Detection device for post insulator leakage current in switch cabinet and evaluation method thereof

Abstract

The invention discloses a device for detecting the leakage current of a post insulator in a switch cabinet and an evaluation method thereof, and relates to the technical field of on-line monitoring of power equipment, the device comprises a TMR sensor, a magnetic ring with an air gap, a signal conditioning module and a microprocessor, the TMR sensor is arranged in the air gap, and the magnetic ring detachably sleeves the post insulator in the switch cabinet; the TMR sensor is used for collecting a magnetic signal caused by the leakage current of the post insulator and transmitting the magnetic signal to the signal conditioning module. The signal conditioning module is used for processing the magnetic signal and sending the processed magnetic signal to the microprocessor; and the microprocessor is used for analyzing the processed magnetic signals and determining multi-dimensional parameters of the leakage current. In the method, the operation state of the post insulator is evaluated by using the multi-dimensional parameters of the leakage current, so that the sensing capability of a weak magnetic field can be greatly improved, and the operation state of the post insulator can be accurately evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of online monitoring of electric power equipment, and in particular to a device for detecting leakage current of a support insulator in a switch cabinet and an evaluation method thereof. Background Art

[0002] Switchgear is a critical control and protection device in power systems. Its long-term, safe and stable operation depends heavily on the insulation performance of its internal post insulators. Post insulators not only support conductors and isolate areas of varying potential, but also directly impact the insulation safety of the entire power system. In actual operation, due to the combined effects of elevated humidity, temperature fluctuations, dust accumulation, and concentrated electric field stress, contamination layers or water film channels can easily form on the surface of post insulators, leading to surface leakage currents. If not effectively monitored, these currents can gradually develop into surface discharges, flashovers, or even equipment failures, resulting in busbar short circuits, system tripping, and even widespread power outages.

[0003] Existing leakage current monitoring technologies primarily rely on contact current transformers or Hall-effect sensors. While these solutions offer some detection capabilities, they are typically bulky, complex to install, have high thresholds, and have limited sensitivity. Furthermore, they are not suitable for the confined, compact, and highly shielded environments found within switchgear. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to address the deficiencies of the existing technology and specifically provide a device for detecting leakage current of a post insulator in a switch cabinet and an evaluation method thereof, which are specifically as follows: 1) In a first aspect, the present invention provides a device for detecting leakage current in a post insulator in a switch cabinet. The specific technical solution is as follows: the device comprises a TMR sensor, a magnetic ring with an air gap, a signal conditioning module, and a microprocessor. The TMR sensor is disposed in the air gap, and the magnetic ring is detachably mounted on a post insulator in the switch cabinet. The TMR sensor is used to: collect the magnetic signal caused by the leakage current of the post insulator and transmit the magnetic signal to the signal conditioning module; The signal conditioning module is used to: process the magnetic signal and send the processed magnetic signal to the microprocessor; The microprocessor is used for analyzing the processed magnetic signal and determining the multi-dimensional parameters of the leakage current.

[0005] The beneficial effects of the device for detecting leakage current of a support insulator in a switch cabinet provided by the present invention are as follows: The magnetic ring used can gather magnetic fields, accurately capture weak magnetic signals, and greatly improve the ability to perceive weak magnetic fields. The device is compact and easy to install. The magnetic ring can be detachably mounted on the support insulator in the switch cabinet, making it easy to install and remove in a small space, reducing installation costs and difficulty and improving installation efficiency. Moreover, the magnetic signal collected by the TMR sensor is processed by the signal conditioning module and analyzed by the microprocessor to determine the multi-dimensional parameters of the leakage current, providing more comprehensive data support for the accurate evaluation of the operating status of the switch cabinet, helping to timely detect potential faults and improve the safety and reliability of the switch cabinet operation.

[0006] Based on the above solution, the device for detecting leakage current of a post insulator in a switch cabinet of the present invention can be further improved as follows.

[0007] Furthermore, the microprocessor is also used to: obtain multi-dimensional environmental parameters in the switch cabinet, and use a preset state evaluation model, combined with the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters in the switch cabinet, to evaluate the operating state of the post insulator, and determine the operating state level of the post insulator. The operating state level of the post insulator is normal, warning, abnormal or serious.

[0008] The beneficial effect of adopting the above-mentioned further scheme is that multi-dimensional environmental parameters such as ambient temperature and relative humidity are key external factors that affect the generation and evolution of leakage current. High humidity conditions easily lead to moisture absorption and dampness on the insulation surface, while high temperature accelerates material aging and reduces insulation strength. The coupling effect of the two significantly increases the risk of partial discharge and flashover. Therefore, adding synchronous analysis of multi-dimensional environmental parameters during leakage current monitoring will reduce the deviation of the monitoring results and improve the accuracy of subsequent fault identification and trend judgment. Moreover, in the present invention, the microprocessor obtains multi-dimensional environmental parameters and combines them with the multi-dimensional parameters of the leakage current, and uses a preset state evaluation model to comprehensively evaluate the operating state of the post insulator, determine its operating state level, and realize real-time and accurate monitoring and evaluation of the operating state of the post insulator, thereby improving the accuracy and reliability of the evaluation, helping to timely discover potential faults, prevent accidents, and ensure the safe and stable operation of the switchgear and power system. At the same time, it provides maintenance personnel with a clear maintenance basis, improving maintenance efficiency and pertinence.

[0009] Furthermore, the microprocessor is specifically configured to: evaluate the variation trend of the leakage current based on the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters in the switch cabinet, and obtain an evaluation result of the variation trend of the leakage current.

[0010] The beneficial effects of adopting the above-mentioned further scheme are: achieving accurate assessment of the leakage current change trend. By combining multi-dimensional parameters, the potential change pattern of the leakage current can be discovered in time, and the possible failure risk of the post insulator can be predicted in advance, providing a basis for taking maintenance measures in advance; improving the accuracy and reliability of monitoring the operating status of the post insulator, ensuring the safe and stable operation of the switchgear, and effectively preventing power outages caused by insulation failures; helping to formulate more reasonable maintenance plans, reduce unnecessary maintenance work, and improve operation and maintenance efficiency.

[0011] Furthermore, it also includes a wireless communication module; the microprocessor is also used to: upload the operating status level of the support insulator, the evaluation results of the leakage current change trend, the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters in the switch cabinet to the remote monitoring platform through the wireless communication module.

[0012] The beneficial effect of adopting this further solution is that, using the wireless communication module, key data such as the operating status level of the post insulators and the results of leakage current trend assessments can be uploaded to the remote monitoring platform in real time, enabling remote, real-time monitoring of the operating status of the post insulators within the switchgear. This not only allows maintenance personnel to monitor the operating status of the post insulators anytime and anywhere, and promptly identify potential fault hazards, but also enables centralized data analysis and management through the remote platform, improving operation and maintenance efficiency, reducing operation and maintenance costs, and ensuring the safe and stable operation of the power system.

[0013] Furthermore, the magnetic ring includes a first arc-shaped magnetic strip and a second arc-shaped magnetic strip, and the first arc-shaped magnetic strip and the second arc-shaped magnetic strip are detachably connected via a mortise and tenon structure.

[0014] The beneficial effect of adopting the above further scheme is: by adopting a combination of the first arc-shaped magnetic strip and the second arc-shaped magnetic strip, and using the mortise and tenon structure to achieve a detachable connection, this design greatly improves the flexibility and practicality of the magnetic ring, and is particularly suitable for the complex spatial environment inside the switch cabinet. First, the detachable connection method allows the magnetic ring to be easily installed and disassembled without the need for complex tools or special operations, greatly reducing the installation difficulty and maintenance costs; secondly, the close fit of the mortise and tenon structure ensures the stability of the magnetic ring after installation, and can effectively gather the magnetic field, thereby improving the accuracy of capturing the leakage current magnetic signal; thirdly, this modular design facilitates the adjustment, replacement or upgrade of the magnetic ring, enhancing the maintainability and adaptability of the system. Overall, this magnetic ring structure can not only meet the installation requirements of a small space, but also ensure the magnetic permeability and stability of the magnetic ring, providing reliable hardware support for the accurate monitoring of leakage current.

[0015] 2) In a second aspect, the present invention further provides a method for evaluating the operating status of a post insulator in a switch cabinet, the specific technical solution of which is as follows: The TMR sensor collects the magnetic signal caused by the leakage current of the post insulator and transmits the magnetic signal to the signal conditioning module. The TMR sensor is located in the air gap of the magnetic ring, which is detachably mounted on the post insulator in the switch cabinet. The signal conditioning module processes the magnetic signal to obtain a processed magnetic signal. processing the processed magnetic signal to determine multi-dimensional parameters of the leakage current; According to the multi-dimensional parameters of the leakage current, the operating status of the post insulator is evaluated and the operating status level of the post insulator is determined.

[0016] Based on the above solution, the operating status evaluation method of a post insulator in a switch cabinet of the present invention can be further improved as follows.

[0017] Furthermore, the method further includes: obtaining multi-dimensional environmental parameters in the switch cabinet; Based on the multi-dimensional parameters of the leakage current, the operating status of the post insulator is evaluated and the operating status level of the post insulator is determined, including: The operating status of the post insulator is evaluated using a preset status assessment model, combined with the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters in the switchgear.

[0018] Furthermore, it also includes: Based on the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters in the switch cabinet, the variation trend of the leakage current is evaluated to obtain the leakage current variation trend evaluation result.

[0019] Furthermore, it also includes: The operating status level of the support insulator, the evaluation results of the leakage current change trend, the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters in the switchgear are uploaded to the remote monitoring platform through the wireless communication module.

[0020] Furthermore, the magnetic ring includes a first arc-shaped magnetic strip and a second arc-shaped magnetic strip, and the first arc-shaped magnetic strip and the second arc-shaped magnetic strip are detachably connected via a mortise and tenon structure.

[0021] It should be noted that the beneficial effects achieved by the technical solution of the second aspect of the present invention and the corresponding possible implementation methods can be found in the above-mentioned technical effects of the first aspect and its corresponding possible implementation methods, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments of the present invention: Figure 1 It is a structural diagram of the magnetic ring; Figure 2This is an application scenario diagram of a closed-loop TMR current sensor; Figure 3 It is a structural diagram of a closed-loop TMR current sensor; Figure 4 Schematic diagram of closed-loop control of closed-loop structure TMR current sensor; Figure 5 The present invention is a flowchart of a method for evaluating the operating status of a post insulator in a switch cabinet according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The principles and features of the present invention are described below. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0024] The following describes in detail the technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems using specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The following embodiments of the present invention are described in conjunction with the accompanying drawings.

[0025] Combine Figure 1 and Figure 2 , a device for detecting leakage current of a post insulator in a switch cabinet according to an embodiment of the present invention is described. The device comprises a TMR sensor, a magnetic ring with an air gap (which may be referred to as an open-type magnetic ring), a signal conditioning module, and a microprocessor. The TMR sensor is disposed in the air gap, and the magnetic ring is detachably mounted on a post insulator in the switch cabinet. Among them, such as Figure 1 As shown, the magnetic ring includes a first arc-shaped magnetic strip and a second arc-shaped magnetic strip (ie Figure 1 The upper and lower parts of the central magnetic ring (the upper part comprises a first curved magnetic strip and the lower part comprises a second curved magnetic strip, or the upper part comprises a second curved magnetic strip and the lower part comprises the first curved magnetic strip) are detachably connected via a mortise and tenon structure. This design ensures high-precision alignment, stable closure, and repeatable assembly of the open-type magnetic ring during installation, offering high structural reliability and engineering adaptability. The magnetic ring has a typical "C-shaped" opening structure, allowing for on-site installation around cylindrical structures such as cables or post insulators. To ensure good alignment, stability, and anti-misalignment at the joint between the first and second curved magnetic strips after the magnetic circuit is closed, a complementary mortise and tenon structure is provided at the open end. This mortise and tenon structure typically consists of a trapezoidal tenon on one side and a corresponding grooved tenon on the other, creating multiple points of contact and inter-face engagement. This provides self-guiding, self-limiting, and anti-rotation structural properties during assembly.

[0026] During the assembly process, the first arc-shaped magnetic strip and the second arc-shaped magnetic strip are matched together through a plug-in mortise and tenon structure. There is a small gap (i.e., air gap) between the mating surfaces. The width of the air gap can be controlled according to the size layout requirements of the TMR sensor to ensure that the magnetic induction flux passes through the sensing unit of the TMR sensor, thereby achieving effective perception of the external magnetic field (i.e., the magnetic flux generated by the leakage current). Compared with the traditional planar docking method, the mortise and tenon structure performs better in structural strength, seismic resistance, and magnetic core assembly consistency. It can effectively reduce the measurement error caused by the offset of the magnetic gap position and improve the overall accuracy and stability of the TMR sensor. In addition, the structure can be reliably closed without the need for additional fasteners. It is suitable for use in power equipment with limited space and high electrical insulation requirements to achieve rapid disassembly and on-site maintenance of the sensor module. The mortise and tenon magnetic ring structure in the present invention combines the plug-in matching concept in traditional structural mechanics with the electromagnetic function requirements of modern sensors. It has excellent structural self-calibration, magnetic flux stability, and on-site engineering adaptability. It is suitable for constructing a highly sensitive and reliable open-type TMR magnetic field detection device (also known as a closed-loop structure type TMR current sensor).

[0027] In another feasible solution, a non-metallic block that matches the cross-section of the magnetic ring can be connected to the air gap of the magnetic ring, and the non-metallic block can be fixedly connected to the magnetic ring by bonding, etc., and the TMR sensor can be embedded in the non-metallic block to better fix the TMR sensor.

[0028] Figure 2 The actual deployment method of the closed-loop structured TMR current sensor in power equipment is shown, with clear structural directionality and functional integrated layout. Figure 2 In the figure, reference numeral 1 indicates the main body of the TMR magnetic ring detection device, i.e. the magnetic ring, which has a TMR sensor integrated inside and is used to sense the weak magnetic flux changes caused by the surface leakage current of the post insulator. The magnetic ring is made of high magnetic permeability material (such as ferrite or nanocrystal), and the structure forms an effective magnetic flux channel by closing the magnetic circuit, and the sensor is arranged at the air gap to achieve high-precision detection of the magnetic induction intensity; reference numeral 2 indicates a mortise and tenon self-positioning clamping structure, which is arranged on the inner side of the magnetic ring and is used to stably clamp the magnetic ring device to the bottom area of ​​the post insulator without destroying the structure of the insulator body. The structure achieves rapid assembly and automatic alignment through mortise and tenon engagement or snap-fit ​​positioning, ensuring that the TMR chip is located above the magnetic flux leakage path, thereby improving measurement stability and sensing accuracy, and having good maintenance convenience and anti-interference ability. Reference numeral 3 is a post insulator, which is a key load-bearing insulating component in the high-voltage distribution system. Figure 2The post insulator in the figure is a typical composite shed structure. A TMR magnetic ring detection device is typically installed near the ground or at the ground lead. By monitoring the magnetic field changes around the leakage current path in real time, it enables early identification of insulator degradation and online warning. Number 4 represents a switchgear. Figure 2 This demonstrates the practical application of TMR magnetic ring detection technology for contactless, online, and intelligent status sensing of post insulators in medium- and high-voltage distribution equipment. This structural design fully considers field conditions such as limited equipment space, complex electromagnetic interference, and high insulation requirements. It offers excellent system compatibility and engineering feasibility, serving as a crucial technical foundation for intelligent operation and maintenance and status monitoring of distribution systems.

[0029] The TMR sensor is used to: collect the magnetic signal caused by the leakage current of the post insulator and transmit the magnetic signal to the signal conditioning module; Among them, a highly sensitive TMR (tunnel magnetoresistance) sensor is arranged near the post insulator (the TMR sensor is arranged in the air gap, and the magnetic ring is detachably mounted on the post insulator in the switch cabinet). The TMR sensor can non-contactly sense the weak magnetic field changes caused by the nA-level leakage current generated on the surface of the post insulator, thereby realizing high-precision current monitoring.

[0030] Among them, the TMR sensor adopts a ring magnetic core structure, and the sensing unit of the TMR sensor is encapsulated in a resin or ceramic shell with high insulation performance to achieve effective isolation from external moisture, heat, electrical interference and mechanical stress, reduce the risk of damage and extend the service life.

[0031] The signal conditioning module is used to process the magnetic signal and send the processed magnetic signal to the microprocessor. The signal conditioning module uses a multi-channel low-noise amplifier, filter, level conversion and analog-to-digital conversion unit to amplify, filter and sample the magnetic signal collected by the TMR sensor and the output signal of the digital integrated temperature and humidity sensor (the signal corresponding to the temperature and relative humidity in the switch cabinet) to obtain the processed magnetic signal.

[0032] The microprocessor is used to analyze the processed magnetic signal and determine the multi-dimensional parameters of the leakage current. The multi-dimensional parameters of the leakage current include the mean value, extreme value, slope and fluctuation rate of the leakage current. The acquisition process is as follows: ① Convert the processed magnetic signal into magnetic field time series data. The processed magnetic signal contains information about the magnetic field that changes over time. Through the corresponding conversion relationship, it is converted into sequence data with time as the horizontal axis and magnetic field intensity as the vertical axis, namely magnetic field time series data, so that the changes of the magnetic field over time can be clearly observed and analyzed.

[0033] ② Based on the pre-established correspondence between the magnetic field and current magnitude, the magnetic field time series data is converted into leakage current time series data. Because there is a certain physical correlation between the magnetic field and current, a relationship or correspondence table between the two can be determined in advance through experiments or theoretical calculations. Given the known magnetic field time series data, the magnetic field intensity value at each time point can be converted into the corresponding leakage current value according to the pre-established correspondence. This results in time series data showing the leakage current changing over time, allowing for subsequent analysis directly from the current perspective.

[0034] ③ Add the current values ​​at each time point in the leakage current time series data, and then divide it by the total number of data points. The result is the mean value of the leakage current.

[0035] ④ In the leakage current time series data, compare the current values ​​at each time point in turn to find the maximum and minimum values. These two extreme values ​​reflect the fluctuation range of the leakage current within the time period; ⑤ Select adjacent time points in the leakage current time series data, subtract the current value at the previous time point from the current value at the next time point, and divide by the time interval between the two time points. This yields the rate of change of the leakage current between these two adjacent time points, also known as the slope. By calculating the slopes of multiple pairs of adjacent time points, you can analyze the steepness of the leakage current's change over time. A positive slope indicates an increasing current, while a negative slope indicates a decreasing current. A larger absolute value of the slope indicates a more dramatic change in current.

[0036] ⑥ First, calculate the difference between each data point and the mean in the leakage current time series data. Then, square these differences, sum them, and divide by the total number of data points to obtain the variance. Finally, square the variance to obtain the standard deviation, which can be used as a measure of volatility. Volatility reflects the dispersion of the leakage current data, that is, the degree to which the current values ​​deviate from the mean. A greater volatility indicates less stable leakage current and potentially higher equipment operational risk.

[0037] Among them, the microprocessor controls the closed-loop structure TMR current sensor in a PID closed-loop manner, combined with Figure 3 and Figure 4 The PID closed-loop method is explained. Figure 4 In the equation, the proportional coefficient K p To quantify I p With B p Relationship between s is the sensitivity of the TMR sensor; Ga is the operational amplifier. p is the primary current; B p For I p The magnetic induction intensity generated in the magnetic core; V s is the output voltage of TMR; Vo is the open-loop output voltage parameter, B s is the feedback current I s The excitation magnetic induction intensity. In the closed-loop control process, the input and output are the primary side current to be measured I p and feedback current I s ,like Figure 3 As shown, the feedback current I s Can be output through the op amp, parameter B s is the feedback current I s The excitation magnetic induction intensity. In the closed-loop control process, the input and output are the primary side current to be measured I p and feedback current I s , it should be noted that, Figure 4 The "TMR chip" in the figure is a TMR sensor.

[0038] Coefficient K f It specifically represents I s With B s The quantitative relationship between: Feedback current I is included for closed-loop control s Therefore, the output voltage of the closed-loop TMR current sensor is: V s =Ks·ΔB=K s ·(B p -B s ), where K s is the sensitivity of the TMR chip, and ΔB is the difference between the magnetic induction intensity Bp excited by the primary current Ip and the magnetic induction intensity Bs excited by the feedback current Is.

[0039] The closed-loop control transfer function and the sensitivity of the closed-loop structure TMR current sensor are: Among them, H(s) represents the feedback function under feedback control, and G(s) is the forward channel transfer function. Figure 2 The structure of the TMR current sensor shown can be deduced as follows: Where, τ c =L c / R c , where L c Impedance R reflecting the inductance of the feedback coil x =R s +R c , R s is the sampling resistor, R c is the equivalent resistance of the feedback coil.

[0040] The second-order transfer function of the closed-loop TMR current sensor is: Among them, τ c The time order of magnitude is much smaller than τ a Without considering the influence of the dynamic response of the feedback coil, the transfer function of the closed-loop TMR current sensor is: Using S I (0) represents the static gain under closed-loop control, and the system time constant is represented by τ, so the above formula can be rewritten as: Among them, the system static gain S I (0) is: Among them, R x < <K s K a K f , so the above formula can be simplified to: The transfer function of the closed-loop structure TMR current sensor under closed-loop control is: From the above analysis, it can be seen that SI(0) characterizes the sensitivity characteristics of the system under closed-loop control, and it also has corresponding physical meaning: that is, the turns ratio parameter between the primary measured current winding and the secondary compensation winding. In order to better control the turns ratio, the primary winding turns are set as the benchmark and the value is 1, and the turns ratio is changed by changing the number of turns of the feedback coil. However, when the number of turns of the feedback coil is fixed, the sensitivity K of the closed-loop structure TMR current sensor is a It will be affected by factors such as nonlinear distortion and temperature drift. To suppress the influence of nonlinear distortion and temperature drift of the TMR chip, the feedback current is kept proportional to the primary current to be measured, thereby improving measurement accuracy.

[0041] For the closed-loop control process, the cutoff frequency f0 is a key indicator of the TMR sensor bandwidth. By analyzing the mathematical model of the TMR sensor transfer function under closed-loop control, the cutoff frequency f0 is obtained as: Where, τ a is the amplifier parameter; K s is the sensitivity; K a Indicates the static gain of the operational amplifier under the influence of the magnetic field; K f Feedback path coefficient; R s is the sampling resistor.

[0042] In the design process of the current measurement part, it is particularly important to select a high-sensitivity measurement chip, so a high-sensitivity TMR chip is selected. At the same time, in order to obtain the ideal bandwidth of the current sensor, the feedback channel coefficient K can be changed. f With the sampling resistor R s But it should be noted that the feedback channel coefficient K f When increasing the bandwidth of a closed-loop TMR current sensor, the resulting increase in the size of the feedback coil must also be considered to avoid loss of sensitivity. By comprehensively considering these multiple factors and taking them into account, the closed-loop TMR current sensor can be optimized.

[0043] Optionally, in the above technical solution, the microprocessor is also used to: obtain multi-dimensional environmental parameters in the switch cabinet, and use a preset state evaluation model, combined with the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters in the switch cabinet, to evaluate the operating state of the post insulator, and determine the operating state level of the post insulator. The operating state level of the post insulator is normal, warning, abnormal or serious.

[0044] The multi-dimensional environmental parameters in the switch cabinet include the temperature and relative humidity in the switch cabinet, which can be collected by digital integrated temperature and humidity sensors and expressed as I 2 C method is uploaded to the control unit, i.e., the microprocessor, so that the microprocessor can obtain the temperature and relative humidity in the switch cabinet in real time.

[0045] In another embodiment, a temperature signal inside the switch cabinet is obtained by a temperature sensor, and the temperature signal inside the switch cabinet is processed by a signal conditioning module to obtain a processed temperature signal, which is sent to a microprocessor. The microprocessor processes the processed temperature signal to obtain the temperature inside the switch cabinet in real time; a relative humidity signal inside the switch cabinet is obtained by a humidity sensor, and the relative humidity signal inside the switch cabinet is processed by a signal conditioning module to obtain a processed relative humidity signal, which is sent to a microprocessor. The microprocessor processes the processed relative humidity signal to obtain the relative humidity inside the switch cabinet in real time.

[0046] Among them, the microprocessor (MCU) can adopt a high-performance embedded processor, which is responsible for the periodic acquisition, cache management and preprocessing of multi-source signals (magnetic signals, signals corresponding to multi-dimensional environmental parameters) (the microprocessor analyzes the processed magnetic signals, the microprocessor processes the processed signals corresponding to the temperature and relative humidity in the switch cabinet, and the microprocessor obtains the multi-dimensional parameters of the leakage current and the temperature and relative humidity in the switch cabinet for fusion processing, including preprocessing operations such as denoising, normalization, time alignment, and anomaly rejection), and extracts key characteristic parameters (multi-dimensional parameters of the leakage current, temperature and relative humidity in the switch cabinet), and then realizes the level identification of the insulation status (the operating status level of the pillar insulator) and trend (leakage current change trend) evaluation through the embedded algorithm (state assessment model).

[0047] In one possible implementation scheme, the preset state assessment model is a trained decision tree model, specifically: Given the nonlinearity, coupling, and uncertainty exhibited by the post insulator leakage current and environmental characteristic data, a decision tree model (also known as a decision tree algorithm) with its excellent interpretability and lightweight computational characteristics was selected as the core component of the embedded machine learning model. This decision tree model is suitable for real-time inference and graded judgment of insulation status in embedded MCU environments.

[0048] In the process of building a decision tree model, assume that the current node contains a sample set D, and each sample corresponds to a state label: y∈{0, 1, 2, 3}, which represent "normal, warning, abnormal, serious" states respectively.

[0049] The commonly used partitioning criterion is information gain: in, is the entropy of the data set D; v represents the subset where the feature X takes the value v.

[0050] The input features of the decision tree model include but are not limited to: the mean, slope, maximum value, fluctuation rate, temperature, relative humidity, temperature change rate, relative humidity change rate, delayed response index between leakage current and environmental parameters (temperature, relative humidity); and change trend indicators within the statistical time window (such as the current slope fitting coefficient).

[0051] The decision tree model is trained based on historical insulator operation data and labeled sample sets. The information gain partitioning criterion is used to select the optimal splitting features and construct the optimal tree structure. The model employs a pruning strategy to avoid overfitting and compresses the depth of decision paths to meet embedded inference efficiency requirements.

[0052] Assume that the input of the decision tree model is a set of multi-source fusion feature vectors: X∈{x1,x2,...,x n}, where x i Represents the i-th input feature. For example, x1 is the mean value of the leakage current, x2 is the fluctuation rate of the leakage current, and x3 is the temperature. These input features constitute the input space matrix (i.e., the multi-source fusion feature vector), which serves as the feature basis of the decision tree model. After training the decision tree model, a trained decision tree model is obtained.

[0053] For a trained decision tree model, the state evaluation function can be formalized as: Where L is the total number of leaf nodes; R i Divide the input space corresponding to the lth leaf node into regions; y i is the output state label corresponding to the leaf node; I(·) is the indicator function, which outputs 1 when the input sample X falls into the region Rl, otherwise it outputs 0. The final output of the model is: y∈{0, 1, 2, 3}, corresponding to "normal, warning, abnormal, severe".

[0054] The model parameters and tree structure of the trained decision tree model can be pre-stored in the MCU program storage space and can also be iteratively updated through remote upgrade methods (such as serial port and wireless OTA). In scenarios where sufficient sample data cannot be obtained during the initial operation of the device, a small sample incremental learning strategy can be combined to achieve online correction and dynamic adaptation of the decision tree structure.

[0055] During operation, the microprocessor collects and preprocesses multi-source feature values ​​in real time and progressively inputs them into each branch node of the decision tree. This rapid state assessment process is achieved through a "feature determination → node transition → result output" process, ultimately outputting the insulation state level (normal, warning, abnormal, severe) and a continuous health index (HealthIndex). The continuous health index is ∈ [0, 1]. The decision tree model's reasoning process eliminates the need for complex iterative calculations, resulting in low resource overhead, strong real-time performance, and adaptability to low-power embedded platforms.

[0056] In another possible implementation scheme, the preset state assessment model is a fuzzy logic reasoning model (also known as a fuzzy logic reasoning system), which sets a threshold value based on the characteristic quantity to perform state classification judgment and determine the operating state level of the support insulator.

[0057] The fuzzy logic reasoning model is a modeling tool based on fuzzy set theory and fuzzy reasoning methods, capable of effectively processing information with uncertainty and ambiguity. It transforms precise numerical inputs into fuzzy linguistic variables, uses fuzzy rules for reasoning, and finally converts the fuzzy reasoning results into precise numerical outputs, thereby enabling evaluation and decision-making for complex systems. The fuzzy logic reasoning model primarily consists of four components: a fuzzification interface, a fuzzy rule base, an inference engine, and a defuzzification interface.

[0058] The specific implementation process for determining the operating status of post insulators using a fuzzy logic inference model is as follows: 1) The previously determined multidimensional leakage current parameters (mean, extreme value, slope, and fluctuation) and the multidimensional environmental parameters within the switchgear (such as temperature and relative humidity) are used as input variables in the fuzzy logic inference model. The output variable is the operating status of the post insulator. Although it is ultimately expressed as normal, warning, abnormal, or severe, it needs to be converted into corresponding numerical values ​​within the model to facilitate fuzzy inference and subsequent defuzzification. For example, these four levels can be represented by 0, 1, 2, and 3, respectively.

[0059] 2) Define appropriate fuzzy sets for each input and output variable. For example, for the mean value of leakage current, you can define fuzzy sets as low, medium, and high; for the temperature inside the switchgear, you can define fuzzy sets as low, moderate, and high. These fuzzy sets use membership functions to describe the degree to which each element belongs to the set.

[0060] 3) Based on actual experience and data distribution, select a suitable membership function for each fuzzy set. Common membership functions include triangular membership function, trapezoidal membership function, Gaussian membership function, etc.

[0061] 4) Invite experts in the power system field to summarize fuzzy rules based on their practical experience for the operating status of post insulators under different combinations of leakage current parameters and switchgear environmental parameters. For example, when the leakage current has a low mean, low extreme value, small slope, and small fluctuation rate, and the temperature and humidity inside the switchgear are within a moderate range, the post insulator's operating status is considered normal. Expert knowledge is converted into fuzzy rules, which typically take the form of "if...then..." For example, "If the leakage current has a low mean, a low extreme value, a small slope, and a small fluctuation rate, and the temperature and humidity inside the switchgear are within a moderate range, then the post insulator's operating status is considered normal."

[0062] 5) Use appropriate fuzzy inference methods, such as the Mamdani inference method or the Larsen inference method. Taking the Mamdani inference method as an example, after the input variables are fuzzified, the activation strength of each rule is calculated based on the antecedent and fuzzy operators (such as AND, OR, etc.) in the fuzzy rule. Then, based on the activation strength and the fuzzy set of the rule's consequent, the output fuzzy set corresponding to each rule is obtained through fuzzy implication relations. The output fuzzy sets corresponding to all fuzzy rules are aggregated to obtain a total output fuzzy set. For example, if the output fuzzy sets of multiple rules all correspond to the normal operating status level of the post insulator, then these fuzzy sets will be merged during aggregation to reflect the comprehensive impact of all rules on the output variable.

[0063] 6) Convert the total output fuzzy set into an accurate numerical value. Common methods include the centroid method, maximum membership method, and weighted average method. For example, when using the centroid method, the centroid position of the total output fuzzy set is calculated. The numerical value corresponding to this centroid position is the quantized value of the operating status level of the post insulator. The quantized value obtained by defuzzification is compared with the pre-set status classification threshold to determine the operating status level of the post insulator. For example, if the quantized value is between 0 and 0.5, it corresponds to a normal state; between 0.5 and 1.5, it corresponds to a warning state; between 1.5 and 2.5, it corresponds to an abnormal state; and between 2.5 and 3, it corresponds to a serious state.

[0064] Optionally, in the above technical solution, the microprocessor is also specifically used to: evaluate the changing trend of the leakage current based on the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters in the switch cabinet, and obtain the evaluation results of the changing trend of the leakage current, and specifically use a time series trend judgment model to analyze the evaluation results of the changing trend of the leakage current (specifically including the growth trend and mutation characteristics of the leakage current).

[0065] The time series trend prediction model can be obtained by training a long short-term memory (LSTM) network or a Transformer. LSTM is a special type of recurrent neural network (RNN) that effectively addresses the vanishing and exploding gradient problems of traditional RNNs when processing long sequences of data. By introducing a gating mechanism, including input, forget, and output gates, it can better control the flow of information, thereby capturing long-term dependencies in time series. This makes LSTM a preferred choice for tasks such as time series forecasting.

[0066] The process of obtaining the time series trend judgment model is as follows: 1) Collect data series on the time-varying multidimensional parameters of the leakage current (mean, extreme value, slope, volatility) and the multidimensional environmental parameters in the switch cabinet (such as temperature, humidity, etc.), and organize them into a time series data set. Since the data dimensions and value ranges of different parameters may vary greatly, the data needs to be normalized and scaled to [0,1] or other appropriate ranges to improve the training efficiency and stability of the model. Specifically, the Min-Max normalization method can be used. According to the characteristics of the time series data, construct input samples and corresponding target values. For each time step t, the data of the previous n time steps are selected as input features, and the leakage current change trend of the current time step t is used as the target value. For example, if n = 10, the input sample is the multidimensional parameter data of 10 consecutive time steps, and the target value is the leakage current change trend of the 10th time step.

[0067] 2) Build a neural network model consisting of multiple LSTM layers. The first layer is an LSTM layer, with an appropriate number of neurons (e.g., 50 or 100) to effectively extract features from the time series. The second layer can include a Dropout layer to prevent overfitting and improve the model's generalization. The final layer is a fully connected layer, which outputs the leakage current trend assessment results, namely, the categories or values ​​of the growth trend and mutation characteristics. Select an appropriate loss function and optimizer to compile the model. For classification problems, if the leakage current trend is classified into different categories (such as stable, slow growth, rapid growth, and mutation), the sparse categorical cross entropy loss function can be used. For regression problems, if the numerical value of the trend is directly predicted, the mean squared error loss function can be used. The Adam optimizer can be selected as the optimizer, which features adaptive learning rate adjustment and accelerates model convergence. The preprocessed training data is input into the constructed LSTM model and trained for multiple iterations. During each training cycle (epoch), the model automatically adjusts the network weights and bias parameters based on the input data and the corresponding target value to minimize the loss function. At the same time, the model is validated using a validation set to monitor its performance on the validation set and prevent overfitting. Based on the model's performance during training, appropriate adjustments are made to the training parameters. For example, the learning rate can be adjusted. A high learning rate can lead to unstable model training, while a low learning rate can slow down training. The batch size can also be adjusted, as the choice of batch size affects the model's update frequency and training efficiency. Model structural parameters such as the number of neurons and the dropout layer's dropout rate can also be adjusted to optimize model performance, thereby obtaining a time series trend prediction model.

[0068] The leakage current growth trend refers to the gradual increase in leakage current over a period of time. This growth can be slow, linear, or nonlinear. This is because during the operation of power equipment, insulation performance gradually degrades due to factors such as aging of insulation materials and accumulation of surface contamination. For example, the insulation resistance of post insulators within switchgear is gradually reduced due to long-term exposure to electric fields, heat, and environmental factors (such as temperature and humidity changes), causing the leakage current to slowly increase over time. Furthermore, if latent defects exist within the equipment, such as partial discharge, the leakage current will also show an increasing trend as the defect continues to develop. By monitoring the growth trend of the leakage current, the insulation aging of post insulators can be predicted in advance.

[0069] A sudden change in leakage current refers to a phenomenon in which the leakage current undergoes a sharp change within a short period of time. This change can occur as a sudden increase or decrease in current, typically manifesting as a spike or dip in time series data. When the switchgear environment is suddenly struck by an abnormality such as a lightning strike or short circuit, this can generate a transient overvoltage or overcurrent in the equipment, leading to a sudden change in leakage current. Alternatively, flashover of the contaminant layer on the surface of the insulator within the equipment under certain conditions (such as a sudden increase in humidity) can cause a sudden increase in leakage current.

[0070] Optionally, the above technical solution further includes a wireless communication module; the microprocessor is further configured to upload all operating status assessment results, the operating status level of the post insulator, the multi-dimensional parameters of the leakage current, and the multi-dimensional environmental parameters within the switchgear to a remote monitoring platform via the wireless communication module. The wireless communication module may be a Wi-Fi communication protocol module, a ZigBee module, an NB-IoT (Narrowband Internet of Things) module, a LoRa (Long Range Radio) module, or a 4G / 5G communication module.

[0071] This is explained by the following examples: 1) TMR sensors are installed on the support insulators inside the switchgear. A microprocessor controls the weak magnetic signal generated by the TMR sensors' non-contact sensing of leakage current. Digital integrated temperature and humidity sensors are also deployed at appropriate locations to obtain real-time multi-dimensional environmental parameters (temperature, relative humidity, etc.) within the switchgear. Both the TMR sensors and the digital integrated temperature and humidity sensors are connected to a signal conditioning module to achieve multi-source sensing.

[0072] 2) The magnetic signal generated by the leakage current is usually at the microvolt to millivolt level and needs to go through the signal conditioning module for low-noise amplification, bandpass filtering, and analog-to-digital conversion (ADC) to improve signal quality and suppress noise interference. Multidimensional environmental parameters can be uploaded to the microprocessor via a digital interface to ensure that all signals are sampled and cached at a unified time base.

[0073] Among them, in the insulator status monitoring system, the magnetic signal corresponding to the leakage current obtained by the TMR sensor is usually in the microvolt to millivolt range. The signal amplitude is weak and easily interfered by environmental noise, and requires a precise signal conditioning process to ensure data quality. The front-end circuit uses a low-noise amplifier to amplify the original signal with high gain to improve the signal-to-noise ratio, and combines it with a bandpass filter to effectively suppress low-frequency drift and high-frequency interference, thereby extracting the effective magnetic field components within the target frequency band. The processed analog signal is further digitally sampled by a high-resolution analog-to-digital converter (ADC) and converted into a format that can be recognized by the digital processing system; the temperature and humidity data are collected by the integrated digital sensor and transmitted through the standard digital communication interface (I 2 C) Upload directly to the microprocessor. To ensure the consistency and synchronization of electrical and environmental data, the system uses a unified time-base control strategy to periodically sample and cache multi-source signals to maintain data timing alignment.

[0074] 3) The microprocessor analyzes the collected time series data (magnetic field time series data, temperature time series data, and relative humidity time series data) to obtain the following multi-source features: ① Multidimensional parameters of leakage current (i.e., multi-source characteristics of leakage current): mean, extreme value, slope, and fluctuation rate of leakage current; ② Multidimensional characteristics of multidimensional environmental parameters: temperature mutation data, relative humidity mutation data, temperature change trend, relative humidity change trend; ③ Fusion characteristics: time-delay coupling and trend correlation between environmental changes and current responses.

[0075] Through multi-parameter fusion, the sensitivity to hidden danger status (operating status levels of abnormal and serious states) and the input dimensions of the model (different status assessment models) are enhanced.

[0076] 4) The microprocessor obtains multi-dimensional environmental parameters within the switchgear and, using a preset state assessment model, combines the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters within the switchgear to assess the operating status of the post insulator and determine the operating status level of the post insulator, which is classified as normal, warning, abnormal, or severe. Based on the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters within the switchgear, the leakage current trend is assessed to obtain a leakage current trend assessment result.

[0077] The microprocessor analyzes and extracts features from the collected multi-dimensional time series data. The leakage current signal features include mean, extreme value, growth slope, and volatility, reflecting its steady-state level and disturbance trend; the environmental parameter features include the degree of mutation, rate of increase, and trend change of temperature and humidity, describing the impact of external stress on the insulation system. Furthermore, the system can calculate fusion features such as the time-delay coupling relationship and trend correlation between environmental changes and current responses to enhance the perception of environmentally induced insulation degradation. The fusion of multi-source data and high-dimensional feature input helps to improve the sensitivity and judgment accuracy of subsequent state assessment models to abnormal conditions, providing technical support for early warning and trend prediction.

[0078] 5) Classification is performed based on the model output and set thresholds, typically including: Normal: leakage current and environmental parameters are stable; Warning: abnormal fluctuation trends exist; Abnormal: reaching the preset safety threshold; Severe: high-risk operating state, power outage and maintenance recommended. Evaluation timestamps and historical trends are also recorded for tracking and tracing.

[0079] 6) Through the wireless communication module, status data (all operating status assessment results, operating status levels of post insulators), raw data (multi-dimensional leakage current parameters and multi-dimensional environmental parameters within the switchgear), and warning information (warning information for different operating status levels) are uploaded in real time to the remote monitoring platform (also known as the backend monitoring platform), enabling remote visualization, automatic recording, and alarm linkage. The backend supports the generation of status curves, abnormal trend charts, and operation and maintenance recommendations.

[0080] In this embodiment, to effectively monitor the insulation condition of post insulators within the switchgear, TMR (tunnel magnetoresistance) magnetic field sensors are installed near critical insulation locations, preferably on the post insulator surface or near potential leakage current paths. TMR sensors are highly sensitive to weak magnetic field variations and can contactlessly sense local magnetic flux density changes caused by leakage current flowing on the insulator surface, enabling highly sensitive detection of current signals in the microampere or even nanoampere range. Compared to traditional current transformers or Hall effect elements, TMR devices offer smaller size, lower power consumption, and higher accuracy, making them particularly suitable for deployment within switchgear environments with limited space, high electric field strength, and complex interference environments. Furthermore, to achieve real-time sensing of environmental factors that affect insulation performance, they are used to continuously monitor key environmental parameters such as temperature and relative humidity. The signals output by these sensors are connected to the system through a signal conditioning module, which includes filtering, amplification, and level conversion circuits to ensure the quality of the original signal and transmission stability. Ultimately, the device can achieve synchronous collection and fusion perception of electrical signals and environmental signals, providing accurate and reliable data support for subsequent status assessment algorithms, thereby building a multi-parameter perception system for early warning of insulation degradation.

[0081] The present invention relates to a device for detecting leakage current in post insulators within a switchgear cabinet, belonging to the technical field of online monitoring and intelligent diagnosis of power equipment. The device implements non-contact detection of weak magnetic flux changes caused by leakage current by deploying TMR magnetic field sensors at key locations on the post insulators. Combined with temperature and humidity sensing modules, it achieves real-time, synchronous sensing of environmental parameters. An embedded microprocessor is used to extract features, perform fusion modeling, and perform status assessment on multi-source data. The magnetic ring adopts an open-type structure and introduces a mechanical mortise and tenon self-positioning design, allowing for rapid assembly onto the insulator surface while retaining an effective magnetic induction air gap to improve magnetic signal consistency and installation stability. The system features low power consumption, high sensitivity, and high integration. It can remotely report status data and warning information to a backend platform via wireless communication, supporting trend curve generation and intelligent diagnosis. The present invention achieves accurate sensing and early warning of the operating status of post insulators without changing the original structure. It is suitable for intelligent monitoring of insulation status in electrical equipment such as medium- and high-voltage switchgear and ring main units, and has excellent engineering adaptability and application value.

[0082] In another embodiment, a leakage current detection device for a post insulator in a switch cabinet of the present invention includes: an open-type magnetic ring structure, the magnetic ring is made of a high magnetic permeability material, and matching mortise and tenon structures are provided at both ends of the split structure to achieve self-positioning and closed installation of the magnetic ring; an air gap is provided in the magnetic ring, and a TMR (tunnel magnetoresistance) magnetic field sensor is embedded in the air gap to sense the magnetic flux changes generated by the leakage current along the surface of the post insulator; a fixed structure is provided on the outside of the magnetic ring to clamp it to the bottom of the post insulator or near the grounding path; the device also includes a signal conditioning module connected to the TMR sensor, the conditioning module includes amplification, filtering and analog-to-digital conversion circuits; the device further includes a microprocessor electrically connected to the signal conditioning module for data acquisition, processing and control; the microprocessor is also connected to a wireless communication module for uploading the processing results to a remote monitoring platform.

[0083] Currently, most temperature and humidity monitoring modules exist as independent systems, lacking an integrated design with electrical parameter monitoring. This makes it difficult to achieve data fusion, unified evaluation, and intelligent analysis, severely restricting the integration and intelligence level of the overall monitoring system. As a new generation of highly sensitive magnetic field sensing elements, TMR (tunnel magnetoresistance) sensors offer outstanding advantages such as small size, low power consumption, fast response, strong anti-interference, and extreme sensitivity to weak magnetic fields. They are particularly suitable for non-contact, high-precision detection of magnetic field changes caused by tiny leakage currents in environments with limited space and complex electromagnetic interference. At the same time, their excellent linear response and stability allow them to be embedded and integrated into multi-parameter sensing platforms, working in conjunction with temperature and humidity sensors to create compact, parameter-interconnected, algorithm-driven intelligent sensing devices.

[0084] Therefore, there is an urgent need to develop a highly integrated intelligent monitoring device based on TMR sensors, which can perform real-time linkage perception of the leakage current of the support insulator and the ambient temperature and humidity without changing the original structure of the switchgear, and realize trend judgment and risk warning of the insulation status through embedded intelligent evaluation algorithms, thereby comprehensively improving the visualization, intelligence and active defense capabilities of the switchgear operation, and providing technical support for the reliable operation of the smart grid.

[0085] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art may adjust the execution order of S1, S2, etc. according to actual conditions, which is also within the scope of protection of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0086] like Figure 5 As shown, a method for evaluating the operating status of a post insulator in a switch cabinet according to an embodiment of the present invention includes the following steps: S1. Collecting magnetic signals caused by leakage current of the post insulator through a TMR sensor and transmitting the magnetic signals to a signal conditioning module. The TMR sensor is located in the air gap of a magnetic ring, which is detachably mounted on the post insulator in the switch cabinet. S2. Processing the magnetic signal through a signal conditioning module to obtain a processed magnetic signal; S3. Processing the processed magnetic signal to determine multi-dimensional parameters of the leakage current; S4. Evaluate the operating status of the post insulator based on the multi-dimensional parameters of the leakage current and determine the operating status level of the post insulator.

[0087] Optionally, in the above technical solution, it further includes: obtaining multi-dimensional environmental parameters in the switch cabinet; Based on the multi-dimensional parameters of the leakage current, the operating status of the post insulator is evaluated and the operating status level of the post insulator is determined, including: The operating status of the post insulator is evaluated using a preset status assessment model, combined with the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters in the switchgear.

[0088] Optionally, in the above technical solution, the following is further included: Based on the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters in the switch cabinet, the variation trend of the leakage current is evaluated to obtain the leakage current variation trend evaluation result.

[0089] Optionally, in the above technical solution, the following is further included: The operating status level of the support insulator, the evaluation results of the leakage current change trend, the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters in the switchgear are uploaded to the remote monitoring platform through the wireless communication module.

[0090] Optionally, in the above technical solution, the magnetic ring includes a first arc-shaped magnetic strip and a second arc-shaped magnetic strip, and the first arc-shaped magnetic strip and the second arc-shaped magnetic strip are detachably connected via a mortise and tenon structure.

[0091] In another embodiment, the method includes: collecting magnetic signals caused by leakage current on the surface of the insulator using a TMR sensor; synchronously collecting ambient temperature and relative humidity parameters; extracting features from the collected signals, including at least the leakage current mean, amplitude, harmonic content, and rate of change; analyzing the extracted multi-dimensional features based on a preset state assessment model; outputting the current state level of the insulator, which may be normal, warning, abnormal, or severe; and uploading the state information and feature data to a remote monitoring platform via a wireless communication module. The state assessment model is a decision tree model or a fuzzy logic inference system, which determines the state classification based on thresholds set based on feature quantities. The assessment model also includes a time series trend judgment module for analyzing the leakage current growth trend and mutation characteristics over multiple cycles. The uploaded data includes a timestamp, state label, feature parameters, and trend analysis results, and triggers a backend platform alarm function in the event of an abnormal state.

[0092] This invention proposes a post insulator condition assessment method that combines TMR magnetoresistive sensing with environmental parameter sensing. The method is suitable for early identification of insulator degradation, trend assessment, and risk warning in electrical equipment such as switchgear. It primarily includes the following aspects.

[0093] Data preprocessing and feature extraction: The microcontroller (MCU) fuses the TMR magnetic signal with the temperature and humidity data, performing preprocessing operations such as denoising, normalization, time alignment, and anomaly removal. It then extracts multiple sets of characteristic parameters, such as leakage current and condition assessment model calculations.

[0094] According to the evaluation results, the insulator status is divided into multiple levels, including normal status, warning status, abnormal status, and serious status.

[0095] Alarm output and data reporting: Once an "abnormal" or "serious" state is detected, the system will upload the alarm signal and the current complete monitoring data to the remote background monitoring platform through the wireless communication module, supporting interface visualization, log recording and trend backtracking functions.

[0096] The present invention provides a device for detecting leakage current of support insulators in a switch cabinet based on a TMR sensor and a method for evaluating their status, which have the significant advantages of compact structure, high sensitivity, strong functional integration and high intelligent evaluation. By adopting a TMR (tunnel magnetoresistance) sensor to achieve non-contact real-time detection of the weak magnetic field generated by the leakage current on the surface of the support insulator, the problem of large size, limited installation and high detection threshold of traditional current transformers is effectively compensated. At the same time, the device integrates a temperature and humidity sensor module, which can synchronously collect environmental information inside the switch cabinet, and combines it with an embedded microprocessor to perform fusion analysis and trend modeling on multi-source data, realizing real-time evaluation and risk warning of the insulator operating status. The system supports multiple wireless communication methods and has remote data reporting and alarm push functions, which facilitates centralized monitoring and intelligent operation and maintenance. The overall solution not only significantly improves the sensitivity and stability of leakage current detection, but also enhances the response capability to insulation degradation under the influence of environmental factors, providing reliable technical support for the digital and intelligent perception of the insulation status inside the switch cabinet, and has broad application prospects and promotion value.

[0097] It should be noted that the specific implementation process of the operating status evaluation method of the support insulator in the switch cabinet provided by the above embodiment is detailed in the specific description of the leakage current detection device for the support insulator in the switch cabinet, which will not be repeated here.

[0098] The above description is merely a preferred embodiment of the present invention and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present invention is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the present invention.

[0099] It should be noted that the terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects and to define a specific order or precedence. Where appropriate, the order used for similar objects may be interchanged, such that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0100] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A device for detecting leakage current of a support insulator in a switch cabinet, characterized in that: The device comprises a TMR sensor, a magnetic ring with an air gap, a signal conditioning module and a microprocessor, wherein the TMR sensor is arranged in the air gap, and the magnetic ring is detachably mounted on a support insulator in a switch cabinet; The TMR sensor is used to: collect a magnetic signal caused by a leakage current of the post insulator, and transmit the magnetic signal to the signal conditioning module; The signal conditioning module is used to process the magnetic signal and send the processed magnetic signal to the microprocessor; the microprocessor is used to analyze the processed magnetic signal and determine the multi-dimensional parameters of the leakage current.

2. The device for detecting leakage current of a support insulator in a switch cabinet according to claim 1, characterized in that: The microprocessor is further configured to obtain multidimensional environmental parameters within the switch cabinet, and utilize a preset state assessment model, in combination with the multidimensional parameters of the leakage current and the multidimensional environmental parameters within the switch cabinet, to assess the operating state of the post insulator, and determine an operating state level of the post insulator, wherein the operating state level of the post insulator is normal, warning, abnormal, or severe.

3. The device for detecting leakage current of a support insulator in a switch cabinet according to claim 2, characterized in that: The microprocessor is further specifically configured to: evaluate a variation trend of the leakage current based on the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters in the switch cabinet, and obtain an evaluation result of the variation trend of the leakage current.

4. The device for detecting leakage current of a support insulator in a switch cabinet according to claim 3, characterized in that: It also includes a wireless communication module; the microprocessor is also used to: upload the operating status level of the post insulator, the leakage current change trend evaluation result, the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters in the switch cabinet to the remote monitoring platform through the wireless communication module.

5. A device for detecting leakage current of a post insulator in a switch cabinet according to any one of claims 1 to 4, characterized in that: The magnetic ring includes a first arc-shaped magnetic strip and a second arc-shaped magnetic strip, and the first arc-shaped magnetic strip and the second arc-shaped magnetic strip are detachably connected via a mortise and tenon structure.

6. A method for evaluating the operating status of a support insulator in a switch cabinet, characterized in that: include: The TMR sensor is arranged in the air gap of a magnetic ring, which is detachably mounted on the post insulator in the switch cabinet. The magnetic signal is transmitted to the signal conditioning module by collecting the magnetic signal caused by the leakage current of the post insulator through the TMR sensor and transmitting the magnetic signal to the signal conditioning module. After processing the magnetic signal by the signal conditioning module, a processed magnetic signal is obtained; processing the processed magnetic signal to determine multi-dimensional parameters of the leakage current; The operating state of the post insulator is evaluated according to the multi-dimensional parameters of the leakage current, and the operating state level of the post insulator is determined.

7. The method for evaluating the operating status of a post insulator in a switch cabinet according to claim 6, characterized in that: Also includes: Acquiring multi-dimensional environmental parameters in the switch cabinet; Evaluating the operating state of the post insulator according to the multi-dimensional parameters of the leakage current to determine the operating state level of the post insulator includes: The operating state of the post insulator is evaluated by using a preset state evaluation model and combining the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters in the switch cabinet.

8. The method for evaluating the operating status of a post insulator in a switch cabinet according to claim 7, characterized in that: Also includes: Based on the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters in the switch cabinet, the variation trend of the leakage current is evaluated to obtain an evaluation result of the variation trend of the leakage current.

9. The method for evaluating the operating status of a post insulator in a switch cabinet according to claim 8, characterized in that: Also includes: The operating status level of the post insulator, the evaluation result of the leakage current change trend, the multi-dimensional parameters of the leakage current and the multi-dimensional environmental parameters in the switch cabinet are uploaded to the remote monitoring platform through a wireless communication module.

10. The method for evaluating the operating status of a post insulator in a switch cabinet according to any one of claims 6 to 9, characterized in that: The magnetic ring includes a first arc-shaped magnetic strip and a second arc-shaped magnetic strip, and the first arc-shaped magnetic strip and the second arc-shaped magnetic strip are detachably connected via a mortise and tenon structure.

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