Automatic feeder control method and system for 10-kilovolt power distribution network

Abstract

The invention discloses a 10 kilovolt power distribution network feeder automation control method and system, particularly relates to the field of power distribution network feeder automation control, and comprises a data batch division step, a data acquisition step, a data analysis step, a data comprehensive evaluation step and a tracking feedback step. The data acquisition step comprises a running state data acquisition unit and a safety data acquisition unit and is used for acquiring target data in real time and transmitting the acquired data to the data analysis step; and the data analysis step comprises a running state data analysis unit and a safety data analysis unit and is used for collecting target data in real time and transmitting the collected data to the data comprehensive evaluation step. The 10 kilovolt power distribution network feeder automation operation data is divided into data batches, the pertinence and effectiveness of data processing are effectively improved, and a solid support is provided for achieving efficient and accurate 10 kilovolt power distribution network feeder automation control and intelligent and reasonable operation and maintenance planning.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network feeder automation control, and more specifically, to a 10 kV distribution network feeder automation control method and system. Background Art

[0002] With the continuous development of power systems and the gradual improvement of the level of intelligence, the role of 10kV distribution network feeders in the power supply network has become increasingly critical, and the requirements for their operation quality and performance have also increased. The 10kV distribution network feeder automation control technology has brought new challenges and opportunities to the operation of the distribution network due to its key impact on the overall power supply reliability and stability of the distribution network. Accurate automation control can ensure the stability of power transmission and fault handling capabilities of the feeder, and meet the needs of high-quality power supply in the distribution network.

[0003] The existing 10 kV distribution network feeder automation control method mainly includes operation data collection step, data communication step, operation status analysis step, and control command output step. Through the operation data collection step, multifunctional sensors are installed at the key nodes of the 10 kV distribution network feeder to collect data such as current, voltage, power, frequency, and switch status of the feeder, so as to realize real-time monitoring of the feeder operation process; through the data communication step, the collected data is reliably transmitted using the verification algorithm to ensure the accuracy and timeliness of the data during the transmission process; through the operation status analysis step, the transmitted data is parsed and analyzed to dig out possible abnormal operation information and potential fault hazards, and provide data support for the stability of the operation quality of the 10 kV distribution network feeder; through the control command output step, the operation and maintenance personnel can clearly grasp the operation status of the feeder, realize remote control of the feeder operation parameters and triggering of protection actions, timely correct the parameters of the operating equipment, and improve the reliability and stability of the 10 kV distribution network feeder operation.

[0004] However, this method still has some shortcomings in practical applications. For example, in terms of data collection, due to the limitations of sensor types and layout positions, some feeder data may be missed, resulting in incomplete collected data, which has an adverse effect on the subsequent operation status analysis; in terms of operation status analysis, facing a large amount of operation data and complex fault diagnosis algorithms, it is easy to cause the analysis results to be inaccurate and in-depth, which may lead to the inability to detect potential faults in feeder operation in a timely manner, reducing the ability to control the operation quality of the 10 kV distribution network feeder.

[0005] Therefore, there is an urgent need to provide a 10 kV distribution network feeder automation control method and system to solve the problems of insufficient data collection and inaccurate analysis results of the existing 10 kV distribution network feeder automation control method, and further improve the quality and reliability of distribution network power supply. 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 10 kV distribution network feeder automation control method and system, 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 10 kV distribution network feeder automation control method, comprising:

[0008] S1, data batch division step: 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;

[0009] S2, data collection step: including an operation status data collection unit and a safety data collection unit, which are used to collect target data in real time and transmit the collected data to the data analysis step. The operation data collection unit is used to collect electromagnetic environment data, equipment status data and line data; the safety data collection unit is used to collect insulation aging data and mechanical abnormality data;

[0010] S3, data analysis step: including an operation status data analysis unit and a safety data analysis unit, which are used to collect target data in real time and transmit the collected data to the data comprehensive evaluation step. The operation status data analysis unit includes an electromagnetic environment data analysis node, an equipment status data analysis node, and a line data analysis node; the safety data analysis unit includes an insulation aging data analysis node and a mechanical abnormality data analysis node;

[0011] S4, data comprehensive evaluation step: including a 10 kV distribution network feeder automation control data analysis unit, which is used to comprehensively analyze the analysis results transmitted by the data analysis step, and transmit the analysis results to the tracking feedback step;

[0012] S5, tracking feedback step: used to establish a preset value of the automation control comprehensive evaluation index, judge the automation control comprehensive evaluation index value according to the preset value of the automation control comprehensive evaluation index, and send a corresponding signal according to the judgment result.

[0013] Preferably, the electromagnetic environment data include the ground potential rise peak value Gp, the spatial electric field distortion area Sa and the electromagnetic pulse duration De; the equipment status data include the insulator surface molecular binding energy Sm, the paint film thickness loss Ta and the transformer core hysteresis loss density Th; the line data include the minimum cable bending radius Mr, the pole tower guy wire elastic modulus attenuation rate Tr and the line hardware heating power Hf.

[0014] Preferably, the insulation aging data include the dielectric loss tangent change rate Mt of the insulation material, the insulation surface leakage current growth rate Sr, and the insulation internal air gap discharge pulse energy Ge; the mechanical abnormality data include the equipment bracket vibration mode change rate Ec, the cable outer sheath tensile strain non-uniformity Ct, and the tower foundation soil pressure change Ts.

[0015] Preferably, the electromagnetic environment data analysis node is used to establish an electromagnetic environment data calculation model, and the electromagnetic environment data transmitted in the data collection step is imported into the electromagnetic environment data calculation model to obtain the electromagnetic environment quality coefficient value. The electromagnetic environment data calculation model is specifically expressed as:

[0016]

[0017] Among them, α i Indicates the electromagnetic environment quality coefficient value calculated for the i-th time, Gp i Indicates the peak value of ground potential collected for the ith time, Sa i Denotes the spatial electric field distortion area collected for the i-th time, De i represents the duration of the electromagnetic pulse collected for the i-th time, and t represents time.

[0018] Preferably, the device status data analysis node is used to establish a device status data calculation model, import the device status data transmitted in the data collection step into the device status data calculation model, and obtain the device health coefficient value. The device status data calculation model is specifically expressed as:

[0019]

[0020] Among them, β i Indicates the equipment health factor value calculated for the i-th time, Sm i represents the molecular binding energy of the insulator surface collected for the i-th time, Ta i represents the paint film thickness loss collected for the i-th time, Th i Represents the transformer core hysteresis loss density collected for the i-th time.

[0021] Preferably, the line data analysis node is used to establish a line data calculation model, import the line data transmitted in the data collection step into the line data calculation model, and obtain the line operation safety factor value. The line data calculation model is specifically expressed as:

[0022]

[0023] Among them, χ i represents the line operation safety factor value calculated for the i-th time, Mr i Indicates the minimum value of the cable bending radius collected for the i-th time, Tr iHf represents the attenuation rate of the elastic modulus of the tower wire collected for the i-th time. i It represents the heating power of the line fittings collected for the i-th time, and π represents the pi.

[0024] Preferably, the insulation aging data analysis node is used to establish an insulation aging data calculation model, and the insulation aging data transmitted in the data acquisition step is imported into the insulation aging data calculation model to obtain an insulation aging risk coefficient value. The insulation aging data calculation model is specifically expressed as:

[0025]

[0026] in, Indicates the insulation aging risk factor value calculated for the i-th time, Mt i It represents the change rate of dielectric loss tangent value of the insulating material collected for the i-th time, Sr i It represents the growth rate of the insulation surface leakage current collected for the i-th time, Ge i It represents the energy of the insulation internal air gap discharge pulse collected for the i-th time, i means starting from the i-th number, and n means ending at the n-th number.

[0027] Preferably, the mechanical abnormality data analysis node is used to establish a mechanical abnormality data calculation model, and the mechanical abnormality data transmitted in the data collection step is imported into the mechanical abnormality data calculation model to obtain a mechanical abnormality risk coefficient value. The mechanical abnormality data calculation model is specifically expressed as:

[0028]

[0029] Among them, l i Indicates the mechanical abnormality risk coefficient value calculated for the i-th time, Ec i Indicates the rate of change of vibration mode of the equipment support collected for the ith time, Ct i Ts represents the tensile strain non-uniformity of the cable sheath collected for the i-th time. i It represents the change of soil pressure on the tower foundation collected for the i-th time.

[0030] Preferably, the 10 kV distribution network feeder automation control data analysis unit is used to establish a 10 kV distribution network feeder automation control data calculation model, and the electromagnetic environment quality coefficient value, equipment health coefficient value, line operation safety coefficient value, insulation aging risk coefficient value and mechanical abnormality risk coefficient value transmitted in the data analysis step are imported into the 10 kV distribution network feeder automation control data calculation model to obtain the automation control comprehensive evaluation index value. The 10 kV distribution network feeder automation control data calculation model is specifically expressed as:

[0031]

[0032] Among them, AC represents the calculated comprehensive evaluation index value of automation control, α i represents the electromagnetic environment quality coefficient value calculated for the i-th time, β i represents the equipment health coefficient value calculated for the i-th time, χ i represents the line operation safety factor value calculated for the i-th time, represents the insulation aging risk factor value calculated for the i-th time, l i It represents the mechanical abnormality risk coefficient value calculated for the i-th time, i means starting from the i-th number, and n means ending at the n-th number.

[0033] Preferably, a 10 kV distribution network feeder automation control system comprises:

[0034] 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;

[0035] Data acquisition module: including operation status data acquisition unit and safety data acquisition unit, used to acquire target data in real time and transmit the acquired data to the data analysis module. The operation data acquisition unit is used to acquire electromagnetic environment data, equipment status data and line data; the safety data acquisition unit is used to acquire insulation aging data and mechanical abnormality data;

[0036] Data analysis module: including an operation status data analysis unit and a safety data analysis unit, which are used to collect target data in real time and transmit the collected data to the data comprehensive evaluation module. The operation status data analysis unit includes an electromagnetic environment data analysis node, an equipment status data analysis node, and a line data analysis node; the safety data analysis unit includes an insulation aging data analysis node and a mechanical abnormality data analysis node;

[0037] Data comprehensive evaluation module: including a 10 kV distribution network feeder automation control data analysis unit, which is used to comprehensively analyze the analysis results transmitted by the data analysis module and transmit the analysis results to the tracking feedback module;

[0038] Tracking feedback module: used to establish a preset value of the automation control comprehensive evaluation index, judge the automation control comprehensive evaluation index value according to the preset value of the automation control comprehensive evaluation index, and send a corresponding signal according to the judgment result.

[0039] Technical effects and advantages of the present invention:

[0040] 1. The present invention effectively improves the pertinence and effectiveness of data processing by dividing the 10 kV distribution network feeder automation operation data into data batches. It adopts multiple channels to collect electromagnetic environment data, equipment status data, line data, insulation aging data and mechanical abnormality data. This multi-faceted data collection method reduces the one-sided limitations of traditional distribution network data acquisition and lays a rich and reliable data foundation for subsequent feeder operation status analysis;

[0041] 2. The present invention uses data analysis to conduct in-depth mining of the collected data, thereby obtaining the electromagnetic environment quality coefficient value, equipment health coefficient value, line operation safety coefficient value, insulation aging risk coefficient value and mechanical abnormality risk coefficient value, and accurately identifying the hidden dangers that may interfere with the normal operation of the feeder. By comprehensively considering and deeply integrating the results of data analysis, the accuracy and practicality of the analysis results are greatly improved;

[0042] 3. The present invention relies on real-time monitoring and continuous follow-up. Once a feeder abnormality is detected, an alarm message is triggered immediately. Relevant operation and maintenance personnel and management personnel can quickly know the operating status and potential crises of the 10 kV distribution network feeder automation, and take timely measures to adjust and optimize the operating strategies and parameters, providing solid support for achieving efficient and accurate 10 kV distribution network feeder automation control and wise and reasonable operation and maintenance planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the structure of the method of the present invention.

[0044] Figure 2 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0045] 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.

[0046] As attached Figure 1 A 10 kV distribution network feeder automation control method is shown, comprising:

[0047] S1. Data batch division step: 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.

[0048] In this embodiment, it should be specifically explained that: the method of dividing by equal time is based on the characteristics of each target data. According to the real-time requirements of each target data, it is collected once at equal intervals through the database and divided into different collection times.

[0049] S2, data collection step: including an operation status data collection unit and a safety data collection unit, which are used to collect target data in real time and transmit the collected data to the data analysis step. The operation data collection unit is used to collect electromagnetic environment data, equipment status data and line data; the safety data collection unit is used to collect insulation aging data and mechanical abnormality data.

[0050] In this embodiment, it is specifically necessary to explain that: the electromagnetic environment data includes the ground potential rise peak value Gp, the spatial electric field distortion area Sa and the electromagnetic pulse duration De; the equipment status data includes the insulator surface molecular binding energy Sm, the paint film thickness loss Ta and the transformer core hysteresis loss density Th; the line data includes the minimum cable bending radius Mr, the tower wire elastic modulus attenuation rate Tr and the line hardware heating power Hf; the insulation aging data includes the dielectric loss tangent value change rate Mt of the insulation material, the insulation surface leakage current growth rate Sr and the insulation internal air gap discharge pulse energy Ge; the mechanical abnormality data includes the equipment bracket vibration mode change rate Ec, the cable outer sheath tensile strain unevenness Ct and the tower foundation soil pressure change Ts.

[0051] The ground potential rise peak value helps to evaluate the performance of the grounding system under fault conditions. An excessively high ground potential rise peak value may cause the potential of the equipment casing to rise, threatening personnel safety and the normal operation of the equipment. By monitoring this parameter, potential ground fault risks can be discovered in a timely manner, providing a basis for the action of protection devices and fault handling. The collection method is: install high-precision potential sensors at key nodes of the ground grid or at the grounding point of the equipment. These sensors can convert changes in ground potential into electrical signals. When a fault occurs or under special electromagnetic conditions, the sensor collects the ground potential signal. After passing through the signal conditioning circuit, the data acquisition device records the change in potential over time and extracts the ground potential rise peak value from it.

[0052] The spatial electric field distortion area may indicate an abnormal enhancement of the local electric field, which may be caused by equipment insulation damage, proximity of foreign objects, etc. Understanding the spatial electric field distortion area helps to quickly locate possible insulation weaknesses or external interference sources, so that timely measures can be taken to avoid the expansion of faults and ensure the safe and stable operation of the power grid. The collection method is: in the space around the distribution network, multiple electric field sensors are installed according to a certain three-dimensional spatial layout. These sensors can measure the electric field strength vector at their location in real time. The data collected by each sensor is transmitted to the data processing center through the communication network. In the data processing center, the electric field distribution model and algorithm are used to calculate the distribution of the spatial electric field based on the electric field strength data measured by the sensor, and then the area of ​​the electric field distortion area is determined.

[0053] The duration of the electromagnetic pulse reflects the characteristics of electromagnetic interference. Understanding the duration of the electromagnetic pulse helps to evaluate the impact of the electromagnetic pulse on the equipment. Electromagnetic pulses of different durations may cause different degrees of damage to the electronic components in the equipment. By monitoring this parameter, a reference can be provided for the anti-electromagnetic interference design and protection measures of the equipment to ensure the reliability of the equipment in a complex electromagnetic environment. The collection method is: install electromagnetic pulse measuring instruments near or in key areas of equipment that may be affected by electromagnetic pulses. These instruments can capture electromagnetic pulse signals and convert them into electrical signals. The measuring instrument has a high-precision time measurement function and can record the start and end time of the electromagnetic pulse. The difference between the two is the duration of the electromagnetic pulse. The collected data can be transmitted to the control system for analysis via wired or wireless communication.

[0054] The molecular binding energy on the surface of the insulator reflects the surface physical and chemical properties of the insulator. The performance of the insulator is crucial to maintaining electrical insulation. Changes in molecular binding energy may mean aging, contamination or damage to the surface of the insulator. Monitoring the molecular binding energy on the surface of the insulator can detect the trend of insulator performance degradation in advance, provide a basis for timely replacement or maintenance of insulators, and avoid failures caused by reduced insulation performance of insulators. The collection method is: using special surface analysis instruments, such as equipment based on photoelectron spectroscopy technology or scanning probe microscopy technology. For insulators in operation, micro sensor probes can be installed on the surface of the insulator, or sampling and testing of the insulator can be carried out during power outages. The sensor probe or detection instrument interacts with the molecules on the surface of the insulator, measures information such as the energy of the electrons emitted or scattered, and then analyzes and calculates the molecular binding energy.

[0055] The paint film thickness loss can evaluate the protective status of the equipment and promptly detect damage to the protective layer on the surface of the equipment, so as to take corresponding maintenance measures, extend the service life of the equipment, and ensure the normal operation and insulation performance of the equipment. The collection method is: using non-contact paint film thickness measurement technology, such as optical interference or electromagnetic induction. A paint film thickness monitoring sensor is installed at a suitable position on the surface of the equipment. The sensor uses optical or electromagnetic principles to calculate the paint film thickness by measuring the difference in optical or electromagnetic properties between the paint film and the base material. During the operation of the equipment, the sensor collects data regularly and transmits the data to the control system. The paint film thickness loss is obtained by comparing the initial paint film thickness data.

[0056] The transformer core hysteresis loss density is helpful to evaluate the operating efficiency and health of the transformer. Too high hysteresis loss density may indicate problems such as degradation of core material performance and abnormal magnetization characteristics, which may affect the output performance and life of the transformer. By monitoring this parameter, potential fault hazards of the transformer can be discovered in time and the operation strategy can be optimized. The collection method is: install magnetic field sensors and temperature sensors around the transformer core. The magnetic field sensor is used to measure the strength and change of the magnetic field in the core, and the temperature sensor is used to measure the core temperature. The hysteresis loss density is calculated by measuring the magnetization curve of the core, combining the volume and temperature information of the core, and using the hysteresis loss calculation formula.

[0057] The minimum bending radius of the cable describes that during the installation and operation of the cable, a bending radius that is too small may cause damage to the cable insulation layer, deformation of the internal wires and other problems, affecting the electrical performance and service life of the cable. Monitoring the minimum bending radius of the cable can ensure that the cable is in a safe installation and operation state, promptly discover potential mechanical damage hazards of the cable, and avoid power outages caused by cable failures. The collection method is: install bending radius sensors at key locations of the cable. The sensor calculates the bending radius by measuring the change in curvature or strain of the cable when it is bent. Then monitor the minimum bending radius of the cable in real time.

[0058] The elastic modulus attenuation rate of the tower guy wire can evaluate the structural stability of the tower, promptly detect the degradation of the guy wire performance, take corresponding maintenance measures, prevent the tower from tilting or collapsing, and ensure the safe operation of the distribution network line. The collection method is: install strain sensors and temperature sensors on the tower guy wires. The strain sensor is used to measure the strain change of the guy wire under stress, and the temperature sensor is used to measure the temperature of the guy wire. According to the principles of material mechanics, combined with the initial elastic modulus, cross-sectional area, stress condition and temperature change of the guy wire, by measuring the strain data at different times, the change of the elastic modulus is calculated, and then the elastic modulus attenuation rate is obtained.

[0059] The heating power of the line fittings helps to promptly discover hidden faults of the fittings, avoid problems such as loose connections and insulation damage caused by overheating of the fittings, and ensure the reliable operation of the line. The collection method is: using infrared thermal imaging technology to regularly perform infrared scanning on the line fittings, and calculating the heating power based on the temperature distribution in the thermal imaging image and the surface area of ​​the fittings combined with the principle of thermal radiation.

[0060] The rate of change of the dielectric loss tangent of the insulating material can reflect the degree of aging of the insulating material, discover the trend of insulation performance degradation in advance, provide a basis for the replacement and maintenance of the insulating material, and ensure the insulation safety of the power grid. The collection method is: a dielectric performance measurement sensor is installed near or inside the insulating material. The sensor measures the capacitance, voltage, current and other parameters of the insulating material under an alternating electric field, calculates the dielectric loss tangent according to the calculation formula of the dielectric loss tangent, and then collects data regularly and calculates the rate of change.

[0061] The insulation surface leakage current growth rate can timely detect the deterioration of the insulation surface, such as the degradation of insulation performance due to pollution or moisture. This helps to take targeted maintenance measures, such as cleaning the insulation surface or improving the operating environment, to prevent insulation failures. The collection method is: a leakage current sensor is installed on the surface of the insulation equipment, and the sensor is electrically connected to the insulation surface. When a leakage current flows, the sensor converts the current signal into a measurable electrical signal. The leakage current is recorded in real time through the data acquisition system, and the current growth rate is calculated within a certain time interval.

[0062] The air gap discharge pulse energy inside the insulation reflects the severity of the discharge. Monitoring this parameter can timely discover potential fault points inside the insulation, evaluate the degree of insulation aging, provide a basis for taking corresponding maintenance or replacement measures, and ensure the safe operation of the power grid. The collection method is: install local discharge detection sensors inside or near the insulating material, such as sensors based on high-frequency current transformers, capacitive couplers or ultrasonic sensors. When air gap discharge occurs inside the insulation, the sensor can capture the pulse signal generated by the discharge. By analyzing the pulse signal, including the amplitude, frequency, duration and other parameters of the pulse, the discharge pulse energy is calculated using the energy calculation formula.

[0063] The vibration modal change rate of the equipment bracket can timely detect mechanical abnormalities of the equipment bracket, ensure the stable operation of the equipment, and avoid equipment damage or abnormal operation due to bracket failure. The collection method is: install vibration sensors, such as acceleration sensors or displacement sensors, at key parts of the equipment bracket. The sensor converts the vibration signal of the bracket into an electrical signal. By performing spectrum analysis on the vibration signal, the frequency, amplitude and other parameters of the vibration mode can be obtained. Collect data at different times and calculate the vibration modal change rate.

[0064] The cable outer sheath tensile strain unevenness can timely discover the potential mechanical damage hidden dangers of the cable outer sheath and ensure the safe operation of the cable. The collection method is: distributed strain sensors are installed on the surface of the cable outer sheath. These sensors can be fiber grating strain sensors or sensors based on the principle of resistance strain gauges. The sensors are distributed along the length and circumference of the cable outer sheath to measure the strain conditions at different positions. The data of each sensor is collected through a data acquisition system to calculate the strain unevenness.

[0065] The change in soil pressure at the tower foundation can timely discover potential problems of the tower foundation, such as uneven settlement, so as to take corresponding reinforcement or adjustment measures to ensure the stability of the tower and prevent the tower from tilting or collapsing. The collection method is: install a soil pressure sensor in the soil around the tower foundation. The sensor can be a sensor based on the principle of piezoresistive, capacitive or vibrating string. The sensor converts the change in soil pressure into an electrical signal. The soil pressure data is regularly collected through the data acquisition system, and the pressure change is calculated.

[0066] S3, data analysis step: including an operation status data analysis unit and a safety data analysis unit, which are used to collect target data in real time and transmit the collected data to the data comprehensive evaluation step. The operation status data analysis unit includes an electromagnetic environment data analysis node, an equipment status data analysis node, and a line data analysis node; the safety data analysis unit includes an insulation aging data analysis node and a mechanical abnormality data analysis node.

[0067] In this embodiment, it is specifically required to be explained that: the electromagnetic environment data analysis node is used to establish an electromagnetic environment data calculation model, and the electromagnetic environment data transmitted in the data collection step is imported into the electromagnetic environment data calculation model to obtain the electromagnetic environment quality coefficient value. The electromagnetic environment data calculation model is specifically expressed as:

[0068]

[0069] Among them, α i Indicates the electromagnetic environment quality coefficient value calculated for the i-th time, Gp i Indicates the peak value of ground potential collected for the ith time, Sa i Denotes the spatial electric field distortion area collected for the i-th time, De i represents the duration of the electromagnetic pulse collected for the i-th time, and t represents time.

[0070] In this embodiment, it is specifically required to be explained that: the device status data analysis node is used to establish a device status data calculation model, and the device status data transmitted in the data collection step is imported into the device status data calculation model to obtain the device health coefficient value. The device status data calculation model is specifically expressed as:

[0071]

[0072] Among them, β i Indicates the equipment health factor value calculated for the i-th time, Sm i represents the molecular binding energy of the insulator surface collected for the i-th time, Ta i represents the paint film thickness loss collected for the i-th time, Th i Represents the transformer core hysteresis loss density collected for the i-th time.

[0073] In this embodiment, it is specifically required to be explained that: the line data analysis node is used to establish a line data calculation model, and the line data transmitted in the data collection step is imported into the line data calculation model to obtain the line operation safety factor value. The line data calculation model is specifically expressed as:

[0074]

[0075] Among them, χ i represents the line operation safety factor value calculated for the i-th time, Mr i Indicates the minimum value of the cable bending radius collected for the i-th time, Tr i Hf represents the attenuation rate of the elastic modulus of the tower wire collected for the i-th time. i It represents the heating power of the line fittings collected for the i-th time, and π represents the pi.

[0076] In this embodiment, it is specifically required to be explained that: the insulation aging data analysis node is used to establish an insulation aging data calculation model, and the insulation aging data transmitted in the data acquisition step is imported into the insulation aging data calculation model to obtain the insulation aging risk coefficient value. The insulation aging data calculation model is specifically expressed as:

[0077]

[0078] in, Indicates the insulation aging risk factor value calculated for the i-th time, Mt i It represents the change rate of dielectric loss tangent value of the insulating material collected for the i-th time, Sr i It represents the growth rate of the insulation surface leakage current collected for the i-th time, Ge i It represents the energy of the insulation internal air gap discharge pulse collected for the i-th time, i means starting from the i-th number, and n means ending at the n-th number.

[0079] In this embodiment, it is specifically required to be explained that: the mechanical abnormality data analysis node is used to establish a mechanical abnormality data calculation model, and the mechanical abnormality data transmitted in the data collection step is imported into the mechanical abnormality data calculation model to obtain a mechanical abnormality risk coefficient value. The mechanical abnormality data calculation model is specifically expressed as:

[0080]

[0081] Among them, l i Indicates the mechanical abnormality risk coefficient value calculated for the i-th time, Ec i Indicates the rate of change of vibration mode of the equipment support collected for the ith time, Ct i Ts represents the tensile strain non-uniformity of the cable sheath collected for the i-th time. i It represents the change of soil pressure on the tower foundation collected for the i-th time.

[0082] S4, data comprehensive evaluation step: including a 10 kV distribution network feeder automation control data analysis unit, which is used to comprehensively analyze the analysis results transmitted by the data analysis step and transmit the analysis results to the tracking feedback step.

[0083] In this embodiment, it is specifically required to be explained that: the 10 kV distribution network feeder automation control data analysis unit is used to establish a 10 kV distribution network feeder automation control data calculation model, and the electromagnetic environment quality coefficient value, equipment health coefficient value, line operation safety coefficient value, insulation aging risk coefficient value and mechanical abnormality risk coefficient value transmitted in the data analysis step are imported into the 10 kV distribution network feeder automation control data calculation model to obtain the automation control comprehensive evaluation index value. The 10 kV distribution network feeder automation control data calculation model is specifically expressed as:

[0084]

[0085] Among them, AC represents the calculated comprehensive evaluation index value of automation control, α i represents the electromagnetic environment quality coefficient value calculated for the i-th time, β i represents the equipment health coefficient value calculated for the i-th time, χ i represents the line operation safety factor value calculated for the i-th time, represents the insulation aging risk factor value calculated for the i-th time, l i It represents the mechanical abnormality risk coefficient value calculated for the i-th time, i means starting from the i-th number, and n means ending at the n-th number.

[0086] S5. Tracking and feedback step: used to establish a preset value of the comprehensive evaluation index for automated control, judge the value of the comprehensive evaluation index for automated control according to the preset value of the comprehensive evaluation index for automated control, and send corresponding signals according to the judgment results.

[0087] In this embodiment, specifically, it should be noted that the preset value of the comprehensive evaluation index for automated control is marked as AC def , when AC def = <AC, a normal signal is sent, which indicates that the preset value of the comprehensive evaluation index for automated control is less than or equal to the value of the comprehensive evaluation index for automated control, indicating that the automated control status of the 10 kV distribution network feeder is good; when AC def > AC, a warning signal is sent to relevant technical management personnel, which indicates that the preset value of the comprehensive evaluation index for automated control is greater than the value of the comprehensive evaluation index for automated control, indicating that the automated control status of the 10 kV distribution network feeder is poor and needs to be adjusted.

[0088] Based on the above solution and appendix Figure 2 , this application embodiment also discloses a 10 kV distribution network feeder automated control system, including:

[0089] Data batch division module: 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;

[0090] Data acquisition module: includes an operating status data acquisition unit and a safety data acquisition unit, used to collect the target data in real time and transmit the collected data to the data analysis module. The operating data acquisition unit is used to collect electromagnetic environment data, equipment status data, and line data; the safety data acquisition unit is used to collect insulation aging data and mechanical anomaly data;

[0091] Data analysis module: includes an operating status data analysis unit and a safety data analysis unit, used to collect the target data in real time and transmit the collected data to the data comprehensive evaluation module. The operating status data analysis unit includes an electromagnetic environment data analysis node, an equipment status data analysis node, and a line data analysis node; the safety data analysis unit includes a collected insulation aging data analysis node and a mechanical anomaly data analysis node;

[0092] Data comprehensive evaluation module: includes a 10 kV distribution network feeder automated control data analysis unit, used to comprehensively analyze the analysis results transmitted by the data analysis module and transmit the analysis results to the tracking and feedback module;

[0093] Tracking feedback module: used to establish a preset value of the automation control comprehensive evaluation index, judge the automation control comprehensive evaluation index value according to the preset value of the automation control comprehensive evaluation index, and send a corresponding signal according to the judgment result.

[0094] The present invention effectively improves the pertinence and effectiveness of data processing by dividing the 10 kV distribution network feeder automation operation data into data batches. Electromagnetic environment data, equipment status data, line data, insulation aging data, and mechanical abnormality data are collected through multiple channels. This multi-faceted data collection method reduces the one-sided limitations of traditional distribution network data acquisition and lays a rich and reliable data foundation for subsequent feeder operation status analysis. With the help of data analysis, the collected data is deeply mined to obtain the electromagnetic environment quality coefficient value, equipment health coefficient value, line operation safety coefficient value, insulation aging risk coefficient value, and mechanical abnormality risk coefficient value, so as to accurately identify the hidden dangers that may interfere with the normal operation of the feeder. By comprehensively considering and deeply integrating the results of data analysis, the accuracy and practicality of the analysis results are greatly improved. With real-time monitoring and continuous follow-up, once a feeder abnormality is detected, an alarm message will be triggered immediately. Relevant operation and maintenance personnel and management personnel can quickly understand the operating status and potential crises of the 10kV distribution network feeder automation, and take timely measures to adjust and optimize the operating strategies and parameters, providing solid support for achieving efficient and accurate 10kV distribution network feeder automation control and wise and reasonable operation and maintenance planning.

[0095] 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;

[0096] 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 10 kV distribution network feeder automation control method, characterized in that: include: S1, data batch division step: 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; S2, data collection step: including an operation status data collection unit and a safety data collection unit, which are used to collect target data in real time and transmit the collected data to the data analysis step. The operation data collection unit is used to collect electromagnetic environment data, equipment status data and line data; the safety data collection unit is used to collect insulation aging data and mechanical abnormality data; S3, data analysis step: including an operation status data analysis unit and a safety data analysis unit, which are used to collect target data in real time and transmit the collected data to the data comprehensive evaluation step. The operation status data analysis unit includes an electromagnetic environment data analysis node, an equipment status data analysis node, and a line data analysis node; the safety data analysis unit includes an insulation aging data analysis node and a mechanical abnormality data analysis node; S4, data comprehensive evaluation step: including a 10 kV distribution network feeder automation control data analysis unit, which is used to comprehensively analyze the analysis results transmitted by the data analysis step, and transmit the analysis results to the tracking feedback step; S5, tracking feedback step: used to establish a preset value of the automation control comprehensive evaluation index, judge the automation control comprehensive evaluation index value according to the preset value of the automation control comprehensive evaluation index, and send a corresponding signal according to the judgment result.

2. A 10 kV distribution network feeder automation control method according to claim 1, characterized in that: The electromagnetic environment data include the ground potential rise peak value Gp, the spatial electric field distortion area Sa and the electromagnetic pulse duration De; the equipment status data include the insulator surface molecular binding energy Sm, the paint film thickness loss Ta and the transformer core hysteresis loss density Th; the line data include the minimum cable bending radius Mr, the pole tower wire elastic modulus attenuation rate Tr and the line hardware heating power Hf.

3. A 10 kV distribution network feeder automation control method according to claim 1, characterized in that: The insulation aging data include the dielectric loss tangent change rate Mt of the insulation material, the insulation surface leakage current growth rate Sr and the insulation internal air gap discharge pulse energy Ge; the mechanical abnormality data include the equipment bracket vibration mode change rate Ec, the cable outer sheath tensile strain unevenness Ct and the tower foundation soil pressure change Ts.

4. A 10 kV distribution network feeder automation control method according to claim 1, characterized in that: The electromagnetic environment data analysis node is used to establish an electromagnetic environment data calculation model, import the electromagnetic environment data transmitted in the data collection step into the electromagnetic environment data calculation model, and obtain the electromagnetic environment quality coefficient value. The electromagnetic environment data calculation model is specifically expressed as: Among them, α i Indicates the electromagnetic environment quality coefficient value calculated for the i-th time, Gp i The peak value of the ground potential collected for the ith time, Sa i Denotes the spatial electric field distortion area collected for the i-th time, De i represents the duration of the electromagnetic pulse collected for the i-th time, and t represents time.

5. A 10 kV distribution network feeder automation control method according to claim 1, characterized in that: The device status data analysis node is used to establish a device status data calculation model, import the device status data transmitted in the data collection step into the device status data calculation model, and obtain the device health coefficient value. The device status data calculation model is specifically expressed as: Among them, β i Indicates the equipment health factor value calculated for the i-th time, Sm i represents the molecular binding energy of the insulator surface collected for the i-th time, Ta i represents the paint film thickness loss collected for the i-th time, Th i Represents the transformer core hysteresis loss density collected for the i-th time.

6. A 10 kV distribution network feeder automation control method according to claim 1, characterized in that: The line data analysis node is used to establish a line data calculation model, import the line data transmitted in the data collection step into the line data calculation model, and obtain the line operation safety factor value. The line data calculation model is specifically expressed as: Among them, χ i represents the line operation safety factor value calculated for the i-th time, Mr i Indicates the minimum value of the cable bending radius collected for the i-th time, Tr i Hf represents the attenuation rate of the elastic modulus of the tower wire collected for the i-th time. i It represents the heating power of the line fittings collected for the i-th time, and π represents the pi.

7. A 10 kV distribution network feeder automation control method and system according to claim 1, characterized in that: The insulation aging data analysis node is used to establish an insulation aging data calculation model, import the insulation aging data transmitted in the data acquisition step into the insulation aging data calculation model, and obtain the insulation aging risk coefficient value. The insulation aging data calculation model is specifically expressed as: in, Indicates the insulation aging risk factor value calculated for the i-th time, Mt i It represents the change rate of dielectric loss tangent value of the insulating material collected for the i-th time, Sr i It represents the growth rate of the insulation surface leakage current collected for the i-th time, Ge i It represents the energy of the insulation internal air gap discharge pulse collected for the i-th time, i means starting from the i-th number, and n means ending at the n-th number.

8. A 10 kV distribution network feeder automation control method according to claim 1, characterized in that: The mechanical abnormality data analysis node is used to establish a mechanical abnormality data calculation model, import the mechanical abnormality data transmitted in the data collection step into the mechanical abnormality data calculation model, and obtain a mechanical abnormality risk coefficient value. The mechanical abnormality data calculation model is specifically expressed as: in, Indicates the mechanical abnormality risk coefficient value calculated for the i-th time, Ec i Indicates the rate of change of vibration mode of the equipment support collected for the ith time, Ct i Ts represents the tensile strain non-uniformity of the cable sheath collected for the i-th time. i It represents the change of soil pressure on the tower foundation collected for the i-th time.

9. A 10 kV distribution network feeder automation control method according to claim 1, characterized in that: The 10 kV distribution network feeder automation control data analysis unit is used to establish a 10 kV distribution network feeder automation control data calculation model, and the electromagnetic environment quality coefficient value, equipment health coefficient value, line operation safety coefficient value, insulation aging risk coefficient value and mechanical abnormality risk coefficient value transmitted in the data analysis step are imported into the 10 kV distribution network feeder automation control data calculation model to obtain the automation control comprehensive evaluation index value. The 10 kV distribution network feeder automation control data calculation model is specifically expressed as: Among them, AC represents the calculated comprehensive evaluation index value of automation control, α i represents the electromagnetic environment quality coefficient value calculated for the i-th time, β i represents the equipment health coefficient value calculated for the i-th time, χ i represents the line operation safety factor value calculated for the i-th time, represents the insulation aging risk factor value calculated for the i-th time, It represents the mechanical abnormality risk coefficient value calculated for the i-th time, i means starting from the i-th number, and n means ending at the n-th number.

10. A 10 kV distribution network feeder automation control system, used to implement a 10 kV distribution network feeder automation control method as described in any one of claims 1 and 9, 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; Data acquisition module: including operation status data acquisition unit and safety data acquisition unit, used to acquire target data in real time and transmit the acquired data to the data analysis module. The operation data acquisition unit is used to acquire electromagnetic environment data, equipment status data and line data; the safety data acquisition unit is used to acquire insulation aging data and mechanical abnormality data; Data analysis module: including an operation status data analysis unit and a safety data analysis unit, which are used to collect target data in real time and transmit the collected data to the data comprehensive evaluation module. The operation status data analysis unit includes an electromagnetic environment data analysis node, an equipment status data analysis node, and a line data analysis node; the safety data analysis unit includes an insulation aging data analysis node and a mechanical abnormality data analysis node; Data comprehensive evaluation module: including a 10 kV distribution network feeder automation control data analysis unit, which is used to comprehensively analyze the analysis results transmitted by the data analysis module and transmit the analysis results to the tracking feedback module; Tracking feedback module: used to establish a preset value of the automation control comprehensive evaluation index, judge the automation control comprehensive evaluation index value according to the preset value of the automation control comprehensive evaluation index, and send a corresponding signal according to the judgment result.

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