Non-invasive ultra-high voltage measuring device and voltage measuring method

Abstract

The application relates to the technical field of voltage measurement, and discloses a non-invasive ultrahigh-voltage voltage measurement device and a voltage measurement method. Through structural design of a metal shell and a metal shielding cavity, combined with fixed installation of a sensor array and inversion calculation of a data processing module, external electromagnetic interference is effectively shielded, electric field data is stably collected, high-precision voltage inversion is realized by using a nonlinear mapping model, and the problems of low measurement precision and poor system robustness caused by sensor displacement and electromagnetic interference in the prior art are solved.

Description

Technical Field

[0001] This application relates to the field of voltage measurement technology, and in particular to a non-invasive ultra-high voltage voltage measuring device and voltage measurement method. Background Technology

[0002] With the rapid expansion of ultra-high voltage (UHV) transmission networks, the demand for real-time monitoring of transmission line voltage in power systems has significantly increased. Conductor voltage, as a core parameter characterizing the health of the power grid, directly affects the safety and protection capabilities of the entire power system through the accuracy and long-term stability of its measurement results. Currently, voltage measurement mainly relies on two technical approaches: contact-based and non-intrusive. Contact-based measurement requires establishing a physical electrical connection with the high-voltage conductor, posing extremely high operational risks under UHV conditions and necessitating a power supply interruption, making it difficult to meet the continuous operation requirements of modern power grids. Non-intrusive measurement, on the other hand, indirectly derives the voltage value by analyzing the electric field distribution around the conductor, avoiding the risks of direct contact and causing minimal interference to line operation. Therefore, it exhibits significant advantages in UHV scenarios.

[0003] However, existing non-invasive ultra-high voltage voltage measurement devices suffer from systemic defects in their structural implementation. In the sensor integration stage, electric field sensing units generally employ a crude installation strategy, lacking both optimized array layouts designed for the non-uniform electric field characteristics surrounding the conductor and precise fixing mechanisms that coordinate with the shell geometry. This design makes the sensors highly susceptible to local electric field distortion interference caused by corona discharge in strong electric field environments. Furthermore, shell vibration or thermal expansion and contraction can induce sensor displacement, resulting in temporal fluctuations and spatial mismatches in the acquired signals, severely weakening the reliability of the electric field data. Regarding the electronic system architecture, data processing units, power supply modules, and wireless communication components are typically arranged in an exposed state without effective physical isolation barriers. When the device is exposed to the strong electromagnetic field generated by the ultra-high voltage conductor, external interference can intrude into sensitive circuits through conduction and radiation, inducing signal baseline drift, data processing logic disorder, and communication link interruptions, leading to distorted measurement results and reduced system robustness. Summary of the Invention

[0004] This application proposes a non-invasive ultra-high voltage measuring device and voltage measuring method, aiming to solve the problem that existing non-invasive voltage measuring devices are prone to interference in strong electric field environments, resulting in low measurement accuracy.

[0005] The first aspect of this application provides a non-invasive ultra-high voltage measurement device, comprising: a metal casing, a sensor array, a metal shielding cavity enclosed by the metal casing, and a data processing module, a power supply module, and a wireless communication module fixed inside the metal shielding cavity; the data processing module is connected to the sensor array, the power supply module, and the wireless communication module. The metal housing includes an upper half-shell and a lower half-shell that are rotatably connected. Through holes for clamping the conductor under test are provided on the upper and lower bottom surfaces of the metal housing. When the metal housing is installed on the conductor under test, the metal housing and the metal shielding cavity are respectively symmetrically arranged with respect to the axial center of the conductor under test. The sensor array is fixed to the inner wall of the lower half shell and extends through the outer wall of the lower half shell; the sensor array is used to collect multiple electric field measurement data at different locations of the metal shell; The metal shielding cavity includes a first shielding cavity disposed on the inner wall of the upper half shell and a second shielding cavity disposed on the inner wall of the lower half shell, wherein the power module, the data processing module and the wireless communication module are distributed in the first shielding cavity and the second shielding cavity; The data processing module is used to perform inversion calculations using a pre-built nonlinear mapping model between voltage and electric field to obtain the voltage of the conductor under test.

[0006] In one feasible embodiment of the first aspect, after the ultra-high voltage measuring device is installed on the conductor under test, the upper half shell, the lower half shell, the first shielding cavity and the second shielding cavity are all electrically connected to the conductor under test and form an equipotential body.

[0007] In one feasible embodiment of the first aspect, both the first shielding cavity and the second shielding cavity are composed of a metal partition plate and a U-shaped plate; The metal partition plate has a semi-circular concave surface with the same radius as the through hole at its center position; The metal partition plate and the U-shaped plate are provided with an interference fit at their edges, and the metal partition plate and the U-shaped plate are installed through the interference fit to form a sealed space.

[0008] In one feasible embodiment of the first aspect, at least one first communication interface is provided in the area of ​​the metal isolation plate other than the semi-circular concave surface. The sensor array, as well as the power module, the data processing module, and the wireless communication module distributed in the first shielding cavity and the second shielding cavity, are electrically connected through the communication interface.

[0009] In one feasible embodiment of the first aspect, a second communication interface is provided on the U-shaped plate of the second shielding cavity, the second communication interface being used to connect the sensor array and the data processing module.

[0010] In one feasible embodiment of the first aspect, an array hole is provided on the side wall of the lower half shell, each electric field sensor in the sensor array is installed in the array hole, and the detection electrode of the electric field sensor is wrapped with insulating material to isolate its electrical connection with the lower half shell.

[0011] In one feasible embodiment of the first aspect, it further includes: a solar panel disposed on the outer surface of the upper shell and electrically connected to the power module.

[0012] In one feasible embodiment of the first aspect, the power module includes an energy storage battery, an energy harvesting circuit board, and a power supply circuit board; The energy harvesting circuit board is connected to the solar panel and the energy storage battery respectively, and is used to convert the solar energy received by the solar panel into electrical energy and store it in the energy storage battery; The power supply circuit board is connected to the energy storage battery and is used to supply power to the sensor array and the wireless communication module based on the voltage in the energy storage battery.

[0013] In one feasible embodiment of the first aspect, the data processing module includes an electric field data processing unit and a voltage extrapolation unit; The electric field data processing unit is used to perform synchronous sampling, noise reduction filtering, amplitude normalization and time alignment processing on the raw electric field signals collected by each electric field sensor in the sensor array to obtain multi-channel electric field feature data. The voltage derivation unit is used to perform inversion calculations on multiple electric field characteristic data based on a pre-constructed nonlinear mapping model between voltage and electric field, and outputs the voltage value of the measured conductor.

[0014] A second aspect of this application provides a voltage measurement method applied to the non-invasive ultra-high voltage voltage measurement device provided above, the method comprising: A non-invasive ultra-high voltage measuring device is fixedly installed on the conductor being measured, so that the metal shell and internal metal shielding cavity of the device are electrically connected to the conductor being measured and form an equipotential body. A sensor array arranged inside the metal casing is used to collect multiple electric field measurement signals located at different spatial positions, wherein each electric field measurement signal corresponds to the composite electric field distribution around the conductor being measured. The multiple electric field measurement signals acquired are synchronously sampled and processed, and noise reduction filtering, amplitude normalization and timing alignment are performed in sequence to obtain multi-channel electric field characteristic data. Based on the spatial position parameters of each electric field sensor relative to the conductor being measured, the multi-channel electric field feature data is subjected to position compensation processing, and the compensated electric field feature data is fused to obtain fused electric field data. The voltage value of the conductor under test is obtained by inverting the fused electric field data by calling the pre-calibrated nonlinear mapping model between voltage and electric field; The voltage value of the conductor under test is stored as a measurement result and / or transmitted to a remote monitoring terminal via wireless communication.

[0015] This application provides a non-invasive ultra-high voltage voltage measurement device and method. The device includes: a metal shell, a sensor array, a metal shielding cavity concentrically arranged with and enclosed by the metal shell, and a data processing module, a power supply module, and a wireless communication module fixed inside the metal shielding cavity. The data processing module is connected to the sensor array, the power supply module, and the wireless communication module. The metal shell consists of an upper shell and a lower shell connected by hinges, and through holes for clamping the conductor under test are provided on the upper and lower bottom surfaces of the metal shell. The sensor array is fixed on the inner wall of the lower shell and extends through the outer wall of the lower shell. The metal shielding cavity includes a first shielding cavity on the inner wall of the upper shell and a second shielding cavity on the inner wall of the lower shell. The power supply module, the data processing module, and the wireless communication module are distributed in the first shielding cavity and the second shielding cavity. The data processing module is used to perform inversion calculations on multiple electric field measurement data collected by the sensor array using a pre-constructed nonlinear mapping model between voltage and electric field to obtain the voltage of the conductor under test.

[0016] In this application, through the structural design of a metal shell and a metal shielding cavity, combined with the fixed installation of the sensor array and the inversion calculation of the data processing module, external electromagnetic interference is effectively shielded, electric field data is stably acquired, and high-precision voltage inversion is achieved by using a nonlinear mapping model, thus solving the problems of low measurement accuracy and poor system robustness caused by sensor displacement and electromagnetic interference in the prior art. Attached Figure Description

[0017] Figure 1 This is a structural block diagram of the non-invasive ultra-high voltage measuring device in this application; Figure 2 This is a schematic diagram of a non-invasive ultra-high voltage measuring device in this application. Figure 3 This is a first-direction cross-sectional view of the non-invasive ultra-high voltage measuring device in this application; Figure 4 This is a schematic diagram of the structure of the second shielding cavity in this application; Figure 5 This is a second-direction cross-sectional view of the non-invasive ultra-high voltage measuring device in this application; Figure 6 This is a schematic diagram of one embodiment of the voltage measurement method in this application. Detailed Implementation

[0018] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein.

[0019] Furthermore, the terms “comprising” or “having” and any variations thereof are intended to cover non-exclusive inclusion, for example, including a system, product, or device is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such products or devices.

[0020] In non-invasive ultra-high voltage measurement technology, the installation structure of electric field sensors lacks array-based arrangement and a fixing mechanism that matches the housing. As a result, the sensor output signal is easily affected by external interference and fluctuates under strong electric field and corona discharge environments. In particular, the data processing module, power supply module and wireless communication module have not implemented effective structural isolation measures, which allows electromagnetic interference to propagate between modules, resulting in a decrease in signal processing accuracy and a reduction in communication link stability.

[0021] For example, in the field monitoring application of 500kV AC transmission lines, the measuring device is installed on the surface of the conductor. Because the sensor array is not precisely positioned using array holes, each electric field sensor undergoes slight displacement under the influence of wind vibration and temperature changes, resulting in inconsistent spatial distribution of the collected electric field data. At the same time, the data processing module and the wireless communication module are placed together in an unshielded cavity. In a strong electric field environment, electromagnetic interference causes data transmission errors, resulting in intermittent interruptions in the communication link.

[0022] To address the aforementioned problems, this application provides a non-invasive ultra-high voltage measurement device, such as... Figure 1 and 2 The diagram shown is a structural block diagram of a non-invasive ultra-high voltage measuring device proposed in this application embodiment. The non-invasive ultra-high voltage measuring device includes: a metal housing 100, a sensor array 200, a metal shielding cavity 300 concentrically arranged with and enclosed by the metal housing 100, and a data processing module 400, a power supply module 500, and a wireless communication module 600 fixed inside the metal shielding cavity 300; the data processing module 400 is connected to the sensor array 200, the power supply module 500, and the wireless communication module 600. The metal housing 100 includes an upper half-shell 110 and a lower half-shell 120 rotatably connected. Through holes 130 for clamping the conductor under test are provided on the upper and lower bottom surfaces of the metal housing 100. When the metal housing 100 is installed on the conductor under test, the metal housing 100 and the metal shielding cavity 300 are symmetrically arranged opposite the axial center of the conductor under test. It should be noted that the through holes 130 are provided on the upper and lower bottom surfaces of the metal housing 100 to allow the conductor under test to pass through the center of the device when the device clamps the conductor under test. The sensor array 200 is fixed to the inner wall of the lower half shell 120 and extends through the outer wall of the lower half shell 120; the sensor array is used to collect multiple electric field measurement data at different locations of the metal shell; The metal shielding cavity 300 includes a first shielding cavity 310 disposed on the inner wall of the upper half shell 110 and a second shielding cavity 320 disposed on the inner wall of the lower half shell 120. The power module 500, the data processing module 400 and the wireless communication module 600 are distributed in the first shielding cavity 310 and the second shielding cavity 320. The data processing module 400 is used to perform inversion calculations using a pre-built nonlinear mapping model between voltage and electric field to obtain the voltage of the conductor under test. Understandably, the metal casing 100 is typically made of conductive material. In this application, the metal casing 100 is designed as an openable structure for easy installation and disassembly; that is, the metal casing 100 consists of two halves, an upper half 110 and a lower half 120, which can rotate around a hinge axis. During installation, the lower half 120 is fixed to the conductor under test, specifically by using a hinge to fix the conductor under test in the position of the through hole 130, thus electrically connecting the conductor under test to the entire metal casing 100.

[0023] Sensor array 200 refers to a collection of multiple electric field sensors arranged in a specific layout. These sensors work together to collect electric field signals around the conductor under test at different spatial locations to obtain more comprehensive and accurate information on the electric field distribution.

[0024] The metal shielding cavity 300 serves as an anti-interference structure, enclosing and housing all functional modules / circuit modules in the device except for the sensor array 200. It is essentially a closed space made of conductive material, its main function being to isolate the internal electronic modules from the external strong electric field environment, preventing electromagnetic interference from affecting the normal operation of the internal circuits, thereby ensuring the reliability of data acquisition and processing. In practical applications, the metal shielding cavity 300 can be configured as one, two, or more. For example, when two are configured, they are respectively installed in the upper half-shell 110 and the lower half-shell 120. All functional modules / circuit modules in the device except for the sensor array 200 are distributed in the upper half-shell 110 and the lower half-shell 120. For example, the power module 500 and the data processing module 400 are located in the upper half-shell 110, and the wireless communication module 600 is located in the lower half-shell 120. The modules in the two half-shells are connected by wires.

[0025] The data processing module 400, as the core of the device, receives the electric fields at different locations of the conductor under test collected by the sensor array 200, and fuses and inverts them to calculate the voltage of the conductor under test. Specifically, it receives the raw electric field data collected by the sensor array 200 and processes it through a series of steps, including but not limited to synchronous sampling, noise reduction filtering, amplitude normalization, timing alignment, and voltage inversion calculation, ultimately outputting the voltage value of the conductor under test. Specifically, the data processing module 400 receives multiple electric field measurement data collected by the sensor array 200. Subsequently, the data processing module 400 uses a pre-built nonlinear mapping model between voltage and electric field to perform inversion calculations on these electric field measurement data. Through this inversion calculation, the voltage value of the conductor under test can be obtained. For example, the nonlinear mapping model can be a lookup table based on empirical data or a mathematical function obtained through curve fitting. The inversion calculation process can be a simple table lookup operation or an iterative solution process.

[0026] Furthermore, the power supply for each functional module is provided by the power module 500, which typically includes energy storage, energy extraction, and power supply circuits.

[0027] After the data processing module 400 calculates the voltage, it transmits the data to the outside world through the wireless communication module 600, specifically via radio waves to the remote monitoring terminal, thereby realizing remote data transmission and monitoring.

[0028] As one implementation, the metal housing 100 can be made of conductive materials such as aluminum alloy or stainless steel to provide good mechanical strength and electromagnetic shielding. The sensor array 200 can consist of multiple electric field sensors, such as planar electrode type or spherical electrode type sensors. The metal shielding cavity 300 can be welded or riveted from materials such as copper plate or galvanized steel plate to form a closed metal box. The data processing module 400, power supply module 500, and wireless communication module 600 can use standard industrial-grade circuit boards and be connected by wires or flexible ribbon cables.

[0029] The metal housing 100 is designed to consist of two main parts: an upper housing 110 and a lower housing 120. These two parts are connected by a hinge, allowing the entire housing to open and close like pliers. This design facilitates the device's ability to surround the conductor under test during installation. Both the upper and lower surfaces of the metal housing 100 have through holes 130 for clamping the conductor under test. The dimensions of these through holes 130 can be preset according to the expected diameter of the conductor under test; for example, they can be designed as circular or elliptical holes. In one implementation, the hinge can be a simple pin structure, allowing the upper housing 110 and lower housing 120 to rotate about an axis. The through holes 130 can be machined with smooth inner walls to reduce wear on the conductor under test.

[0030] The sensor array 200 is fixed to the inner wall of the lower housing 120. To ensure that the sensors can effectively sense external electric fields, each electric field sensor in the sensor array 200 is configured to extend beyond the outer wall of the lower housing 120. For example, the sensors can be mounted on the inner wall of the lower housing 120 by means of threaded fixing, snap-fit ​​fixing, or adhesive bonding. The sensitive part of the sensor can extend to the outside of the lower housing 120, directly exposed to the electric field around the conductor being measured.

[0031] like Figure 3 As shown, the metal shielding cavity 300 is further subdivided into two parts: a first shielding cavity 310 and a second shielding cavity 320. The first shielding cavity 310 is disposed on the inner wall of the upper shell 110, while the second shielding cavity 320 is disposed on the inner wall of the lower shell 120. The power module 500, the data processing module 400, and the wireless communication module 600 are distributed inside the first shielding cavity 310 and the second shielding cavity 320. For example, the data processing module 400 and the power module 500 can be placed in the second shielding cavity 320, while the wireless communication module 600 can be placed in the first shielding cavity 310, or all modules can be centrally placed in one shielding cavity and physically isolated by internal partitions. This distribution method aims to optimize the use of internal space and further enhance electromagnetic compatibility.

[0032] The following example provides a more detailed explanation of the above technical solution. Suppose that in a power transmission line monitoring scenario, non-invasive voltage measurement is required on an operating ultra-high voltage transmission line. Traditional measurement methods may face safety risks and data instability. The non-invasive ultra-high voltage measurement device provided in this embodiment is deployed in this scenario.

[0033] First, the metal casing 100 of the device is opened, with its upper half 110 and lower half 120 connected by hinges, allowing the device to open like jaws. The operator wraps the device around the power line under test, allowing the power line to pass through the pre-set through holes 130 on the upper and lower bottom surfaces of the metal casing 100. Subsequently, the upper half 110 and lower half 120 are closed and clamped, thereby firmly fixing the device to the power line under test. During this process, the metal casing 100 forms an electrical connection with the power line under test and serves as the external equipotential body of the device.

[0034] Once the device is secured, its internal sensor array 200 begins operation. Multiple electric field sensors in this array are fixed to the inner wall of the lower housing 120, with their sensitive portions extending beyond the outer wall of the lower housing 120, directly exposed to the electric field around the power transmission line being measured. These sensors are precisely positioned to collect electric field measurement data at different spatial points around the power transmission line. This securing method ensures that the sensors maintain stable spatial positions even under external environmental influences such as strong winds and vibrations, avoiding measurement errors caused by sensor movement or positional shifts.

[0035] Meanwhile, the data processing module 400, power supply module 500, and wireless communication module 600 inside the device are securely fixed within the metal shielding cavity 300. The metal shielding cavity 300 is concentrically arranged with and encloses the metal outer shell 100, forming a double-shielded structure. The metal shielding cavity 300 includes a first shielding cavity 310 located on the inner wall of the upper shell 110 and a second shielding cavity 320 located on the inner wall of the lower shell 120, with the modules distributed within these two shielding cavities. For example, the data processing module 400 and power supply module 500 can be located in the second shielding cavity 320, while the wireless communication module 600 is located in the first shielding cavity 310. This robust metal shielding structure effectively isolates the electromagnetic interference from external strong electric fields on the internal precision electronic components, ensuring the reliability of data processing and wireless communication, and preventing measurement data distortion or communication interruption caused by electromagnetic noise.

[0036] The multiple electric field measurement data collected by the sensor array 200 are transmitted to the data processing module 400. After receiving this raw electric field data, the data processing module 400 performs inversion calculations using a pre-constructed nonlinear mapping model between voltage and electric field. This nonlinear mapping model is established based on extensive experimental data and theoretical analysis, and can accurately reflect the complex nonlinear relationship between voltage and electric field under ultra-high voltage conditions. Through this inversion calculation, the data processing module 400 can accurately derive the real-time voltage value of the measured transmission line. For example, if the sensor array 200 collects a specific set of electric field distribution data, the data processing module 400 will quickly calculate the corresponding voltage value based on the model and use it as the final measurement result.

[0037] Thus, through its unique structural design, including the openable metal housing 100, the stably mounted sensor array 200, and the metal shielded cavity 300 of the internal modules, the device works together to achieve non-invasive, high-precision, and high-reliability measurement of the voltage of ultra-high voltage transmission lines.

[0038] In summary, this application provides a non-invasive ultra-high voltage measurement device. Addressing the problem of insufficient data stability caused by the simple installation method and lack of reasonable arrangement of electric field sensors in the prior art, this application provides a more stable and accurate sensor installation solution by fixing the sensor array 200 to the inner wall of the lower half shell 120 and allowing it to protrude through the outer wall of the lower half shell 120.

[0039] To address the problem in existing technologies where the data processing module 400, power supply module 500, and wireless communication module 600 lack effective structural isolation and are susceptible to electromagnetic interference from strong electric fields, a metal shielding cavity 300 is introduced, concentrically positioned and enclosed by the metal casing 100. This metal shielding cavity 300 is further subdivided into a first shielding cavity 310 and a second shielding cavity 320, used to properly distribute and secure the internal electronic modules. This double-metal shielding structure provides robust electromagnetic protection for the internal precision electronic components, effectively blocking electromagnetic interference generated by external ultra-high voltage and strong electric fields. In contrast to existing technologies where modules are not effectively isolated, the solution in this embodiment ensures the reliability of data processing and wireless communication in strong electric field environments, avoiding measurement data distortion or communication interruptions caused by electromagnetic noise, thereby guaranteeing the stable operation of the entire measurement system.

[0040] Furthermore, the data processing module 400 uses a pre-built nonlinear mapping model between voltage and electric field to perform inversion calculations to obtain the voltage of the conductor under test. This inversion calculation method based on a nonlinear model can more accurately capture the complex nonlinear relationship between voltage and electric field under ultra-high voltage conditions, providing higher measurement accuracy compared to simple linear or empirical models.

[0041] In this regard, this application further proposes that after the ultra-high voltage measuring device is installed on the conductor under test, the upper half shell 110, the lower half shell 120, the first shielding cavity 310 and the second shielding cavity 320 are all electrically connected to the conductor under test and form an equipotential body.

[0042] In this embodiment, the inner surfaces of the upper shell 110 and the lower shell 120 can be in direct, tight contact with the conductor under test, or a reliable electrical path can be established with the conductor under test through conductive components such as conductive contact pieces or conductive elastic contacts provided on the inner walls. Simultaneously, the first shielding cavity 310 and the second shielding cavity 320 can also be electrically connected to the inner walls of the upper shell 110 and the lower shell 120 through their edges, for example, using conductive pads, conductive springs, or direct metal contact. These methods ensure that a stable electrical path is formed between the metal components of the device and the conductor under test. "Forming an equipotential body" means that through the aforementioned electrical connection, the potentials of the upper shell 110, the lower shell 120, the first shielding cavity 310, and the second shielding cavity 320 are consistent with the potential of the conductor under test. This ensures that there is no potential difference between these metal components and the conductor under test, thereby creating a relatively stable electric field environment outside the device and preventing the device itself from interfering with the electric field distribution around the conductor under test.

[0043] The solution of this application electrically connects the metal casing 100 and the internal metal shielding cavity 300 of the ultra-high voltage measuring device to the conductor under test, so that these metal parts of the device are at the same potential as the conductor under test, thereby forming an equipotential body. When the device is installed on the conductor under test, the upper half-shell 110, the lower half-shell 120, the first shielding cavity 310, and the second shielding cavity 320 act as a whole, and their potential is forced to be consistent with that of the conductor under test. The formation of this equipotential body effectively eliminates the potential difference that may exist between the device's own metal structure and the conductor under test, thereby avoiding local electric field distortion caused by potential difference. The sensor array 200 performs measurements in such a stable and predictable electric field environment, and the electric field data it collects can more accurately reflect the true electric field distribution around the conductor under test, reducing external interference and measurement errors. The data processing module 400 uses this high-precision electric field data to perform inversion calculations, thereby obtaining a more accurate voltage value of the conductor under test.

[0044] For example, when the non-invasive ultra-high voltage measuring device clamps the conductor under test through its through-hole 130, the inner walls of the upper shell 110 and the lower shell 120 can be designed with multiple conductive protrusions or conductive brushes. These protrusions or brushes make close contact with the surface of the conductor under test during clamping, thereby establishing an electrical connection. Simultaneously, the edges of the first shielding cavity 310 and the second shielding cavity 320 can adopt an overlapping structure, supplemented with conductive sealing strips or conductive adhesive, to ensure a reliable electrical connection between them and the inner walls of the upper shell 110 and the lower shell 120. In this way, the metal outer shell 100 and the internal shielding cavity of the device form a unified equipotential region with the conductor under test after installation.

[0045] Furthermore, this embodiment also proposes that both the first shielding cavity 310 and the second shielding cavity 320 are composed of a metal isolation plate 301 and a U-shaped plate 302; as shown Figure 4 As shown, the metal isolation plate 301 has a semi-circular concave surface 3011 with the same radius as the through hole 130 at its center position; the metal isolation plate 301 and the U-shaped plate 302 have an interference fit structure at their edges, and the metal isolation plate 301 and the U-shaped plate 302 are installed through the interference fit structure to form a sealed space that can shield signals.

[0046] It should be noted that the metal isolation plate 301 is a plate-like structure made of conductive material, used to spatially separate different areas while providing electromagnetic shielding. It can be planar or have a specific shape, such as cutouts or holes, to accommodate other components. Its main function is to construct the internal partition of the shielding cavity and serve as a base for mounting other electronic components.

[0047] The U-shaped plate 302 is a metal sheet with a U-shaped cross-section, possessing certain structural strength and covering capacity. The U-shaped plate 302 can be used to form the sidewalls or top cover of a shielded cavity, working in conjunction with the metal isolation plate 301 to form a closed shielding structure. Its U-shaped structure helps enhance the rigidity of the cavity and provides additional protection for internal components. A semi-circular concave surface 3011 is provided in the middle of the metal isolation plate 301, the radius of which is designed to be the same as the radius of the through hole 130 on the device housing. When the device clamps the conductor under test, the conductor can smoothly pass through this concave surface. This design ensures that when the conductor under test passes through the shielded cavity, the shielded cavity can tightly surround the conductor under test, maintaining the continuity of the shield and contributing to the formation of a stable electrical connection.

[0048] Interference fits include press-fit, expansion fit, or contraction fit. The sealed space formed by the interlocking interference fits creates a signal-shielding environment. This refers to a physically enclosed and electromagnetically continuous region formed internally after the metal isolation plate 301 and the U-shaped plate 302 are tightly connected using the interference fit. This space effectively blocks external electromagnetic interference signals from entering and prevents electromagnetic radiation from leaking from sensitive internal electronic components, thereby protecting the normal operation of the internal data processing module 400, power supply module 500, and wireless communication module 600, and ensuring the accuracy of data collected by the sensor array 200.

[0049] In this embodiment, the first shielding cavity 310 and the second shielding cavity 320 are designed to consist of a metal isolation plate 301 and a U-shaped plate 302, and are installed using an interference fit at their edges. This interference fit ensures a tight mechanical connection and continuous electrical contact between the metal isolation plate 301 and the U-shaped plate 302, thereby forming a physically sealed and electromagnetically continuous shielding space. This shielding space can effectively isolate the internal power module 500, data processing module 400, and wireless communication module 600 from the external environment, block external electromagnetic interference, and prevent electromagnetic radiation leakage from internal electronic components. In addition, the semi-circular concave surface 3011 with the same radius as the through hole 130 in the middle of the metal isolation plate 301 allows the conductor under test to pass smoothly through the shielding cavity when the device clamps the conductor under test. At the same time, the semi-circular concave surface 3011 fits tightly with the conductor under test, further enhancing the electrical connection and shielding effect between the shielding cavity and the conductor under test. Through this structure, the entire shielded cavity, together with the metal shell 100 and the conductor under test, forms a stable and reliable equipotential body, providing a safe and low-interference working environment for the internal sensitive electronic components, thereby ensuring the accuracy of ultra-high voltage measurement and the long-term stability of the device.

[0050] For example, the metal isolation plate 301 can be made of 2mm thick aluminum alloy sheet, with its surface anodized to enhance corrosion resistance. The U-shaped plate 302 can be formed from aluminum alloy sheet of the same thickness through a bending process, with its U-shaped opening matching the edge dimensions of the metal isolation plate 301. In the middle of the metal isolation plate 301, a semi-circular groove can be machined using CNC milling, with its radius precisely matching the radius of the device through hole 130. The interference fit structure can be designed with a raised dovetail groove around the edge of the metal isolation plate 301, and a slightly smaller dovetail tenon around the corresponding edge of the U-shaped plate 302. During assembly, a special fixture presses the dovetail tenon into the dovetail groove, forming a tight mechanical lock and electrical connection. This connection method not only provides excellent electromagnetic shielding performance but also effectively prevents dust and moisture from entering, ensuring stable operation of internal electronic components in harsh environments.

[0051] In another embodiment, it is proposed that at least one first communication interface 3012 is provided in the area of ​​the metal isolation plate 301 other than the semi-circular concave surface 3011; the sensor array 200, and the power module 500, the data processing module 400 and the wireless communication module 600 distributed in the first shielding cavity 310 and the second shielding cavity 320 are electrically connected through the first communication interface 3012.

[0052] Understandably, the first communication interface 3012 is a communication connection for all circuit modules within the first shielded cavity 310 and the second shielded cavity 320. Specifically, it connects the circuit modules in the two shielded cavities to the data processing module 400. Its design is intended to allow signal or power lines to pass through the shielded or sealed walls while maintaining the shielding integrity and sealing of the walls.

[0053] In practical applications, the first communication interface 3012 can be implemented in various forms. For example, it can be a feedthrough connector with glass-metal or ceramic-metal seals, or a USB or bus connector. It maintains the sealing and electromagnetic shielding performance of the shielded cavity while allowing electrical signals to pass through by embedding the conductor in the insulating material and hermetically welding or sealing it with the metal housing 100.

[0054] Furthermore, the first communication interface 3012 can also be an interface employing optical isolation technology. This involves converting electrical signals into optical signals for transmission, and then converting them back to electrical signals on the other side, thereby achieving complete electrical isolation and excellent electromagnetic compatibility. Electrical connection via the first communication interface refers to the establishment of necessary electrical pathways between the sensor array 200, power module 500, data processing module 400, and wireless communication module 600, using the first communication interface 3012 as an intermediary. This connection can be achieved by connecting the corresponding pins or interfaces of each module to the internal terminals of the first communication interface 3012, while the external terminals of the first communication interface 3012 are connected to the sensor array 200 or external lines that need to cross a shielding wall.

[0055] This application utilizes the first communication interface 3012 as a special electrical path, designed to penetrate the metal isolation plate 301. This allows the electric field data collected by the sensor array 200 to be transmitted to the data processing module 400, the power supply module 500 to supply power to the sensor array 200 and other modules, and the data processing module 400 to exchange data with the wireless communication module 600. In this way, all necessary electrical connections are established. Simultaneously, the design of the first communication interface 3012 itself (e.g., employing sealed feedthrough technology) ensures that the sealing and electromagnetic shielding performance of the metal shielding cavity 300 are unaffected. This enables the internal power supply module 500, data processing module 400, and wireless communication module 600 to operate stably in a highly shielded environment, effectively avoiding the influence of external electromagnetic interference on the measurement results and preventing electromagnetic radiation generated by internal electronic equipment from interfering with external electric field measurements, thereby ensuring the accuracy and reliability of ultra-high voltage measurements.

[0056] For example, the first communication interface 3012 can specifically be multiple high-frequency sealed feedthrough connectors. These connectors are precisely embedded in the reserved holes of the metal isolation plate 301 and hermetically welded to the metal isolation plate 301 to ensure the sealing and electromagnetic continuity of the shielding cavity. For example, a multi-pin feedthrough connector can be provided on the metal isolation plate 301 to introduce multiple electric field signal lines of the sensor array 200 into the second shielding cavity 320 and connect to the input terminal of the data processing module 400. At the same time, another feedthrough connector can also be provided to lead out the power supply line output by the power module 500 to provide operating power to the sensor array 200. In addition, if the data processing module 400 and the wireless communication module 600 are located in different shielding cavities, or need to communicate across the metal isolation plate 301, similar feedthrough connectors can also be used to achieve electrical connection between them. The outer shell of these feedthrough connectors maintains good electrical contact with the metal isolation plate 301, while the internal conductive pins are isolated from the outer shell by insulating material, thereby allowing electrical signal transmission while effectively blocking electromagnetic wave leakage or intrusion.

[0057] By setting a first communication interface 3012 on the metal isolation plate 301, and electrically connecting the sensor array 200, power module 500, data processing module 400, and wireless communication module 600 through this interface, not only is the necessary data and power transmission between the functional modules ensured, but more importantly, the design of this communication interface maintains the airtightness and electromagnetic shielding effect of the metal shielding cavity 300. This significantly improves the device's resistance to electromagnetic interference, protects internal sensitive electronic components from the influence of external strong electric field environments, and avoids interference from internal electronic equipment to external electric field measurements. This greatly improves the accuracy and stability of ultra-high voltage measurements, ensuring the long-term reliable operation of the device in complex electromagnetic environments.

[0058] In this regard, this application further proposes that a second communication interface 3021 is provided on the U-shaped plate 302 of the second shielding cavity 320, and the second communication interface 3021 is used to connect the sensor array 200 and the data processing module 400.

[0059] In other words, the second communication interface 3021 is a physical or logical interface used to establish electrical connections between different modules to transmit data or signals. Specifically, it can be a multi-pin connector, achieving connection through plugging and unplugging; it can also be a coaxial connector suitable for high-frequency signal transmission; or it can be a fiber optic interface used for electrical isolation and electromagnetic interference immunity. The second communication interface 3021 is provided on the U-shaped plate 302 of the second shielding cavity 320, meaning that the interface is integrated or installed on the U-shaped plate 302 constituting the second shielding cavity 320. In this case, the communication interface can be directly embedded or molded into the material of the U-shaped plate 302, ensuring a tight connection between the interface and the U-shaped plate 302 to reduce electromagnetic leakage; or an opening can be pre-drilled in the U-shaped plate 302, and a standard communication interface module with shielding function can be installed through threads, snaps, or welding, with additional electromagnetic sealing treatment around the interface, such as using conductive gaskets or conductive adhesive.

[0060] Furthermore, the output of the sensor array 200 can be connected to the external port of the second communication interface 3021 via a shielded cable, while the internal port is connected to the data processing module 400 via internal wiring.

[0061] In practical applications, the second communication interface 3021 can also be a multi-core aviation socket with a shielded housing. This aviation socket is installed on the U-shaped plate 302 of the second shielding cavity 320 by threading or welding. Its external port connects to the output cable of the sensor array 200, while its internal port connects to the input terminal of the data processing module 400 via a flexible flat cable or shielded wire. The metal housing 100 of the aviation socket makes good contact with the metal material of the U-shaped plate 302, forming an electrical connection, thereby ensuring the continuity of the shielding cavity. Furthermore, to further enhance the shielding effect, conductive gaskets can be used for sealing at the junction of the aviation socket and the U-shaped plate 302. The analog signals collected by each electric field sensor in the sensor array 200 are collected through their respective signal conditioning circuits, converged via a multi-core cable, and finally transmitted to the data processing module 400 through this aviation socket.

[0062] By setting a second communication interface 3021 on the U-shaped plate 302 of the second shielding cavity 320 and using it to connect the sensor array 200 and the data processing module 400, this application effectively solves the problem of simultaneously ensuring the reliability, anti-interference ability, and integrity of the shielding cavity in signal transmission between the sensor array 200 and the internal data processing module 400. This design provides a dedicated and protected signal transmission path, significantly reducing the impact of external electromagnetic interference on the sensor signal and ensuring signal integrity and transmission quality. Simultaneously, because the interface is tightly integrated with the shielding cavity structure, shielding failure due to signal line penetration is avoided, thereby ensuring that the data processing module 400 operates efficiently in a stable electromagnetic environment, thus improving the accuracy and stability of ultra-high voltage measurement.

[0063] In this embodiment, an array hole 121 is provided on the side wall of the lower half shell 120. Each electric field sensor in the sensor array 200 is installed in the array hole 121, and the detection electrode of the electric field sensor is wrapped with insulating material to isolate its electrical connection with the lower half shell 120.

[0064] The array aperture 121 is actually composed of multiple holes pre-machined or formed on the sidewalls of the lower half-shell 120 for accommodating and fixing the electric field sensor. These holes can be formed using various methods such as drilling, laser cutting, stamping, or precision casting, depending on the layout requirements of the sensor array 200. Their shape and size match the shape of the electric field sensor to ensure that the sensor can be installed stably and accurately. For example, the array aperture 121 can be circular, square, or a custom-shaped hole according to the sensor structure, and its arrangement can be linear, matrix, or ring-shaped. The detection electrode of the electric field sensor is the core component used to sense and collect electric field signals. Wrapping with insulating material refers to covering the outside of the detection electrode with one or more layers of material with high dielectric strength and good insulation properties. This wrapping can be achieved in various ways, such as encapsulating the detection electrode entirely in an insulating medium such as epoxy resin or silicone rubber to form an integrated insulating probe; or coating the surface of the detection electrode with an insulating coating such as insulating varnish or polyimide film; or using insulating sleeves such as ceramics or polytetrafluoroethylene (PTFE) to physically isolate the detection electrode from the surrounding environment.

[0065] Here, by setting array holes 121 on the side wall of the lower half-shell 120, a precise and stable mounting position is provided for each electric field sensor in the sensor array 200, ensuring that the sensors can be deployed according to the preset spatial layout, thereby accurately capturing the electric field distribution around the conductor being measured. Simultaneously, by wrapping the detection electrodes of the electric field sensors with insulating material, even if the lower half-shell 120 is electrically connected to the conductor being measured after the device is installed and forms an equipotential body, the detection electrodes can remain electrically isolated from this equipotential body. This isolation mechanism effectively avoids direct electrical contact between the detection electrodes and the lower half-shell 120, thereby preventing current leakage, short-circuit risks, and distortion or interference of the electric field measurement signal caused by potential differences. Therefore, the electric field sensors can operate stably and accurately under high-voltage environments, providing reliable raw electric field data for subsequent voltage inversion calculations by the data processing module 400, thus ensuring the measurement accuracy and operational reliability of the entire non-invasive ultra-high voltage measurement device.

[0066] In one specific implementation, the lower shell 120 can be made of an aluminum alloy material with good electrical conductivity. Multiple circular array holes 121 can be precisely drilled on the sidewall of the lower shell 120 using CNC machining technology. The diameter of these array holes 121 is slightly larger than the outer diameter of the electric field sensor. Each electric field sensor in the sensor array 200 can be a miniature capacitive electric field sensor, whose detection electrode is integrally encapsulated in a special epoxy resin with high insulation strength during manufacturing, forming an insulated probe. During installation, these insulated probes are inserted into the array holes 121 and further sealed and fixed using insulated threaded fasteners or insulating adhesive to ensure reliable electrical isolation between them and the lower shell 120.

[0067] This application further proposes that the aforementioned non-invasive ultra-high voltage measuring device also includes a solar panel 700, which is disposed on the outer surface of the upper shell 110 and electrically connected to the power module 500.

[0068] In this embodiment, the solar panel 700 serves as the energy harvesting device and can be made of different types of photovoltaic materials such as monocrystalline silicon, polycrystalline silicon, or amorphous silicon. For example, a high-efficiency monocrystalline silicon solar panel can be selected to provide high energy conversion efficiency within a limited installation area; alternatively, a flexible thin-film solar panel can be used to better adapt to the curved structure or irregular shape of the upper shell 110.

[0069] The solar panel 700 is disposed on the outer surface of the upper shell 110 to maximize its area and efficiency for receiving sunlight. This arrangement can be achieved through various structures; for example, the solar panel 700 can be securely installed on the outer surface of the upper shell 110 by means of structural adhesive bonding, bolt fixing, or snap-fit ​​connection.

[0070] Specifically, a groove matching the size of the solar panel 700 is pre-reserved on the outer surface of the upper shell 110. This groove is secured by embedded installation and sealant to enhance the overall integrity and environmental adaptability of the device. The solar panel 700 is electrically connected to the power module 500, ensuring that the generated electrical energy can be effectively transmitted to the power module 500 for management and utilization. This electrical connection can be achieved through wires, connectors, or traces on an integrated circuit board. For example, the output terminal of the solar panel 700 can be connected to the charging input interface of the power module 500 via a waterproof connector to ensure connection reliability and normal operation in harsh environments.

[0071] A self-sufficient energy supply system is constructed by mounting a solar panel 700 on the outer surface of the upper shell 110 of the non-invasive ultra-high voltage voltage measuring device and electrically connecting it to the power module 500. When the device is installed on the conductor under test and exposed to the external environment, the solar panel 700 can continuously receive sunlight and convert light energy into electrical energy. This electrical energy is then transmitted to the power module 500. The power module 500 is responsible for managing the received electrical energy, such as charging the internal energy storage battery 510 through the charging management circuit, or directly powering core components such as the sensor array 200, data processing module 400, and wireless communication module 600 within the device. This design allows the device to utilize renewable energy sources in the environment to continuously provide itself with power, greatly reducing its dependence on external power sources. In this way, the power module 500 of the device can obtain continuous energy replenishment, thereby ensuring that the entire measuring device can operate stably for a long time without human intervention, performing continuous ultra-high voltage measurements.

[0072] In one specific implementation, a 5W monocrystalline silicon solar panel 700 can be selected and bonded to a pre-reserved flat area on the outer surface of the upper shell 110 using weather-resistant epoxy resin adhesive. The output leads of the solar panel 700 are connected to the charging management unit inside the power module 500 via an IP67-rated waterproof connector. This charging management unit integrates a maximum power point tracking (MPPT) controller, which can dynamically adjust the charging strategy based on the output characteristics of the solar panel 700 and the charging state of the energy storage battery 510 to maximize the utilization efficiency of solar energy.

[0073] This application further proposes that the power module 500 includes an energy storage battery 510, an energy harvesting circuit board 520, and a power supply circuit board 530; the energy harvesting circuit board 520 is connected to the solar panel 700 and the energy storage battery 510 respectively, and is used to convert the solar energy received by the solar panel 700 into electrical energy and store it in the energy storage battery 510; the power supply circuit board 530 is connected to the energy storage battery 510 and is used to supply power to the sensor array 200 and the wireless communication module 600 based on the voltage in the energy storage battery 510.

[0074] The energy storage battery 510 can be a rechargeable battery such as a lithium-ion battery, a nickel-metal hydride battery, or a lead-acid battery. The energy harvesting circuit board 520 may include a maximum power point tracking (MPPT) controller, a charging management chip, a DC-DC converter, etc., and its function is to convert the unstable solar energy received by the solar panel 700 into electrical energy suitable for charging the energy storage battery 510 and to efficiently store it. For example, an MPPT controller based on a buck-boost topology can be used, or a charging management IC with overcharge and over-discharge protection functions can be used.

[0075] The power supply circuit board 530 may include a voltage regulator (such as an LDO or switching regulator), overcurrent protection circuit, undervoltage protection circuit, etc. Its function is to provide a stable and reliable power supply to each load module according to the voltage state of the energy storage battery 510, ensuring that they operate normally under rated voltage. For example, a multi-output DC-DC converter can be used to provide customized power supplies for modules with different voltage requirements.

[0076] By subdividing the power module 500 into an energy storage battery 510, an energy harvesting circuit board 520, and a power supply circuit board 530, and clearly defining their connections and functional divisions, a complete energy management system is formed. The energy harvesting circuit board 520 efficiently captures energy from the solar panel 700 and converts it into electrical energy, storing it in the energy storage battery 510, effectively solving the intermittent and unstable problems of solar power supply. The energy storage battery 510 acts as an energy buffer, ensuring a continuous energy supply. The power supply circuit board 530 obtains energy from the energy storage battery 510 and stably outputs it to the sensor array 200 and the wireless communication module 600, ensuring the stable operation of these key components under various operating conditions. This collaborative working mechanism enables the entire device to overcome the impact of environmental factors on power supply, achieving long-term, reliable, and autonomous operation, thereby ensuring the accuracy of ultra-high voltage measurement and the continuity of data transmission.

[0077] For example, the energy storage battery 510 can be a high-energy-density lithium iron phosphate battery pack, which has a long cycle life and good temperature characteristics to adapt to outdoor ultra-high-voltage environments. The energy harvesting circuit board 520 can integrate a solar charge controller with maximum power point tracking (MPPT) function, such as a Buck-Boost topology-based controller, which can dynamically adjust charging parameters according to the output characteristics of the solar panel 700 to ensure that solar energy is converted into electrical energy and charged into the energy storage battery 510 with the highest efficiency under different light conditions. The energy harvesting circuit board 520 can also include overcharge, over-discharge, and over-temperature protection functions to extend the service life of the energy storage battery 510 and improve system safety. The power supply circuit board 530 can be designed as a multi-output DC-DC converter, for example, one outputting 5V voltage to supply the sensor array 200, and another outputting 3.3V voltage to supply the wireless communication module 600. It also integrates undervoltage lockout and short-circuit protection functions to ensure that a stable power supply can still be provided to the critical modules when the voltage of the energy storage battery 510 fluctuates or the load is abnormal, avoiding measurement interruption or data loss due to power instability.

[0078] This application further proposes a data processing module 400 including an electric field data processing unit and a voltage derivation unit. The electric field data processing unit is a logic or physical module specifically responsible for preprocessing the original electric field signal, improving its quality for subsequent voltage derivation. This unit can be a dedicated digital signal processor (DSP), a specific software algorithm running in a microcontroller (MCU), or implemented using a field-programmable gate array (FPGA). The voltage derivation unit is a logic or physical module specifically responsible for performing voltage inversion calculations, calculating the voltage value of the measured conductor based on the processed electric field characteristic data and a nonlinear mapping model. This unit can be a high-performance microprocessor running complex mathematical models and algorithms, or a hardware accelerator integrated into an application-specific integrated circuit (ASIC).

[0079] In this embodiment, the electric field data processing unit performs synchronous sampling, denoising filtering, amplitude normalization, and timing alignment processing on the raw electric field signals collected by each electric field sensor in the sensor array 200 to obtain multi-channel electric field feature data. Synchronous sampling aims to ensure that signals from different electric field sensors are collected at the same time point, eliminating data inconsistencies caused by sampling time deviations. This can be achieved by triggering all analog-to-digital converters (ADCs) to sample using a unified clock source, or by interpolating and resampling asynchronously acquired data using software algorithms. Denoising filtering is used to eliminate random noise, power frequency interference, etc., present in the raw electric field signal, improving the signal-to-noise ratio. This processing can employ digital filters, such as Butterworth filters and Chebyshev filters, or algorithms such as wavelet transform and moving average. Amplitude normalization is used to adjust the amplitude of the electric field signals collected by different sensors to a uniform scale or range, eliminating the influence of individual sensor differences and environmental factors on the amplitude, facilitating subsequent model processing. This can be achieved using methods such as max-min normalization and Z-score normalization, or by calibration based on the sensor's calibration coefficients. Timing alignment processing aims to ensure precise alignment of multiple electric field signals on the time axis, eliminating time offsets caused by transmission delays and processing delays, and guaranteeing data consistency. This can be achieved through cross-correlation algorithms, dynamic time warping (DTW) algorithms, or by utilizing timestamp information from synchronous sampling. After the above processing, the resulting multi-channel electric field characteristic data is a set of electric field measurement data that accurately reflects the electric field distribution around the measured conductor, exhibiting consistency and a high signal-to-noise ratio.

[0080] The voltage derivation unit uses a pre-built nonlinear mapping model between voltage and electric field to perform inverse calculations on multiple channels of electric field characteristic data, outputting the voltage value of the measured conductor. The pre-built nonlinear mapping model between voltage and electric field is a mathematical model describing the complex nonlinear relationship between the voltage of the measured conductor and the surrounding electric field distribution. As the core algorithm, it converts the processed electric field characteristic data into actual voltage values. The inverse calculation derives the voltage value of the measured conductor from the electric field characteristic data based on the nonlinear mapping model. This calculation can employ iterative optimization algorithms, neural network models, support vector machines, or other machine learning methods, or methods based on polynomial fitting and lookup tables. Finally, it outputs the voltage value of the measured conductor, providing the final measurement result for users or systems to monitor, analyze, and control.

[0081] In practical applications, the data processing module 400 can be an embedded system, such as a circuit board based on an ARM Cortex-M series microcontroller (e.g., STM32H7 series) or a high-performance DSP chip (e.g., TI C6000 series). The electric field data processing unit can trigger multiple analog-to-digital converter channels to start conversion simultaneously through the microcontroller's internal timer, achieving synchronous sampling. Noise denoising and filtering can be implemented by running a digital signal processing algorithm library on the microcontroller to implement FIR or IIR filters, for example, using a 10th-order Butterworth low-pass filter to filter out high-frequency noise. Amplitude normalization can be implemented in software, performing a linear mapping based on the sensor's factory calibration parameters and the real-time acquired maximum / minimum amplitude. Timing alignment processing can be performed by implementing an algorithm based on a cross-correlation function in the microcontroller to calculate the delay between each signal and perform time compensation. The voltage extrapolation unit can utilize the floating-point arithmetic capabilities of the microcontroller or DSP to execute a pre-stored nonlinear mapping model. For example, this model can be a polynomial regression model, whose coefficients are obtained through high-voltage laboratory calibration and stored in memory before the device leaves the factory. When the processed electric field characteristic data is received, the voltage derivation unit substitutes these data into the polynomial equation to calculate the voltage value of the conductor being measured.

[0082] Compared to directly using raw electric field data for inversion, the above technical solution provides more stable and accurate voltage measurement results, especially in complex electromagnetic environments, effectively improving the overall performance and applicability of non-invasive ultra-high voltage measurement devices.

[0083] This application further proposes that the electric field data processing unit is also used to perform position-weighted processing on the electric field characteristic data based on the spatial position parameters of each electric field sensor in the device structure, so as to compensate for the influence of the distance difference between different sensors and the conductor under test on the electric field measurement results.

[0084] The spatial position parameters refer to the precise geometric coordinates of each electric field sensor within the entire measuring device relative to the conductor being measured or a reference point of the device. These parameters are determined during the device design and manufacturing process and can be pre-stored in the data processing module 400. For example, the position of each sensor can be defined using a three-dimensional coordinate system, or described by the radial distance and circumferential angle between the sensor and the central axis of the conductor being measured. These parameters form the basis for precise position compensation processing.

[0085] The position-weighted processing of the electric field characteristic data specifically involves assigning different weights to the electric field characteristic data output by different sensors based on the relative spatial relationship between the electric field sensor and the conductor being measured. For example, sensors closer to the conductor may be assigned higher weights, or the data from sensors at different locations may be differentiated according to the theoretical model of the electric field distribution. This position-weighted processing eliminates or reduces electric field measurement errors caused by varying distances between the sensor and the conductor. In ultra-high voltage electric field environments, the change in electric field strength with distance is non-linear and may be affected by factors such as device structure and the surrounding environment. Precise position compensation ensures the comparability of sensor data from different locations when participating in voltage inversion calculations, thereby improving the accuracy and reliability of the overall measurement system.

[0086] The proposed solution, based on the electric field data processing unit's synchronous sampling, noise reduction filtering, amplitude normalization, and timing alignment of the raw electric field signals acquired by the sensor array 200 to obtain multiple electric field characteristic data, further introduces position-weighted processing based on the spatial position parameters of each electric field sensor within the device structure. Specifically, after receiving the pre-processed electric field characteristic data, the electric field data processing unit combines it with pre-stored precise spatial position information of each electric field sensor relative to the conductor being measured. Due to the complex nonlinear relationship between electric field strength and distance, the electric field values ​​measured by sensors at different locations will vary depending on their distance from the conductor being measured. To eliminate the measurement bias caused by this distance difference, the electric field data processing unit applies specific weighting factors to each electric field characteristic data based on these spatial position parameters. These weighting factors can be determined based on electric field theoretical models, experimental calibration results, or empirical formulas, aiming to "calibrate" the data from sensors at different locations to a unified reference standard, or to assign them the appropriate relative importance in subsequent voltage extrapolation. Through this position-weighted processing, even if the distances between each sensor in the sensor array 200 and the conductor under test are not exactly the same, the output electric field characteristic data can be effectively corrected, thereby providing a more accurate and consistent input to the voltage extrapolation unit and significantly improving the reliability of the final voltage measurement results.

[0087] The electric field data processing unit can be an embedded microcontroller, such as a digital signal processor (DSP) based on an ARM Cortex-M series processor (e.g., STM32F4 series). This microcontroller internally stores spatial position parameters, such as the radial distance and circumferential angle of each electric field sensor in the sensor array 200 relative to the central axis of the conductor being measured. After the sensor array 200 acquires the electric field signal and performs preliminary processing, the electric field data processing unit consults a pre-established weighting coefficient lookup table or executes a weighting algorithm based on these preset spatial position parameters. For example, sensors closer to the conductor being measured can be assigned a larger weight, while sensors farther away can be assigned a smaller weight, or compensation can be made based on the attenuation of electric field strength with distance. This weighting algorithm can be a polynomial function, with coefficients obtained by calibrating the device under a standard high-voltage environment. The processed electric field characteristic data is then passed to the voltage derivation unit for the final voltage inversion calculation.

[0088] This application further proposes that the aforementioned nonlinear mapping model is a nonlinear fitting model based on a polynomial function, wherein the order of the polynomial function is pre-set according to the voltage level of the conductor being measured and the application scenario. For example, a first-order polynomial is y=ax+b, and a second-order polynomial is y=ax^2+bx+c. The choice of order directly affects the complexity and fitting ability of the model. A lower order may lead to underfitting, failing to fully capture the nonlinear characteristics of the data; while an excessively high order may lead to overfitting, making the model overly sensitive to noise and reducing its generalization ability. Therefore, choosing an appropriate polynomial order is key to constructing an effective nonlinear fitting model. The order of the polynomial function is not fixed but needs to be adjusted according to the specific measurement conditions. The voltage level of the conductor being measured, such as 110kV, 220kV, or 500kV, will affect the complexity and nonlinear intensity of the electric field distribution. Different application scenarios, such as laboratory calibration environments, outdoor transmission line monitoring, or substation partial discharge detection, have different environmental noise, interference sources, and requirements for measurement accuracy. Pre-setting the order means selecting the most suitable order for the current measurement task based on prior knowledge and experience before building or deploying the model, in order to balance the model's fitting accuracy and generalization ability. This setting can be done through expert experience, experimental analysis, or cross-validation.

[0089] The proposed solution concretizes the nonlinear mapping model between voltage and electric field into a nonlinear fitting model based on a polynomial function. It allows for the pre-setting of the order of the polynomial function according to the voltage level of the measured conductor and the application scenario, thereby optimizing the computational accuracy and adaptability of the voltage inference unit. After the sensor array 200 acquires multiple electric field measurement data, the electric field data processing unit preprocesses them to generate multiple electric field characteristic data. Subsequently, the voltage inference unit uses this optimized nonlinear fitting model to perform inversion calculations on these electric field characteristic data. By selecting a polynomial function of an appropriate order, the model can more accurately capture the complex nonlinear relationship between ultra-high voltage electric field and voltage, avoiding underfitting or overfitting problems that may arise from a fixed model. For example, in lower voltage levels or relatively simple electric field environments, a lower-order polynomial may be chosen to maintain the simplicity and robustness of the model; while in ultra-high voltage or complex electric field environments, a higher-order polynomial may be needed to accurately describe its strong nonlinear characteristics. This adjustable order setting mechanism allows the nonlinear mapping model to better adapt to different measurement conditions, improving the accuracy and reliability of voltage inversion calculations.

[0090] The nonlinear mapping model can be constructed using a third- or fourth-order polynomial function fitted using the least squares method. For example, when measuring voltage on a 220kV transmission line, considering the nonlinear characteristics of the electric field distribution at this voltage level, the order of the polynomial function can be set to third order. This third-order polynomial function can be expressed as V = aE^3 + bE^2 + cE + d, where V represents the voltage of the conductor being measured, E represents the electric field characteristic data collected by the sensor array 200, and a, b, c, and d are undetermined coefficients. These coefficients can be obtained by fixing a non-invasive ultra-high voltage measuring device to the conductor with a known voltage in a standard high-voltage laboratory environment, simultaneously collecting electric field data and reference voltage values, and then fitting these data using the least squares method. When the measurement scenario is switched to a 500kV transmission line, due to the more significant nonlinear effect of the electric field, the order of the polynomial function can be adjusted to fourth order to improve the fitting accuracy of the model. This pre-setting of the order and calibration of the model parameters ensures that the voltage inversion unit can call the most suitable nonlinear mapping model to perform accurate voltage inversion calculations under different voltage levels and application scenarios.

[0091] This application further proposes that the polynomial function in the above-mentioned nonlinear mapping model is a third-order or higher polynomial function, and its coefficients are obtained by calibrating the sensor array 200 under a standard high-pressure environment.

[0092] Specifically, a polynomial function is a mathematical expression composed of variables and coefficients through addition, subtraction, multiplication, and non-negative integer exponentiation. Its order is the exponent of the highest-order term in the polynomial. Choosing a third-order or higher polynomial function means that the model has sufficient complexity and flexibility to better fit the complex nonlinear relationship between voltage and electric field. For example, a third-order polynomial can be used, such as `V=aE^3+bE^2+cE+d`, where `V` represents voltage, `E` represents electric field, and `a, b, c, d` are undetermined coefficients; or, to further improve the fitting accuracy, a fourth-order or higher polynomial can be used, such as `V=aE^4+bE^3+cE^2+dE+f`. This choice ensures that the model can capture the highly nonlinear characteristics that the electric field distribution may exhibit under ultra-high voltage environments, avoiding the underfitting problems that may be caused by lower-order polynomials.

[0093] Meanwhile, to ensure the accuracy of the model, its coefficients are obtained by calibrating the sensor array 200 under a standard high-voltage environment. Calibration refers to the process of determining the model parameters (coefficients of the polynomial function) under known input (standard high voltage) and known output (electric field measurement values ​​of the sensor array 200). A standard high-voltage environment typically refers to a laboratory or test field with a precise and controllable high-voltage source and reference measurement equipment. In this way, it can be ensured that the obtained model coefficients accurately reflect the response characteristics of the device under actual operating conditions, thereby improving the accuracy and reliability of voltage measurement. For example, a non-invasive ultra-high voltage measuring device can be installed on a standard high-voltage conductor of a known voltage level, and the applied voltage can be gradually changed while recording the electric field data collected by the sensor array 200. Subsequently, mathematical optimization algorithms such as least squares method and regression analysis are used to solve for the coefficients of the polynomial function based on these paired voltage and electric field data. In addition, a piecewise calibration method can also be used, that is, calibration is performed separately in different voltage ranges to obtain the corresponding polynomial function coefficients, in order to further optimize the measurement accuracy across the entire range.

[0094] The proposed solution concretizes the nonlinear mapping model into a third-order or higher polynomial function and combines this with calibration of the sensor array 200 under a standard high-voltage environment to obtain its coefficients. This constructs a voltage extrapolation model that can fully characterize the complex nonlinear relationship between ultra-high voltage and electric field while ensuring accurate and reliable parameters. Specifically, after the sensor array 200 acquires electric field data around the conductor under test, the voltage extrapolation unit in the data processing module 400 calls a pre-calibrated polynomial function model with a third-order or higher complexity. This model, with its higher order, can effectively fit the complexity of the ultra-high-voltage electric field distribution, avoiding the large errors that may arise from simpler models. Furthermore, since the coefficients of the model are precisely determined through actual measurement and data fitting under a strictly controlled standard high-voltage environment, rather than relying on theoretical estimations or empirical values, the accuracy and reliability of the model parameters are greatly improved. Therefore, when electric field data is input into this model for inversion calculation, it can output a highly accurate and stable voltage value of the conductor under test, effectively solving the problem of limited measurement accuracy caused by inaccurate nonlinear mapping model parameters.

[0095] As a specific implementation method, the voltage derivation unit can employ a third-order polynomial function `V=A`. E^3+B E^2+C E+D` represents the nonlinear relationship between voltage `V` and electric field `E`, where `A, B, C, D` are coefficients to be determined. These coefficients can be obtained through calibration in a standard high-voltage laboratory. For example, a non-invasive ultra-high-voltage measuring device is fixedly mounted on a test conductor of a known voltage level. Voltages from 100kV to 500kV are applied progressively using a high-precision voltage source, and after stabilization at each voltage point, multiple electric field measurement data collected by the sensor array 200 are recorded. For example, at 100kV, the sensor array 200 outputs electric field data E1; at 200kV, it outputs E2, and so on. These paired (V, E) data are input into the calibration module of the voltage extrapolation unit. This module can run a least-squares regression algorithm to fit the optimal third-order polynomial coefficients `A, B, C, D` based on these data points. These coefficients are then stored in the calibration unit of the data processing module 400 for subsequent voltage extrapolation calculations.

[0096] This application further proposes that the voltage inference unit is also used to perform data fusion processing on the multi-channel voltage inversion results from the sensor array 200, so as to reduce the impact of local electric field distortion caused by ion flow and corona discharge on the voltage measurement accuracy.

[0097] The data fusion processing of multiple voltage inversion results from sensor array 200 involves combining multiple voltage values ​​independently obtained from each electric field sensor in the array 200 to obtain a more accurate and stable final voltage measurement. This processing method effectively utilizes the redundancy of multi-channel measurement information, improving measurement robustness. Data fusion processing can be implemented in ways including, but not limited to: weighted averaging based on statistical principles, assigning different weights to each inversion result based on factors such as reliability, historical error, or sensor location, and then performing a weighted average; or using outlier removal algorithms to identify and exclude inversion results that significantly deviate from the normal range, and then averaging or medianizing the remaining results; or using more complex fusion algorithms, such as Kalman filtering and Bayesian estimation, to dynamically fuse multiple data streams. Through data fusion processing, the impact of local electric field distortion caused by ion flow and corona discharge on voltage measurement accuracy can be effectively reduced. Ion flow and corona discharge are common phenomena in ultra-high voltage environments. They can cause local charge accumulation and electric field distortion around the conductor being measured, leading to deviations in the local electric field values ​​measured by a single electric field sensor from the actual situation. By fusing voltage results obtained from multiple sensors at different spatial locations, they can be mutually verified and complemented, thereby reducing the influence of local distortion on a single sensor and improving the accuracy and reliability of the overall voltage measurement.

[0098] This application's solution addresses the problem of local electric field distortion caused by ion flow and corona discharge affecting voltage measurement accuracy under ultra-high pressure environments by enabling the voltage inversion unit to perform data fusion processing on multiple voltage inversion results from the sensor array 200. Specifically, after multiple electric field sensors in the sensor array 200 collect electric field signals from different locations around the conductor being measured, the electric field data processing unit in the data processing module 400 first performs synchronous sampling, noise reduction filtering, amplitude normalization, and time alignment processing on these raw electric field signals to obtain multiple electric field feature data. Subsequently, the voltage inversion unit uses a pre-constructed nonlinear mapping model between voltage and electric field to independently invert and calculate each electric field feature data, thereby obtaining multiple independent voltage inversion results. Since the electric field distortion caused by ion flow and corona discharge is usually localized, the degree and manner in which sensors at different locations are affected may differ. Therefore, by fusing these multi-channel voltage inversion results—for example, through weighted averaging, outlier removal, or consistency verification—the weight of inversion results significantly affected by local distortions can be effectively identified and reduced, or outliers can be directly removed. This allows for the comprehensive utilization of information from multiple sensors, resulting in a more accurate and stable final voltage measurement. This processing method enables the device to maintain high measurement accuracy and robustness even in complex ultra-high voltage environments with local electric field interference.

[0099] In the aforementioned non-invasive ultra-high voltage measurement device, after receiving multiple electric field characteristic data output by the electric field data processing unit, the voltage inference unit independently calls a pre-calibrated nonlinear mapping model to perform inversion calculations for each electric field characteristic data, thereby obtaining multiple voltage inversion results. For example, if the sensor array 200 contains four electric field sensors, four voltage inversion results, V1, V2, V3, and V4, will be obtained. To reduce the influence of local electric field distortion, the voltage inference unit can perform data fusion processing on these four results. Specifically, a weighted average processing based on statistical weights can be used. For example, different weights W1, W2, W3, and W4 can be assigned according to the accuracy shown by each sensor during the calibration process or its spatial location in the device (e.g., its distance from the area where corona discharge may occur), and then the final voltage value V_final = (W1, W2, W3, and W4) can be calculated. V1+W2 V2+W3 V3+W4 V4) / (W1+W2+W3+W4). Furthermore, outlier removal can be combined. For example, first calculate the average and standard deviation of V1, V2, V3, and V4, then remove any results exceeding the average ± 2 times the standard deviation, and finally average the remaining results. Alternatively, a consistency check can be performed; if the difference between any two voltage inversion results exceeds a preset threshold, an alarm is triggered or a more conservative fusion strategy is adopted.

[0100] This application further proposes that the data fusion processing includes at least one of the following: weighted average processing based on statistical weights, outlier removal processing, and consistency verification processing.

[0101] Data fusion processing is a process of integrating data from multiple sources or at multiple time points to obtain more comprehensive, accurate, and reliable information. Its role is to improve measurement accuracy, enhance system robustness, and reduce uncertainty. Besides the methods mentioned in this application, data fusion processing can also be achieved through Kalman filtering, Bayesian fusion, or neural network fusion. Weighted averaging based on statistical weights involves assigning different weights to each data point according to its statistical characteristics, such as variance, confidence level, or historical performance, and then performing an average calculation. This method allows more reliable data to contribute more to the final result, thereby reducing the impact of unreliable data. The determination of weights can be based on various factors such as the sensor's own accuracy, its distance from the measured conductor, the stability of historical data, or real-time data quality assessment. Outlier removal aims to identify and remove data points that significantly deviate from the majority of the data in the dataset, preventing outliers from having an excessive impact on the fusion result, thereby improving data purity. Commonly used outlier removal methods include the 3σ criterion, box plot method, or distance-based outlier detection algorithms. Consistency verification is used to check whether different data points in a dataset meet certain preset logical relationships or physical constraints, in order to ensure the internal logical consistency of the data and to identify and correct contradictory data. For example, it can be used to cross-verify measurement results from different sensors or to compare them with historical trends or physical model predictions.

[0102] This application's solution systematically addresses the problem of local electric field distortion caused by complex environmental factors such as ion flow and corona discharge by applying at least one of the following methods to the data fusion process: weighted averaging based on statistical weights, outlier removal, and consistency verification. Specifically, after the voltage inversion unit performs inversion calculations on the multi-channel electric field data collected by the sensor array 200 to obtain preliminary voltage inversion results, outlier removal is first performed to identify and remove extreme abnormal data points caused by transient strong interference (such as partial discharge), ensuring that the data basis for subsequent processing is relatively pure. Subsequently, consistency verification is performed by comparing the logical relationships or physical constraints between the inversion results of different sensors to further verify the reliability of the data and mark or correct inconsistent data. Finally, the data after outlier removal and consistency verification is weighted based on statistical weights. Different weights are assigned to each sensor's inversion result according to its reliability (e.g., based on its historical accuracy, relative position to the measured conductor, etc.) to perform a weighted average, thereby obtaining a final, more accurate, and robust measured conductor voltage value. This phased, multi-strategy fusion processing mechanism enables more effective response to measurement challenges in complex electromagnetic environments, based on inversion calculations using a pre-built nonlinear mapping model between voltage and electric field, significantly improving the accuracy and reliability of voltage measurement results.

[0103] The following is a concrete example. When performing data fusion processing on multiple voltage inversion results from sensor array 200, an improved 3σ criterion can be used first to remove outliers. This involves calculating the average and standard deviation of all voltage inversion results, and marking data points exceeding the average ± 3 times the standard deviation as outliers and removing them. Next, a consistency check is performed. For example, a relative deviation threshold (e.g., 5%) can be set. If the relative deviation between the voltage values ​​obtained from any two sensors exceeds this threshold, inconsistency is considered to exist, requiring further analysis or remeasurement. For data that passes the check, a weighted average based on statistical weights can be used. For example, the weight can be determined based on the average distance between each electric field sensor and the conductor being measured. Sensors that are closer to the conductor have higher weights in their inversion results, thus obtaining the final voltage measurement result.

[0104] This application further proposes that the wireless communication module 600 adopts a low-power wireless communication protocol to periodically or on demand send the voltage measurement results output by the voltage extrapolation unit to a remote monitoring terminal.

[0105] The wireless communication module 600 is a component in the device responsible for exchanging data with external systems. Its function is to transmit the voltage measurement results generated internally by the device to a remote location. This module can be a standalone communication chip, such as a cellular communication module (e.g., 2G / 3G / 4G / 5G module), LoRa module, NB-IoT module, etc., or it can be a communication interface integrated into the data processing module 400, transmitting and receiving wireless signals through an external antenna. The low-power wireless communication protocol is a communication standard or specification specifically designed for data transmission with minimal energy consumption under limited energy supply. Common low-power wireless communication protocols include LoRaWAN, NB-IoT, Zigbee, Bluetooth Low Energy (BLE), etc., or proprietary low-power protocols optimized for specific application scenarios. These protocols significantly reduce energy consumption during communication by optimizing data packet structure, sleep mechanisms, transmission rates, and modulation methods. The voltage measurement result is the voltage value of the measured conductor calculated by the voltage derivation unit in the data processing module 400, usually represented in digital form, and may include information such as voltage amplitude and phase. The periodic transmission refers to automatically triggering data transmission at preset time intervals (e.g., every minute, every hour, or every day) to provide a continuous monitoring data stream. The on-demand transmission refers to triggering data transmission only based on specific conditions (e.g., voltage exceeding a preset threshold, detection of an abnormal event) or receiving a query command from a remote monitoring terminal. The remote monitoring terminal is a device or system that receives, stores, processes, and displays voltage measurement results; it can be a server, cloud platform, mobile application, SCADA system, etc., enabling users to remotely monitor the voltage status of the measured conductor.

[0106] The solution in this application achieves efficient transmission of voltage measurement results by tightly integrating the wireless communication module 600 with the voltage deduction unit in the data processing module 400. Specifically, after the voltage deduction unit completes the calculation of the voltage value of the measured conductor, the wireless communication module 600 is activated to perform the data transmission task. To address the energy constraints and long-term operational requirements typically faced by ultra-high voltage measurement devices, the wireless communication module 600 is configured to employ a low-power wireless communication protocol. This protocol minimizes energy consumption during data transmission, thereby significantly extending the device's battery life and maintenance cycle. Furthermore, to further optimize data transmission efficiency and responsiveness, the device provides a flexible data transmission strategy. Periodic transmission ensures that the remote monitoring terminal continuously receives the latest voltage data, facilitating trend analysis and routine status monitoring. Simultaneously, through an on-demand transmission mechanism, the device can instantly upload critical data upon detecting specific events (such as abnormal voltage fluctuations) or receiving remote instructions, avoiding unnecessary continuous communication and further conserving valuable energy while ensuring timely information delivery. This solution, which combines low-power communication technology and intelligent transmission strategies, enables non-invasive ultra-high voltage measurement devices to achieve efficient, reliable, and energy-saving remote data transmission in harsh operating environments.

[0107] Once the voltage calculation unit in the data processing module 400 successfully calculates the voltage value of the conductor under test, the voltage measurement result is encapsulated into a data packet. The wireless communication module 600 can use the LoRaWAN protocol for data transmission. The LoRaWAN protocol, with its long-range and low-power characteristics, is well-suited for data transmission in vast areas such as power lines where infrastructure may be lacking. The device can be configured to automatically send the latest voltage measurement result to the remote monitoring terminal via the LoRaWAN network every 15 minutes. Furthermore, to handle emergencies, the device can set a voltage change threshold; for example, if the voltage measurement result fluctuates by more than 5% within a short period, the wireless communication module 600 will immediately trigger a data upload, promptly reporting the anomaly to the remote monitoring terminal. The remote monitoring terminal can be a server deployed in the cloud, receiving data forwarded by the LoRaWAN gateway and monitoring the voltage measurement results.

[0108] This application further proposes that the wireless communication module 600 and the data processing module 400 work together to trigger the immediate upload of voltage measurement results when a change in the voltage of the measured conductor exceeding a preset threshold is detected.

[0109] The collaborative operation between the wireless communication module 600 and the data processing module 400 refers to the establishment of an effective communication link and cooperation mechanism between them to achieve information sharing and task coordination. For example, the data processing module 400 can send control commands or data upload requests to the wireless communication module 600, while the wireless communication module 600 can provide feedback on communication status or receive confirmation information to the data processing module 400. This collaboration can be implemented through hardware interfaces such as Serial Peripheral Interface (SPI), Integrated Circuit Bus (I2C), or Universal Asynchronous Receiver / Transmitter (UART), or through software protocols such as message queues or shared memory. The detection process, in which the voltage of the measured conductor is detected to exceed a preset threshold, is executed by the data processing module 400. Its core function is to monitor the measured conductor voltage value output by the voltage deduction unit in real time and compare it with the preset threshold. The preset threshold can be set according to the actual application scenario and safety requirements. For example, it can be set as the absolute value of the voltage exceeding a certain upper or lower limit, or the rate of change of the voltage (such as the magnitude of voltage rise or fall in a short period of time) exceeding a certain specific value. Detection methods may include continuous sampling and comparison, average or variance analysis based on a sliding window, or using state machine logic to determine whether the voltage has entered an abnormal range. The immediate uploading of the triggered voltage measurement results means that after detecting an abnormal voltage change, the wireless communication module 600 immediately initiates the data transmission process, sending the latest voltage measurement result to the remote monitoring terminal, rather than waiting for the next preset period or triggering on demand. This triggering mechanism can be initiated by the data processing module 400 after detecting an anomaly by sending a specific command or interrupt signal to the wireless communication module 600. Upon receiving the trigger signal, the wireless communication module 600 will prioritize the uploading task and may employ a faster transmission rate or a more reliable communication strategy to ensure timely data delivery.

[0110] This application's solution addresses the problem of untimely uploading of abnormal voltage changes by enabling the wireless communication module 600 and data processing module 400 to work collaboratively. Specifically, the data processing module 400 continuously receives and processes electric field data collected by the sensor array 200, and uses a pre-built nonlinear mapping model between voltage and electric field to perform inversion calculations to obtain the voltage value of the measured conductor. During this process, the data processing module 400 not only performs routine voltage calculations but also monitors these voltage values ​​in real time, comparing them with preset thresholds. Once a change in the voltage of the measured conductor exceeding the preset threshold is detected, the data processing module 400 immediately generates an instant upload request or instruction and sends it to the wireless communication module 600 via its internal communication interface. Upon receiving this instruction, the wireless communication module 600 interrupts or prioritizes any ongoing periodic or on-demand transmission tasks, and immediately encapsulates the latest voltage measurement results and sends them to the remote monitoring terminal via a low-power wireless communication protocol. This mechanism ensures that, at critical moments, such as when the power grid experiences faults or abnormal fluctuations, abnormal voltage information can be rapidly transmitted, providing real-time early warnings and decision-making support to the remote monitoring terminal. Through this close collaboration and instant response mechanism, the device can transform from passive data reporting to proactive early warning of abnormal events, significantly improving the real-time performance and reliability of ultra-high voltage measurement.

[0111] The data processing module 400 can integrate a microcontroller (MCU) that continuously runs a voltage monitoring algorithm. This algorithm constantly retrieves the latest voltage measurement value from the voltage estimation unit and compares it with high-voltage and low-voltage thresholds stored in non-volatile memory. For example, if the voltage value exceeds a preset ±10% normal fluctuation range for three consecutive sampling periods, the microcontroller determines that the voltage has changed beyond the preset threshold. At this time, the microcontroller sends a specific "urgent upload" command to the wireless communication module 600 via a Universal Asynchronous Receiver / Transmitter (UART) interface. Upon receiving this command, the wireless communication module 600, such as a LoRaWAN-based module, immediately initiates the LoRaWAN transmission process, sending the latest voltage measurement data packet to the LoRaWAN gateway, which then forwards it to the remote monitoring terminal. To ensure immediacy, this urgent upload can be set to high priority and may even employ an acknowledgment mechanism to ensure successful data packet delivery.

[0112] This application further proposes that the aforementioned data processing module 400 also includes a calibration unit for storing the calibration parameters of the sensor array 200, the parameters of the nonlinear mapping model, and historical voltage measurement data, so as to support subsequent voltage extrapolation correction and long-term operating status analysis.

[0113] The calibration unit is a functional module within the data processing module 400. Its main function is to provide a dedicated storage and management mechanism for storing important parameters related to the long-term operation and accuracy maintenance of the measuring device. It can be a standalone storage chip, such as EEPROM, flash memory, or an SD card, or a specific area allocated within the internal memory of the data processing module 400. Its implementation can be through hardware circuit integration or through software logic managed on a general-purpose storage medium. Storing the calibration parameters of the sensor array 200 means that the calibration unit can store calibration data obtained during factory or periodic calibration of the sensor array 200. These parameters may include the gain, zero-point drift, and linearity correction coefficients of each electric field sensor. The storage method can be to embed these parameters in the storage medium in the form of lookup tables, coefficient matrices, or functional expressions, so that the data processing module 400 can perform accurate compensation when processing the raw electric field signal. Storing the nonlinear mapping model parameters means that the calibration unit can store the specific parameters of the nonlinear mapping model used to invert the electric field data into voltage values. For example, if the model is a polynomial function, its coefficients of each order are stored; if it is a neural network model, its weights and biases are stored. These parameters are the core of the model, and their accuracy directly affects the accuracy of voltage extrapolation. Storage methods can include directly storing numerical values, storing model configuration files, or storing binary data. Storing historical voltage measurement data allows the calibration unit to record the measurement results of the device on the voltage of the conductor being measured at different points in time. This historical data can include auxiliary information such as voltage values, measurement timestamps, and environmental conditions. Storing this data helps analyze the long-term operating trend of the conductor being measured and evaluate the performance stability of the measuring device itself. Storage methods can include circular buffers, time-series databases, or log files. Supporting subsequent voltage extrapolation correction means that the data processing module 400 can dynamically adjust and optimize the voltage extrapolation process using stored calibration parameters and model parameters. For example, when sensor performance drift is detected, real-time compensation can be performed using stored calibration parameters; when changes in environmental conditions cause a decrease in model accuracy, fine-tuning or reloading a more suitable model can be performed based on stored model parameters. This ensures that the measuring device maintains high measurement accuracy during long-term operation. Supporting long-term operational status analysis means that the operating status of the measuring device can be evaluated and diagnosed by storing historical voltage measurement data. For example, by analyzing historical data, the long-term drift trend of the sensor array 200 can be discovered, and the maintenance cycle can be predicted; it is also possible to evaluate the performance of the device under different operating conditions by comparing measurement results at different time periods, providing data support for the optimization and upgrading of the device.

[0114] In this application, the data processing module 400 integrates a calibration unit to effectively manage key operating parameters and historical measurement data. Specifically, the calibration unit is configured to store the calibration parameters of the sensor array 200. These parameters are invoked by the electric field data processing unit during synchronous sampling, noise reduction filtering, amplitude normalization, and timing alignment processing after the sensor array 200 acquires the raw electric field signal, to accurately calibrate the raw signal and ensure the accuracy of the electric field characteristic data. Simultaneously, the calibration unit also stores the parameters of the nonlinear mapping model. These parameters are the core basis for the voltage extrapolation unit's inversion calculations. By invoking these parameters, the voltage extrapolation unit can accurately convert the processed electric field characteristic data into the voltage value of the measured conductor. Furthermore, the calibration unit is responsible for recording and storing historical voltage measurement data. This data not only provides a foundation for subsequent voltage extrapolation corrections—for example, by analyzing historical data to identify model deviations or sensor drift and thus adjust calibration parameters or model parameters—but also provides valuable information for long-term operational status analysis of the device, enabling the device to perform self-diagnosis and performance evaluation. In this way, the calibration unit works closely with other units of the data processing module 400 to form a closed-loop calibration, measurement, correction and analysis system, which significantly improves the measurement accuracy and reliability of the non-invasive ultra-high voltage measurement device during long-term operation.

[0115] The data processing module 400 can use a high-performance microcontroller as its core, and integrate or expand a non-volatile memory, such as EEPROM or NAND Flash, as the physical carrier of the calibration unit. Within this memory, a dedicated area can be allocated to store the calibration parameters of the sensor array 200. For example, a data block can be reserved for each electric field sensor to record its gain correction coefficient and zero-point offset at different temperatures. Nonlinear mapping model parameters can be stored as an array of polynomial coefficients; for example, for a third-order polynomial model, four floating-point coefficients can be stored. Historical voltage measurement data can be organized according to timestamps, with each record containing the measured voltage value, measurement time, and ambient temperature, and a circular storage mechanism is used, automatically overwriting the oldest data when the storage space is full. When the device is running in the field, the electric field data processing unit of the data processing module 400 reads the corresponding calibration parameters from the calibration unit for real-time correction when processing the raw electric field signals acquired by the sensor array 200. Subsequently, the voltage extrapolation unit calls upon the stored nonlinear mapping model parameters to perform inversion calculations on the corrected electric field characteristic data. Simultaneously, the voltage measurement results obtained from each calculation are written to the historical voltage measurement data area of ​​the calibration unit. During device maintenance or upgrades, the system can connect to the data processing module 400 via an external interface to read and analyze historical data from the calibration unit, or update the calibration parameters and nonlinear mapping model parameters of the sensor array 200 to adapt to new operating environments or improve measurement accuracy.

[0116] In summary, through the structural design of the metal shell and metal shielding cavity, combined with the fixed installation of the sensor array and the inversion calculation of the data processing module, external electromagnetic interference is effectively shielded, electric field data is stably acquired, and high-precision voltage inversion is achieved by using a nonlinear mapping model. This solves the problems of low measurement accuracy and poor system robustness caused by sensor displacement and electromagnetic interference in the existing technology.

[0117] Based on the non-invasive ultra-high voltage measuring device provided above, this application further proposes a voltage measurement method, such as... Figure 6 As shown, the method includes the following steps: 610. The non-invasive ultra-high voltage measuring device is fixedly installed on the conductor being measured, so that the metal shell and internal metal shielding cavity of the device are electrically connected to the conductor being measured and form an equipotential body; In this step, a non-invasive ultra-high voltage measuring device is fixedly installed on the conductor under test, ensuring that the device's metal casing and internal metal shielding cavity are electrically connected to the conductor, thus forming an equipotential body. This step is fundamental to ensuring measurement accuracy. By forming an equipotential body between the device and the conductor under test, external electric field interference can be effectively shielded, and a stable reference potential can be provided for the internal sensor. For example, the device can be securely fixed to the conductor under test using mechanical clamps or bolts, while conductive pads or spring contacts ensure a reliable low-impedance electrical path is formed between the metal casing and the conductor under test.

[0118] 620. Multiple electric field measurement signals located at different spatial positions are collected by a sensor array arranged inside a metal casing. Each electric field measurement signal corresponds to the composite electric field distribution around the conductor being measured. Specifically, electric field sensors based on the principle of capacitance or Pockels sensors based on the photoelectric effect can be used. These sensors are strategically arranged inside the device to capture information about the intensity and direction of the electric field around the conductor being measured at different spatial points.

[0119] The sensor array consists of multiple MEMS electric field sensors. As the front-end sensitive device for voltage measurement, the MEMS electric field sensor uses insulating materials such as polytetrafluoroethylene and ceramics to isolate its detection electrodes from the structural shell of the measuring device, ensuring that it is electrically insulated from high-voltage equipment.

[0120] 630. The multiple electric field measurement signals acquired are synchronously sampled and processed, and noise reduction filtering, amplitude normalization and timing alignment are performed in sequence to obtain multi-channel electric field characteristic data. This step can be achieved using a unified sampling clock or a high-precision timestamp. Noise reduction filtering eliminates the influence of environmental noise and sensor noise on the signal; for example, a digital low-pass filter or a Kalman filter can be used. Amplitude normalization adjusts the output signals of different sensors to a uniform dimension or range to eliminate the effects of individual sensor differences and inconsistent amplification factors; for example, this can be achieved by dividing by the maximum amplitude or a preset reference value. Timing alignment corrects for phase delays that may occur during signal transmission or processing, ensuring precise time alignment of all signals; for example, this can be achieved using cross-correlation algorithms or phase-locked loop techniques.

[0121] 640. Based on the spatial position parameters of each MEMS electric field sensor relative to the conductor under test, position compensation processing is performed on the multi-channel electric field characteristic data, and the compensated electric field characteristic data is fused to obtain fused electric field data. In this embodiment, specific correction coefficients are applied to the readings of each sensor according to a pre-calibrated spatial geometric model. The fusion operation then combines the compensated multi-channel electric field characteristic data to form a more stable and accurate electric field data. For example, a weighted average method can be used, where the weights can be determined based on the reliability of the sensor or its distance from the conductor being measured, or a more complex sensor data fusion algorithm can be used.

[0122] 650. Call the pre-calibrated nonlinear mapping model between voltage and electric field to perform inversion calculation on the fused electric field data to obtain the voltage value of the conductor under test; It should be noted that this nonlinear mapping model was established through precise calibration of the device under standard high-voltage conditions. It can accurately describe the complex nonlinear relationship between the voltage of the measured conductor and the surrounding electric field. For example, this model can be a polynomial fitting model or a neural network-based model. By inputting the fused electric field data into this model, the actual voltage value of the measured conductor can be accurately deduced.

[0123] 660. Store the voltage value of the conductor under test as a measurement result and / or transmit it to a remote monitoring terminal via wireless communication.

[0124] In this embodiment, a non-invasive ultra-high voltage measurement device is precisely installed and forms an equipotential body with the conductor under test. A sensor array is used to collect multi-point electric field signals, and these signals undergo a series of refined digital processing steps, including synchronous sampling, noise reduction filtering, amplitude normalization, and timing alignment, to obtain high-quality electric field characteristic data. Based on this, further position compensation and data fusion are performed, effectively eliminating the influence of spatial differences and local distortions, generating more reliable fused electric field data. Finally, by calling a pre-built nonlinear mapping model for inversion calculation, the voltage value of the conductor under test can be accurately derived. This systematic approach fully utilizes the hardware advantages of the device, such as the stable measurement environment provided by the metal shielded cavity and the powerful computing capabilities of the data processing module, effectively overcoming the challenges of interference susceptibility and complex nonlinear relationships in electric field measurements under ultra-high voltage environments, significantly improving the accuracy and reliability of voltage measurements.

[0125] During installation, a specially designed conductive clamp tightly holds the metal casing of the device to the conductor under test of the ultra-high voltage transmission line, ensuring a good electrical connection and forming a stable equipotential shielding environment. The sensor array consists of eight miniature capacitive electric field sensors, evenly distributed on the inner wall of the lower half of the shell. Their detection electrodes protrude through the outer wall of the lower half of the shell via insulating encapsulation, directly exposed to the electric field surrounding the conductor under test. The analog signal output from each capacitive electric field sensor is fed into a high-precision analog-to-digital converter (ADC). All ADCs are synchronously triggered by a master clock in the data processing module, sampling synchronously at a sampling rate of 10kHz. The sampled digital signals are first denoised using a digital Butterworth low-pass filter to remove high-frequency noise above 500Hz. Subsequently, the data processing module normalizes the amplitude of each signal according to preset calibration parameters and uses a cross-correlation algorithm to perform timing alignment to compensate for transmission delay. Next, based on the precise three-dimensional coordinates of each sensor within the device, the data processing module applies a geometric compensation model established based on finite element simulation results to perform position compensation for each electric field characteristic data. The compensated data is fused using a weighted average algorithm, where the weights are dynamically adjusted based on the consistency of each sensor's response under different electric field intensities, resulting in a comprehensive fused electric field data. Finally, the data processing module calls a pre-stored fifth-order polynomial nonlinear fitting model, using the fused electric field data as input, to derive the real-time voltage value of the measured conductor. This voltage value is updated once per second and stored in the data processing module's internal flash memory, while simultaneously being transmitted to a remote monitoring center via a built-in LoRa wireless communication module.

[0126] The following section, using the specific structure of the non-invasive ultra-high voltage voltage measurement device provided in the above embodiments, explains the measurement principle of this voltage measurement method. This method actually employs an electric field-sensitive element based on MEMS technology to achieve a large-range voltage measurement. A nonlinear mapping model (which is essentially a function relating the electric field distribution to the voltage) can be constructed using Helmholtz's law. When the high-voltage line potential is... At that time, the electric field distribution around it is as follows: Where d is the distance from the center of the axial section of the high-voltage line, r is the radius of the high-voltage line, h is the height above the ground, ε represents the dielectric constant, and E(d) represents the electric field strength at a distance d from the center of the section. This represents the potential of the high-voltage line. Since r is a constant, the height h of the high-voltage line is generally also fixed as a constant. This represents a univariate function indicating the distance of the electric field sensing element from the center of the axial section of the high-voltage line. The sensor array uses MEMS electric field sensors arranged on the high-voltage line. When the distance *d* between the sensing element and the center of the axial section of the high-voltage line is fixed, the electric field value at that location is also determined. In the ideal case where only an electrostatic field exists, the electric field value at that location changes linearly with the change in the high-voltage line potential. A first-order linear equation can be solved to obtain the electric field value. This allows for the measurement of the voltage value of high-voltage lines.

[0127] Before actual measurement, the sensor of the device needs to be calibrated in a standard high-voltage environment according to the voltage level and height of the actual test line. The nonlinear relationship fitting equation between the high-voltage line voltage and the sensor output is obtained, thereby retrieving the actual high-voltage line voltage value and obtaining the above-mentioned function model.

[0128] Regarding the voltage measurement method, the distance d from the sensitive measuring electrode of each MEMS electric field sensor to the axial center of the high-voltage line is not equal, and the output electric field value is also different, corresponding to... , ... First, the MEMS electric field sensor is calibrated in a standard electric field environment to obtain... ,in, This is a nonlinear relationship function obtained after calibration in a standard electric field environment, which can be used to calculate the potential value of the high-voltage line.

[0129] In this embodiment, a non-invasive ultra-high voltage measurement device is fixed to a high-voltage conductor. A standard high voltage U is applied to calibrate the MEMS electric field sensor integrated within the device. Based on the actual calibration data, a third-order polynomial fit is sufficient to meet the voltage measurement accuracy requirements. The nonlinear relational function in the equation can be represented by the following relational equation matrix: , , , .

[0130] Fitting coefficients for each MEMS sensor , , , All of these can be determined through calibration, and thus the voltage can be derived. The system performs multi-channel data fusion processing on the voltage outputs of four sensors to achieve high-precision measurement of high-voltage lines.

[0131] The above technical solution effectively addresses the challenges of electric field signal susceptibility to environmental interference, uneven spatial distribution, and complex nonlinear relationships between voltage and electric field in ultra-high voltage (UHV) voltage measurements. This method significantly improves the accuracy and robustness of electric field measurements through refined signal processing, spatial compensation, and data fusion techniques. Furthermore, it achieves high-precision inversion of the voltage of the measured conductor using a nonlinear mapping model. This enables stable and reliable voltage measurement results even in complex and variable UHV environments, providing a solid data foundation for the safe operation and condition monitoring of power systems.

[0132] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A non-invasive ultra-high voltage measuring device, characterized in that, include: The system comprises a metal casing, a sensor array, a metal shielding cavity enclosed by the metal casing, and a data processing module, a power supply module, and a wireless communication module fixed inside the metal shielding cavity; the data processing module is connected to the sensor array, the power supply module, and the wireless communication module. The metal housing includes an upper half-shell and a lower half-shell that are rotatably connected. Through holes for clamping the conductor under test are provided on the upper and lower bottom surfaces of the metal housing. When the metal housing is installed on the conductor under test, the metal housing and the metal shielding cavity are respectively symmetrically arranged with respect to the axial center of the conductor under test. The sensor array is fixed to the inner wall of the lower half shell and extends through the outer wall of the lower half shell; the sensor array is used to collect multiple electric field measurement data at different locations of the metal shell; The metal shielding cavity includes a first shielding cavity disposed on the inner wall of the upper half shell and a second shielding cavity disposed on the inner wall of the lower half shell, wherein the power module, the data processing module and the wireless communication module are distributed in the first shielding cavity and the second shielding cavity; The data processing module is used to perform inversion calculations using a pre-built nonlinear mapping model between voltage and electric field to obtain the voltage of the conductor under test.

2. The non-invasive ultra-high voltage measuring device according to claim 1, characterized in that, After the ultra-high voltage measuring device is installed on the conductor being measured, the upper half shell, the lower half shell, the first shielding cavity, and the second shielding cavity are all electrically connected to the conductor being measured and form an equipotential body.

3. The non-invasive ultra-high voltage measuring device according to claim 2, characterized in that, Both the first shielding cavity and the second shielding cavity are composed of a metal partition plate and a U-shaped plate; The metal partition plate has a semi-circular concave surface with the same radius as the through hole at its center position; The metal partition plate and the U-shaped plate are provided with an interference fit at their edges, and the metal partition plate and the U-shaped plate are installed through the interference fit to form a sealed space.

4. The non-invasive ultra-high voltage measuring device according to claim 3, characterized in that, At least one first communication interface is provided in the area outside the semi-circular concave surface of the metal isolation plate; The sensor array, as well as the power module, the data processing module, and the wireless communication module distributed in the first shielding cavity and the second shielding cavity, are electrically connected through the communication interface.

5. The non-invasive ultra-high voltage measuring device according to claim 4, characterized in that, The second shielding cavity has a second communication interface on its U-shaped plate, which is used to connect the sensor array and the data processing module.

6. The non-invasive ultra-high voltage measuring device according to any one of claims 1-5, characterized in that, An array hole is provided on the side wall of the lower half shell. Each electric field sensor in the sensor array is installed in the array hole, and the detection electrode of the electric field sensor is wrapped with insulating material to isolate its electrical connection with the lower half shell.

7. The non-invasive ultra-high voltage measuring device according to any one of claims 1-5, characterized in that, Also includes: A solar panel is disposed on the outer surface of the upper shell and is electrically connected to the power module.

8. The non-invasive ultra-high voltage measuring device according to claim 7, characterized in that, The power module includes an energy storage battery, an energy harvesting circuit board, and a power supply circuit board; The energy harvesting circuit board is connected to the solar panel and the energy storage battery respectively, and is used to convert the solar energy received by the solar panel into electrical energy and store it in the energy storage battery; The power supply circuit board is connected to the energy storage battery and is used to supply power to the sensor array and the wireless communication module based on the voltage in the energy storage battery.

9. The non-invasive ultra-high voltage measuring device according to any one of claims 1-5, characterized in that, The data processing module includes an electric field data processing unit and a voltage extrapolation unit; The electric field data processing unit is used to perform synchronous sampling, noise reduction filtering, amplitude normalization and time alignment processing on the raw electric field signals collected by each electric field sensor in the sensor array to obtain multi-channel electric field feature data. The voltage derivation unit is used to perform inversion calculations on multiple electric field characteristic data based on a pre-constructed nonlinear mapping model between voltage and electric field, and outputs the voltage value of the measured conductor.

10. A voltage measurement method, characterized in that, The method, applied to the non-invasive ultra-high voltage measuring device as described in any one of claims 1-9, comprises: A non-invasive ultra-high voltage measuring device is fixedly installed on the conductor being measured, so that the metal shell and internal metal shielding cavity of the device are electrically connected to the conductor being measured and form an equipotential body. A sensor array arranged inside the metal casing is used to collect multiple electric field measurement signals located at different spatial positions, wherein each electric field measurement signal corresponds to the composite electric field distribution around the conductor being measured. The multiple electric field measurement signals acquired are synchronously sampled and processed, and noise reduction filtering, amplitude normalization and timing alignment are performed in sequence to obtain multi-channel electric field characteristic data. Based on the spatial position parameters of each electric field sensor relative to the conductor being measured, the multi-channel electric field feature data is subjected to position compensation processing, and the compensated electric field feature data is fused to obtain fused electric field data. The voltage value of the conductor under test is obtained by inverting the fused electric field data by calling the pre-calibrated nonlinear mapping model between voltage and electric field; The voltage value of the conductor under test is stored as a measurement result and / or transmitted to a remote monitoring terminal via wireless communication.

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