Substation clamp type adaptive fast grounding device and monitoring method

Abstract

The application belongs to the technical field of power safety, and particularly relates to a transformer substation hoop type self-adaptive rapid grounding device and a monitoring method. The device comprises: a grounding device body comprising a grounding terminal for grounding; a sensor module comprising a resistance sensor, a soil temperature and humidity sensor, a current transformer, a voltage sensor, a three-axis acceleration sensor, a GPS positioning module and an environmental temperature and humidity sensor, which are used to collect data of the resistance of the grounding terminal, the current of the grounding terminal, the voltage of the grounding terminal, the vibration frequency and amplitude of the grounding terminal, the longitude and latitude of the grounding terminal, the soil humidity, the soil temperature, the air humidity and the air temperature; and a controller module connected with the sensor module, which is used to collect, store and process the data collected by the sensor module to obtain processed data, and execute alarm logic judgment based on the processed data. The lever type rapid buckle designed grounding device body can realize clamping and fixing by single hand operation.

Description

Technical Field

[0001] This invention belongs to the field of power safety technology, specifically relating to a substation clamp-type adaptive fast grounding device and monitoring method. Background Technology

[0002] In power construction and maintenance operations, grounding devices are critical equipment for ensuring the safety of workers. Currently, commonly used grounding devices primarily rely on a mechanical connection method where bolts are tightened between the wire lugs and the grounding stake. While this meets basic safety grounding requirements, installation requires manual tightening of bolts, a cumbersome process that takes an average of 2 to 3 minutes per installation. In large-scale power maintenance operations, frequent installation and disassembly significantly reduce overall work efficiency and impact construction progress. Furthermore, existing devices cannot monitor key parameters such as grounding resistance, current, and voltage in real time, relying on periodic inspections or reactive handling after faults for maintenance. Statistics show that the accident tracing rate using traditional methods is less than 30%, failing to meet the real-time and precise safety management requirements of modern power systems. Summary of the Invention

[0003] The purpose of this invention is to provide a substation clamp-type adaptive fast grounding device and monitoring method, which solves the problems of cumbersome installation and operation and incomplete monitoring of existing grounding devices in the background art.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: A substation clamp-type adaptive fast grounding device includes: The grounding device body includes a grounding terminal for grounding; The sensor module includes a resistance sensor, a soil temperature and humidity sensor, a current transformer, a voltage sensor, a triaxial accelerometer, a GPS positioning module, and an environmental temperature and humidity sensor. It is used to collect data on grounding terminal resistance, grounding terminal current, grounding terminal voltage, grounding terminal vibration frequency and amplitude, grounding terminal latitude and longitude, soil moisture, soil temperature, air humidity, and air temperature. The controller module, connected to the sensor module, is used to collect, store, and process the data collected by the sensor module to obtain result data, and to perform alarm logic judgment based on the result data. The communication module is signal-connected to the controller module and is used to transmit data processed by the controller module. Power supply module, used for power supply; The specific steps for collecting, storing, and processing the data to obtain the processed data, and then performing alarm logic judgments based on the processed data, include: The sensor module collects vibration signals, fault current signals, and grounding resistance values. The vibration signal is input into a pre-constructed normal vibration mode model to obtain the judgment result of mechanical loosening abnormality; The fault current signal is subjected to point gradient analysis to locate the electrical abnormal fault segment and obtain the fault segment location result. The judgment result and the location result are fused to obtain the comprehensive confidence level; Based on the confidence level, combined with environmental parameter deviations and equipment status deviations, the dynamic alarm threshold for grounding resistance is calculated. The system compares the grounding resistance value with the dynamic alarm threshold, combines the comprehensive confidence level with the confidence threshold, triggers the corresponding level of alarm signal, and outputs maintenance suggestions that include the fault type and location.

[0005] Preferably, the grounding device body includes a grounding terminal, a clamp, a clamping block, and a handle. Two clamping blocks are clamped onto the grounding terminal, and the clamp is clamped onto the clamping block. A nut is fixedly provided on the clamp. The two ends of the clamp are connected by a screw. One end of the screw is threadedly connected to the nut, and a locking pin is fixedly provided on the other end of the screw. The locking pin is perpendicularly connected to the screw. The locking pin and the handle are rotatably connected. The handle can rotate circumferentially along the locking pin. A connecting lug is also provided on the clamp.

[0006] Preferably, the two clamping blocks are symmetrically placed on the grounding terminal. The clamping blocks are cylindrical blocks with a semi-circular cross-section, and a clearance groove corresponding to the grounding terminal is opened at the center of the semi-circle.

[0007] In a second aspect, the present invention provides a monitoring method for a clamp-type adaptive fast grounding device in a substation, which employs the aforementioned grounding device and includes the following specific steps: Insert the clamp-type grounding device into the grounding pile and clamp it in place with one hand. Vibration signals, fault current signals, and grounding resistance values ​​are collected through sensor modules within the grounding device; The vibration signal is input into a pre-constructed normal vibration mode model to obtain the judgment result of mechanical loosening abnormality; The fault current signal is subjected to point gradient analysis to locate the electrical abnormal fault segment and obtain the fault segment location result. The judgment result and the location result are fused to obtain the comprehensive confidence level; Based on the confidence level, combined with environmental parameter deviations and equipment status deviations, the dynamic alarm threshold for grounding resistance is calculated. The system compares the grounding resistance value with the dynamic alarm threshold, and combines the comprehensive confidence level with the confidence threshold to trigger an alarm signal of the corresponding level. It also outputs maintenance suggestions containing the fault type and location through the communication module.

[0008] The step of acquiring vibration signals, fault current signals, and grounding resistance values ​​through the sensor module within the grounding device includes: The vibration acceleration time-series data of the grounding lead at the moment of the fault is obtained by using an accelerometer as a vibration signal; Obtain fault current waveform data through a current transformer; The grounding resistance value is obtained by using a resistance sensor.

[0009] Preferably, in the step of inputting the vibration signal into a pre-constructed normal vibration mode model to obtain the determination result of mechanical loosening abnormality, the normal vibration mode model includes a GNAR model and a particle filter, specifically: Vibration signals of the grounding device under historical health conditions are obtained as training samples; The training samples are fitted using a GNAR model, which is as follows: ; In the formula, for t The amplitude of vibration at any given moment; , These are model coefficients, reflecting the impact of historical data and noise; , The order of autoregression; For external incentives, It is Gaussian white noise; After fitting, determine the model order and the corresponding model coefficients; Real-time collected vibration signals The input is fed into a particle filter, which outputs an estimate of the vibration signal at time t. ; Calculate the measured vibration signal and particle filter estimate The residuals between: ; Obtain the residual sequence within the preset time window ; Based on the statistical characteristics of the historical residual sequence, the judgment threshold is calculated: ; In the formula, The mean, Standard deviation; If the residuals of M consecutive sampling points all exceed the judgment threshold If so, it is determined that the grounding device has a mechanical loosening abnormality; Output vibration anomaly determination results.

[0010] Preferably, the step of performing point gradient analysis on the fault current signal to locate the electrical abnormal fault segment and obtain the fault segment location result includes: Acquire the fault current signal, simultaneously acquire the voltage values ​​of accessible points, and arrange the voltage values ​​in spatial order to obtain a voltage sequence; Calculate the potential gradient between adjacent accessible points in the voltage sequence, and mark the conductor segment whose potential gradient exceeds a preset threshold as a candidate fault segment; Calculate the resistance increment for each candidate fault segment and construct a resistance increment sequence; Using the zero resistance increment sequence under fault-free conditions as a reference sequence, a grey relational analysis is performed on the resistance increment sequence to calculate the comprehensive relational degree of each conductor segment. The conductor segment with the lowest overall correlation degree and below the preset correlation degree threshold is identified as the fault segment, and the location result including the fault segment location and resistance increment value is output.

[0011] Preferably, the step of fusing the judgment result and the positioning result to obtain the comprehensive confidence level includes: Obtain the mechanical loosening anomaly judgment result and the confidence level of the judgment result output by the vibration analysis algorithm; Obtain the fault segment location results and the confidence level of the location results output by the fault detection algorithm; Based on the current working conditions, the preset dynamic weight allocation model is invoked to determine the fusion weight of the vibration analysis results and the fusion weight of the fault detection results; Calculate the preliminary fusion confidence based on the weighted fusion rules; The DS evidence theory is used to perform conflict analysis on the judgment result and the location result, and the evidence conflict factor is calculated. If the evidence conflict factor exceeds the preset conflict threshold, the conflict resolution mechanism is activated to correct the preliminary fusion confidence and obtain the corrected fusion confidence. The maximum value between the initial fusion confidence score and the corrected fusion confidence score is selected as the overall confidence score.

[0012] Preferably, in the step of calculating the dynamic alarm threshold of grounding resistance based on confidence level, combined with environmental parameter deviation and equipment status deviation, the dynamic alarm threshold is calculated using the formula: ; In the formula, Fixed thresholds according to IEC standards; = 实测 基准 This represents the deviation of environmental parameters; 基准 Typical environmental parameters; = 实测 历史 This represents the equipment status deviation; 历史 This represents the historical average value of the equipment under healthy conditions. , These are the environmental condition adjustment factor and the condition adjustment factor, respectively. Assign confidence weights to data fusion; For the first Dynamic weights of each data source; This represents the state confidence level after data fusion.

[0013] Preferably, the step of triggering an alarm signal of the corresponding level when the grounding resistance value is lower than the dynamic alarm threshold but the confidence level is higher than the preset confidence threshold specifically includes: When the grounding resistance value is greater than the dynamic alarm threshold, it is determined to be a level one alarm of resistance exceeding the standard; When the grounding resistance value is less than or equal to the dynamic alarm threshold, the overall confidence level is further compared with the preset confidence threshold: When the overall confidence level is greater than or equal to the confidence level threshold, it is determined to be a level 2 alarm indicating that the fusion confidence level has exceeded the standard; If the overall confidence level is less than the confidence level threshold, the system is considered normal and monitoring continues.

[0014] The beneficial effects of this invention are as follows: This invention utilizes a lever-type quick-clamp design for the grounding device body, enabling single-handed clamping and fixing, reducing installation time, improving installation efficiency, and significantly enhancing the efficiency of power construction and maintenance. The dual copper block conductive structure and serrated conductive texture design ensure that the contact resistance between the grounding device and the grounding stake remains stable below 0.5mΩ, and a pressure sensor monitors the clamping force in real time to prevent poor contact due to loosening or corrosion. Integrated multi-modal sensors for grounding resistance, soil temperature and humidity, grounding current, voltage, vibration, and tilt angle comprehensively monitor the electrical performance, mechanical stability, and environmental factors of the grounding system, providing data support for fault early warning.

[0015] The method in this invention uses a dynamic weight allocation model to adaptively adjust the weights of vibration analysis and fault detection results according to the working conditions, thus avoiding the limitations of a single algorithm. It introduces DS evidence theory to perform conflict analysis on multi-source judgment results. When the conclusions of different algorithms contradict each other, a conflict resolution mechanism is activated to correct the fusion confidence. This ensures that the system can still make a robust and reliable comprehensive diagnosis even when multi-source information is inconsistent, significantly improving the accuracy of decision-making.

[0016] By fitting the vibration modes under normal working conditions using a GNAR model and then using a particle filter for state tracking, mechanical loosening faults are transformed into statistical anomalies in the model residuals, which can distinguish between fault impacts and random environmental noise, and achieve sensitive capture of early signs of connection loosening. The fault section is initially screened based on potential gradient analysis, and then the fault location is accurately located through grey relational analysis. By utilizing the distribution characteristics of voltage at multiple measurement points, the single-point measurement error is suppressed, and the accurate location of hidden faults such as conductor breakage or corrosion is achieved. The threshold adaptive algorithm comprehensively considers the coupled influence of environmental factors and the equipment's own state on electrical parameters, and constructs a multi-factor correction model, which enables the alarm threshold to dynamically drift with changes in objective conditions. This solves the inherent false alarm and missed alarm problems of fixed thresholds when the environment changes drastically or the equipment deteriorates, making the alarm logic more in line with physical reality. Attached Figure Description

[0017] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a three-dimensional structural diagram of a substation clamp-type adaptive fast grounding device according to Embodiment 1 of the present invention; Figure 2 This is a top view of a substation clamp-type adaptive fast grounding device according to Embodiment 1 of the present invention.

[0018] The components are: 1. clamp; 2. grounding terminal; 3. clamping block; 4. handle; 5. connecting pin; 6. handle; 7. connecting ear; 8. screw; 9. nut. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0020] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0021] In the description of this invention, it should be understood that the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0022] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more. It should be noted in the description of this invention that, unless otherwise explicitly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0023] Example 1 like Figures 1-2 As shown, a substation clamp-type adaptive fast grounding device includes: The grounding device body includes a grounding terminal for grounding; Specifically, the grounding device body includes a grounding terminal, a clamp, a clamping block, and a handle. Two clamping blocks are clamped onto the grounding terminal, and the clamp is clamped onto the clamping block. A nut is fixedly installed on the clamp. The two ends of the clamp are connected by a screw. One end of the screw is threadedly connected to the nut, and a locking pin is fixedly installed on the other end of the screw. The locking pin is perpendicularly connected to the screw, and the locking pin is rotatably connected to the handle. The handle can rotate circumferentially along the locking pin. A connecting lug is also provided on the clamp.

[0024] The two clamping blocks are symmetrically placed on the grounding terminal. The clamping block is a cylindrical block with a semi-circular cross-section, and a clearance groove corresponding to the grounding terminal is opened at the center of the semi-circle.

[0025] As a preferred example of the above embodiments, the clamping block is a copper block made of C11000 copper with an electrical conductivity ≥98% IACS.

[0026] As a preferred example of the above embodiments, the clamp is made of high-strength aluminum alloy with a tensile strength ≥320MPa.

[0027] As a preferred example of the above embodiment, the clamp is rotatably provided with a connecting pin, the first end of the handle is rotatably connected to the connecting pin, the first end of the handle is configured as a lever-type snap fastener, and the second end of the handle has a through groove corresponding to the connecting ear. In use, first rotate the handle along the axial direction of the snap pin. The rotation of the handle drives the snap pin to rotate, which in turn drives the screw to rotate, thereby adjusting the distance between the two ends of the clamp and thus adjusting the tightness of the clamp. This allows the two clamping blocks to press against the grounding terminal. After pressing, rotate the handle circumferentially along the snap pin to flip the handle to the connecting ear for mechanical limiting and locking. The clamping and installation time can be ≤5 seconds with one-handed operation.

[0028] As a preferred example of the above embodiments, the clamp is provided with a handle for easy gripping and leverage.

[0029] As a preferred example of the above embodiments, an electromagnetic lock is also provided on the second end of the handle, forming a dual locking system of electromagnetic locking and mechanical limiting.

[0030] As a preferred example of the above embodiments, the copper block surface is designed with serrated conductive textures to increase the contact area (contact resistance ≤0.5mΩ), and an integrated pressure sensor array (range 0-500N, accuracy ±1N) is used to monitor the clamping force in real time.

[0031] As a preferred example of the above embodiments, the clamp opening is provided with an anti-accidental opening lock buckle, which can be removed with special tools to prevent accidental loosening during operation.

[0032] As a preferred example of the above embodiment, the clamping block has a limit hole along the radial direction, and the clamp has a through hole at the position corresponding to the limit hole. In use, first clamp the two clamping blocks onto the grounding nail to form a wrap. Then, hold the handle and put the clamping ring over the clamping blocks. Rotate the handle around the screw circumferentially. The rotation of the handle drives the connecting pin to rotate, which in turn drives the screw to rotate. Since the screw and nut are threadedly connected, the rotation of the screw and the relative movement of the nut cause the clamping ring to tighten and fix. After tightening, rotate the handle axially along the connecting pin. The handle and the connecting pin rotate relative to each other until the end rotates to the connecting ear. Insert the pin into the connecting ear to fix the position of the handle, thus completing the installation.

[0033] The sensor module includes a resistance sensor, a soil temperature and humidity sensor, a current transformer, a voltage sensor, a triaxial accelerometer, a GPS positioning module, and an ambient temperature and humidity sensor. It is used to collect data on grounding terminal resistance, grounding terminal current, grounding terminal voltage, grounding terminal vibration frequency and amplitude, grounding terminal latitude and longitude, soil moisture, soil temperature, air humidity, and air temperature. The sensors in the sensor module are connected to the controller module via an RS485 bus.

[0034] The controller module, connected to the sensor module, is used to collect, store, and process the data collected by the sensor module to obtain processed data, and to perform alarm logic judgment based on the processed data. The specific steps for collecting, storing, and processing the data to obtain the processed data, and then performing alarm logic judgments based on the processed data, include: The sensor module collects vibration signals, fault current signals, and grounding resistance values. The vibration signal is input into a pre-constructed normal vibration mode model to obtain the judgment result of mechanical loosening abnormality; The fault current signal is subjected to point gradient analysis to locate the electrical abnormal fault segment and obtain the fault segment location result. The judgment result and the location result are fused to obtain the comprehensive confidence level; Based on the confidence level, combined with environmental parameter deviations and equipment status deviations, the dynamic alarm threshold for grounding resistance is calculated. The system compares the grounding resistance value with the dynamic alarm threshold, combines the comprehensive confidence level with the confidence threshold, triggers the corresponding level of alarm signal, and outputs maintenance suggestions that include the fault type and location. The communication module is signal-connected to the controller module and is used to transmit data processed by the controller module. Power supply module, used for power supply.

[0035] Preferably, the communication module adopts a dual-mode communication architecture; Specifically, as an application example, it is used for data monitoring: The grounding resistance value is uploaded every 10 seconds. If the resistance is detected to be >0.1Ω for 3 consecutive times, a local audible and visual alarm (≥95dB) is triggered and pushed to the safety officer's terminal.

[0036] As an application example, it is used in substation expansion projects: Deploy two LoRaWAN gateways to support low-power wide-area coverage and achieve full site coverage (radius ≥ 1km).

[0037] Configure Bluetooth Mesh repeater nodes to solve indoor signal jamming issues; It employs the AES-128 encryption algorithm, combined with a dynamic token authentication mechanism, to ensure data transmission security.

[0038] Preferably, the power supply module uses a combination of supercapacitor (capacity 1F, withstand voltage 5.5V) and high-energy battery for continuous operation for ≥180 days, with the battery serving as a backup power source; the sleep current is ≤10μA, the operating current is ≤150mA, and it supports temporary USB power supply to achieve low-power power supply.

[0039] As a preferred example, the controller module integrates an AI module, which can run a monitoring method for a substation clamp-type adaptive fast grounding device, analyze the data transmitted by the sensor module in real time, make rapid local decisions through threshold judgment and trend prediction, and forward the decision results through the communication module.

[0040] Example 2 A monitoring method for a substation clamp-type adaptive fast grounding device, using the aforementioned grounding device, includes the following specific steps: S1. Insert the clamp-type grounding device into the grounding pile and clamp it in place with one hand. Specifically, first, clamp the two clamping blocks onto the grounding nail to form a wrap. Then, hold the handle and put the clamping ring over the clamping blocks. Rotate the handle around the screw circumferentially. The rotation of the handle drives the connecting pin to rotate, which in turn drives the screw to rotate. Since the screw and nut are threadedly connected, the rotation of the screw and the relative movement of the nut cause the clamping ring to tighten and fix. After tightening, rotate the handle axially along the connecting pin. The handle and the connecting pin rotate relative to each other, and the end rotates to the connecting ear. Insert the pin into the connecting ear to fix the position of the handle, completing the one-handed installation.

[0041] S2. Vibration signals, fault current signals, and grounding resistance values ​​are collected through the sensor module inside the grounding device; Specifically: The vibration acceleration time-series data of the grounding down conductor at the moment of fault is obtained by an accelerometer as a vibration signal with an accuracy range of ±0.1g or ±0.1Hz. This signal is used to monitor the physical stability of the grounding device and prevent loosening or breakage of the connection due to vibration. It also monitors the tilt angle of the grounding device. The vibration signal is processed by particle filtering. The fault current waveform data is obtained by using a current transformer to determine whether there is an abnormal current path. The accuracy range is ±1% or ±0.1A, whichever is larger. The grounding resistance value is obtained by a resistance sensor with an accuracy range of ±0.5% or ±0.01Ω, whichever is larger; the grounding resistance value is denoised using wavelet denoising.

[0042] Preferred options also include: Soil volumetric moisture content is obtained by using a soil moisture sensor, and the effect of soil moisture on grounding resistance (the higher the moisture, the lower the resistance) is analyzed to help identify the reasons for changes in grounding performance. Soil temperature is obtained through a soil temperature sensor, and the effect of temperature on soil resistivity (resistivity decreases as temperature increases) is used to correct the grounding resistance measurement. The voltage sensor obtains the ground electrode voltage to ground, which is used to detect ground fault current or leakage current and determine whether there is an abnormal current path for equipment leakage or short circuit. The GPS positioning module obtains latitude and longitude coordinates, records the geographical location of the grounding device, facilitates maintenance and management, and links it to environmental data, including soil type and climate conditions. By acquiring air temperature and humidity data through environmental temperature and humidity sensors, the impact of environmental factors on grounding performance can be comprehensively analyzed, such as high temperature and humidity accelerating corrosion, and low temperature causing soil freezing resistance to increase.

[0043] Preferably, a weighted multimodal fusion decision model is used to eliminate redundancy and conflicts in multi-source data. The core formula is: ; In the formula, for t Real-time decision output, health level, 0-100 points; For the first Static parameters (such as grounding resistance value R, soil resistivity) ρ The normalized value of ) ∈[1,n]; For the first Quasi-dynamic characteristics (such as vibrational energy density) E v Fault current energy E f The normalized value of ) ∈[1,m]; and All are weight coefficients, determined using the Analytic Hierarchy Process (AHP), and satisfy the following conditions: ; For correction items, such as ambient temperature compensation value; Breaking through the limitations of traditional single-parameter monitoring, it integrates three modal data—grounding resistance, vibration signal, and fault arc characteristics—to improve the accuracy of fault identification.

[0044] During a single-phase ground fault, a sudden increase in grounding resistance, a peak vibration energy density, and a fault current energy pulse occur simultaneously. Fusion decision-making can eliminate environmental interference, such as lightning strikes; the analytic hierarchy process (AHP) is introduced to dynamically adjust the weights. , It is adaptable to different working conditions, such as wet or dry soil.

[0045] Preferably, the decision output D(t) is mapped to the failure probability. : ; In the formula, The attenuation coefficient is determined by fitting historical fault data, thus realizing the quantitative transformation from health score to fault probability.

[0046] As a specific example: The substation grounding grid dimensions are 100m×100m, with vertical grounding electrodes (φ50mm galvanized steel pipe, 2.5m long) spaced 4m apart, and horizontal grounding electrodes (40×4mm² galvanized flat steel) buried at a depth of 0.8m. Soil resistivity: ρ =100Ω m; Fault type: Single-phase ground fault, fault current I=5kA, duration 0.2s.

[0047] Grounding resistance: During a fault, it suddenly increases from 0.8Ω to 1.2Ω (simulating poor contact); Vibration signal: fundamental frequency 50Hz, superimposed with a 1000Hz high-frequency component (simulating electric arc discharge), amplitude increased from 0.5mm / s² to 2.0mm / s²; Fault current energy: ; Data normalization: Grounding resistance ; Vibrational energy density ; Fault current energy .

[0048] Analytic Hierarchy Process (AHP): Grounding resistance weight w 1 = 0.4, vibrational energy density v 1=0.3, fault current energy v 2 = 0.3.

[0049] Decision output: D( t = 0.4 × 0.33 + 0.3 × 0.33 + 0.3 × 0.44 = 0.363 Corresponding health level 36 (warning status), probability of failure =1 e 0.5×0.363 =16%.

[0050] Experimental results: The algorithm successfully captured fault characteristics, and the output results were consistent with the actual settings.

[0051] S3. Input the vibration signal into the pre-built normal vibration mode model to obtain the judgment result of mechanical loosening abnormality; The normal vibration mode model includes a GNAR model and a particle filter, specifically: Vibration signals of the grounding device under historical health conditions are obtained as training samples; The training samples are fitted using a GNAR model, which is as follows: ; In the formula, for t The amplitude of vibration at any given moment; , These are model coefficients, reflecting the influence of historical data and noise; , The order of autoregression; For external incentives, It is Gaussian white noise; After fitting, determine the model order and the corresponding model coefficients; Real-time collected vibration signals The input is fed into a particle filter, which outputs an estimate of the vibration signal at time t. ; Calculate the measured vibration signal and particle filter estimate The residuals between: ; Obtain the residual sequence within the preset time window ; Based on the statistical characteristics of the historical residual sequence, the judgment threshold is calculated: ; In the formula, The mean, Standard deviation; If the residuals of M consecutive sampling points all exceed the judgment threshold If so, it is determined that the grounding device has a mechanical loosening abnormality; Output vibration anomaly determination results.

[0052] As a specific example: Grounding grid dimensions: 200m × 200m, conductor spacing 10m; Soil resistivity: ρ = 100Ω m; Fault type: Single-phase ground fault, fault current I=10kA; Vibration measurement points: edge node (point A) and center node (point B) of the grounding grid.

[0053] Deduction steps: Data generation: The grounding grid vibration signal during a simulated fault is based on a fundamental frequency of 100Hz (twice the power frequency), superimposed with high-frequency noise (above 2kHz, simulating partial discharge).

[0054] Example data (point A): ; GNAR model fitting: Choosing p=2 and q=1, we fit the data at point A and obtain the coefficients: a1=0.8, a2= 0.3, b1=0.2; Model output The error compared with the actual data is less than 5%.

[0055] Particle filter fault detection: Initialize the particle set (1000 particles), state vector =[ , (Frequency, Amplitude); Iteratively update particle weights and calculate residuals. .

[0056] Result: The residual at point A significantly exceeds the threshold at 100Hz. τ Point B shows no abnormalities.

[0057] Fault location: Based on the residual distribution and the grounding grid model, the fault point was located as a loose conductor near point A (consistent with the actual setup).

[0058] The algorithm successfully extracted 100Hz fault features, eliminated high-frequency noise interference, and achieved conductor-level positioning accuracy (within 10m); it also worked for different soil resistivities (50–300Ω). m) Simulated data verification showed a detection accuracy of >90%.

[0059] S4. Perform point gradient analysis on the fault current signal to locate the electrical abnormal fault segment and obtain the location result of the fault segment. Specifically: Acquire the fault current signal, simultaneously acquire the voltage values ​​of accessible points, and arrange the voltage values ​​in spatial order to obtain a voltage sequence; Calculate the potential gradient between adjacent accessible points in the voltage sequence, and mark the conductor segment whose potential gradient exceeds a preset threshold as a candidate fault segment; Preferred, the first conductor potential gradient Fault diagnosis formula: ; In the formula, For the first Voltage at accessible points of a conductor segment; For the first The distance between two accessible points of a conductor segment; When the potential gradient When the gradient threshold is exceeded (e.g., 0.5 V / m), it is identified as a potential fault, and candidate fault segments are obtained.

[0060] Calculate the resistance increment for each candidate fault segment and construct a resistance increment sequence; Using the zero resistance increment sequence under fault-free conditions as a reference sequence, a grey relational analysis is performed on the resistance increment sequence to calculate the comprehensive relational degree of each conductor segment. The preferred formula for fault location using grey relational analysis is: ; In the formula, This is the sequence of resistance increments under standard conditions, which is 0 when there is no fault. For the first The measured resistance increment sequence of a conductor segment; The resolution coefficient is set to 0.5, which is used to adjust the sensitivity of the correlation to differences. This is the correlation coefficient, ranging from 0 to 1. A larger value indicates a higher correlation coefficient. The stronger the correlation between a conductor segment and the fault state (i.e., the more likely it is to be a fault point); This is a reference sequence under standard conditions, representing the resistance increment of each conductor when there is no fault; it is typically a 0 sequence. For the first Measured reference sequence of the conductor segment.

[0061] As a specific example: If the third conductor fails =[0,0,95,0,…,0].

[0062] Extreme value calculation symbols:

[0063] Physical meaning: The minimum difference between the measured sequence and the reference sequence among all conductor segments (i.e., the difference closest to the normal state).

[0064] Calculation steps: For each conductor segment Calculate the absolute difference between it and the reference sequence. (The length is obtained) n (the difference sequence); The minimum value of the difference sequence for all conductor segments is taken (i.e., the global minimum difference).

[0065] Assumption =[0,0,0], =[1,2,3], =[0.5,1.5,0]; | |=[1,2,3],| |=[0.5,1.5,0]; = (1,2,3,0.5,1.5,0)=0.

[0066] Physical meaning: The maximum difference between the measured sequence and the reference sequence among all conductor segments (i.e., the most unusual difference).

[0067] Calculate the absolute difference sequence for each conductor segment; Take the maximum value of the difference sequence for all conductor segments (i.e., the global maximum difference).

[0068] = (1,2,3,0.5,1.5,0)=3.

[0069] The conductor segment with the lowest overall correlation degree and below the preset correlation degree threshold is identified as the fault segment, and the location result including the fault segment location and resistance increment value is output.

[0070] As a specific example two: Raw data: Grounding resistance R 原始 =0.75Ω, soil temperature T=30 C, humidity H=70%; The accessible point voltage sequence is V = [0.12, 0.15, 0.18, ..., 0.09]V (a total of 10 points).

[0071] Correction calculation: R 校正 =0.75×[1 0.01×25 / (30) 25) + 0.05 × 60 / (70) 60)]=0.937Ω Potential gradient calculation: =5 | 0.18 0.15|=0.6V / m (Normal) =5 | 0.22 0.18 | = 0.2V / m (Normal) Fault location and verification: Resistance increment sequence: Standard sequence =[0,0,…,0]; Measured sequence =[0,0,95,0,…,45,0] (Fault section resistance increment).

[0072] Grey relational analysis: Calculate the correlation of each segment The conductor in the 3rd row and 4th column was found. =0.32 (lowest), identified as a fault point; When multiple components fail, the second row and second column... =0.41, 4th row, 5th column =0.38, all below the threshold of 0.5.

[0073] S5. The judgment result and the positioning result are fused to obtain the comprehensive confidence level; Specifically: Obtain the mechanical loosening anomaly judgment result and the confidence level of the judgment result output by the vibration analysis algorithm; Obtain the fault segment location results and the confidence level of the location results output by the fault detection algorithm; Based on the current working conditions, the preset dynamic weight allocation model is invoked to determine the fusion weight of the vibration analysis results and the fusion weight of the fault detection results; Calculate the preliminary fusion confidence based on the weighted fusion rules; The DS evidence theory is used to perform conflict analysis on the judgment result and the location result, and the evidence conflict factor is calculated. If the evidence conflict factor exceeds the preset conflict threshold, the conflict resolution mechanism is activated to correct the preliminary fusion confidence and obtain the corrected fusion confidence. The maximum value between the initial fusion confidence score and the corrected fusion confidence score is selected as the overall confidence score.

[0074] S6. Based on the confidence level, combined with the environmental parameter deviation and equipment status deviation, calculate the dynamic alarm threshold of the grounding resistance; Specifically: Obtain the resistance value directly measured by the grounding resistance sensor. ; The directly measured resistance value is corrected to obtain the corrected grounding resistance value. : ; In the formula, Soil temperature deviation is calculated as the difference between the measured soil temperature and the soil temperature reference value. Among them, soil temperature benchmark value It is 25℃; Soil moisture deviation is calculated as the measured soil moisture content minus the soil moisture baseline value. Among them, soil moisture benchmark value It is 60%; These are temperature and humidity correction coefficients, with values ​​determined based on soil type. For sandy soil: =0.02, =0.03; Clay: =0.01, =0.05.

[0075] The dynamic alarm threshold is calculated using the following formula: ; In the formula, Fixed thresholds according to IEC standards; = 实测 基准 This represents the deviation of environmental parameters; 基准 Typical environmental parameters; = 实测 历史 This represents the equipment status deviation; 历史 This represents the historical average value of the equipment under healthy conditions. , These are the environmental condition adjustment factor and the condition adjustment factor, respectively. Assign confidence weights to data fusion; For the first Dynamic weights of each data source; This represents the state confidence level after data fusion.

[0076] Dynamic alarm threshold It is used to determine whether the grounding system is abnormal in real time, and triggers an alarm when the measured value is greater than the threshold.

[0077] As a specific example: The effectiveness of the threshold adaptive algorithm was verified by combining typical operating conditions of the grounding system of a 220kV substation during the rainy season (high humidity) and the dry season (low temperature) with lightning strike faults and equipment aging scenarios.

[0078] The collected data is as follows:

[0079] Rainy season operating conditions (high humidity): Environmental parameters: =85% 60% = 25% =0 (newly commissioned equipment); Calculation of dynamic thresholds for rainy season operating conditions: =1 (1+0.03 25 / 60 0)+0.8 (0.4 0.7 + 0.3 0.1 + 0.3 0.6) = 1.40Ω Calculation of dynamic thresholds for dry season operating conditions: Environmental parameters: (Abnormal vibration); Data fusion confidence: The fault detection algorithm outputs a "lightning strike" confidence of 0.8, the vibration analysis outputs a "loosening" confidence of 0.7, and the overall confidence γ=0.75; According to the dynamic threshold formula Substitute The calculation yields: ; The measured grounding resistance was 0.65Ω < 1.50Ω, but the vibration amplitude of 35mm / s² was greater than the threshold of 20mm / s². Based on the data fusion, the confidence level of "lightning strike" + "loosening" was 0.75, triggering a level two alarm (requiring planned maintenance).

[0080] Result comparison: Fixed threshold method: 23% false alarm rate during the rainy season (due to resistance fluctuations caused by high humidity), and 18% false alarm rate during the dry season (because the threshold does not take vibration anomalies into account). Adaptive algorithm: The false alarm rate during the rainy season was reduced to 5%, and the false alarm rate during the dry season was reduced to 2%. Furthermore, the fault was located by data fusion as "lightning strike causing loose down conductor", which was consistent with the actual maintenance results.

[0081] S7. Compare the grounding resistance value and the dynamic alarm threshold, and combine the comprehensive confidence level with the confidence threshold to trigger the corresponding alarm signal. Then, output maintenance suggestions containing the fault type and location via the communication module. The alarm signals specifically include: When the grounding resistance value is greater than the dynamic alarm threshold, it is determined to be a level one alarm of resistance exceeding the standard; When the grounding resistance value is less than or equal to the dynamic alarm threshold, the overall confidence level is further compared with the preset confidence threshold: When the overall confidence level is greater than or equal to the confidence level threshold, it is determined to be a level 2 alarm indicating that the fusion confidence level has exceeded the standard; If the overall confidence level is less than the confidence level threshold, the system is considered normal and monitoring continues.

[0082] ; In the formula, The threshold is set according to IEC standards: 1Ω for 110kV substations and 0.5Ω for 220kV substations. The environmental risk level is determined by a combination of soil resistivity and corrosion rate, and includes levels 1-5.

[0083] As a specific example of this judgment, the data of a certain 110kV substation (soil type: silty clay, backfill depth 0.8m) in July 2024 (rainy season) and January 2025 (dry season) are compared as shown in Table 1.

[0084] Table 1

[0085] First, calculate the correction resistor: Rainy season (ΔT=0℃, ΔH=+20%):

[0086] Dry season (ΔT= 30℃, ΔH=0%)

[0087] Environmental risk level: Rainy season (ρ=50Ω) m, v=0.2mm / year): Risk level 2 (medium); Dry season (ρ=150Ω) m, v=0.1mm / year): Risk level 3 (high).

[0088] Status determination: rainy season: The condition is "good"; dry season: =0.734Ω≤1Ω, but risk level 3, status "warning".

[0089] Results explanation: High humidity during the rainy season reduces soil resistivity, resulting in good grounding conditions. The dry season and low humidity lead to increased resistivity, which, combined with the risk of high soil resistivity, necessitates early warning and recommendations to add resistance reduction measures (such as deep well grounding).

[0090] Taking a lightning strike failure scenario during the dry season as an example, the following is an application example: An abnormal high-frequency signal (35 mm / s²) was detected by the vibration sensor, and a 25 kA lightning strike current was recorded by the fault current sensor. After wavelet denoising, the grounding resistance value was 0.65 Ω (no significant change), and the residual of the vibration signal after GNAR model processing exceeded the judgment threshold.

[0091] Analysis: Vibration analysis algorithm determines mechanical loosening (residual) =8.7); The fault detection algorithm locates the faulty section as the grounding down conductor through potential gradient analysis; The data fusion algorithm assigns a weight of 0.7 to vibration analysis and a weight of 0.3 to fault detection, with a comprehensive confidence level of 0.82. The threshold adaptive algorithm dynamically adjusts the alarm threshold to 1.12Ω (original baseline 1Ω).

[0092] Decision output: The measured grounding resistance value is 0.65Ω < 1.12Ω, but the vibration is abnormal and the data fusion confidence level is > 0.8, triggering a level 2 alarm.

[0093] Control execution: Push alarm information to the operation and maintenance terminal, and suggest "planned maintenance of the down-line connection parts".

[0094] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0095] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or equivalent to the scope of this invention are included in this invention.

Claims

1. A substation clamp-type adaptive fast grounding device, characterized in that, include: The grounding device body includes a grounding terminal for grounding; The sensor module includes a resistance sensor, a soil temperature and humidity sensor, a current transformer, a voltage sensor, an acceleration sensor, a GPS positioning module, and an environmental temperature and humidity sensor, used to collect data on grounding terminal resistance, grounding terminal current, grounding terminal voltage, grounding terminal vibration frequency and amplitude, grounding terminal latitude and longitude, soil moisture, soil temperature, air humidity, and air temperature. The controller module, connected to the sensor module, is used to collect, store, and process the data collected by the sensor module to obtain processed data, and to perform alarm logic judgment based on the processed data. The communication module is signal-connected to the controller module and is used to transmit data processed by the controller module. Power supply module, used for power supply; The specific steps for collecting, storing, and processing the data to obtain the processed data, and then performing alarm logic judgments based on the processed data, include: The sensor module collects vibration signals, fault current signals, and grounding resistance values. The vibration signal is input into a pre-constructed normal vibration mode model to obtain the judgment result of mechanical loosening abnormality; The fault current signal is subjected to point gradient analysis to locate the electrical abnormal fault segment and obtain the fault segment location result. The judgment result and the location result are fused to obtain the comprehensive confidence level; Based on the confidence level, combined with environmental parameter deviations and equipment status deviations, the dynamic alarm threshold for grounding resistance is calculated. The system compares the grounding resistance value with the dynamic alarm threshold, combines the comprehensive confidence level with the confidence threshold, triggers the corresponding level of alarm signal, and outputs maintenance suggestions that include the fault type and location.

2. The substation clamp-type adaptive fast grounding device as described in claim 1, characterized in that, The grounding device body includes a grounding terminal, a clamp, a clamping block, and a handle. Two clamping blocks are clamped onto the grounding terminal, and the clamp is clamped onto the clamping block. A nut is fixedly installed on the clamp. The two ends of the clamp are connected by a screw. One end of the screw is threadedly connected to the nut, and a locking pin is fixedly installed on the other end of the screw. The locking pin is perpendicularly connected to the screw, and the locking pin is rotatably connected to the handle. The handle can rotate circumferentially along the locking pin. A connecting lug is also provided on the clamp.

3. The substation clamp-type adaptive fast grounding device as described in claim 2, characterized in that, The two clamping blocks are symmetrically placed on the grounding terminal. The clamping blocks are cylindrical blocks with a semi-circular cross-section, and a clearance groove corresponding to the grounding terminal is opened at the center of the semi-circle.

4. A monitoring method for a substation clamp-type adaptive fast grounding device, characterized in that, The grounding device as described in any one of claims 1-3 includes the following specific steps: Insert the clamp-type grounding device into the grounding pile and clamp it in place with one hand. Vibration signals, fault current signals, and grounding resistance values ​​are collected through sensor modules within the grounding device; The vibration signal is input into a pre-constructed normal vibration mode model to obtain the judgment result of mechanical loosening abnormality; The fault current signal is subjected to point gradient analysis to locate the electrical abnormal fault segment and obtain the fault segment location result. The judgment result and the location result are fused to obtain the comprehensive confidence level; Based on the confidence level, combined with environmental parameter deviations and equipment status deviations, the dynamic alarm threshold for grounding resistance is calculated. The system compares the grounding resistance value with the dynamic alarm threshold, and combines the comprehensive confidence level with the confidence threshold to trigger an alarm signal of the corresponding level. It also outputs maintenance suggestions containing the fault type and location through the communication module.

5. The monitoring method for a substation clamp-type adaptive fast grounding device as described in claim 4, characterized in that, The step of acquiring vibration signals, fault current signals, and grounding resistance values ​​through the sensor module within the grounding device includes: The vibration acceleration time-series data of the grounding lead at the moment of the fault is obtained by using an accelerometer as a vibration signal; Obtain fault current waveform data through a current transformer; The grounding resistance value is obtained by using a resistance sensor.

6. The monitoring method for a substation clamp-type adaptive fast grounding device as described in claim 4, characterized in that, In the step of inputting the vibration signal into a pre-constructed normal vibration mode model to obtain the determination result of mechanical loosening abnormality, the normal vibration mode model includes a GNAR model and a particle filter, specifically: Vibration signals of the grounding device under historical health conditions are obtained as training samples; The training samples are fitted using a GNAR model, which is as follows: ； In the formula, for t The amplitude of vibration at any given moment; , These are model coefficients, reflecting the influence of historical data and noise; , The order of autoregression; For external incentives, It is Gaussian white noise; After fitting, determine the model order and the corresponding model coefficients; Real-time collected vibration signals The input is fed into a particle filter, which outputs an estimate of the vibration signal at time t. ; Calculate the measured vibration signal and particle filter estimate The residuals between: ； Obtain the residual sequence within the preset time window ; Based on the statistical characteristics of the historical residual sequence, the judgment threshold is calculated: ； In the formula, The mean, Standard deviation; If the residuals of M consecutive sampling points all exceed the judgment threshold If so, it is determined that the grounding device has a mechanical loosening abnormality; Output vibration anomaly determination results.

7. The monitoring method for a substation clamp-type adaptive fast grounding device as described in claim 4, characterized in that, The steps for performing point gradient analysis on the fault current signal to locate the electrical fault segment and obtain the fault segment location result include: Acquire the fault current signal, simultaneously acquire the voltage values ​​of accessible points, and arrange the voltage values ​​in spatial order to obtain a voltage sequence; Calculate the potential gradient between adjacent accessible points in the voltage sequence, and mark the conductor segment whose potential gradient exceeds a preset threshold as a candidate fault segment; Calculate the resistance increment for each candidate fault segment and construct a resistance increment sequence; Using the zero resistance increment sequence under fault-free conditions as a reference sequence, a grey relational analysis is performed on the resistance increment sequence to calculate the comprehensive relational degree of each conductor segment. The conductor segment with the lowest overall correlation degree and below the preset correlation degree threshold is identified as the fault segment, and the location result including the fault segment location and resistance increment value is output.

8. The monitoring method for a substation clamp-type adaptive fast grounding device as described in claim 4, characterized in that, The step of fusing the judgment result and the location result to obtain the comprehensive confidence level includes: Obtain the mechanical loosening anomaly judgment result and the confidence level of the judgment result output by the vibration analysis algorithm; Obtain the fault segment location results and the confidence level of the location results output by the fault detection algorithm; Based on the current working conditions, the preset dynamic weight allocation model is invoked to determine the fusion weight of the vibration analysis results and the fusion weight of the fault detection results; Calculate the preliminary fusion confidence based on the weighted fusion rules; The DS evidence theory is used to perform conflict analysis on the judgment result and the location result, and the evidence conflict factor is calculated. If the evidence conflict factor exceeds the preset conflict threshold, the conflict resolution mechanism is activated to correct the preliminary fusion confidence and obtain the corrected fusion confidence. The maximum value between the initial fusion confidence score and the corrected fusion confidence score is selected as the overall confidence score.

9. The monitoring method for a substation clamp-type adaptive fast grounding device as described in claim 4, characterized in that, In the step of calculating the dynamic alarm threshold of grounding resistance based on confidence level, combined with environmental parameter deviation and equipment status deviation, the dynamic alarm threshold is calculated using the formula: ； In the formula, Fixed thresholds according to IEC standards; = 实测 基准 This represents the deviation of environmental parameters; 基准 Typical environmental parameters; = 实测 历史 This represents the equipment status deviation; 历史 This represents the historical average value of the equipment under healthy conditions. , These are the environmental condition adjustment factor and the condition adjustment factor, respectively. Assign confidence weights to data fusion; For the first Dynamic weights of each data source; This represents the state confidence level after data fusion.

10. The monitoring method for a substation clamp-type adaptive fast grounding device as described in claim 4, characterized in that, When the grounding resistance value is lower than the dynamic alarm threshold but the confidence level is higher than the preset confidence threshold, an alarm signal of the corresponding level is triggered, specifically including: When the grounding resistance value is greater than the dynamic alarm threshold, it is determined to be a level one alarm of resistance exceeding the standard; When the grounding resistance value is less than or equal to the dynamic alarm threshold, the overall confidence level is further compared with the preset confidence threshold: When the overall confidence level is greater than or equal to the confidence level threshold, it is determined to be a level 2 alarm indicating that the fusion confidence level has exceeded the standard; If the overall confidence level is less than the confidence level threshold, the system is considered normal and monitoring continues.

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