High-voltage power distribution cabinet intelligent adjusting system based on cloud control

Abstract

The invention discloses a high-voltage power distribution cabinet intelligent adjustment system based on cloud control, and particularly relates to the field of high-voltage power distribution cabinet intelligent adjustment, and the system comprises a data batch division module, a high-voltage power distribution cabinet data collection module, a high-voltage power distribution cabinet data analysis module, a comprehensive data evaluation module, and a real-time early warning module. The data batch division module is used for determining to-be-collected data as target data and dividing the target data into different batches according to an equitime division mode; the high-voltage power distribution cabinet data acquisition module comprises an electrical operation data acquisition unit and an environment state data acquisition unit; the high-voltage power distribution cabinet data analysis module comprises an electrical operation data analysis unit and an environment state data analysis unit; the comprehensive data evaluation module comprises a high-voltage power distribution cabinet intelligent adjustment reasonability analysis unit; and the real-time early warning module is used for establishing an intelligent adjustment reasonability index preset value of the high-voltage power distribution cabinet.

Description

Technical Field

[0001] The present invention relates to the technical field of high-voltage power distribution cabinets, and more specifically, to an intelligent adjustment system for high-voltage power distribution cabinets based on cloud control. Background Art

[0002] With the rapid development of information technology, the application of intelligent technology in power systems is becoming more and more extensive. As a key equipment in the power system, the stability and safety of the high-voltage distribution cabinet are crucial to the normal operation of the entire power system.

[0003] The existing intelligent adjustment system of high-voltage distribution cabinet mainly includes: data acquisition and transmission module, cloud processing and analysis module, intelligent adjustment module and human-computer interaction module. Among them, the data acquisition and transmission module is responsible for real-time collection of the operation data of the high-voltage distribution cabinet, such as voltage, current, and power, and uploads the data to the cloud through a wireless network; the cloud processing and analysis module uses big data analysis and machine learning algorithms to conduct in-depth mining and processing of the collected data to achieve real-time monitoring of the operation status of the high-voltage distribution cabinet and fault warning; the intelligent adjustment module automatically adjusts the operation parameters of the high-voltage distribution cabinet according to the analysis results of the cloud to ensure that it is in the best operating state; the human-computer interaction interface provides users with intuitive and convenient operation and query functions.

[0004] However, in the existing high-voltage distribution cabinet regulation system, too much attention is paid to the voltage and current data of the high-voltage distribution cabinet during data collection. There is a problem that the collected data is incomplete and single, which leads to limitations in the analysis results, affects the accuracy of data analysis, and further affects the operating efficiency of the high-voltage distribution cabinet.

[0005] Therefore, there is an urgent need for an intelligent adjustment system for high-voltage distribution cabinets based on cloud control to solve the problems of insufficient comprehensive data collection and inaccurate analysis results in the existing system. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a high-voltage distribution cabinet intelligent adjustment system based on cloud control, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0007] To achieve the above object, the present invention provides the following technical solution: a high-voltage distribution cabinet intelligent adjustment system based on cloud control, comprising:

[0008] Data batch division module: used to determine the data to be collected as target data, divide the target data into different batches according to equal time division, and mark them as 1, 2, ..., n in sequence;

[0009] High-voltage distribution cabinet data acquisition module: including an electrical operation data acquisition unit and an environmental status data acquisition unit, which are used to acquire target data in real time and transmit the acquired data to the high-voltage distribution cabinet data analysis module; the electrical operation data acquisition unit is used to acquire advanced electrical characteristic data, electrical health status data and electrical transient data; the environmental status data acquisition unit is used to acquire micro-environment meteorological data, environmental pollution data and safety protection data;

[0010] High-voltage distribution cabinet data analysis module: including an electrical operation data analysis unit and an environmental status data analysis unit, which are used to analyze the data transmitted by the high-voltage distribution cabinet data acquisition module and transmit the analysis results to the comprehensive data evaluation module; the electrical operation data analysis unit includes an advanced electrical characteristic data analysis node, an electrical health status data analysis node, and an electrical transient data analysis node; the environmental status data analysis unit includes a micro-environment meteorological data analysis node, an environmental pollution data analysis node, and a safety protection data analysis node;

[0011] Comprehensive data evaluation module: including a high-voltage distribution cabinet intelligent adjustment rationality analysis unit, which is used to analyze the data transmitted by the high-voltage distribution cabinet data analysis module and transmit the analysis results to the real-time warning module;

[0012] Real-time warning module: used to establish a preset value of the rationality index of the intelligent adjustment of the high-voltage distribution cabinet, judge the rationality index value of the intelligent adjustment of the high-voltage distribution cabinet according to the preset value of the rationality index of the high-voltage distribution cabinet, send a corresponding signal according to the judgment result and save the data in the database.

[0013] Preferably, the advanced electrical characteristic data include dielectric loss tangent Dt, local discharge inception voltage Dv and arc reignition voltage Av; the electrical health status data include insulation resistance value Ir, capacitor loss rate Cr and temperature rise rate Tr; the electrical transient data include transient recovery voltage Rv, transient overvoltage duration Td and operating overvoltage multiple Mo.

[0014] Preferably, the micro-environment meteorological data include the dew point temperature Ct inside the cabinet, the air flow rate Ca inside the cabinet and the wind speed Cs outside the cabinet; the environmental pollution data include the salt spray concentration Sc, the sulfur dioxide content Sd and the ultraviolet intensity Uv; the safety protection data include the vibration acceleration Va, the number of cabinet door openings Dc and the number of illegal intrusion alarms Ia.

[0015] Preferably, the advanced electrical data analysis node is used to establish an advanced electrical data calculation model, and the advanced electrical data transmitted by the high-voltage distribution cabinet data acquisition module is imported into the advanced electrical data calculation model to obtain the performance benchmark coefficient value of the high-voltage distribution cabinet; the high-voltage electrical data calculation model is specifically expressed as:

[0016]

[0017] in, Indicates the performance benchmark coefficient value of the high-voltage distribution cabinet calculated for the i-th time; Dt i represents the dielectric loss tangent of the ith acquisition, Dt i-1 represents the dielectric loss tangent collected for the i-1th time; Dv i represents the partial discharge inception voltage collected for the i-th time, Dv i-1 Av represents the partial discharge inception voltage collected for the i-1th time; i represents the arc reignition voltage collected for the ith time, Av def Indicates the safe value of arc restrike voltage.

[0018] Preferably, the electrical health status data analysis node is used to establish an electrical health status data calculation model, and the electrical health status data transmitted by the high-voltage distribution cabinet data acquisition module is imported into the electrical health status data calculation model to obtain the health coefficient value of the high-voltage distribution cabinet; the electrical health status data model is specifically expressed as:

[0019]

[0020] Among them, l i Ir represents the health factor value of the high-voltage distribution cabinet calculated for the i-th time; i Indicates the insulation resistance value collected for the i-th time, Ir i-1 Indicates the insulation resistance value collected for the i-1th time; Cr i represents the capacitor loss rate collected for the i-th time; Tr i represents the temperature rise rate of the ith acquisition, Tr def Indicates the safe value of the temperature rise rate.

[0021] Preferably, the electrical transient data analysis node is used to establish an electrical transient data calculation model, import the electrical transient data transmitted by the high-voltage distribution cabinet data acquisition module into the electrical transient data calculation model, and obtain the dynamic response characteristic value of the high-voltage distribution cabinet; the electrical transient data model is specifically expressed as:

[0022]

[0023] Among them, α i Rv represents the dynamic response characteristic value of the high-voltage distribution cabinet calculated for the i-th time; i represents the transient recovery voltage collected for the i-th time, Rv i-1 represents the transient recovery voltage collected for the i-1th time; Td i represents the duration of transient overvoltage collected for the i-th time, Indicates the average value of transient overvoltage duration; Mo i It represents the operating overvoltage multiple collected for the i-th time. Indicates the average value of the operating overvoltage multiple.

[0024] Preferably, the microenvironment meteorological data analysis node is used to establish a microenvironment meteorological data calculation model, and the microenvironment meteorological data transmitted by the high-voltage distribution cabinet data acquisition module is imported into the microenvironment meteorological data calculation model to obtain the microenvironment safety factor value of the high-voltage distribution cabinet; the electrical transient data model is specifically expressed as:

[0025]

[0026] Among them, β i represents the microenvironmental safety factor value of the high-voltage distribution cabinet calculated for the i-th time, Ct i Indicates the dew point temperature in the cabinet collected for the i-th time, Ct def Indicates the threshold value of the minimum dew point temperature in the cabinet; Ca i represents the air flow rate in the cabinet collected for the i-th time, Ca def Indicates the threshold value of the maximum air velocity in the cabinet; Cs i Indicates the wind speed outside the cabinet collected for the i-th time.

[0027] Preferably, the environmental pollution data analysis node is used to establish an environmental pollution data calculation model, import the environmental pollution data transmitted by the high-voltage distribution cabinet data acquisition module into the environmental pollution data calculation model, and obtain the environmental pollution risk coefficient value of the high-voltage distribution cabinet; the environmental pollution data model is specifically expressed as:

[0028]

[0029] Among them, η i Sc represents the environmental pollution risk factor value of the high-voltage distribution cabinet calculated for the i-th time; i represents the salt spray concentration collected for the i-th time; Sd i Indicates the sulfur dioxide content collected for the i-th time; Uv i Indicates the ultraviolet intensity collected for the i-th time.

[0030] Preferably, the safety protection data analysis node is used to establish a safety protection data calculation model, import the safety protection data transmitted by the high-voltage distribution cabinet data acquisition module into the safety protection data calculation model, and obtain the protection safety factor value of the high-voltage distribution cabinet; the safety protection data model is specifically expressed as:

[0031]

[0032] Among them, μ i Represents the protection safety factor value of the high-voltage distribution cabinet calculated for the i-th time; Vai represents the vibration acceleration collected for the ith time; Dc i Ia represents the number of cabinet door openings and closings collected for the i-th time; i Indicates the number of illegal intrusion alarms collected for the i-th time.

[0033] Preferably, the high-voltage distribution cabinet intelligent adjustment rationality analysis unit is used to establish a high-voltage distribution cabinet intelligent adjustment rationality calculation model, and the high-voltage distribution cabinet performance benchmark coefficient value, high-voltage distribution cabinet health coefficient value, high-voltage distribution cabinet dynamic response characteristic value, high-voltage distribution cabinet microenvironment safety coefficient value, high-voltage distribution cabinet environmental pollution risk coefficient value and high-voltage distribution cabinet protection safety coefficient value transmitted by the high-voltage distribution cabinet data analysis module are introduced into the high-voltage distribution cabinet intelligent adjustment rationality calculation model to obtain the high-voltage distribution cabinet intelligent adjustment rationality index value; the high-voltage distribution cabinet intelligent adjustment rationality calculation model is specifically expressed as:

[0034]

[0035] Among them, A represents the calculated rationality index value of intelligent regulation of high-voltage distribution cabinet, represents the performance benchmark coefficient value of the high-voltage distribution cabinet calculated for the i-th time, Indicates the minimum value of the calculated performance benchmark coefficient of the high-voltage distribution cabinet. Indicates the maximum value of the calculated performance benchmark coefficient of the high-voltage distribution cabinet; l i represents the health factor value of the high-voltage distribution cabinet calculated for the i-th time, l min Indicates the minimum value of the calculated health factor of the high-voltage distribution cabinet, l max Indicates the maximum value of the calculated health factor of the high-voltage distribution cabinet; α i represents the dynamic response characteristic value of the high-voltage distribution cabinet calculated for the i-th time, α min represents the minimum value of the dynamic response characteristic of the high-voltage distribution cabinet, α max Indicates the maximum value of the calculated dynamic response characteristic of the high-voltage distribution cabinet; β i represents the microenvironmental safety factor value of the high-voltage distribution cabinet calculated for the i-th time, β min Indicates the minimum value of the calculated microenvironmental safety factor of the high-voltage distribution cabinet, β max Indicates the maximum value of the calculated microenvironmental safety factor of the high-voltage distribution cabinet; η i represents the environmental pollution risk factor value of the high-voltage distribution cabinet calculated for the i-th time, η min Represents the minimum value of the calculated environmental pollution risk factor of the high-voltage distribution cabinet, η max Indicates the maximum value of the calculated environmental pollution risk factor of the high-voltage distribution cabinet; μ i represents the protection safety factor value of the high-voltage distribution cabinet calculated for the i-th time, μ min Indicates the minimum value of the calculated protection safety factor of the high-voltage distribution cabinet, μ maxrepresents the calculated maximum value of the protection safety factor of the high-voltage distribution cabinet; λ represents the rationality index value of the intelligent adjustment of the high-voltage distribution cabinet and other influencing factors. Since the influence is small, no specific limitation is made in this embodiment.

[0036] Technical effects and advantages of the present invention:

[0037] 1. The present invention divides the relevant data of the high-voltage distribution cabinet into multiple data batches through the data batch division module, which significantly enhances the target and practicality of data acquisition; the high-voltage distribution cabinet data acquisition module collects advanced electrical characteristic data, electrical health status data, electrical transient data, micro-environment meteorological data, environmental pollution data and safety protection data, and collects data from multiple dimensions, effectively avoiding the singleness of the data, and laying a solid foundation for subsequent in-depth analysis;

[0038] 2. The present invention uses the high-voltage distribution cabinet data analysis module to deeply mine and analyze the collected data, and obtains the performance benchmark coefficient value of the high-voltage distribution cabinet, the health coefficient value of the high-voltage distribution cabinet, the dynamic response characteristic value of the high-voltage distribution cabinet, the micro-environment safety coefficient value of the high-voltage distribution cabinet, the environmental pollution risk coefficient value of the high-voltage distribution cabinet, and the protection safety coefficient value of the high-voltage distribution cabinet, which intuitively shows the potential risks and improvement space in the operation of the high-voltage distribution cabinet;

[0039] 3. The present invention integrates and comprehensively evaluates the output results of the data analysis algorithm module through a comprehensive data evaluation module, thereby further improving the accuracy and practical value of the analysis results; finally, through a real-time early warning module, the analysis results are displayed in real time and an early warning function is provided, so that operation and maintenance personnel and management can instantly grasp the operating status of the high-voltage distribution cabinet and achieve efficient management and maintenance decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] As attached Figure 1 The high-voltage distribution cabinet intelligent adjustment system shown in the figure based on cloud control includes a data batch division module, a high-voltage distribution cabinet data acquisition module, a high-voltage distribution cabinet data analysis module, a comprehensive data evaluation module, and a real-time warning module.

[0043] The data batch division module is used to determine the data to be collected as target data, divide the target data into different batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence;

[0044] What needs to be specifically explained in this embodiment is that the equal time division method is used to divide each target data according to its characteristics through the background information management system, and each target data is divided into different collection times according to its real-time requirements, and a system database is established to store it.

[0045] The high-voltage distribution cabinet data acquisition module includes an electrical operation data acquisition unit and an environmental status data acquisition unit, which are used to collect target data in real time and transmit the collected data to the high-voltage distribution cabinet data analysis module; the electrical operation data acquisition unit is used to collect advanced electrical characteristic data, electrical health status data and electrical transient data; the environmental status data acquisition unit is used to collect micro-environment meteorological data, environmental pollution data and safety protection data.

[0046] In this embodiment, it is specifically necessary to explain that: the advanced electrical characteristic data include dielectric loss tangent Dt, partial discharge starting voltage Dv and arc reignition voltage Av; the electrical health status data include insulation resistance value Ir, capacitor loss rate Cr and temperature rise rate Tr; the electrical transient data include transient recovery voltage Rv, transient overvoltage duration Td and operating overvoltage multiple Mo.

[0047] The dielectric loss tangent indicates the energy loss of the insulating material under the action of the electric field, which means the aging or moisture condition of the insulating material. The collection method is: using a dielectric loss tester, by connecting the test instrument with the relevant electrical interface of the distribution cabinet, applying a specific test voltage and frequency, measuring the energy loss of the insulating material under the action of the electric field, and uploading the test results to the cloud server; the partial discharge starting voltage indicates the lowest voltage at which discharge begins to occur in the local insulation area of ​​the equipment, indicating the insulation performance condition. The collection method is: using a partial discharge tester, gradually increasing the voltage until discharge begins in the local insulation area of ​​the equipment, recording the voltage value at this time as the starting voltage, and using the tester The communication interface transmits data to the cloud; the arc reignition voltage indicates the lowest voltage when the arc is reignited after being extinguished, reflecting the damage of the equipment. The collection method is: by simulating the process of arc extinguishing and reigniting, a high-precision voltage measuring instrument is used to capture the voltage value when the arc reignites, and then the data is sent to the cloud server via wireless transmission; the insulation resistance value is an important indicator for measuring the insulation performance of electrical equipment, and its value directly reflects the quality of the insulation material and the insulation state of the equipment; the capacitor loss rate indicates the ratio of the energy loss of the capacitor to the stored energy under the action of the electric field, indicating the performance of the capacitor, and the collection method is: using a capacitor loss tester, The capacitor is subjected to specific tests to measure the ratio of its energy loss to stored energy under the action of the electric field, and then the test results are uploaded to the cloud; the temperature rise rate reflects the heat generation of the equipment during operation and is an important indicator for evaluating whether the equipment has an overheating risk. The collection method is: install temperature sensors at key parts of the distribution cabinet, monitor the equipment temperature in real time, and calculate the temperature rise rate, and then upload the data to the cloud for further analysis and processing; the transient recovery voltage represents the transient voltage that the equipment is subjected to after the switch operation, reflecting the insulation breakdown of the equipment. The collection method is: use a high-speed voltage sensor to monitor the voltage waveform after the switch operation in real time to capture the transient recovery voltage. The peak value of the voltage is measured, and the data is then collected through high-speed data acquisition and uploaded to the cloud; the transient overvoltage duration indicates the duration of the transient overvoltage, reflecting the damage to the equipment, and its collection method is: using an oscilloscope acquisition card with high-speed sampling technology to record the waveform of the transient overvoltage, calculating the duration of the overvoltage through waveform analysis, and uploading the result to the cloud; the operating overvoltage multiple indicates the ratio of the operating overvoltage to the normal operating voltage, reflecting the threat to the insulation of the equipment, and its collection method is: by comparing the peak value or effective value of the operating overvoltage with the normal operating voltage, the overvoltage multiple is calculated, and then the calculation result is uploaded to the cloud server for storage and analysis.

[0048] In this embodiment, it is specifically necessary to explain that: the micro-environment meteorological data includes the dew point temperature Ct inside the cabinet, the air flow rate Ca inside the cabinet and the wind speed Cs outside the cabinet; the environmental pollution data includes the salt spray concentration Sc, the sulfur dioxide content Sd and the ultraviolet intensity Uv; the safety protection data includes the vibration acceleration Va, the number of cabinet door openings Dc and the number of illegal intrusion alarms Ia.

[0049] The dew point temperature in the cabinet indicates the temperature when the air inside the distribution cabinet reaches a saturated state, reflecting the insulation performance of the equipment. The collection method is: use a temperature and humidity sensor to monitor the temperature and humidity inside the distribution cabinet in real time, use the dew point calculation formula or algorithm to convert the temperature and humidity values ​​into dew point temperature, and then upload the result to the cloud; the air flow rate in the cabinet indicates the flow rate of the air inside the distribution cabinet, reflecting the heat dissipation and insulation performance of the equipment. The collection method is: use an air flow rate sensor to monitor the air flow rate inside the distribution cabinet in real time, and upload the measurement results to the cloud server wirelessly; the wind speed outside the cabinet indicates the wind speed of the external environment of the distribution cabinet, reflecting the heat dissipation of the equipment. The collection method is: : Install a wind speed and direction sensor on the outside of the distribution cabinet, use an ultrasonic anemometer to monitor the wind speed of the external environment in real time, and then send the data to the cloud through wireless transmission; the salt spray concentration represents the salt spray concentration in the environment around the distribution cabinet, reflecting the corrosion of the equipment, and its collection method is: use a salt spray concentration sensor to regularly collect air samples in the environment around the distribution cabinet, and obtain the salt spray concentration through chemical analysis and physical measurement, and then enter the results and upload them to the cloud through wireless communication; the sulfur dioxide content represents the sulfur dioxide concentration in the environment around the distribution cabinet, reflecting the corrosion and insulation performance of the equipment, and its collection method is: install a sulfur dioxide sensor, through electrochemical and infrared absorption sensors, The sulfur dioxide concentration in the environment around the power distribution cabinet is monitored in real time, and the measurement results are uploaded to the cloud server wirelessly; the ultraviolet intensity indicates the ultraviolet intensity of the external environment of the power distribution cabinet, reflecting the aging of the equipment shell, and its collection method is: an ultraviolet sensor is installed outside the power distribution cabinet, and the ultraviolet intensity of the external environment is monitored in real time through a photoresistor and an ultraviolet radiometer, and then the data is sent to the cloud through wireless transmission; the vibration acceleration indicates the vibration acceleration of the power distribution cabinet, reflecting equipment damage and loose connections, and its collection method is: using a vibration sensor, through piezoelectric and capacitive accelerometers, to monitor the vibration acceleration of the power distribution cabinet in real time, and the measurement results are uploaded to the cloud server wirelessly. The data is uploaded to the cloud server for further analysis and processing; the number of door openings and closings indicates the number of times the distribution cabinet door is opened and closed, reflecting loose connections and damage to the equipment, and is collected in the following manner: door magnetic sensors and proximity switches are installed on the distribution cabinet door. Whenever the cabinet door is opened or closed, the sensor sends a signal to the data acquisition system, and the system records the number of openings and closings and uploads the data to the cloud; the number of illegal intrusion alarms indicates the number of illegal intrusions that occur in the distribution cabinet, reflecting the status of safety accidents, and is collected in the following manner: the security system monitors abnormal conditions around the distribution cabinet, and when illegal intrusion is detected, the security system triggers an alarm and uploads the alarm signal and the number of times to the cloud server for recording and analysis.

[0050] The high-voltage distribution cabinet data analysis module includes an electrical operation data analysis unit and an environmental status data analysis unit, which are used to analyze the data transmitted by the high-voltage distribution cabinet data acquisition module and transmit the analysis results to the comprehensive data evaluation module; the electrical operation data analysis unit includes an advanced electrical characteristics data analysis node, an electrical health status data analysis node and an electrical transient data analysis node; the environmental status data analysis unit includes a micro-environment meteorological data analysis node, an environmental pollution data analysis node and a safety protection data analysis node.

[0051] In this embodiment, it is specifically required to be explained that: the advanced electrical data analysis node is used to establish an advanced electrical data calculation model, and the advanced electrical data transmitted by the high-voltage distribution cabinet data acquisition module is imported into the advanced electrical data calculation model to obtain the performance benchmark coefficient value of the high-voltage distribution cabinet; the high-voltage electrical data calculation model is specifically expressed as:

[0052]

[0053] in, Indicates the performance benchmark coefficient value of the high-voltage distribution cabinet calculated for the i-th time; Dt i represents the dielectric loss tangent of the ith acquisition, Dt i-1 represents the dielectric loss tangent collected for the i-1th time; Dv i represents the partial discharge inception voltage collected for the i-th time, Dv i-1 Av represents the partial discharge inception voltage collected for the i-1th time; i represents the arc reignition voltage collected for the ith time, Av def Indicates the safe value of arc restrike voltage.

[0054] In this embodiment, it is specifically required to be explained that: the electrical health status data analysis node is used to establish an electrical health status data calculation model, and the electrical health status data transmitted by the high-voltage distribution cabinet data acquisition module is imported into the electrical health status data calculation model to obtain the health coefficient value of the high-voltage distribution cabinet; the electrical health status data model is specifically expressed as:

[0055]

[0056] Among them, l i Ir represents the health factor value of the high-voltage distribution cabinet calculated for the i-th time; i Indicates the insulation resistance value collected for the i-th time, Ir i-1 Indicates the insulation resistance value collected for the i-1th time; Cr i represents the capacitor loss rate collected for the i-th time; Tr i represents the temperature rise rate of the ith acquisition, Tr def Indicates the safe value of the temperature rise rate.

[0057] In this embodiment, it is specifically required to be explained that: the electrical transient data analysis node is used to establish an electrical transient data calculation model, and the electrical transient data transmitted by the high-voltage distribution cabinet data acquisition module is imported into the electrical transient data calculation model to obtain the dynamic response characteristic value of the high-voltage distribution cabinet; the electrical transient data model is specifically expressed as:

[0058]

[0059] Among them, α i Rv represents the dynamic response characteristic value of the high-voltage distribution cabinet calculated for the i-th time; i represents the transient recovery voltage collected for the i-th time, Rv i-1 represents the transient recovery voltage collected for the i-1th time; Td i represents the duration of transient overvoltage collected for the i-th time, Indicates the average value of transient overvoltage duration; Mo i It represents the operating overvoltage multiple collected for the i-th time. Indicates the average value of the operating overvoltage multiple.

[0060] In this embodiment, it is specifically required to be explained that: the micro-environment meteorological data analysis node is used to establish a micro-environment meteorological data calculation model, and the micro-environment meteorological data transmitted by the high-voltage distribution cabinet data acquisition module is imported into the micro-environment meteorological data calculation model to obtain the micro-environment safety factor value of the high-voltage distribution cabinet; the electrical transient data model is specifically expressed as:

[0061]

[0062] Among them, β i represents the microenvironmental safety factor value of the high-voltage distribution cabinet calculated for the i-th time, Ct i Indicates the dew point temperature in the cabinet collected for the i-th time, Ct def Indicates the threshold value of the minimum dew point temperature in the cabinet; Ca i represents the air flow rate in the cabinet collected for the i-th time, Ca def Indicates the threshold value of the maximum air velocity in the cabinet; Cs i Indicates the wind speed outside the cabinet collected for the i-th time.

[0063] In this embodiment, it is specifically required to be explained that: the environmental pollution data analysis node is used to establish an environmental pollution data calculation model, and the environmental pollution data transmitted by the high-voltage distribution cabinet data acquisition module is imported into the environmental pollution data calculation model to obtain the environmental pollution risk coefficient value of the high-voltage distribution cabinet; the environmental pollution data model is specifically expressed as:

[0064]

[0065] Among them, η i Sc represents the environmental pollution risk factor value of the high-voltage distribution cabinet calculated for the i-th time; i represents the salt spray concentration collected for the i-th time; Sd i Indicates the sulfur dioxide content collected for the i-th time; Uv i Indicates the ultraviolet intensity collected for the i-th time.

[0066] In this embodiment, it is specifically required to be explained that: the safety protection data analysis node is used to establish a safety protection data calculation model, and the safety protection data transmitted by the high-voltage distribution cabinet data acquisition module is imported into the safety protection data calculation model to obtain the protection safety factor value of the high-voltage distribution cabinet; the safety protection data model is specifically expressed as:

[0067]

[0068] Among them, μ i Represents the protection safety factor value of the high-voltage distribution cabinet calculated for the i-th time; Va i represents the vibration acceleration collected for the ith time; Dc i Ia represents the number of cabinet door openings and closings collected for the i-th time; i Indicates the number of illegal intrusion alarms collected for the i-th time.

[0069] The comprehensive data evaluation module includes a high-voltage distribution cabinet intelligent adjustment rationality analysis unit, which is used to analyze the data transmitted by the high-voltage distribution cabinet data analysis module and transmit the analysis results to the real-time warning module.

[0070] In this embodiment, it is specifically required to be explained that: the high-voltage distribution cabinet intelligent adjustment rationality analysis unit is used to establish a high-voltage distribution cabinet intelligent adjustment rationality calculation model, and the high-voltage distribution cabinet performance benchmark coefficient value, high-voltage distribution cabinet health coefficient value, high-voltage distribution cabinet dynamic response characteristic value, high-voltage distribution cabinet microenvironment safety coefficient value, high-voltage distribution cabinet environmental pollution risk coefficient value and high-voltage distribution cabinet protection safety coefficient value transmitted by the high-voltage distribution cabinet data analysis module are introduced into the high-voltage distribution cabinet intelligent adjustment rationality calculation model to obtain the high-voltage distribution cabinet intelligent adjustment rationality index value; the high-voltage distribution cabinet intelligent adjustment rationality calculation model is specifically expressed as:

[0071]

[0072] Among them, A represents the calculated rationality index value of intelligent regulation of high-voltage distribution cabinet, represents the performance benchmark coefficient value of the high-voltage distribution cabinet calculated for the i-th time, Indicates the minimum value of the calculated performance benchmark coefficient of the high-voltage distribution cabinet. Indicates the maximum value of the calculated performance benchmark coefficient of the high-voltage distribution cabinet; l irepresents the health factor value of the high-voltage distribution cabinet calculated for the i-th time, l min Indicates the minimum value of the calculated health factor of the high-voltage distribution cabinet, l max Indicates the maximum value of the calculated health factor of the high-voltage distribution cabinet; α i represents the dynamic response characteristic value of the high-voltage distribution cabinet calculated for the i-th time, α min represents the minimum value of the dynamic response characteristic of the high-voltage distribution cabinet, α max Indicates the maximum value of the calculated dynamic response characteristic of the high-voltage distribution cabinet; β i represents the microenvironmental safety factor value of the high-voltage distribution cabinet calculated for the i-th time, β min Indicates the minimum value of the calculated microenvironmental safety factor of the high-voltage distribution cabinet, β max Indicates the maximum value of the calculated microenvironmental safety factor of the high-voltage distribution cabinet; η i represents the environmental pollution risk factor value of the high-voltage distribution cabinet calculated for the i-th time, η min Represents the minimum value of the calculated environmental pollution risk factor of the high-voltage distribution cabinet, η max Indicates the maximum value of the calculated environmental pollution risk factor of the high-voltage distribution cabinet; μ i represents the protection safety factor value of the high-voltage distribution cabinet calculated for the i-th time, μ min Indicates the minimum value of the calculated protection safety factor of the high-voltage distribution cabinet, μ max represents the calculated maximum value of the protection safety factor of the high-voltage distribution cabinet; λ represents the rationality index value of the intelligent adjustment of the high-voltage distribution cabinet and other influencing factors. Since the influence is small, no specific limitation is made in this embodiment.

[0073] The real-time warning module is used to establish a preset value of the intelligent adjustment rationality index of the high-voltage distribution cabinet, judge the value of the intelligent adjustment rationality index of the high-voltage distribution cabinet according to the preset value of the intelligent adjustment rationality index of the high-voltage distribution cabinet, send a corresponding signal according to the judgment result and save the data in the database.

[0074] In this embodiment, it should be specifically noted that the preset value of the rationality index of the intelligent adjustment of the high-voltage distribution cabinet is marked as A. def , when A>=A def When A is equal to or greater than the preset value of the rationality index of intelligent regulation of the high-voltage distribution cabinet, a normal signal is issued, indicating that the comprehensive operation status of the high-voltage distribution cabinet is good; when A def When , it means that the intelligent adjustment rationality index value of the high-voltage distribution cabinet is less than the preset value of the intelligent adjustment rationality index of the high-voltage distribution cabinet, a warning signal will be issued to the relevant technical personnel, which indicates that the comprehensive operating status of the high-voltage distribution cabinet is poor; and the intelligent adjustment rationality index value of the high-voltage distribution cabinet is recorded and uploaded to the database.

[0075] ​The present invention divides the relevant data of the high-voltage distribution cabinet into multiple data batches through the data batch division module, which significantly enhances the target and practicality of data acquisition; collects advanced electrical characteristic data, electrical health status data, electrical transient data, micro-environment meteorological data, environmental pollution data and safety protection data through the high-voltage distribution cabinet data acquisition module, and collects data from multiple dimensions, effectively avoiding the singleness of the data, and laying a solid foundation for subsequent in-depth analysis; and deeply mines and analyzes the collected data through the high-voltage distribution cabinet data analysis module to obtain the performance benchmark coefficient value of the high-voltage distribution cabinet and the health status of the high-voltage distribution cabinet. The health coefficient value, dynamic response characteristic value of the high-voltage distribution cabinet, micro-environment safety factor value of the high-voltage distribution cabinet, environmental pollution hazard factor value of the high-voltage distribution cabinet and protection safety factor value of the high-voltage distribution cabinet intuitively show the potential risks and improvement space in the operation of the high-voltage distribution cabinet; through the comprehensive data evaluation module, the output results of the data analysis algorithm module are integrated and comprehensively evaluated, which further improves the accuracy and practical value of the analysis results; finally, through the real-time early warning module, the analysis results are displayed in real time and an early warning function is provided, which is convenient for operation and maintenance personnel and management to immediately grasp the operating status of the high-voltage distribution cabinet and achieve efficient management and maintenance decisions.

[0076] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0077] Finally: The above description is only a preferred embodiment of the present invention and is 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 in the protection scope of the present invention.

Claims

1. A high-voltage distribution cabinet intelligent adjustment system based on cloud control, characterized in that: include: Data batch division module: used to determine the data to be collected as target data, divide the target data into different batches according to equal time division, and mark them as 1, 2, ..., n in sequence; High-voltage distribution cabinet data acquisition module: including electrical operation data acquisition unit and environmental status data acquisition unit, used to collect target data in real time and transmit the collected data to the high-voltage distribution cabinet data analysis module; The electrical operation data acquisition unit is used to collect high-level electrical characteristic data, electrical health status data and electrical transient data; the environmental status data acquisition unit is used to collect micro-environment meteorological data, environmental pollution data and safety protection data; High-voltage distribution cabinet data analysis module: including an electrical operation data analysis unit and an environmental status data analysis unit, used to analyze the data transmitted by the high-voltage distribution cabinet data acquisition module and transmit the analysis results to the comprehensive data evaluation module; The electrical operation data analysis unit includes an advanced electrical characteristic data analysis node, an electrical health status data analysis node, and an electrical transient data analysis node; The environmental status data analysis unit includes a micro-environment meteorological data analysis node, an environmental pollution data analysis node, and a safety protection data analysis node; Comprehensive data evaluation module: including a high-voltage distribution cabinet intelligent adjustment rationality analysis unit, which is used to analyze the data transmitted by the high-voltage distribution cabinet data analysis module and transmit the analysis results to the real-time warning module; Real-time warning module: used to establish a preset value of the rationality index of the intelligent adjustment of the high-voltage distribution cabinet, judge the rationality index value of the intelligent adjustment of the high-voltage distribution cabinet according to the preset value of the rationality index of the high-voltage distribution cabinet, send a corresponding signal according to the judgment result and save the data in the database.

2. According to the cloud-controlled high-voltage distribution cabinet intelligent adjustment system of claim 1, it is characterized by: The advanced electrical characteristic data include dielectric loss tangent Dt, partial discharge inception voltage Dv and arc reignition voltage Av; the electrical health status data include insulation resistance value Ir, capacitor loss rate Cr and temperature rise rate Tr; the electrical transient data include transient recovery voltage Rv, transient overvoltage duration Td and operating overvoltage multiple Mo.

3. According to the cloud-controlled high-voltage distribution cabinet intelligent adjustment system of claim 1, it is characterized by: The micro-environment meteorological data includes the dew point temperature Ct inside the cabinet, the air flow rate Ca inside the cabinet and the wind speed Cs outside the cabinet; the environmental pollution data includes the salt spray concentration Sc, the sulfur dioxide content Sd and the ultraviolet intensity Uv; the safety protection data includes the vibration acceleration Va, the number of cabinet door openings Dc and the number of illegal intrusion alarms Ia.

4. According to the cloud-controlled high-voltage distribution cabinet intelligent adjustment system of claim 1, it is characterized by: The advanced electrical data analysis node is used to establish an advanced electrical data calculation model, import the advanced electrical data transmitted by the high-voltage distribution cabinet data acquisition module into the advanced electrical data calculation model, and obtain the performance benchmark coefficient value of the high-voltage distribution cabinet; the high-voltage electrical data calculation model is specifically expressed as: in, Indicates the performance benchmark coefficient value of the high-voltage distribution cabinet calculated for the i-th time; Dt i represents the dielectric loss tangent of the ith acquisition, Dt i-1 represents the dielectric loss tangent collected for the i-1th time; Dv i represents the partial discharge inception voltage collected for the i-th time, Dv i-1 Av represents the partial discharge inception voltage collected for the i-1th time; i represents the arc reignition voltage collected for the ith time, Av def Indicates the safe value of arc restrike voltage.

5. According to the cloud-controlled high-voltage distribution cabinet intelligent adjustment system of claim 1, it is characterized by: The electrical health status data analysis node is used to establish an electrical health status data calculation model, import the electrical health status data transmitted by the high-voltage distribution cabinet data acquisition module into the electrical health status data calculation model, and obtain the health coefficient value of the high-voltage distribution cabinet; the electrical health status data model is specifically expressed as: Among them, l i Ir represents the health factor value of the high-voltage distribution cabinet calculated for the i-th time; i Indicates the insulation resistance value collected for the i-th time, Ir i-1 Indicates the insulation resistance value collected for the i-1th time; Cr i represents the capacitor loss rate collected for the i-th time; Tr i represents the temperature rise rate of the ith acquisition, Tr def Indicates the safe value of the temperature rise rate.

6. According to the cloud-controlled high-voltage distribution cabinet intelligent adjustment system of claim 1, it is characterized by: The electrical transient data analysis node is used to establish an electrical transient data calculation model, import the electrical transient data transmitted by the high-voltage distribution cabinet data acquisition module into the electrical transient data calculation model, and obtain the dynamic response characteristic value of the high-voltage distribution cabinet; the electrical transient data model is specifically expressed as: Among them, α i Rv represents the dynamic response characteristic value of the high-voltage distribution cabinet calculated for the i-th time; i represents the transient recovery voltage collected for the i-th time, Rv i-1 represents the transient recovery voltage collected for the i-1th time; Td i represents the duration of transient overvoltage collected for the i-th time, Indicates the average value of transient overvoltage duration; Mo i It represents the operating overvoltage multiple collected for the i-th time. Indicates the average value of the operating overvoltage multiple.

7. The intelligent adjustment system for high-voltage power distribution cabinet based on cloud control according to claim 1 is characterized in that: The micro-environment meteorological data analysis node is used to establish a micro-environment meteorological data calculation model, import the micro-environment meteorological data transmitted by the high-voltage distribution cabinet data acquisition module into the micro-environment meteorological data calculation model, and obtain the micro-environment safety factor value of the high-voltage distribution cabinet; the electrical transient data model is specifically expressed as: Among them, β i represents the microenvironmental safety factor value of the high-voltage distribution cabinet calculated for the i-th time, Ct i Indicates the dew point temperature in the cabinet collected for the i-th time, Ct def Indicates the threshold value of the minimum dew point temperature in the cabinet; Ca i represents the air flow rate in the cabinet collected for the i-th time, Ca def Indicates the threshold value of the maximum air velocity in the cabinet; Cs i Indicates the wind speed outside the cabinet collected for the i-th time.

8. The intelligent adjustment system for high-voltage power distribution cabinet based on cloud control according to claim 1 is characterized in that: The environmental pollution data analysis node is used to establish an environmental pollution data calculation model, import the environmental pollution data transmitted by the high-voltage distribution cabinet data acquisition module into the environmental pollution data calculation model, and obtain the environmental pollution risk coefficient value of the high-voltage distribution cabinet; the environmental pollution data model is specifically expressed as: Among them, η i Sc represents the environmental pollution risk factor value of the high-voltage distribution cabinet calculated for the i-th time; i represents the salt spray concentration collected for the i-th time; Sd i Indicates the sulfur dioxide content collected for the i-th time; Uv i Indicates the ultraviolet intensity collected for the i-th time.

9. The intelligent adjustment system for high-voltage power distribution cabinet based on cloud control according to claim 1 is characterized in that: The safety protection data analysis node is used to establish a safety protection data calculation model, import the safety protection data transmitted by the high-voltage distribution cabinet data acquisition module into the safety protection data calculation model, and obtain the protection safety factor value of the high-voltage distribution cabinet; the safety protection data model is specifically expressed as: Among them, μ i Represents the protection safety factor value of the high-voltage distribution cabinet calculated for the i-th time; Va i represents the vibration acceleration collected for the ith time; Dc i Ia represents the number of cabinet door openings and closings collected for the i-th time; i Indicates the number of illegal intrusion alarms collected for the i-th time.

10. The intelligent adjustment system for high-voltage power distribution cabinet based on cloud control according to claim 1 is characterized in that: The high-voltage distribution cabinet intelligent adjustment rationality analysis unit is used to establish a high-voltage distribution cabinet intelligent adjustment rationality calculation model, and import the high-voltage distribution cabinet performance benchmark coefficient value, high-voltage distribution cabinet health coefficient value, high-voltage distribution cabinet dynamic response characteristic value, high-voltage distribution cabinet microenvironment safety coefficient value, high-voltage distribution cabinet environmental pollution risk coefficient value and high-voltage distribution cabinet protection safety coefficient value transmitted by the high-voltage distribution cabinet data analysis module into the high-voltage distribution cabinet intelligent adjustment rationality calculation model to obtain the high-voltage distribution cabinet intelligent adjustment rationality index value; the high-voltage distribution cabinet intelligent adjustment rationality calculation model is specifically expressed as: Among them, A represents the calculated rationality index value of intelligent regulation of high-voltage distribution cabinet, represents the performance benchmark coefficient value of the high-voltage distribution cabinet calculated for the i-th time, Indicates the minimum value of the calculated performance benchmark coefficient of the high-voltage distribution cabinet. Indicates the maximum value of the calculated performance benchmark coefficient of the high-voltage distribution cabinet; l i represents the health factor value of the high-voltage distribution cabinet calculated for the i-th time, l min Indicates the minimum value of the calculated health factor of the high-voltage distribution cabinet, l max Indicates the maximum value of the calculated health factor of the high-voltage distribution cabinet; α i represents the dynamic response characteristic value of the high-voltage distribution cabinet calculated for the i-th time, α min represents the minimum value of the dynamic response characteristic of the high-voltage distribution cabinet, α max Indicates the maximum value of the calculated dynamic response characteristic of the high-voltage distribution cabinet; β i represents the microenvironmental safety factor value of the high-voltage distribution cabinet calculated for the i-th time, β min Indicates the minimum value of the calculated microenvironmental safety factor of the high-voltage distribution cabinet, β max Indicates the maximum value of the calculated microenvironmental safety factor of the high-voltage distribution cabinet; η i represents the environmental pollution risk factor value of the high-voltage distribution cabinet calculated for the i-th time, η min Represents the minimum value of the calculated environmental pollution risk factor of the high-voltage distribution cabinet, η max Indicates the maximum value of the calculated environmental pollution risk factor of the high-voltage distribution cabinet; μ i represents the protection safety factor value of the high-voltage distribution cabinet calculated for the i-th time, μ min Indicates the minimum value of the calculated protection safety factor of the high-voltage distribution cabinet, μ max represents the calculated maximum value of the protection safety factor of the high-voltage distribution cabinet; λ represents the rationality index value of the intelligent adjustment of the high-voltage distribution cabinet and other influencing factors. Since the influence is small, it is not specifically limited in this embodiment.

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