Electric field sensor optimization method and system for multi-voltage high-voltage transmission electricity verification

Abstract

The invention discloses an electric field sensor optimization method and system for multi-voltage high-voltage power transmission electricity verification, and relates to the technical field related to high-voltage electricity verification, and the method comprises the steps: laying a sensor network in a power transmission line; selecting and activating the electric field sensor according to the measurement feedback of the pre-measurement sensor; environment data of the power transmission line is collected and transmitted to the central processing platform, control compensation of the activated electric field sensor is carried out, and electric field collection is executed; performing digital twin modeling of the power transmission line according to the power transmission task; gradient interpolation compensation of the electric field data set is carried out according to the electric field gradient distribution, and then acquisition optimization of the electric field sensor is completed. The technical problems in the prior art that the measurement precision of an electric field sensor is influenced by voltage fluctuation, electromagnetic interference and a complex environment, so that the high-voltage transmission electricity testing accuracy, the fault diagnosis capability and the line operation reliability are poor are solved. The technical effects of improving the high-voltage power transmission electricity testing accuracy, the fault diagnosis capability and the line operation reliability are achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to high-voltage electrical testing, and specifically to an electric field sensor optimization method and system for multi-voltage high-voltage transmission electrical testing. Background Art

[0002] High-voltage transmission lines with multiple voltage levels are crucial for the long-distance, high-capacity transmission of electricity. With the development of smart grids, the voltage levels of transmission lines are constantly increasing, and the structure of transmission networks is becoming increasingly complex, placing higher demands on the safe operation and maintenance of transmission lines. As a key step in the operation and maintenance of high-voltage transmission lines, the accuracy and reliability of electric field testing directly impact the stable operation of electricity. On the one hand, the measurement accuracy of electric field sensors in multi-voltage environments is easily affected by line voltage fluctuations and electromagnetic interference, making it difficult to meet the requirements of high-precision electric field testing. On the other hand, in complex and changing environmental conditions such as strong winds, rain, snow, and high temperatures, sensor performance significantly degrades, resulting in large deviations in electric field test data and even misjudgments, seriously threatening the safe operation of transmission lines. Furthermore, as the scale of transmission lines continues to expand, single-point measurement methods cannot fully reflect the overall electric field distribution of transmission lines, making it difficult to achieve real-time and accurate monitoring of the transmission line's operating status.

[0003] Therefore, in the current related technologies, there is a technical problem that the measurement accuracy of electric field sensors is affected by voltage fluctuations, electromagnetic interference and complex environments, resulting in poor accuracy of high-voltage transmission power testing, fault diagnosis capabilities and line operation reliability. Summary of the Invention

[0004] This application solves the technical problem in the prior art that the measurement accuracy of electric field sensors is affected by voltage fluctuations, electromagnetic interference and complex environment, resulting in poor accuracy, fault diagnosis capability and line operation reliability of high-voltage transmission power testing, by providing an electric field sensor optimization method and system for multi-voltage high-voltage transmission power testing. This achieves the technical effect of improving the accuracy, fault diagnosis capability and line operation reliability of high-voltage transmission power testing.

[0005] The present application provides an electric field sensor optimization method for multi-voltage high-voltage transmission power testing, the method comprising: deploying a sensor network on a transmission line, wherein the sensor network integrates a plurality of electric field sensors, and the sensor network is a multi-level sensor network; activating a predicted measurement sensor in the sensor network, and selectively activating the electric field sensors in the sensor network based on the measurement feedback of the predicted measurement sensor; performing environmental data collection of the transmission line through the deployed environmental sensors, transmitting the environmental data collection results to a central processing platform, controlling and compensating the activated electric field sensors based on the feedback signals of the central processing platform, performing electric field collection, and establishing an electric field data set; obtaining the transmission task of the transmission line, performing digital twin modeling of the transmission line based on the transmission task, and establishing an electric field gradient distribution using the digital twin modeling results; performing gradient interpolation compensation of the electric field data set based on the electric field gradient distribution, and completing the collection optimization of the electric field sensor using the gradient interpolation compensation results.

[0006] In a possible implementation, the electric field sensor optimization method for multi-voltage high-voltage transmission and electrical testing also performs the following processing: extracting load change characteristics based on the transmission task, and extracting predicted electric field characteristics based on the measurement feedback; obtaining sensor characteristics of the electric field sensors in the sensor network, the sensor characteristics including position characteristics, acquisition accuracy characteristics, anti-interference characteristics, and sensor state characteristics; inputting the load change characteristics, the predicted electric field characteristics, and the sensor characteristics as input data into the adaptation and matching network to establish an adaptation and matching result; and selecting and activating the electric field sensor based on the adaptation and matching result.

[0007] In a possible implementation, the electric field sensor optimization method for multi-voltage high-voltage transmission and electrical testing also performs the following processing: activating the electric field accuracy loss layer within the adaptive matching network, performing measurement accuracy loss analysis based on the input data, and establishing a first adaptation result; activating the load adaptability loss layer within the adaptive matching network, performing load adaptability analysis based on the input data, and establishing a second adaptation result; activating the health and reliability analysis layer within the adaptive matching network, performing adaptation analysis based on the input data, and establishing a third adaptation result; synchronizing the first adaptation result, the second adaptation result, and the third adaptation result to the global analysis layer to perform coverage evaluation, and outputting the adaptation matching result based on the coverage evaluation result.

[0008] In a possible implementation, the electric field sensor optimization method for multi-voltage high-voltage transmission power testing also performs the following processing: using the first adaptation result, the second adaptation result, and the third adaptation result to perform activation selection under comprehensive adaptation to generate a basic activation selection result; performing a monitoring coverage evaluation of the transmission line on the basic activation selection result; adding a new activation sensor based on the monitoring coverage evaluation result; and outputting an adaptation matching result based on the newly added activation sensor and the basic activation selection result.

[0009] In a possible implementation, the electric field sensor optimization method for multi-voltage high-voltage transmission and electrical testing also performs the following processing: configuring a gradient compensation channel according to the electric field gradient distribution; inputting the electric field data set with position identification as input data into the gradient compensation channel, performing distortion correction of the electric field gradient, and completing gradient interpolation compensation.

[0010] In a possible implementation, the electric field sensor optimization method for multi-voltage high-voltage transmission and electrical testing also performs the following processing: configuring a virtual sensor mapped to the activated electric field sensor; calling the simulation database of the central processing platform, performing scenario simulation under the environmental data collection results, and performing adaptive simulation testing on the virtual sensor; executing control optimization according to the adaptive simulation test results and establishing a feedback signal.

[0011] In a possible implementation, the electric field sensor optimization method for multi-voltage high-voltage transmission and electrical inspection also performs the following processing: using the electric field sensor after acquisition optimization to perform electric field data acquisition, and performing acquisition status self-test; if the acquisition status self-test result fails to meet the preset threshold, activating the redundant sensor to re-acquire the electric field data, and completing the transmission and electrical inspection management according to the acquisition results of the redundant sensor.

[0012] The present application also provides an electric field sensor optimization system for multi-voltage high-voltage transmission power testing, the system comprising: a sensor network deployment unit, for deploying a sensor network on a transmission line, wherein the sensor network integrates a plurality of electric field sensors, and the sensor network is a multi-level sensor network; a sensor selection and activation unit, for activating the predicted measurement sensors in the sensor network, and selectively activating the electric field sensors in the sensor network based on the measurement feedback of the predicted measurement sensors; an environmental data acquisition unit, for performing environmental data acquisition of the transmission line through the deployed environmental sensors, transmitting the environmental data acquisition results to a central processing platform, performing control compensation of the activated electric field sensors based on the feedback signals of the central processing platform, performing electric field acquisition, and establishing an electric field data set; a digital twin modeling unit, for obtaining the transmission task of the transmission line, performing digital twin modeling of the transmission line based on the transmission task, and establishing an electric field gradient distribution using the digital twin modeling results; a sensor acquisition optimization unit, for performing gradient interpolation compensation of the electric field data set based on the electric field gradient distribution, and completing acquisition optimization of the electric field sensor using the gradient interpolation compensation results.

[0013] The electric field sensor optimization method and system for multi-voltage high-voltage transmission power testing proposed in this application is intended to deploy a sensor network on the transmission line; select and activate electric field sensors based on the measurement feedback of the pre-measurement sensors in the sensor network; collect environmental data of the transmission line through environmental sensors, transmit it to the central processing platform, and perform control compensation of the activated electric field sensors to perform electric field collection; perform digital twin modeling of the transmission line according to the transmission task; perform gradient interpolation compensation of the electric field data set according to the electric field gradient distribution, and then complete the collection optimization of the electric field sensor. This solves the technical problem in the prior art that the measurement accuracy of electric field sensors is affected by voltage fluctuations, electromagnetic interference and complex environments, resulting in poor accuracy, fault diagnosis capability and line operation reliability of high-voltage transmission power testing, and achieves the technical effect of improving the accuracy, fault diagnosis capability and line operation reliability of high-voltage transmission power testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0015] Figure 1 A flow chart of the electric field sensor optimization method for multi-voltage high-voltage transmission and electrical testing provided in an embodiment of the present application.

[0016] Figure 2 Schematic diagram of the structure of the electric field sensor optimization system for multi-voltage high-voltage transmission and electrical testing provided in an embodiment of the present application.

[0017] Explanation of the accompanying drawings: sensor network deployment unit 10, sensor selection and activation unit 20, environmental data acquisition unit 30, digital twin modeling unit 40, sensor acquisition optimization unit 50. DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0021] The embodiment of the present application provides an electric field sensor optimization method for multi-voltage high-voltage transmission and electric detection, such as Figure 1 As shown, the method includes: Step S100: deploying a sensor network on a power transmission line. The sensor network integrates a plurality of electric field sensors, and the sensor network is a multi-layered sensor network.

[0022] Preferably, a multi-level sensor network is deployed on the transmission line using multiple electric field sensors, wherein the multiple electric field sensors are integrated in the sensor network. Specifically, sensors are deployed at different heights of the high-voltage transmission line (such as the conductor layer, the overhead ground wire layer, and the top of the tower) to cover the three-dimensional spatial electric field distribution of the transmission line from the conductor to the ground. For example, high-precision electric field sensors (such as quantum electric field sensors, optical fiber electric field sensors, etc.) are deployed at high levels (near the high-voltage conductor) to directly measure the electric field strength in the strong electric field area around the conductor. Electric field sensors are deployed at middle levels (near insulator strings or hardware) to monitor sudden changes in the electric field distribution (such as local discharge potential risk areas). Electric sensors are deployed at low levels (near the tower foundation or the ground). At the same time, long-distance transmission lines are divided into several monitoring sections (such as one section every 10 kilometers). Sensor nodes are deployed at a certain interval (such as every 500 meters) in each section to form a horizontal hierarchical structure from section level to node level, thereby achieving grid coverage of the electric field along the line. Through the layered layout in the vertical and horizontal directions, the electric field distribution details at different locations of the transmission line (such as the strong electric field area near the conductor and the electric field distortion points on the insulator surface) can be captured, avoiding the limitations of single-point measurement and improving the spatial resolution and reliability of electric field measurement.

[0023] Step S200: activating the pre-measurement sensor in the sensor network, and selectively activating the electric field sensor in the sensor network based on the measurement feedback of the pre-measurement sensor.

[0024] Step S200 further includes step S210, extracting load change characteristics according to the power transmission task, and extracting predicted electric field characteristics according to the measurement feedback; step S220, obtaining sensor characteristics of the electric field sensor in the sensor network, the sensor characteristics including position characteristics, acquisition accuracy characteristics, anti-interference characteristics, and sensor state characteristics; step S230, inputting the load change characteristics, the predicted electric field characteristics, and the sensor characteristics as input data into the adaptation matching network to establish an adaptation matching result; step S240, selecting and activating the electric field sensor according to the adaptation matching result.

[0025] Preferably, the predictive measurement sensors in the sensor network are activated, and the optimal configuration of sensor resources is achieved through intelligent matching of load characteristics, electric field characteristics and sensor characteristics. The predictive measurement sensors refer to basic sensors deployed at key positions of the transmission lines, which are used to obtain initial electric field data and transmission task parameters in real time. In multi-voltage lines, the predictive measurement sensors perceive in advance the current transmission voltage level, active / reactive power, voltage fluctuation frequency and other load change characteristics (such as voltage fluctuation amplitude, load curve slope, high-frequency voltage regulation scenario or low-frequency stable transmission scenario). Different load characteristics correspond to different electric field distribution requirements. For example, high-voltage loads require higher-precision sensors to monitor strong electric field areas near the conductors. At the same time, based on the measurement feedback, predictive measurement electric field characteristics such as initial electric field strength, electric field gradient change rate, and abnormal field strength point location (such as electric field peak near the conductor, electric field gradient of the insulator string) are extracted to judge the complexity of the current electric field distribution. For example, areas with larger gradients require higher density or precision sensor coverage.

[0026] Preferably, the electric field sensors in the sensor network are analyzed to obtain sensor features, including location features, acquisition accuracy features, anti-interference features, and sensor state features, which are used to match task requirements. Specifically, the location feature refers to the relative relationship between the sensor deployment location and the transmission line, the acquisition accuracy feature refers to the measurement capability parameter of the sensor, the anti-interference feature refers to the ability of the sensor to resist external interference, and the state feature refers to the current available state of the sensor. The specific sensor feature matching data is shown in Table 1: Table 1 Electric field sensor feature matching data table

[0027] Preferably, the load change characteristics, predicted electric field characteristics and sensor characteristics are converted into structured data. For example, the load characteristics are "500kV full load operation, voltage fluctuation frequency <0.1Hz", the electric field characteristics are "the average electric field near the conductor is 300kV / m, the gradient change rate is 4kV / m", and the sensor characteristics are "sensor A is located 1.5 meters below the conductor, accuracy ±1%, anti-interference level IP68, power 80%". These three types of characteristics are input as input data into the adaptive matching network to perform matching, wherein the adaptive matching network includes an electric field accuracy loss layer, a load adaptability loss layer and a health reliability analysis layer. Specifically, the electric field accuracy loss layer is used to quantify the impact of different sensor configurations on the electric field measurement accuracy, the load adaptability loss layer is used to evaluate the response capability of the sensor configuration to changes in power transmission tasks, and the health reliability analysis layer is used to predict the risk of sensor failure based on real-time status. Finally, the output results of the three network layers are fused to generate an adaptive matching result. Finally, the electric field sensors are selected and activated based on the adaptation and matching results. That is, sensor resources are dynamically allocated to optimize resource consumption while ensuring measurement accuracy. When the power transmission task changes (such as voltage level switching, load start and stop) or the environmental conditions deteriorate (such as sudden heavy rain), the activation list is adjusted in real time to ensure that the sensor combination is always in the optimal state.

[0028] Furthermore, step S230 also includes step S231, activating the electric field accuracy loss layer in the adaptive matching network, performing measurement accuracy loss analysis based on the input data, and establishing a first adaptation result; step S232, activating the load adaptability loss layer in the adaptive matching network, performing load adaptability analysis based on the input data, and establishing a second adaptation result; step S233, activating the health reliability analysis layer in the adaptive matching network, performing adaptation analysis based on the input data, and establishing a third adaptation result; step S234, synchronizing the first adaptation result, the second adaptation result, and the third adaptation result to the global analysis layer to perform coverage evaluation, and outputting the adaptation matching result based on the coverage evaluation result.

[0029] Preferably, the predicted electric field characteristics (such as electric field gradient, abnormal point location), sensor acquisition accuracy characteristics (resolution, error rate) and location characteristics (spatial relationship between deployment location and electric field distortion area) are input into the electric field accuracy loss layer in the adaptation matching network to perform measurement accuracy loss analysis, that is, compare the predicted electric field characteristics (such as gradient, extreme value) with the sensor acquisition accuracy characteristics (error rate, resolution), calculate the theoretical accuracy loss of the key area, and then combine the location characteristics to evaluate the impact of the sensor deployment position on the measurement results, ensure that the sensor configuration meets the measurement accuracy requirements of the electric field distribution, and finally output the first adaptation result, that is, the sensor combination that meets the accuracy requirements, such as "sensor with error ≤±2% and <3 meters away from the conductor".

[0030] Preferably, the load change characteristics (voltage level, power fluctuation frequency), sensor dynamic response indicators (sampling frequency, range switching time) and historical task execution data (such as sensor failure probability under different loads) are input into the load adaptability loss layer in the adaptation matching network, and load adaptability analysis is performed, that is, according to the load change characteristics (voltage level, power fluctuation frequency), the dynamic performance of the adapted sensor is screened, and then the sensor failure cases under similar loads in the historical data are analyzed, and models that are easily interfered with are excluded to ensure that the sensor works stably under the current load conditions. Finally, the second adaptation result is output, that is, the sensor combination that adapts to the load task, such as "sensor with sampling frequency ≥ 50Hz and range covering the current voltage".

[0031] Preferably, the sensor status characteristics (battery power, communication quality, historical faults) and environmental data (temperature and humidity, wind speed, electromagnetic interference intensity) are input into the health reliability analysis layer within the adaptation matching network for adaptation analysis, that is, the real-time health is evaluated based on the status characteristics (battery power, communication quality, historical faults), and then the risk of sensor performance degradation is predicted in combination with environmental data (such as high temperature, strong wind) to avoid the use of faulty or high-risk sensors. Finally, the third adaptation result is output, that is, a sensor combination with a qualified health status, such as "sensors with battery power > 50% and signal > -80dBm".

[0032] Preferably, the first, second, and third adaptation results are synchronized to the global analysis layer for coverage evaluation. Specifically, the three-layer adaptation results are input into the global analysis layer for a logical intersection operation to ensure that the sensors ultimately activated meet all conditions, including accuracy, load adaptability, and health and reliability. For example, in high-voltage sections, the first adaptation result (accuracy) is a mandatory condition, while the second and third adaptation results serve as auxiliary screening conditions. If there is a conflict between the three layers of results (e.g., a sensor meets accuracy but has a critical health), the weights are dynamically adjusted based on the priority of the power transmission task. For example, under normal operating conditions, the accuracy weight (40%) is greater than the load adaptability weight (30%), and the health and reliability weight (30%) is adjusted. Under emergency conditions (such as fault repair), the weight is adjusted to health and reliability weight (50%), accuracy weight (30%), and load adaptability weight (20%). This ultimately generates an activation list of sensors that meet all constraints, a standby list of sensors that meet some of the constraints and can serve as candidates, and a dormant list of sensors that do not meet the current task requirements.

[0033] Furthermore, step S234 also includes step a, using the first adaptation result, the second adaptation result, and the third adaptation result to perform activation selection under comprehensive adaptation to generate a basic activation selection result; step b, performing a monitoring coverage evaluation of the transmission line on the basic activation selection result; step c, adding a new activation sensor based on the monitoring coverage evaluation result; and step d, outputting an adaptation matching result based on the newly added activation sensor and the basic activation selection result.

[0034] Preferably, the first adaptation result, the second adaptation result, and the third adaptation result are used to perform activation selection under comprehensive adaptation, such as performing a logical intersection operation to generate a sensor combination that meets the basic conditions. As the basic activation selection result, there may be monitoring coverage blind spots, such as no sensors are activated at key locations (such as insulator strings), insufficient sensor density in high-gradient areas, etc.; then, the electric field distribution is simulated by the digital twin of the transmission line, and the sensor positions in the basic activation list are compared to evaluate the monitoring coverage of the transmission line for the basic activation selection results, including spatial coverage evaluation, precision coverage evaluation, and redundant coverage evaluation. Specifically, spatial coverage evaluation refers to analyzing whether the sensor positions in the basic activation list cover the key monitoring points of the transmission line, such as the conductor-insulator connection of each tower, the river crossing section, and the crossing point; precision coverage evaluation refers to checking whether the high voltage / high gradient area is covered by high-precision sensors (error ≤±2%); redundant coverage evaluation refers to evaluating whether multiple types of sensors (such as capacitive + optical fiber) are deployed at important nodes (such as the entrance and exit of the hub substation) for cross-validation.

[0035] Preferably, based on the coverage evaluation results, new sensors are activated through a dynamic adjustment mechanism. Specifically, sensors near electric field distortion points such as uncovered insulator strings and wire clamps are activated first (even if their health status is medium, such as 40% battery). At the same time, low-precision sensors are replaced in high-voltage sections, and spare high-precision nodes are activated (such as quantum sensors with an error of ±1%). Then, anti-interference sensors are added to hub nodes to form a "master-backup" monitoring pair (such as activating capacitive + MEMS sensors at the same time). The newly added sensors meet at least one core dimension requirement (accuracy / load / health). For example, the health status of sensors in key locations can be relaxed to a battery level of >20%; redundant nodes can accept slightly lower accuracy (error ≤±3%), but ensure that the anti-interference capability meets the standard (IP65). The dynamic adjustment mechanism can be to re-execute the coverage evaluation every 15 minutes. If the transmission task changes (such as voltage level adjustment) or the sensor status changes (such as the battery level of a node is less than 20%), the blind spot filling process is automatically triggered. Finally, the newly activated sensors and basic activation selection results are integrated to output the final adaptation matching results, ensuring that the sensor activation strategy not only meets the multi-dimensional performance requirements but also fully covers the monitoring needs of the transmission line.

[0036] Step S300: Environmental data collection of the transmission line is performed through the deployed environmental sensors, and the environmental data collection results are transmitted to the central processing platform. Control compensation of the activated electric field sensors is performed according to the feedback signal of the central processing platform, electric field collection is performed, and an electric field data set is established.

[0037] Preferably, a variety of environmental sensors are evenly distributed along the transmission line (for example, one set of environmental sensors is deployed on each tower), and are deployed densely in high-risk areas (such as areas with frequent lightning and heavy pollution) to perform environmental data collection on the transmission line, including using meteorological sensors to collect temperature, humidity, air pressure, wind speed, and precipitation, using electromagnetic interference sensors to obtain radio frequency field strength and power frequency harmonic components, using vibration / tilt sensors to collect tower vibration amplitude and insulator inclination, and using pollution sensors to obtain salt density and ash density values ​​on the insulator surface, thereby forming environmental data collection results.

[0038] Preferably, the environmental data collection results are transmitted in real time to a central processing platform via a wireless communication network (such as 5G or Beidou short messages). Specifically, the central processing platform analyzes the patterns of electric field measurement errors under the same historical environmental conditions, establishes a correlation between environmental factors and electric field measurement errors, and generates a feedback signal. Dynamic control compensation of the activated electric field sensor is then performed based on the feedback signal, including adaptive adjustment of sensor parameters. For example, when strong electromagnetic interference is detected (such as a radio frequency field strength greater than 100dBμV / m), the sampling frequency of the electric field sensor is automatically increased (such as from 20Hz to 100Hz) to suppress noise through high-frequency sampling. In a high-humidity environment (such as humidity greater than 90%), the capacitive sensor is switched to a moisture-resistant operating mode (such as activating an internal heating module to reduce condensation), or a spare fiber optic sensor (not sensitive to humidity) is temporarily activated. If the power transmission task adjustment causes the voltage to increase (such as from 220kV to 500kV), the sensor range is automatically expanded (such as from 0-300kV / m to 0-600kV / m) to avoid data saturation. Finally, the electric field is collected according to the sensor configuration after control compensation, and the electric field sensor data and environmental data are stored synchronously to form a structured electric field data set.

[0039] Furthermore, step S300 also includes step S310, configuring a virtual sensor mapped to the activated electric field sensor; step S320, calling the simulation database of the central processing platform, executing scene simulation under the environmental data acquisition results, and performing adaptive simulation testing on the virtual sensor; step S330, executing control optimization according to the adaptive simulation test results and establishing a feedback signal.

[0040] Preferably, a mapped virtual sensor is created for each activated electric field sensor based on the factory test data of the sensor, and is corrected through historical operation data. For example, the influence coefficient of humidity on the sensor is adjusted according to the actual error statistical results. The virtual sensor includes physical properties such as position coordinates, measurement range, accuracy parameters (error rate, resolution), and environmental response characteristics such as temperature and humidity drift curves, electromagnetic interference sensitivity curves, and mechanical vibration transfer functions; then the simulation database of the central processing platform is called to perform scenario simulation under the environmental data collection results. Specifically, the measurement performance of the simulation sensor under current conditions is simulated with real-time environmental data (such as temperature and humidity, electromagnetic interference intensity), and the extreme performance of the sensor is simulated and tested with historical extreme environmental data (such as the maximum wind speed in previous years and the peak field strength of thunderstorms).

[0041] Preferably, the virtual sensor is then subjected to adaptive simulation testing. This involves applying different control compensation strategies to the virtual sensor, observing changes in its output data, and evaluating sensor accuracy, response speed, reliability, and resource consumption. This involves, for example, adjusting parameters (e.g., changing the sampling frequency or range), hardware reconfiguration (e.g., switching to a backup sensor type), or layout optimization (e.g., adjusting the sensor installation angle or adding redundant nodes). This involves determining the sensor's measurement error rate, the time from interference occurrence to stable measurement, data efficiency (the proportion of outliers), and power consumption and communication traffic. This generates adaptive simulation test results. Finally, based on the adaptive simulation test results, control optimization is performed, for example, with the goal of minimizing error and power consumption, iteratively searching for the optimal control parameter combination, outputting the optimal control strategy, and converting it into executable control instructions. For example, a digital signal of a parameter adjustment instruction is sent to the sensor via an API.

[0042] Step S400: Acquire the transmission task of the transmission line, perform digital twin modeling of the transmission line according to the transmission task, and establish the electric field gradient distribution using the digital twin modeling result.

[0043] Preferably, the transmission task of the transmission line is obtained, such as the transmission power, transmission time, voltage level, whether it is a special period (such as peak load period), etc. These task parameters directly affect the operating status of the transmission line (such as current and voltage fluctuations) and the electric field distribution characteristics; then the digital twin modeling of the transmission line is carried out according to the transmission task, that is, through sensor data, historical operation data and physical models, a real-time mirror model of the transmission line is constructed in the virtual space to simulate its real operating status. Specifically, the transmission task parameters (such as power, voltage, etc.) are input, combined with the line physical characteristics (such as conductor model, spacing, insulation configuration) and environmental conditions (such as temperature) , humidity, wind speed), build a dynamic simulation model, which can reflect the electrical characteristics (such as current distribution, electric field strength) and mechanical characteristics (such as conductor tension and vibration) of the line under the task in real time, and then obtain the digital twin modeling results; and use the digital twin modeling results to establish the electric field gradient distribution, where the electric field gradient refers to the change law of the magnitude and direction of the electric field intensity in the space around the transmission line with the position, that is, the digital twin model outputs the electric field distribution of the transmission line under the current task, for example, the electric field intensity is the highest on the surface of the conductor and gradually decays towards the surrounding space; the electric field superposition effect between conductors of different phases, etc., and then generate the electric field gradient distribution, including the electric field gradient distribution.

[0044] Step S500 , performing gradient interpolation compensation on the electric field data set according to the electric field gradient distribution, and completing acquisition optimization of the electric field sensor using the gradient interpolation compensation result.

[0045] Step S500 further includes step S510, configuring a gradient compensation channel according to the electric field gradient distribution; step S520, inputting the electric field data set with position identification as input data into the gradient compensation channel, performing distortion correction of the electric field gradient, and completing gradient interpolation compensation.

[0046] Preferably, the electric field gradient information generated by the digital twin model is used to correct the spatial distortion of the sensor collected data, and the monitoring blind spots are filled by the interpolation algorithm, ultimately achieving high-precision restoration of the electric field measurement. Specifically, the difference between the sensor position and the theoretical field strength is analyzed according to the electric field gradient distribution, and a position-error mapping relationship is established, and then a correction model, namely the gradient compensation channel, is established; then the electric field data set with the position identifier (such as the tower number, the vertical distance from the conductor, and the horizontal coordinate) is used as input data and input into the gradient compensation channel to perform electric field gradient distortion correction, that is, the original measurement value is corrected according to the sensor position, and the error caused by environmental interference (such as temperature and humidity drift, electromagnetic noise) is corrected at the same time, that is, the compensation result is corrected twice in combination with the environmental data.

[0047] Preferably, the compensated electric field values ​​are then compared with the theoretical gradients of the digital twin model, and a difference matrix is ​​calculated. If the difference exceeds a threshold (e.g., ±5 kV / m·m), it is determined to be a measurement anomaly or local field strength distortion. Nearby backup sensors are activated for cross-validation. Finally, using the corrected discrete sensor data, an interpolation algorithm is used to fit the electric field gradient distribution across the entire transmission line. Data in uncovered areas is then filled in, and an optimized electric field dataset is output, containing corrected data for all sensor locations and virtual data points filled in by interpolation. Finally, the gradient interpolation compensation results are used to optimize electric field sensor acquisition. For example, sensor density can be automatically increased or sampling frequency can be increased in areas where gradients vary dramatically after interpolation. High field strength areas detected by interpolation can be replaced with high-voltage sensors (e.g., quantum electric field sensors). In areas with high interpolation uncertainty, multiple sensor types can be activated for redundant measurement. Gradient compensation can reduce measurement bias caused by sensor position errors, promptly detect local electric field distortion, and thus improve measurement accuracy and fault warning capabilities.

[0048] Furthermore, step S500 also includes step S530, using the electric field sensor after acquisition optimization to perform electric field data acquisition and perform acquisition status self-test; step S540, if the acquisition status self-test result fails to meet the preset threshold, the redundant sensor is activated to re-acquire the electric field data, and the power transmission inspection management is completed according to the acquisition results of the redundant sensor.

[0049] Preferably, electric field data collection is performed using an electric field sensor after acquisition optimization, including real-time collection of data such as electric field strength, gradient changes, position tags, and synchronous recording of acquisition time, sensor status parameters (such as power level, signal strength), and performing acquisition status self-test, including using a built-in self-test program to analyze the waveform of the acquired data in real time (such as whether there is spike noise or periodic distortion); remotely monitor the sensor hardware status through a central processing platform (such as obtaining power level and signal strength through the IoT protocol), and then generate acquisition status self-test results. If the self-test results of the collection status do not meet the preset threshold (such as data deviation rate > 8% or sensor power < 20%), the redundancy mechanism is triggered, that is, the redundant sensors are activated to re-collect electric field data. For example, the backup sensors on the same tower or nearby locations are activated first, and the same type of sensors are called (for example, when a capacitive sensor fails, the same type of backup node is activated), or sensors based on different principles are switched to. The redundant sensors collect electric field data at a higher sampling frequency (such as twice the conventional frequency) or a higher precision mode (such as activating the enhanced measurement gear of the sensor) to improve data credibility. Finally, the transmission power inspection management is completed based on the collection results of the redundant sensors, including the output of the power inspection results, such as the final valid data (field strength value and gradient value after redundancy verification), sensor status report (normal / faulty / redundancy activation) and exception handling records (such as redundancy activation time and reason), and the validity, reliability and fault tolerance of the electric field data are ensured.

[0050] In the above, refer to Figure 1 The electric field sensor optimization method for multi-voltage high-voltage transmission power detection according to an embodiment of the present invention is described in detail. Figure 2 An electric field sensor optimization system for multi-voltage high-voltage transmission and electric inspection according to an embodiment of the present invention is described.

[0051] The electric field sensor optimization system for multi-voltage high-voltage transmission power testing according to the embodiment of the present invention is used to solve the technical problem in the prior art that the measurement accuracy of electric field sensors is affected by voltage fluctuations, electromagnetic interference and complex environments, resulting in poor accuracy, fault diagnosis capability and line operation reliability of high-voltage transmission power testing, thereby achieving the technical effect of improving the accuracy, fault diagnosis capability and line operation reliability of high-voltage transmission power testing. Figure 2 As shown, the electric field sensor optimization system for multi-voltage high-voltage transmission power testing includes: a sensor network deployment unit 10, a sensor selection and activation unit 20, an environmental data acquisition unit 30, a digital twin modeling unit 40, and a sensor acquisition optimization unit 50.

[0052] The sensor network deployment unit 10 is used to deploy a sensor network on the transmission line, wherein the sensor network integrates multiple electric field sensors, and the sensor network is a multi-level sensor network; the sensor selection and activation unit 20 is used to activate the predicted measurement sensors in the sensor network, and select and activate the electric field sensors in the sensor network according to the measurement feedback of the predicted measurement sensors; the environmental data acquisition unit 30 is used to perform environmental data acquisition of the transmission line through the deployed environmental sensors, transmit the environmental data acquisition results to the central processing platform, control and compensate the activated electric field sensors according to the feedback signal of the central processing platform, perform electric field acquisition, and establish an electric field data set; the digital twin modeling unit 40 is used to obtain the transmission task of the transmission line, perform digital twin modeling of the transmission line according to the transmission task, and establish the electric field gradient distribution using the digital twin modeling results; the sensor acquisition optimization unit 50 is used to perform gradient interpolation compensation of the electric field data set according to the electric field gradient distribution, and complete the acquisition optimization of the electric field sensor using the gradient interpolation compensation result.

[0053] The specific configuration of the sensor selection and activation unit 20 will be described in detail below. The sensor selection and activation unit 20 further includes: extracting load change characteristics based on the power transmission task, extracting predicted electric field characteristics based on the measurement feedback; obtaining sensor characteristics of the electric field sensors in the sensor network, wherein the sensor characteristics include location characteristics, acquisition accuracy characteristics, anti-interference characteristics, and sensor state characteristics; inputting the load change characteristics, the predicted electric field characteristics, and the sensor characteristics as input data into the adaptive matching network to establish an adaptive matching result; and selecting and activating the electric field sensors based on the adaptive matching result.

[0054] The specific configuration of the sensor selection and activation unit 20 will be described in detail below. The sensor selection and activation unit 20 further includes: activating the electric field accuracy loss layer within the adaptive matching network, performing measurement accuracy loss analysis based on the input data, and establishing a first adaptation result; activating the load adaptability loss layer within the adaptive matching network, performing load adaptability analysis based on the input data, and establishing a second adaptation result; activating the health and reliability analysis layer within the adaptive matching network, performing adaptation analysis based on the input data, and establishing a third adaptation result; synchronizing the first, second, and third adaptation results to the global analysis layer for coverage evaluation, and outputting the adaptation matching result based on the coverage evaluation result.

[0055] The specific configuration of the sensor selection and activation unit 20 will be described in detail below. The sensor selection and activation unit 20 further includes: performing activation selection under comprehensive adaptation using the first adaptation result, the second adaptation result, and the third adaptation result to generate a basic activation selection result; evaluating the monitoring coverage of the transmission line based on the basic activation selection result; adding activated sensors based on the monitoring coverage evaluation result; and outputting an adaptation matching result based on the added activated sensors and the basic activation selection result.

[0056] The specific configuration of the sensor acquisition optimization unit 50 will be described in detail below. The sensor acquisition optimization unit 50 further includes: configuring a gradient compensation channel based on the electric field gradient distribution; inputting the electric field dataset with position identifiers as input data into the gradient compensation channel, performing electric field gradient distortion correction, and completing gradient interpolation compensation.

[0057] The specific configuration of the environmental data acquisition unit 30 will be described in detail below. The environmental data acquisition unit 30 further includes: configuring a virtual sensor mapped to the activated electric field sensor; invoking the simulation database of the central processing platform to perform scenario simulation based on the environmental data acquisition results; and performing adaptive simulation testing on the virtual sensor; executing control optimization based on the adaptive simulation test results and establishing a feedback signal.

[0058] The specific configuration of sensor acquisition optimization unit 50 will be described in detail below. Sensor acquisition optimization unit 50 further includes: utilizing the optimized electric field sensor to acquire electric field data and performing a self-test of the acquisition status; if the self-test result fails to meet a preset threshold, activating a redundant sensor to reacquire electric field data, and completing power transmission inspection management based on the acquisition results of the redundant sensor.

[0059] The electric field sensor optimization system for multi-voltage high-voltage transmission and electrical testing provided by the embodiment of the present invention can execute the electric field sensor optimization method for multi-voltage high-voltage transmission and electrical testing provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0060] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0061] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. An electric field sensor optimization method for multi-voltage high-voltage transmission power testing, characterized in that: The method comprises: A sensor network is laid out on the power transmission line, wherein the sensor network integrates a plurality of electric field sensors and is a multi-layered sensor network; activating the pre-measurement sensor in the sensor network, and selectively activating the electric field sensor in the sensor network based on the measurement feedback of the pre-measurement sensor; Performing environmental data collection on the transmission line through the deployed environmental sensors, transmitting the environmental data collection results to the central processing platform, controlling and compensating the activated electric field sensors based on the feedback signals from the central processing platform, performing electric field collection, and establishing an electric field data set; Obtaining a transmission task of the transmission line, performing digital twin modeling of the transmission line according to the transmission task, and establishing an electric field gradient distribution using the digital twin modeling results; Gradient interpolation compensation of the electric field data set is performed according to the electric field gradient distribution, and the acquisition optimization of the electric field sensor is completed using the gradient interpolation compensation result.

2. The electric field sensor optimization method for multi-voltage high-voltage transmission and electric testing according to claim 1 is characterized in that: The selective activation of electric field sensors in the sensor network according to the measurement feedback of the pre-measurement sensor includes: Extracting load variation characteristics according to the power transmission task, and extracting predicted electric field characteristics according to the measurement feedback; Acquiring sensor characteristics of the electric field sensors in the sensor network, the sensor characteristics including position characteristics, acquisition accuracy characteristics, anti-interference characteristics, and sensor state characteristics; Input the load change characteristics, the predicted electric field characteristics, and the sensor characteristics as input data into an adaptive matching network to establish an adaptive matching result; The electric field sensor is selectively activated according to the adaptation and matching result.

3. The electric field sensor optimization method for multi-voltage high-voltage transmission and electric testing according to claim 2 is characterized in that: The input to the adaptive matching network includes: activating an electric field accuracy loss layer in the adaptive matching network, performing a measurement accuracy loss analysis based on the input data, and establishing a first adaptation result; activating a load adaptability loss layer within the adaptive matching network, performing load adaptability analysis based on the input data, and establishing a second adaptation result; activating a health reliability analysis layer within the adaptation and matching network, performing adaptation analysis based on the input data, and establishing a third adaptation result; The first adaptation result, the second adaptation result, and the third adaptation result are synchronized to the global analysis layer to perform coverage evaluation, and the adaptation matching result is output according to the coverage evaluation result.

4. The electric field sensor optimization method for multi-voltage high-voltage transmission and electric testing according to claim 3 is characterized in that: The step of synchronizing the first adaptation result, the second adaptation result, and the third adaptation result to the global analysis layer to perform coverage evaluation, and outputting the adaptation matching result according to the coverage evaluation result, includes: performing activation selection under comprehensive adaptation using the first adaptation result, the second adaptation result, and the third adaptation result to generate a basic activation selection result; Performing a monitoring coverage evaluation of the transmission line on the basic activation selection result; Add activated sensors based on the monitoring coverage evaluation results; An adaptation matching result is output according to the newly activated sensor and the basic activation selection result.

5. The electric field sensor optimization method for multi-voltage high-voltage transmission and electric testing according to claim 1 is characterized in that: The performing gradient interpolation compensation of the electric field data set according to the electric field gradient distribution includes: configuring a gradient compensation channel according to the electric field gradient distribution; The electric field data set with position identification is used as input data and input into the gradient compensation channel to perform distortion correction of the electric field gradient and complete gradient interpolation compensation.

6. The electric field sensor optimization method for multi-voltage high-voltage transmission and electric testing according to claim 1, characterized in that: After the environmental data collection results are transmitted to the central processing platform, the following steps are included: configuring a virtual sensor mapped to the activated electric field sensor; Calling the simulation database of the central processing platform, executing the scenario simulation under the environmental data collection results, and performing the adaptation simulation test on the virtual sensor; Control optimization is performed based on the adaptive simulation test results to establish a feedback signal.

7. The electric field sensor optimization method for multi-voltage high-voltage transmission and electric testing according to claim 1, characterized in that: After the acquisition optimization of the electric field sensor is completed by using the gradient interpolation compensation result, the method includes: Use the optimized electric field sensor to collect electric field data and perform a self-check of the collection status; If the self-test result of the collection status fails to meet the preset threshold, the redundant sensor is activated to re-collect the electric field data, and the power transmission inspection management is completed according to the collection results of the redundant sensor.

8. The electric field sensor optimization system for multi-voltage high-voltage transmission and power testing is characterized by: The system is used to implement the electric field sensor optimization method for multi-voltage high-voltage transmission power testing according to any one of claims 1 to 7, and the system includes: A sensor network deployment unit, configured to deploy a sensor network on a power transmission line, wherein the sensor network integrates a plurality of electric field sensors and is a multi-layered sensor network; A sensor selection and activation unit is used to activate the pre-measurement sensor in the sensor network and to selectively activate the electric field sensor in the sensor network according to the measurement feedback of the pre-measurement sensor; An environmental data acquisition unit is used to collect environmental data of the transmission line through deployed environmental sensors, transmit the environmental data collection results to the central processing platform, control and compensate the activated electric field sensors according to the feedback signals from the central processing platform, perform electric field collection, and establish an electric field data set; a digital twin modeling unit, configured to obtain a transmission task of the transmission line, perform digital twin modeling of the transmission line according to the transmission task, and establish an electric field gradient distribution using the digital twin modeling result; The sensor acquisition optimization unit is used to perform gradient interpolation compensation on the electric field data set according to the electric field gradient distribution, and complete the acquisition optimization of the electric field sensor using the gradient interpolation compensation result.

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