High-protection intelligent high-voltage cabinet

Abstract

The application relates to the technical field of high-voltage cabinet management and relates to an intelligent high-voltage cabinet with high protection, which is characterized in that, based on operation condition parameters, operation condition change analysis is carried out to obtain operation condition change characteristic data; according to the operation condition change characteristic data, temperature rapid change and dew point temperature prediction analysis are carried out to obtain condensation risk data; based on the condensation risk data, dynamic temperature control strategy correlation mining is carried out to obtain dynamic temperature control strategy correlation data; according to the dynamic temperature control strategy correlation data, heater control parameter matching is carried out to generate heater control parameter matching data; high-voltage cabinet preventive control planning and design are carried out to obtain high-voltage cabinet preventive control data; through correlation analysis and dynamic regulation and control of the operation condition-temperature-condensation, the real-time response capability of the temperature control system to operation condition change is ensured, the damage of transient condensation to precise electronic elements in the cabinet is avoided, the service life of the high-voltage cabinet equipment is prolonged, and the later maintenance cost is reduced.

Description

Technical Field

[0001] This invention belongs to the field of high-voltage switchgear management technology and relates to a highly protective intelligent high-voltage switchgear. Background Technology

[0002] High-voltage switchgear is a core and critical piece of electrical equipment in a power system, undertaking important responsibilities for power distribution, control, and protection. Its operating status directly affects grid security and power supply stability. In actual operation, high-voltage switchgear experiences various complex conditions, including load switching, short-circuit fault current surges, and intermittent operation. These conditions can easily cause rapid temperature fluctuations within the switchgear. When the load suddenly increases, conductors and internal components heat up rapidly, driving a rapid rise in the surrounding air temperature; conversely, when the load suddenly decreases, the temperature drops rapidly. This rapid temperature change significantly affects the dew point temperature inside the switchgear. Even if the absolute humidity remains unchanged, the relative humidity may momentarily exceed the saturation point during the temperature drop phase, leading to transient condensation. This transient condensation adheres to the surfaces of insulating components, metal conductors, and precision electronic components inside the switchgear, seriously threatening the safe operation of the high-voltage switchgear. This highlights the importance of optimizing anti-condensation strategies for high-voltage switchgear.

[0003] Current strategies for preventing condensation in high-voltage switchgear still have some areas that need optimization, specifically in the following aspects: 1. Current anti-condensation strategies for high-voltage switchgear do not take into account the dynamic changes in operating conditions, and mostly adopt a reactive heating start-up mode, lacking the ability to respond in real time to rapid temperature changes caused by changes in operating conditions. This passive approach is unable to keep up with the formation of transient condensation, which can easily lead to moisture aging of the insulation materials inside the switchgear, a decrease in insulation strength, and corrosion of metal components. This increases the risk of faults such as short circuits, creepage, and jamming of the switching mechanism. It can not only cause permanent damage to the high-voltage switchgear equipment, but also potentially cause power grid outages, resulting in significant economic losses, and seriously affecting the normal power supply for industrial production and residential life.

[0004] 2. Currently, there is a lack of accurate prediction mechanisms and dynamic control methods for transient condensation in high-voltage switchgear, and no correlation analysis or parameter calibration of operating conditions, temperature, and condensation has been conducted. This makes it impossible to identify risk points of condensation caused by rapid temperature changes in advance, making proactive prevention and control difficult. It cannot prevent damage to the precision electronic components inside the high-voltage switchgear, shortening equipment lifespan and increasing subsequent maintenance costs. Furthermore, it cannot guarantee the accuracy and scientific rigor of high-voltage switchgear operation analysis, making it difficult to effectively reduce power system safety hazards caused by condensation and bringing long-term negative impacts on the reliability of power supply. Summary of the Invention

[0005] In view of the problems existing in the prior art, the present invention provides a highly protective intelligent high-voltage switchgear to solve the above-mentioned technical problems.

[0006] To achieve the above and other objectives, the technical solution adopted by the present invention is as follows: This invention provides a highly protective intelligent high-voltage switchgear, comprising: High-voltage switchgear operating condition data acquisition module: acquires the operating condition parameters and ambient humidity data of the high-voltage switchgear; performs operating condition change analysis based on the operating condition parameters to obtain characteristic data of operating condition changes; High-voltage switchgear condensation risk prediction module: Based on the characteristic data of operating condition changes, it performs temperature rapid change and dew point temperature prediction analysis to obtain condensation risk data; Based on the condensation risk data, it performs dynamic temperature control strategy correlation mining to obtain dynamic temperature control strategy correlation data; Dynamic temperature control strategy correlation mining includes associating predefined heater control strategies according to the condensation risk level, including heater start-up time, heating power and heating duration. High-voltage switchgear preventive control module: Matches heater control parameters based on dynamic temperature control strategy correlation data to generate heater control parameter matching data; and performs high-voltage switchgear preventive control planning and design based on heater control parameter matching data to obtain high-voltage switchgear preventive control data.

[0007] As described above, the high-protection intelligent high-voltage switchgear provided by the present invention has at least the following beneficial effects: This invention provides a highly protective intelligent high-voltage switchgear. It acquires operating parameters and ambient humidity data from the switchgear's operating condition data acquisition module, and analyzes these parameters to obtain characteristic data of these changes. A condensation risk prediction module then performs temperature rapid change and dew point temperature prediction analysis based on the characteristic data, generating condensation risk data. This data is then used for dynamic temperature control strategy correlation mining, matching condensation risk levels with predefined control strategies such as heater start-up time, heating power, and heating duration to obtain dynamic temperature control strategy correlation data. Finally, a preventative control module matches heater control parameters based on the dynamic temperature control strategy correlation data, generating heater control parameter matching data. This data is then used to design preventative control plans for the high-voltage switchgear, resulting in preventative control data. This effectively solves the limitations of current high-voltage switchgear anti-condensation strategies. On the one hand, by combining the dynamic changes in operating conditions, the system enables accurate prediction and proactive prevention of condensation risks, completely overcoming the passive limitations of traditional reactive heating start-up modes. This effectively reduces the risks of insulation material aging, metal component corrosion, and faults such as short circuits and creepage, preventing permanent damage to high-voltage switchgear and grid outages, reducing significant economic losses, and ensuring normal power supply for industrial production and residential life. On the other hand, through the correlation analysis and dynamic control of operating conditions, temperature, and condensation, the system ensures the real-time response capability of the temperature control system to changes in operating conditions, preventing damage to precision electronic components inside the switchgear from transient condensation, extending the service life of the high-voltage switchgear, reducing subsequent maintenance costs, and ensuring the accuracy and scientific nature of high-voltage switchgear operation analysis. This effectively reduces power system safety hazards caused by condensation and provides strong support for the reliability of power supply. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a schematic diagram showing the connection of each module of the high-voltage switchgear of the present invention. Detailed Implementation

[0010] The following description, in conjunction with the implementation of this invention, is merely an example and illustration of the concept of this invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the inventive concept or exceed the scope defined in these claims, all of which should fall within the protection scope of this invention.

[0011] Example 1

[0012] Please see Figure 1 As shown, a highly protective intelligent high-voltage switchgear includes a high-voltage switchgear operating condition data acquisition module, a high-voltage switchgear condensation risk prediction module, and a high-voltage switchgear prevention and control module. The modules described above are connected via wired and / or wireless means to enable data transmission between them. High-voltage switchgear operating condition data acquisition module: acquires the operating condition parameters and ambient humidity data of the high-voltage switchgear; performs operating condition change analysis based on the operating condition parameters to obtain characteristic data of operating condition changes.

[0013] For example, the data acquisition logic of the high-voltage switchgear operating condition data acquisition module is as follows: Acquire the operating condition parameters and ambient humidity data of the high-voltage switchgear; among which, the operating condition parameters include load current data, short-circuit fault current impact event data, and intermittent operation mode data, and the ambient humidity data includes cabinet temperature, cabinet relative humidity, ambient temperature, and ambient humidity; Operating condition parameters are processed into vector data to generate a vector sequence of operating condition data. The vectorization process includes converting load current data, short-circuit fault current impact event data, and intermittent operation mode data into vector data in time series form. Operating condition change patterns are extracted based on the operating condition data vector sequence to obtain operating condition change pattern data; the operating condition change pattern extraction includes identifying the pattern features of load switching events, short-circuit fault current impact events and intermittent operation events from the operating condition data vector sequence. Based on the operating condition change pattern data, operating condition change characteristic analysis is performed to obtain operating condition change characteristic data. The operating condition change characteristic analysis includes frequency statistics and duration analysis of load switching events, amplitude and time feature extraction of short-circuit fault current impact, and periodicity and switching point identification of intermittent operation modes to generate operating condition change characteristic data.

[0014] It should be added that the logic for extracting operating condition change modes also includes: Load switching event identification is performed on the operating condition data vector sequence to obtain load switching event sequence data; load switching event identification includes detecting abrupt changes in load current from the operating condition data vector sequence and generating a sequence of occurrence time points and durations of load switching events through threshold comparison; Short-circuit fault current impact event identification is performed on the operating condition data vector sequence to obtain short-circuit fault event sequence data; short-circuit fault current impact event identification includes extracting abnormal peak values ​​of current amplitude from the operating condition data vector sequence, and generating amplitude characteristics and time point sequences of short-circuit fault events through time window analysis; Intermittent operation events are identified from the working condition data vector sequence to obtain intermittent operation event sequence data. The intermittent operation event identification includes identifying periodic interruption patterns of the operating state from the working condition data vector sequence, and generating the cycle length and switching point sequence of intermittent operation through pattern matching. Based on load switching event sequence data, short-circuit fault event sequence data, and intermittent operation event sequence data, pattern feature synthesis is performed to obtain operating condition change pattern data. Pattern feature synthesis includes integrating load switching event sequence data, short-circuit fault event sequence data, and intermittent operation event sequence data into a unified pattern feature vector, and generating operating condition change pattern data through feature fusion.

[0015] For example, the logic for generating characteristic data of changing operating conditions is as follows: Based on the operating condition change pattern data, the frequency and duration of load switching events are statistically analyzed to obtain load switching characteristic data. The frequency and duration analysis of load switching events includes extracting the number of occurrences of load switching events and the duration of each event from the operating condition change pattern data, and generating load switching frequency and average duration data through statistical calculations. Short-circuit fault current impact amplitude and time characteristics are extracted based on operating condition change mode data to obtain short-circuit impact characteristic data; the extraction of short-circuit fault current impact amplitude and time characteristics includes identifying the peak current amplitude and event occurrence time of the short-circuit fault event from the operating condition change mode data, and calculating the amplitude change rate and time interval characteristics. Based on the data of changing operating conditions, the periodicity and switching points of intermittent operation modes are identified to obtain intermittent operation feature data; the identification of periodicity and switching points of intermittent operation modes includes detecting the cycle length and mode switching time points of intermittent operation from the data of changing operating conditions, and generating periodic indicators and switching point sequences through pattern matching. Operating condition change feature data is obtained by integrating load switching feature data, short-circuit impact feature data, and intermittent operation feature data. The integration of operating condition change feature data includes combining load switching feature data, short-circuit impact feature data, and intermittent operation feature data into feature vectors, and generating operating condition change feature data through data fusion.

[0016] High-voltage switchgear condensation risk prediction module: Based on the characteristic data of operating condition changes, it performs temperature rapid change and dew point temperature prediction analysis to obtain condensation risk data; Based on the condensation risk data, it performs dynamic temperature control strategy correlation mining to obtain dynamic temperature control strategy correlation data; Dynamic temperature control strategy correlation mining includes associating predefined heater control strategies according to the condensation risk level, including heater start-up time, heating power and heating duration.

[0017] For example, the working logic of the high-voltage switchgear condensation risk prediction module includes the following steps: Temperature rapid change prediction analysis is performed based on operating condition change characteristic data to obtain temperature rapid change prediction data. The temperature rapid change prediction analysis includes establishing a temperature change model based on load switching characteristic data, short circuit impact characteristic data and intermittent operation characteristic data in the operating condition change characteristic data to predict the rate of temperature change and temperature extreme points in the cabinet in the short term.

[0018] It should be added that the prediction of the rate of temperature change and extreme temperature points inside the cabinet in the short term includes: Based on the load switching characteristic data in the operating condition change characteristic data, load switching temperature change trend analysis is performed to obtain the load switching temperature change trend; the load switching temperature change trend analysis includes generating the cabinet temperature change trend curve caused by the load switching event through trend fitting based on the load switching frequency and duration in the load switching characteristic data. Based on the short-circuit impact characteristic data in the operating condition change characteristic data, the short-circuit impact temperature change trend is analyzed to obtain the short-circuit impact temperature change trend; the short-circuit impact temperature change trend analysis includes, based on the current amplitude peak value and time interval characteristics in the short-circuit impact characteristic data, the instantaneous temperature change trend inside the cabinet caused by the short-circuit impact event is generated through peak impact assessment. Based on the intermittent operation characteristic data in the operating condition change characteristic data, the intermittent operation temperature change trend is analyzed to obtain the intermittent operation temperature change trend; the intermittent operation temperature change trend analysis includes generating the periodic change trend of cabinet temperature caused by intermittent operation by using pattern superposition based on the cycle length and switching point sequence in the intermittent operation characteristic data; Based on the load switching temperature change trend, short-circuit impact temperature change trend, and intermittent operation temperature change trend, the temperature change rate is calculated and the temperature extreme point is identified to obtain temperature rapid change prediction data. The temperature change rate calculation and temperature extreme point identification include comprehensively superimposing the load switching temperature change trend, short-circuit impact temperature change trend, and intermittent operation temperature change trend, and generating a short-term cabinet temperature change rate and temperature extreme point sequence through change rate analysis and extreme point detection.

[0019] Dew point temperature prediction analysis is performed based on rapid temperature change prediction data and operating condition change characteristic data to obtain dew point temperature prediction data. The dew point temperature prediction analysis includes establishing a dew point temperature calculation model based on the temperature change rate in the rapid temperature change prediction data and the relative humidity data inside the cabinet to predict the short-term trend of dew point temperature change.

[0020] It should be added that the prediction of short-term dew point temperature changes includes: Temperature time series modeling is performed based on the temperature change rate and temperature extreme points in the temperature rapid change prediction data to obtain a temperature prediction model; temperature time series modeling includes generating a short-term cabinet temperature prediction sequence by sequence fitting based on the temperature change rate and temperature extreme points. Humidity time series modeling is performed based on the relative humidity data inside the cabinet from the operating condition change characteristic data to obtain a humidity prediction model; humidity time series modeling includes generating a short-term humidity prediction sequence inside the cabinet based on the relative humidity data inside the cabinet through trend analysis. A dew point temperature calculation model is established based on a temperature prediction model and a humidity prediction model. The establishment of the dew point temperature calculation model includes coupling the temperature prediction sequence in the temperature prediction model and the humidity prediction sequence in the humidity prediction model, and generating the dew point temperature calculation model through the dew point temperature calculation formula. The dew point temperature calculation is based on the difference comparison and saturated water vapor pressure derivation of the corresponding values ​​of the temperature prediction sequence and the humidity prediction sequence. Short-term dew point temperature change trend prediction is performed based on the dew point temperature calculation model to obtain dew point temperature prediction data. The short-term dew point temperature change trend prediction includes using the dew point temperature calculation model to calculate the short-term temperature prediction sequence and humidity prediction sequence, and generating the dew point temperature change trend curve and the dew point temperature extreme point sequence.

[0021] Condensation risk level assessment is conducted based on rapid temperature change prediction data and dew point temperature prediction data to obtain condensation risk data. The condensation risk level assessment includes comparing the difference between the cabinet temperature in the rapid temperature change prediction data and the dew point temperature in the dew point temperature prediction data, and classifying the condensation risk level based on the magnitude of the difference.

[0022] It should be added that the logic for obtaining condensation risk data is as follows: The cabinet internal temperature sequence is extracted based on the rapid temperature change prediction data, and the dew point temperature sequence is extracted based on the dew point temperature prediction data, resulting in cabinet internal temperature sequence data and dew point temperature sequence data; the extraction includes obtaining the time series value of the cabinet internal temperature from the rapid temperature change prediction data and obtaining the time series value of the dew point temperature from the dew point temperature prediction data. Temperature difference is calculated based on the cabinet internal temperature sequence data and dew point temperature sequence data to obtain temperature difference sequence data. The temperature difference calculation includes subtracting each temperature value in the cabinet internal temperature sequence data from the corresponding dew point temperature value in the dew point temperature sequence data to generate the temperature difference sequence. The risk level of condensation is classified based on temperature difference sequence data to obtain condensation risk level sequence data. The classification of condensation risk level includes classifying the levels according to the magnitude of the difference in the temperature difference sequence data and using a predefined risk threshold to generate a condensation risk level sequence. Condensation risk data is generated based on condensation risk level sequence data. The generation of condensation risk data includes integrating condensation risk level sequence data into condensation risk data, including information on risk level, risk occurrence time, and risk duration.

[0023] Dynamic temperature control strategy association mining is performed based on condensation risk data to obtain dynamic temperature control strategy association data. Dynamic temperature control strategy association mining includes associating predefined heater control strategies with condensation risk levels in the condensation risk data to generate control parameter sequences for heater start-up time, heating power, and heating duration.

[0024] It should be added that the specific steps for linking data to the dynamic temperature control strategy are as follows: Based on condensation risk data, condensation risk level identification is performed to obtain condensation risk level sequence data. Condensation risk level identification includes extracting condensation risk level information from condensation risk data and generating time series data of condensation risk levels through serialization processing. Predefined heater control strategy matching is performed based on condensation risk level sequence data to obtain heater control strategy data; the predefined heater control strategy matching includes querying a predefined strategy mapping table based on the risk level value in the condensation risk level sequence data to generate the corresponding heater control strategy sequence. The heater start-up time is calculated based on heater control strategy data and condensation risk data to obtain heater start-up time series data. The heater start-up time calculation includes generating the heater start-up time series by calculating the time offset based on the risk occurrence time point in the condensation risk data and the start-up conditions in the heater control strategy data. Heating power and heating duration are determined based on heater control strategy data and condensation risk data to obtain heating power sequence data and heating duration sequence data. The determination of heating power and heating duration includes generating heating power sequence and heating duration sequence by matching parameters based on the risk level and risk duration in the condensation risk data and the power setting rules and duration parameters in the heater control strategy data. Dynamic temperature control strategy correlation data is generated based on heater start-up time series data, heating power series data, and heating duration series data. The generation of dynamic temperature control strategy correlation data includes integrating heater start-up time series data, heating power series data, and heating duration series data into a control parameter sequence, and generating dynamic temperature control strategy correlation data through data encapsulation.

[0025] High-voltage switchgear preventive control module: Matches heater control parameters based on dynamic temperature control strategy correlation data to generate heater control parameter matching data; heater control parameter matching includes matching specific heater control parameters based on dynamic temperature control strategy correlation data, such as heater power level, heating time sequence, and spatial distribution parameters; uses heater control parameter matching data to perform high-voltage switchgear preventive control planning and design to obtain high-voltage switchgear preventive control data; high-voltage switchgear preventive control planning and design includes generating preventive heating control command sequences and integrating real-time monitoring feedback to adjust control parameters.

[0026] For example, the high-voltage switchgear prevention and control module includes: Step A1: Match heater power levels based on dynamic temperature control strategy association data to obtain heater power level data; heater power level matching includes generating the corresponding heater power level sequence based on the heating power sequence data in the dynamic temperature control strategy association data through the level mapping relationship; Step A2: Perform heating time series matching based on the dynamic temperature control strategy association data to obtain heating time series data; heating time series matching includes generating a heating time series by integrating the heater start-up time series data and heating duration series data from the dynamic temperature control strategy association data. Step A3: Match heater spatial distribution parameters based on dynamic temperature control strategy association data to obtain heater spatial distribution parameter data; heater spatial distribution parameter matching includes generating heater layout parameters in the high-voltage cabinet by optimizing the spatial distribution based on the heating power distribution characteristics in the dynamic temperature control strategy association data. Step A4: Generate a control command sequence based on heater power level data, heating time series data, and heater spatial distribution parameter data to obtain a preventive heating control command sequence; the control command sequence generation includes integrating heater power level data, heating time series data, and heater spatial distribution parameter data into a control command sequence; Step A5: Dynamically adjust the control parameters based on the preventive heating control command sequence and real-time monitoring feedback data to obtain the preventive control data of the high-voltage switchgear; the dynamic adjustment of control parameters includes real-time correction and optimization of the preventive heating control command sequence based on the changes in temperature and humidity inside the switchgear in the real-time monitoring feedback data.

[0027] Step A1 includes the following steps: Step A11: Extract heating power sequence data based on the dynamic temperature control strategy association data to obtain heating power sequence data; the extraction of heating power sequence data includes identifying heating power sequence information from the dynamic temperature control strategy association data and generating a time series of heating power values ​​through data parsing; Step A12: Divide the power value range based on the heating power sequence data to obtain power range division data; the power value range division includes classifying the range according to the power value distribution in the heating power sequence data through a predefined power level threshold, and generating a power range interval sequence; Step A13: Match the gear mapping relationship based on the power range division data to obtain the gear mapping relationship data; the gear mapping relationship matching includes querying the predefined gear mapping table according to the power range interval in the power range division data, and generating the corresponding heater power gear mapping sequence. Step A14: Generate heater power level sequence based on level mapping relationship data to obtain heater power level data; heater power level sequence generation includes converting the level mapping sequence in the level mapping relationship data into a specific heater power level control sequence, and generating heater power level data through data encapsulation.

[0028] Step A2 includes the following steps: Step A21: Extract heater start-up time series data based on dynamic temperature control strategy association data to obtain heater start-up time series data; heater start-up time series data extraction includes identifying heater start-up time information from dynamic temperature control strategy association data and generating heater start-up time point sequence through time data parsing; Step A22: Extract heating duration sequence data based on dynamic temperature control strategy association data to obtain heating duration sequence data; heating duration sequence data extraction includes identifying heating duration information from dynamic temperature control strategy association data and generating heating duration value sequence through duration parsing; Step A23: Perform time series alignment processing based on heater start-up time series data and heating duration series data to obtain time series aligned data; the time series alignment processing includes matching the time points in the heater start-up time series data with the duration values ​​in the heating duration series data to generate a time-duration correspondence sequence; Step A24: Integrate and generate heating time series data based on time series aligned data; the heating time series integration and generation includes calculating and generating a complete heating time series containing start time, end time and duration based on the time-duration correspondence in the time series aligned data through time intervals.

[0029] Step A3 includes the following steps: Step A31: Extract heating power distribution features based on dynamic temperature control strategy association data to obtain heating power distribution feature data; heating power distribution feature extraction includes identifying the spatial distribution information of heating power from dynamic temperature control strategy association data, and generating a distribution feature sequence of heating power in the high-voltage cabinet through feature parsing; Step A32: Perform temperature field simulation analysis inside the high-voltage cabinet based on heating power distribution characteristic data to obtain temperature field distribution data; the temperature field simulation analysis inside the high-voltage cabinet includes generating a temperature field distribution map inside the high-voltage cabinet by simulating the power distribution characteristics in the heating power distribution characteristic data through a heat conduction model. Step A33: Perform heater layout optimization analysis based on temperature field distribution data to obtain heater layout optimization data; the heater layout optimization analysis includes generating the optimal layout scheme of the heater in the high-voltage cabinet based on the temperature distribution in the temperature field distribution data through a spatial optimization algorithm; Step A34: Generate spatial distribution parameters based on heater layout optimization data to obtain heater spatial distribution parameter data; the generation of spatial distribution parameters includes converting the layout scheme in the heater layout optimization data into specific heater installation location parameters and coverage range parameters, and generating heater spatial distribution parameter data through parameter integration.

[0030] Step A4 includes the following steps: Step A41: Generate power control command parameters based on heater power level data to obtain power control command parameter data; the generation of power control command parameters includes generating the corresponding power control command parameter sequence through parameter conversion based on the power level sequence in the heater power level data; Step A42: Generate time control command parameters based on heating time series data to obtain time control command parameter data; the generation of time control command parameters includes generating a time control command parameter sequence by mapping the start time, end time and duration in the heating time series data; Step A43: Generate spatial control command parameters based on heater spatial distribution parameter data to obtain spatial control command parameter data; the generation of spatial control command parameters includes generating a spatial control command parameter sequence through spatial parameter conversion based on the installation location and coverage parameters in the heater spatial distribution parameter data; Step A44: Integrate the control command sequence based on the power control command parameter data, time control command parameter data, and space control command parameter data to obtain a preventive heating control command sequence; the control command sequence integration includes aligning and fusing the power control command parameter data, time control command parameter data, and space control command parameter data according to the time sequence to generate a preventive heating control command sequence.

[0031] Step A5 includes the following steps: Step A51: Extract the cabinet temperature and humidity data based on the real-time monitoring feedback data to obtain real-time temperature and humidity data; the extraction of cabinet temperature and humidity data includes identifying the cabinet temperature and humidity values ​​from the real-time monitoring feedback data, and generating real-time temperature and humidity sequences through data parsing; Step A52: Perform control effect deviation analysis based on real-time temperature and humidity data and preventive heating control command sequence to obtain control effect deviation data; the control effect deviation analysis includes comparing the real-time temperature sequence and humidity sequence with the expected control target in the preventive heating control command sequence, and generating temperature control deviation and humidity control deviation sequences by calculating the difference; Step A53: Calculate the control parameter correction based on the control effect deviation data to obtain the corrected control parameter data; the control parameter correction calculation includes optimizing the power, time and space parameters in the preventive heating control command sequence in real time based on the temperature control deviation and humidity control deviation in the control effect deviation data through a proportional adjustment algorithm; Step A54: Generate preventive control data for the high-voltage switchgear based on the corrected control parameter data to obtain the preventive control data for the high-voltage switchgear; the generation of preventive control data for the high-voltage switchgear includes integrating the corrected control parameter data into the final control command sequence, and generating the preventive control data for the high-voltage switchgear through data encapsulation.

[0032] It should be noted that the interval and threshold sizes are set for ease of comparison. The size of the threshold depends on the amount of sample data and the base number set by those skilled in the art for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value. Furthermore, the above formulas are all dimensionless calculations, and the formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0033] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0034] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0035] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0036] In conclusion, the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A highly protective intelligent high-voltage switchgear, characterized in that, include: High-voltage switchgear operating condition data acquisition module: acquires the operating condition parameters and ambient humidity data of the high-voltage switchgear; performs operating condition change analysis based on the operating condition parameters to obtain characteristic data of operating condition changes; High-voltage switchgear condensation risk prediction module: Based on the characteristic data of operating condition changes, it performs temperature rapid change and dew point temperature prediction analysis to obtain condensation risk data; Based on the condensation risk data, it performs dynamic temperature control strategy correlation mining to obtain dynamic temperature control strategy correlation data; Dynamic temperature control strategy correlation mining includes associating predefined heater control strategies according to the condensation risk level, including heater start-up time, heating power and heating duration. High-voltage switchgear preventive control module: Matches heater control parameters based on dynamic temperature control strategy correlation data to generate heater control parameter matching data; and performs high-voltage switchgear preventive control planning and design based on heater control parameter matching data to obtain high-voltage switchgear preventive control data. The data acquisition logic of the high-voltage switchgear operating condition data acquisition module is as follows: Acquire the operating condition parameters and ambient humidity data of the high-voltage switchgear; among which, the operating condition parameters include load current data, short-circuit fault current impact event data, and intermittent operation mode data, and the ambient humidity data includes cabinet temperature, cabinet relative humidity, ambient temperature, and ambient humidity; Operating condition parameters are processed into vector data to generate a vector sequence of operating condition data. The vectorization process includes converting load current data, short-circuit fault current impact event data, and intermittent operation mode data into vector data in time series form. Operating condition change patterns are extracted based on the operating condition data vector sequence to obtain operating condition change pattern data; the operating condition change pattern extraction includes identifying the pattern features of load switching events, short-circuit fault current impact events and intermittent operation events from the operating condition data vector sequence. Based on the operating condition change pattern data, operating condition change characteristic analysis is performed to obtain operating condition change characteristic data. The operating condition change characteristic analysis includes frequency statistics and duration analysis of load switching events, amplitude and time feature extraction of short-circuit fault current impact, and periodicity and switching point identification of intermittent operation modes to generate operating condition change characteristic data. The working logic of the high-voltage switchgear condensation risk prediction module includes the following steps: Temperature rapid change prediction analysis is performed based on operating condition change characteristic data to obtain temperature rapid change prediction data. Temperature rapid change prediction analysis includes establishing a temperature change model based on load switching characteristic data, short circuit impact characteristic data and intermittent operation characteristic data in the operating condition change characteristic data to predict the rate of temperature change and temperature extreme points in the cabinet in the short term. Dew point temperature prediction analysis is performed based on rapid temperature change prediction data and operating condition change characteristic data to obtain dew point temperature prediction data. The dew point temperature prediction analysis includes establishing a dew point temperature calculation model based on the temperature change rate in the rapid temperature change prediction data and the relative humidity data in the cabinet to predict the dew point temperature change trend in the short term. Condensation risk level assessment is conducted based on rapid temperature change prediction data and dew point temperature prediction data to obtain condensation risk data. The condensation risk level assessment includes comparing the difference between the cabinet temperature in the rapid temperature change prediction data and the dew point temperature in the dew point temperature prediction data, and classifying the condensation risk level based on the magnitude of the difference. Dynamic temperature control strategy association mining is performed based on condensation risk data to obtain dynamic temperature control strategy association data. Dynamic temperature control strategy association mining includes associating predefined heater control strategies with condensation risk levels in the condensation risk data to generate control parameter sequences for heater start-up time, heating power, and heating duration.

2. A high-protection intelligent high-voltage switchgear according to claim 1, characterized in that, The logic for generating characteristic data of changing operating conditions is as follows: Based on the operating condition change pattern data, the frequency and duration of load switching events are statistically analyzed to obtain load switching characteristic data. The frequency and duration analysis of load switching events includes extracting the number of occurrences of load switching events and the duration of each event from the operating condition change pattern data, and generating load switching frequency and average duration data through statistical calculations. Short-circuit fault current impact amplitude and time characteristics are extracted based on operating condition change mode data to obtain short-circuit impact characteristic data. The extraction of short-circuit fault current impact amplitude and time characteristics includes identifying the peak current amplitude and the time point of occurrence of short-circuit fault events from operating condition change mode data, and calculating the amplitude change rate and time interval characteristics; Based on the data of changing operating conditions, the periodicity and switching points of intermittent operation modes are identified to obtain intermittent operation feature data; the identification of periodicity and switching points of intermittent operation modes includes detecting the cycle length and mode switching time points of intermittent operation from the data of changing operating conditions, and generating periodic indicators and switching point sequences through pattern matching. Operating condition change feature data is obtained by integrating load switching feature data, short-circuit impact feature data, and intermittent operation feature data. The integration of operating condition change feature data includes combining load switching feature data, short-circuit impact feature data, and intermittent operation feature data into feature vectors, and generating operating condition change feature data through data fusion.

3. A high-protection intelligent high-voltage switchgear according to claim 1, characterized in that, Predict the rate of temperature change and extreme temperature points inside the cabinet in the short term, including: Based on the load switching characteristic data in the operating condition change characteristic data, load switching temperature change trend analysis is performed to obtain the load switching temperature change trend; the load switching temperature change trend analysis includes generating the cabinet temperature change trend curve caused by the load switching event through trend fitting based on the load switching frequency and duration in the load switching characteristic data. Based on the short-circuit impact characteristic data in the operating condition change characteristic data, the short-circuit impact temperature change trend is analyzed to obtain the short-circuit impact temperature change trend; the short-circuit impact temperature change trend analysis includes, based on the current amplitude peak value and time interval characteristics in the short-circuit impact characteristic data, the instantaneous temperature change trend inside the cabinet caused by the short-circuit impact event is generated through peak impact assessment. Based on the intermittent operation characteristic data in the operating condition change characteristic data, the intermittent operation temperature change trend is analyzed to obtain the intermittent operation temperature change trend; the intermittent operation temperature change trend analysis includes generating the periodic change trend of cabinet temperature caused by intermittent operation by using pattern superposition based on the cycle length and switching point sequence in the intermittent operation characteristic data; Based on the load switching temperature change trend, short-circuit impact temperature change trend, and intermittent operation temperature change trend, the temperature change rate is calculated and the temperature extreme point is identified to obtain temperature rapid change prediction data. The temperature change rate calculation and temperature extreme point identification include comprehensively superimposing the load switching temperature change trend, short-circuit impact temperature change trend, and intermittent operation temperature change trend, and generating a short-term cabinet temperature change rate and temperature extreme point sequence through change rate analysis and extreme point detection.

4. A high-protection intelligent high-voltage switchgear according to claim 1, characterized in that, Predicting short-term dew point temperature trends, including: Temperature time series modeling is performed based on the temperature change rate and temperature extreme points in the temperature rapid change prediction data to obtain a temperature prediction model; temperature time series modeling includes generating a short-term cabinet temperature prediction sequence by sequence fitting based on the temperature change rate and temperature extreme points. Humidity time series modeling is performed based on the relative humidity data inside the cabinet from the operating condition change characteristic data to obtain a humidity prediction model; humidity time series modeling includes generating a short-term humidity prediction sequence inside the cabinet based on the relative humidity data inside the cabinet through trend analysis. A dew point temperature calculation model is established based on a temperature prediction model and a humidity prediction model. The establishment of the dew point temperature calculation model includes coupling the temperature prediction sequence in the temperature prediction model and the humidity prediction sequence in the humidity prediction model, and generating the dew point temperature calculation model using a dew point temperature calculation formula. The dew point temperature calculation is based on the corresponding values ​​of the temperature prediction sequence and the humidity prediction sequence. First, the saturated vapor pressure is calculated using an empirical formula for the saturated vapor pressure at the current temperature. Then, the actual vapor pressure is inferred from the humidity prediction sequence. Next, the difference between the actual vapor pressure and the saturated vapor pressure at the corresponding temperature is compared and coupled. Numerical inversion is performed based on the thermodynamic mapping relationship between saturated vapor pressure and dew point temperature to generate the dew point temperature calculation model. Short-term dew point temperature change trend prediction is performed based on the dew point temperature calculation model to obtain dew point temperature prediction data. The short-term dew point temperature change trend prediction includes using the dew point temperature calculation model to calculate the short-term temperature prediction sequence and humidity prediction sequence, and generating the dew point temperature change trend curve and the dew point temperature extreme point sequence.

5. A high-protection intelligent high-voltage switchgear according to claim 1, characterized in that, The high-voltage switchgear prevention and control module includes: A1. Heater power level matching is performed based on the dynamic temperature control strategy association data to obtain heater power level data; heater power level matching includes generating the corresponding heater power level sequence based on the heating power sequence data in the dynamic temperature control strategy association data through the level mapping relationship; A2. Based on the dynamic temperature control strategy association data, perform heating time series matching to obtain heating time series data; heating time series matching includes generating a heating time series by integrating the heater start-up time series data and heating duration series data from the dynamic temperature control strategy association data. A3. Based on the dynamic temperature control strategy association data, perform heater spatial distribution parameter matching to obtain heater spatial distribution parameter data; heater spatial distribution parameter matching includes generating heater layout parameters in the high-voltage cabinet by optimizing the spatial distribution based on the heating power distribution characteristics in the dynamic temperature control strategy association data. A4. Based on heater power level data, heating time series data, and heater spatial distribution parameter data, a control command sequence is generated to obtain a preventive heating control command sequence; the control command sequence generation includes integrating heater power level data, heating time series data, and heater spatial distribution parameter data into a control command sequence; A5. Based on the preventive heating control command sequence and real-time monitoring feedback data, the control parameters are dynamically adjusted to obtain the preventive control data of the high-voltage switchgear; the dynamic adjustment of control parameters includes real-time correction and optimization of the preventive heating control command sequence according to the changes in temperature and humidity inside the switchgear in the real-time monitoring feedback data.

6. A high-protection intelligent high-voltage switchgear according to claim 5, characterized in that A3 include: Step A31: Extract heating power distribution features based on dynamic temperature control strategy association data to obtain heating power distribution feature data; heating power distribution feature extraction includes identifying the spatial distribution information of heating power from dynamic temperature control strategy association data, and generating a distribution feature sequence of heating power in the high-voltage cabinet through feature parsing; Step A32: Perform temperature field simulation analysis inside the high-voltage cabinet based on heating power distribution characteristic data to obtain temperature field distribution data; the temperature field simulation analysis inside the high-voltage cabinet includes generating a temperature field distribution map inside the high-voltage cabinet by simulating the power distribution characteristics in the heating power distribution characteristic data through a heat conduction model. Step A33: Perform heater layout optimization analysis based on temperature field distribution data to obtain heater layout optimization data; the heater layout optimization analysis includes generating the optimal layout scheme of the heater in the high-voltage cabinet based on the temperature distribution in the temperature field distribution data through a spatial optimization algorithm; Step A34: Generate spatial distribution parameters based on heater layout optimization data to obtain heater spatial distribution parameter data; the generation of spatial distribution parameters includes converting the layout scheme in the heater layout optimization data into specific heater installation location parameters and coverage range parameters, and generating heater spatial distribution parameter data through parameter integration.

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