Microelectrode array-based system and method for detecting free carbon particles in transformer oil

Abstract

The application provides a transformer oil free carbon particle detection system and method based on a microelectrode array, and the system comprises a sampling module, a microelectrode array sensor module and a signal processing unit; the sampling module comprises an oil inlet pipeline and a filtering device; an oil sample is transported from a transformer oil tank to the microelectrode array sensor module through the oil inlet pipeline; the filtering device is arranged in the oil inlet pipeline; a filtering membrane is arranged in the filtering device to remove impurities in the oil sample; the microelectrode array sensor module comprises a microelectrode array sensor; the microelectrode array sensor is a nano electrode arranged on a silicon substrate; the surface of the microelectrode array sensor is coated with graphene oxide to adsorb free carbon particles in the oil sample; when the free carbon particles are adsorbed to the surface of the microelectrode array sensor, an adsorption signal is generated; the signal processing unit is used for extracting the characteristic frequency and intensity of carbon particle vibration in the adsorption signal, and classifying the carbon particles according to the characteristic frequency and intensity of the carbon particles; and the device structure of the application is compact, the maintenance cost is reduced, and the operation process is simplified.

Description

Technical Field

[0001] This invention belongs to the field of transformer impurity identification, specifically relating to a system and method for detecting free carbon particles in transformer oil based on a microelectrode array. Background Technology

[0002] As a core piece of equipment for power transmission and distribution, the reliability of transformers directly affects the safe operation of the power grid. Insulating oil, as a crucial medium within the transformer, serves both insulating and cooling functions. However, with long-term operation, various impurities accumulate in the insulating oil, primarily free carbon particles. These carbon particles are generated due to factors such as insulation material deterioration and oil decomposition. Their presence not only reduces the oil's insulating properties but can also cause serious problems such as discharge, localized overheating, and even equipment failure. Therefore, detection and monitoring technologies for free carbon particles in transformer oil are particularly important. Current technologies mainly employ optical, filtration, and chemical analysis methods for carbon particle detection. However, these methods typically suffer from low detection sensitivity, complex operation, and poor real-time performance, making it difficult to meet the precise monitoring requirements of modern power systems for transformer operating conditions.

[0003] Existing methods for detecting particulate impurities in transformer oil primarily rely on offline detection technology, typically requiring multiple steps such as sampling, transportation, and laboratory analysis—a cumbersome and time-consuming process. Because it is offline, it is difficult to reflect changes in particulate impurities in transformer oil in a timely manner, failing to meet the requirements for real-time monitoring of transformer operating status. Furthermore, existing detection equipment is usually large, expensive, and has high maintenance costs, making it unsuitable for field applications. It also requires professional operation, increasing labor costs and operational risks. Due to the inability to perform real-time online detection, the complexity and time-consuming nature of the detection process, high maintenance costs, and operational complexity, existing technologies are insufficient to meet the need for efficient and accurate monitoring of particulate impurities in transformer oil.

[0004] Prior art document 1 (CN 117213864 A, publication date 2023.12.12) proposes an intelligent detection method and system for lubricating oil metal shavings particles suitable for engines, including: acquiring a primary amplified signal, a secondary amplified signal, and a vibration signal corresponding to a lubricating oil metal shavings particle sensor; performing noise reduction processing; obtaining the metal shavings particle identification result and observing the statistical distribution series of particles in the oil circuit. The oil particle detection method provided in prior art document 1 cannot adjust the oil sample flow rate in real time according to the concentration state of metal shavings particles in the oil, resulting in a large detection error.

[0005] Prior art document 2 (CN 116202915 A, publication date 2023.06.02) discloses a method for testing impurity particles in lubricating oil using ferrography. The method involves collecting an oil sample from an equipment and injecting it into a sample bottle. The sample is then heated in an oven at 65°C until the water content is ≤20ppm. Afterward, the sample is removed and ultrasonically vibrated. Next, the iron content of the oil sample is tested. Based on the iron content, the oil sample is diluted to the appropriate concentration. Then, the kinematic viscosity of the oil sample is tested. Based on the kinematic viscosity, a viscosity dilution ratio is designed to further dilute the oil sample. An analytical ferrography spectrometer is then used to prepare the spectra. Finally, the particles in the spectra are observed using a ferrographic microscope. Initially, observation is performed using a low-power eyepiece. Once the eyepiece field of view is clear, the lens of a digital device is aligned with the image in the eyepiece, and fine adjustments are made to ensure a clear image on the digital device. Then, a high-power eyepiece is used for observation. However, the oil particle detection method provided in prior art document 2 requires laboratory analysis and cannot meet the requirements for real-time monitoring of transformer operating conditions. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an online detection device for carbon particles in transformer oil based on a microelectrode array sensor. This device enables real-time detection of free carbon particles in transformer oil and has the advantages of high detection accuracy, simple operation, and low maintenance costs. It can not only detect free carbon particles in oil in real time but also identify changes in particle concentration, thus having broad application prospects and market value.

[0007] The present invention adopts the following technical solution.

[0008] This invention provides an online detection system for free carbon particles in transformer oil based on a microelectrode array, comprising: a sampling module, a microelectrode array sensor module, a signal processing unit, a communication module, a remote monitoring platform, and an alarm unit;

[0009] The sampling module includes an oil inlet pipe and a filter device. The oil sample is transported from the transformer oil tank to the microelectrode array sensor module through the oil inlet pipe. The filter device is located inside the oil inlet pipe and is equipped with a filter membrane to remove impurities from the oil sample.

[0010] The microelectrode array sensor module includes a microelectrode array sensor, which is a nanoelectrode arranged on a silicon substrate. The surface of the microelectrode array sensor is coated with graphene oxide to adsorb free carbon particles in the oil sample. When the free carbon particles are adsorbed onto the surface of the microelectrode array sensor, an adsorption signal is generated.

[0011] The signal processing unit is used to extract the characteristic frequencies and intensities of carbon particle vibrations in the adsorption signal, and to classify the carbon particles according to their characteristic frequencies and intensities.

[0012] Preferably, the sampling module further includes a servo motor, a micro pump, and a valve; the valve, micro pump, and servo motor are all located outside the electrostatic shielding layer, which covers the outside of the oil inlet pipe; the micro pump is used to drive the oil sample flow, the valve is used to regulate the oil sample flow rate, and the servo motor is used to control the opening of the valve to regulate the oil sample flow rate.

[0013] Preferably, the microelectrode array sensor module further includes a temperature sensor and a pressure sensor; the microelectrode array sensor, temperature sensor, and pressure sensor are all installed inside the oil detection chamber; the oil outlet pipe is connected to the oil detection chamber; and the pressure sensor is connected to the servo motor via a servo motor control line.

[0014] Preferably, the microelectrode array sensor adopts a hexagonal honeycomb arrangement, with nanoelectrodes evenly spaced on a square silicon substrate with a side length of c. From the first row of nanoelectrodes to the row with the largest number of nanoelectrodes, the distance between the leftmost and rightmost nanoelectrodes and the edge of the silicon substrate decreases row by row by a reduction of Δd. The distance between the leftmost and rightmost nanoelectrodes in the row with the largest number of nanoelectrodes and the edge of the silicon substrate is d. max The distance from the leftmost and rightmost nanoelectrodes to the edge of the silicon substrate increases row by row from the row with the largest number of nanoelectrodes to the last row, with an increase of Δd.

[0015] This invention also provides an online detection method for free carbon particles in transformer oil based on a microelectrode array, implemented using the aforementioned online detection system for carbon particles in transformer oil based on a microelectrode array, comprising:

[0016] The oil sample acquired by the sampling module flows into the microelectrode array sensor module through the oil inlet pipe; the pressure and temperature of the oil sample are monitored in real time by the pressure and temperature sensors of the microelectrode array sensor module, and the micro pump and valve of the sampling module are adjusted according to the real-time changes in the pressure and temperature of the oil sample to regulate the flow rate and velocity of the oil sample.

[0017] After adjusting the flow rate and velocity of the oil sample, the microelectrode array sensor generates an adsorption signal when it adsorbs free carbon particles in the oil sample.

[0018] The signal processing unit extracts the characteristic frequencies and intensities of particle vibrations from the adsorption signal and classifies the carbon particles based on their characteristic frequencies and intensities.

[0019] Preferably, the micropump of the sampling module is adjusted according to the real-time changes in the pressure and temperature of the oil sample, and the flow rate of the micropump after adjustment satisfies the following relationship:

[0020]

[0021] In the formula, Q adjust This is the flow rate after adjustment by the micro pump, measured in meters per second (m³).3 / s;K p The first proportionality coefficient has a value of 1.2; P target The target pressure is expressed in MPa; P actual The actual collected oil sample pressure is expressed in MPa; μ(T) is the oil sample viscosity, expressed in Pa·s, determined by temperature T, with a viscosity influence coefficient α of 0.8; ΔP is the pressure change of the oil sample, expressed in MPa, with an influence coefficient β of 0.5; C part C0 is the carbon particle concentration in the oil sample, in mg / L; C0 is the initial carbon particle concentration, in mg / L; γ and δ are the linear and nonlinear influence coefficients of particle concentration on flow rate regulation, respectively; D0 particle K is the particle size of carbon particles, measured in μm; viscosity λ is the coefficient related to viscosity and particle size; λ1 is the adjustment coefficient of particle size on viscosity; ζ is the nonlinear influence coefficient of particle concentration on flow rate regulation.

[0022] Preferably, the valve of the sampling module is adjusted according to the real-time changes in the pressure and temperature of the oil sample, and the adjusted valve opening satisfies the following relationship:

[0023]

[0024] In the formula, α adjust The adjusted valve opening is expressed in degrees Celsius (°). α0 represents the initial valve opening in degrees Celsius (°). γ1 is the coefficient of temperature influencing valve opening, with a value of 0.3. ΔT represents the temperature change in degrees Celsius (°C). γ2 is the coefficient of pressure change influencing valve opening, with a value of 0.4. ΔP represents the pressure change of the oil sample in MPa. part C0 is the initial carbon particle concentration in the oil sample, also in mg / L; β1 is the coefficient of influence of particle concentration on valve opening, with a value of 0.5; η is the nonlinear response coefficient of particle concentration, with a value of 1.2. adjust The flow rate of the micro pump after adjustment, in meters. 3 / s, Q max Maximum flow rate, unit: m³ 3 / s refers to the flow rate when the valve opening is adjusted to its maximum; ζ v D is the coefficient representing the secondary influence of flow rate on valve opening. particle α1 represents the particle size of the carbon particles, in μm; α2 is the coefficient of influence of particle size on valve opening; γ3 is the response coefficient of particle concentration to valve opening; and k is the adjustment coefficient of the Sigmoid function.

[0025] Preferably, adjusting the flow rate and volume of the oil sample further includes:

[0026] By combining PID control and machine learning algorithms for adjustment, the control output satisfies the following relationship:

[0027]

[0028] Where u(t) is the control output, the adjustment amount of the micro pump flow rate or valve opening; e(t) is the error signal, the difference between the target pressure and the actual pressure; t is the oil sample detection time; K c The second proportionality coefficient has a value of 0.8; K i K is the integral coefficient, with a value of 0.5. d ω is the differential coefficient, with a value of 0.3; ω1 is the adjustment coefficient of flow rate to PID feedback; Q actual Actual flow rate, unit: m 3 / s, Q max Maximum flow rate, unit: m³ 3 / s; ω2 is the adjustment coefficient of particle concentration for PID feedback; C part C0 represents the initial carbon particle concentration in the oil sample, in mg / L; μ is the nonlinear response coefficient of particle concentration to detection sensitivity.

[0029] Preferably, the signal processing unit uses Fourier transform and wavelet transform algorithms to analyze and process the adsorption signal, converting the time-domain signal detected by the microelectrode array sensor into a frequency-domain signal, extracting the characteristic frequency and intensity of the free carbon particles. The characteristic frequency includes the resonant frequency offset of the carbon particles, and the intensity includes the resonant signal attenuation intensity of the carbon particles. The wavelet transform algorithm is used to distinguish carbon particles of different sizes, and the SVM algorithm is used to classify the data and determine the carbon particle content in the oil sample.

[0030] Preferably, the signal processing unit classifies carbon particles based on their characteristic frequencies and intensities, including:

[0031] The carbon particles are classified according to the resonant frequency offset Δf of the adsorption signal and the attenuation intensity I of the resonant signal. When 0 < Δf ≤ 10 kHz and I ≤ 20 dB, the carbon particles are classified as low-concentration small particles; when 0 < Δf ≤ 10 kHz and I > 20 dB, the carbon particles are classified as high-concentration small particles; and when Δf > 10 kHz, the carbon particles are classified as large-size conductive particles.

[0032] The beneficial effects of this invention are that, compared with the prior art,

[0033] 1. This invention employs a microelectrode array sensor. Through a nanoelectrode array coated with graphene oxide, it can efficiently detect free carbon particles in transformer oil. Graphene oxide possesses excellent conductivity and a large specific surface area, enhancing the sensor's adsorption and reaction efficiency for carbon particles. The sensor monitors the adsorption of carbon particles and changes in current through electrochemical reactions, exhibiting high sensitivity and the ability to detect low concentrations of carbon particles. The high surface area and fine structure of the nanoelectrodes ensure high-resolution detection performance, accurately capturing minute signal changes. Advanced signal amplification and processing technologies further improve detection accuracy and stability, providing real-time and accurate carbon particle concentration data. A biomimetic nanocoating is applied to the surface of the nanoelectrodes. By constructing a nanoscale rough structure and low surface energy material, superoleophobic properties are achieved, thereby preventing carbon particle deposition on the electrode surface.

[0034] 2. In the process of oil sample collection, this invention comprehensively considers oil sample pressure, viscosity, particle concentration and particle size. By adjusting the opening of the micro pump and valve, the flow rate and flow rate of the oil sample are dynamically adjusted, ensuring the flow stability of the oil sample under different conditions and avoiding particle deposition or uneven flow. In particular, it maintains high accuracy in the detection of small-diameter carbon particles.

[0035] 3. The signal processing unit in this invention integrates advanced signal processing algorithms such as Fourier transform and wavelet transform, which can efficiently process the detection signal, filter out noise and extract key signal features, thus ensuring the accuracy of the detection results.

[0036] 4. The device of the present invention has a compact structure and is easy to install. It is suitable for on-site online testing of transformers, reduces maintenance costs, and simplifies the operation process. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the online detection system for carbon particles in transformer oil based on a microelectrode array in this invention;

[0038] Figure 2 This is a schematic diagram of the microelectrode array in this invention;

[0039] Figure 3 This is a schematic diagram illustrating the working principle of the sampling module and the microelectrode array sensor module in this invention;

[0040] Figure 4 This is a schematic diagram of the signal processing unit and communication system in this invention;

[0041] The main markings in the attached figures are as follows:

[0042] 1-Silicon substrate; 2-Nano electrode; 21-Papillary structure; 3-Filtration device; 31-Filtration membrane; 4-Oil inlet pipe; 5-Oil outlet pipe; 6-Oil detection chamber; 7-Microelectrode array sensor; 8-Electrostatic shielding layer; 9-Pressure sensor; 10-Valve; 11-Servo motor; 12-Servo motor control line; 13-Pressure signal processor; 14-Micro pump. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0044] The present invention provides an online detection system for carbon particles in transformer oil based on a microelectrode array, characterized in that the device includes: a sampling module, a microelectrode array sensor module, a signal processing unit, a communication module, and an alarm unit.

[0045] The sampling module includes an oil inlet pipe 4, a valve 10, a micro pump 14, a servo motor 11, a servo motor control line 12, and a filter device 3. The oil inlet pipe 4 is covered with an electrostatic shielding layer 8. The valve 10, the micro pump 14, and the servo motor 11 are located outside the electrostatic shielding layer 8. The servo motor 11 is connected to the pressure signal processor 13 through the servo motor control line 12. The servo motor 11 is used to control the valve 10 to adjust the oil sample flow rate. The filter device 3 is located inside the oil inlet pipe 4, and the filter membrane 31 is located inside the filter device 3 to remove impurities from the oil sample.

[0046] The micropump 14 provides a stable flow rate ranging from 0.1 to 1 L / min, ensuring that the oil sample is uniformly transferred to the subsequent detection module. The valve 10 regulates the oil sample flow rate, typically controlling it between 1 and 5 mL / min to ensure flow stability and prevent particle deposition or uneven flow. The micropump 14 and valve 10 each perform different functions and work together to ensure a stable delivery of the oil sample to the sensor module for detection.

[0047] Before entering the detection device, the oil sample is filtered by filter 3 to ensure detection accuracy. To prevent carbon particles from adhering to the sampling pipe due to electrostatic attraction during sampling, the system integrates an electrostatic shielding layer 8 to ensure that the carbon particles remain suspended during flow. The sampling module controls the oil sample flow rate through a micro pump 14 and a valve 10, maintaining the flow rate between 1 and 5 ml / min to ensure the stability of the oil sample flow rate and avoid particle deposition or uneven flow. The system also monitors the temperature and pressure of the oil sample in real time. The temperature range is set from 40℃ to 80℃, and the cooling device is activated when the temperature exceeds 85℃. The pressure range is set from 0.5 to 2 MPa. If the pressure fluctuation is greater than ±0.1 MPa, the system automatically adjusts the micro pump 14 and valve 10 to ensure that the temperature and pressure of the oil sample remain stable, avoiding affecting the properties and distribution of particles.

[0048] In this invention, several improvements have been made to the flow rate regulation algorithm of the micropump 14 to improve the detection accuracy of small-diameter carbon particles. Small-diameter carbon particles are easily affected by flow rate; excessively high flow rates may lead to particle aggregation, while excessively low flow rates may lead to particle deposition. Therefore, precise flow rate regulation is crucial for the detection of small-diameter carbon particles. Smaller particles typically tend to deposit easily at low flow rates and tend to aggregate at high flow rates. To prevent this phenomenon, the flow rate regulation algorithm incorporates a term incorporating the influence of particle size on flow resistance. Furthermore, this invention provides an improved flow rate regulation algorithm for the micropump 14, which dynamically adjusts the oil sample flow rate by comprehensively considering factors such as oil sample pressure, viscosity, particle concentration, and particle size, thereby improving the accuracy and stability of particle detection in the oil of the microelectrode array. The flow rate regulation algorithm formula is as follows:

[0049]

[0050] Among them: Q adjust This is the flow rate after adjustment by the micro pump 14, in cubic meters per second (m³). 3 / s;K p The first proportionality coefficient was experimentally calibrated to a value of 1.2; P target Target pressure, unit is MPa; P actual The actual collected oil sample pressure is expressed in MPa; μ(T) is the oil sample viscosity, expressed in Pa·s, determined by temperature T, with its viscosity influence coefficient α experimentally calibrated to 0.8; ΔP is the pressure change of the oil sample, which is the difference between the actual collected oil sample pressure and the initial oil sample pressure, expressed in MPa, with its influence coefficient β experimentally calibrated to 0.5; G part C0 is the carbon particle concentration in the oil sample, in mg / L; C0 is the initial carbon particle concentration, in mg / L; γ and δ are the linear and nonlinear influence coefficients of particle concentration on flow rate regulation, respectively, and their values ​​were experimentally calibrated to be 1.0 and 1.5, respectively; Dparticle K represents the particle size of the carbon particles, measured in μm, and its particle size influence coefficient δ is experimentally calibrated to be 0.6. viscosity λ is the coefficient related to viscosity and particle size, and its value is 0.9 after experimental calibration; λ1 is the adjustment coefficient of particle size on viscosity, and its value is 0.4 after experimental calibration; ζ is the nonlinear influence coefficient of particle concentration on flow rate regulation, and its specific value is 0.075 after experimental calibration.

[0051] Target pressure P target The system's preset ideal oil sample pressure is the actual pressure P. actual Data is collected in real time by pressure sensor 9. (P in the formula) target -P actual The pressure difference () represents the difference between the target pressure and the actual pressure, reflecting the deviation in the oil sample's flow state. The system adjusts the flow rate of the micro-pump 14 based on this pressure difference. The first proportionality coefficient K... p The influence of pressure difference on flow rate is adjusted. Viscosity μ(T) changes with temperature; the viscosity of the oil sample decreases as temperature increases, increasing its fluidity and thus affecting the flow rate. The effect of viscosity on flow rate is adjusted by the coefficient α to ensure stable flow of the oil sample at different temperatures. The pressure change ΔP reflects the instability of the oil sample flow; the effect of pressure fluctuations on flow rate regulation is adjusted by the coefficient β.

[0052] Concentration of carbon particles C in oil sample part Increasing particle concentration increases fluid viscosity, thus affecting flow rate. The effect of particle concentration on flow rate is non-linear, and an exponential relationship is used for modeling, with coefficients γ and δ adjusting for the linear and non-linear effects of concentration on flow rate, respectively. For small-diameter carbon particles, their flowability is more easily affected by changes in flow velocity. The D in the formula... particle The nonlinear effect of particle size on flow rate was considered. Smaller particles are easily affected by changes in flow velocity; both excessively high and low flow velocities can affect particle distribution and detection accuracy. By adjusting δ, the system can optimize the flow velocity, ensuring that small particles are not aggregated due to excessively high flow velocities.

[0053] First, the system calculates the flow rate adjustment range based on the difference between the target pressure and the actual pressure. The first proportionality coefficient K... p This indicates the degree of response of flow rate regulation to pressure difference. When the difference between the target pressure and the actual pressure is large, the system adjusts the flow rate via micro-pump 14 to ensure that the oil sample flow rate is consistent with the target flow rate. Next, the flow rate regulation also considers the effect of oil sample temperature on fluidity. Through μ(T) in the formula, the system can consider the impact of temperature changes on flow rate in real time, thereby avoiding flow rate instability caused by temperature fluctuations.

[0054] Furthermore, the concentration of carbon particles in the oil sample directly affects its flowability. Increased particle concentration increases the flow resistance of the oil sample, thereby affecting the stability of flow velocity and flow rate. Therefore, the system incorporates a nonlinear effect term related to particle concentration. When the particle concentration is high, the system compensates for the increased flow resistance by reducing the flow rate. The effect of particle size is also taken into account, especially for small-diameter carbon particles, which are very sensitive to changes in flow velocity. This is achieved by introducing particle size... The system can prevent excessive flow rates from causing particle aggregation, ensuring a uniform distribution of small particles.

[0055] Therefore, the entire flow regulation process considers multiple factors: pressure difference, oil sample temperature, particle concentration, and particle size. The system collects real-time data on pressure, temperature, particle concentration, and particle size, substitutes these factors into a formula to calculate the flow regulation range, and precisely adjusts the flow rate by controlling the rotational speed of the micro-pump 14 via a stepper motor. This regulation ensures the flow stability of the oil sample under different conditions, avoids particle deposition or uneven flow, and maintains high accuracy, especially in the detection of small-diameter carbon particles.

[0056] To precisely control the oil sample flow rate, this invention proposes an improved valve opening adjustment formula. This formula comprehensively considers the effects of multiple factors such as temperature, pressure, particle concentration, flow rate, and particle size, dynamically adjusting the valve opening to ensure a stable oil sample flow rate. The formula is:

[0057]

[0058] Where: α adjust Let α0 be the initial valve opening (°C) after adjustment, γ1 be the influence coefficient of temperature on valve opening (0.3, calibrated experimentally), and ΔT be the temperature change (°C). Let γ2 be the influence coefficient of pressure change on valve opening (0.4, calibrated experimentally), and ΔP be the pressure change of the oil sample (MPa). part Q represents the carbon particle concentration in the oil sample (unit: mg / L), C0 represents the initial concentration (unit: mg / L), β1 represents the influence coefficient of particle concentration on the opening degree of valve 10, which is experimentally calibrated to a value of 0.5, and η represents the nonlinear response coefficient of particle concentration, which is experimentally calibrated to a value of 1.2. adjust The adjusted flow rate of micro pump 14 (unit: m³) 3 / s), Q max Maximum flow rate (unit: m³) 3 / s) refers to the flow rate when the valve opening is adjusted to its maximum at 10 degrees; ζ vThe value is the secondary influence coefficient of flow rate on valve opening degree 10. For linear valves, the value is set to 0.5, and for nonlinear valves, the value is set to 2.0. (D) particle α1 represents the particle size of the carbon particles (unit: μm), α2 is the influence coefficient of particle size on the opening degree of valve 10, which is experimentally calibrated to a value of 0.6. γ3 is the response coefficient of particle concentration to the opening degree of valve 10, which is experimentally calibrated to a value of 0.7, and k is the adjustment coefficient of the Sigmoid function, which is experimentally calibrated to a value of 0.9.

[0059] This formula introduces the Sigmoid function. This makes the effect of particle concentration smoother, avoiding drastic fluctuations in valve 10 opening when concentration changes are too large. Furthermore, the formula also considers the secondary effect of flow rate on valve 10 opening. By adjusting the relationship between the flow rate and the valve opening, the stability of the oil sample flow rate is ensured.

[0060] When adjusting the valve opening to 10 degrees, the system first collects the oil sample's temperature change ΔT, pressure fluctuation ΔP, and particle concentration C in real time. part Traffic Q adjust and particle size D particle Temperature changes affect the viscosity of the oil sample, which in turn affects the flow rate. The system responds to temperature changes via γ1·ΔT, adjusting the valve opening 10 accordingly to minimize the impact of temperature fluctuations on the flow rate. For example, when the oil sample temperature increases, the viscosity decreases, the flow rate increases, and the valve opening 10 needs to be appropriately reduced. Simultaneously, the system monitors the impact of pressure changes ΔP on the oil sample flow rate. When pressure fluctuations are significant, the valve opening 10 will be appropriately increased to ensure stable flow and prevent uneven particle distribution.

[0061] Particle concentration C part Increasing this will reduce the fluidity of the oil sample, requiring a reduction in the valve opening (10). This is achieved through the formula... With the addition of the sigmoid function term, the system can precisely adjust the opening of valve 10 to ensure that the impact of particle concentration changes on the flow rate is effectively controlled. For example, when the particle concentration is high, oil sample flow becomes more difficult, and the opening of valve 10 will be appropriately reduced to maintain a stable flow rate.

[0062] Traffic Q adjust Changes in this directly affect the flow rate of the oil sample, and the system uses... The system monitors changes in flow rate and adjusts the valve opening (10). When the flow rate is too high, the oil sample velocity increases, which may cause particles to not be evenly distributed. In this case, the valve opening (10) will decrease to ensure a moderate flow rate and prevent particles from flowing too fast or too slow.

[0063] In addition, particle size D particleThe impact on valve opening degree 10 is also significant. Small-diameter carbon particles are particularly susceptible to changes in flow rate; excessively high flow rates may lead to particle aggregation, while excessively low flow rates may result in particle deposition. The system utilizes α²·D in the formula... particle Adjust the valve opening by 10 degrees to ensure uniform distribution and stable flow of small particles.

[0064] During the valve 10 opening adjustment process, the system collects data such as oil sample flow rate, temperature, pressure, particle concentration, and particle size in real time, and substitutes this data into the above formula for calculation. Then, the system precisely adjusts the valve 10 opening through the servo motor 11 to ensure stable oil sample flow rate and avoid particle deposition or aggregation, especially when detecting small-diameter carbon particles, ensuring high-precision detection.

[0065] To improve the system's response speed and detection accuracy, and to address the impact of various factors such as particle concentration, flow rate, and pressure fluctuations, this invention combines PID control and machine learning optimization algorithms to further optimize the flow rate and valve opening adjustment. PID control adjusts the rotation of the servo motor 11 through real-time feedback, thereby regulating the flow rate of the micro-pump 14 and the valve opening to achieve optimal system response.

[0066]

[0067] Where: u(t) is the control output, representing the adjustment amount of the flow rate of the micro pump 14 or the opening degree of the valve 10; e(t) is the error signal, i.e., the difference between the target pressure and the actual pressure; K c The second proportionality coefficient, calibrated experimentally to a value of 0.8; K i K is the integral coefficient, and its value was experimentally calibrated to be 0.5. d ω is the differential coefficient, whose value was experimentally calibrated to 0.3; ω1 is the adjustment coefficient of flow rate to PID feedback, whose value was experimentally calibrated to 0.7; Q actual Actual flow rate (unit: m³) 3 / s), Q max Maximum flow rate (unit: m³) 3 / s); ω2 is the adjustment coefficient of particle concentration for PID feedback, which is experimentally calibrated to be 0.9; C part C0 represents the carbon particle concentration in the oil sample (mg / L), C0 represents the initial concentration (mg / L), and μ represents the nonlinear response coefficient of particle concentration to detection sensitivity, which was calibrated to 1.3 through experiments.

[0068] This formula, by combining PID control principles with feedback mechanisms for flow rate and particle concentration, achieves precise regulation of the flow rate of the micro pump 14 and the opening degree of the valve 10. During system operation, the target pressure P is first collected in real time. target and actual pressure Pactual The error signal e(t) is calculated. The error signal e(t) is used to generate the proportional term, integral term, and differential term, respectively, through the coefficient K. c K i and K d Adjustment. The proportional term is used to quickly respond to the current error, the integral term is used to eliminate long-term steady-state errors, and the derivative term is used to predict the trend of error changes, reducing system overshoot and oscillations.

[0069] To further optimize the system response, feedback terms for flow rate and particle concentration are introduced into the formula. Flow rate feedback term. Used to control the secondary impact of flow rate on system response, ensuring system stability even with flow rate changes. Particle concentration feedback term. This is used to adjust the effect of particle concentration on the system, and the nonlinear response coefficient μ makes the effect of particle concentration changes on the control quantity smoother.

[0070] During implementation, the system calculates the control quantity u(t) based on real-time collected target pressure, actual pressure, flow rate, and particle concentration data, using the formula described above. The servo motor 11 then precisely adjusts the flow rate of the micro-pump 14 and the opening of the valve 10. For example, when the target pressure differs significantly from the actual pressure, the error signal e(t) increases, and the system quickly adjusts the control quantity using a proportional term to reduce the error. When the particle concentration increases, the system... The control volume is dynamically adjusted to ensure that the oil sample's fluidity is not affected. Additionally, the flow rate feedback item... It can appropriately reduce the control quantity when the flow rate is too high, so as to avoid uneven particle distribution caused by excessively fast flow rate.

[0071] like Figure 2 As shown, the microelectrode array sensor 7 adopts a hexagonal honeycomb arrangement, with nano-sized electrodes 2 evenly spaced on the silicon substrate 1. Specifically, the diameter of the nanoelectrode 2 is 50 nm, and the spacing between the electrodes 2 is precisely controlled within the range of 50–100 nm, forming a high-density, low-interference detection network.

[0072] In this embodiment, the microelectrode array sensor 7 adopts a hexagonal honeycomb arrangement, with the nanoelectrodes 2 evenly spaced on a square silicon substrate 1 with a side length of c. From the first row of nanoelectrodes 2 to the row with the largest number of nanoelectrodes 2, the distance between the leftmost and rightmost nanoelectrodes 2 and the edge of the silicon substrate 1 decreases row by row, with a reduction of Δd, where Δd = 50 mm. The distance between the leftmost and rightmost nanoelectrodes 2 in the row with the largest number of nanoelectrodes 2 and the edge of the silicon substrate 1 is d. max =150mm; the number of nanoelectrodes 2 increases from the row with the largest number to the last row, and the distance between the leftmost and rightmost nanoelectrodes 2 and the edge of the silicon substrate 1 increases row by row, with an increase of Δd; the row spacing of the nanoelectrodes in the vertical direction is 86.6nm;

[0073] The first row contains N nanoelectrodes 2, each with a diameter of 50 nm and a center-to-center distance of 100 nm. The distances d1 between the leftmost and rightmost nanoelectrodes 2 in the first row and the edge of the silicon substrate 1 are both d1. The second row contains N+1 nanoelectrodes 2; the third row contains N+2 nanoelectrodes 2; this number increases sequentially until the row with the largest number of nanoelectrodes 2 is reached, then decreases sequentially until the nanoelectrodes 2 are arranged in a regular hexagonal pattern. This hexagonal honeycomb arrangement not only maximizes the utilization of the silicon substrate 1 surface area but also ensures the uniformity of the electric field distribution, significantly improving the capture efficiency and detection sensitivity of free carbon particles in transformer oil. The sensor surface is preferably coated with a nanometer-thick layer of graphene oxide. Due to its unique two-dimensional structure and high specific surface area, graphene oxide can efficiently capture free carbon particles in the oil sample through π-π interactions and electrostatic adsorption mechanisms. Furthermore, the excellent conductivity of graphene oxide further enhances the electrode's response to changes in conductivity after carbon particle adsorption. Platinum is preferably used as the sensor electrode material. Platinum has good conductivity, chemical stability, and corrosion resistance, enabling it to operate stably for a long time in the high-temperature and high-humidity environment of transformer oil, ensuring the reliability and service life of the detection device.

[0074] To further improve the performance of microelectrode arrays, particularly to prevent carbon particle deposition on the electrode surface and enhance the long-term stability of detection, this invention proposes a biomimetic nanocoating design. The biomimetic coating design is inspired by the microstructure of a lotus leaf surface, whose superhydrophobic properties allow water droplets to form spheres and easily roll off. A similar principle can be applied to the microelectrode surface; by constructing a nanoscale rough structure and low surface energy materials, superoleophobic properties are achieved, thereby preventing carbon particle deposition on the electrode surface.

[0075] The surface microstructure of the coating employs a nanoscale columnar or conical array, similar to the papillary structure on the surface of a lotus leaf 21. This structure significantly increases surface roughness, thereby enhancing oleophobic properties. The specific formula is as follows:

[0076] θ CB =arccos(r·cosθ0+f-1)

[0077] Where, θ CB Let θ be the apparent contact angle (Cassie-Baxter model), r be the roughness factor, θ0 be the inherent contact angle of the material, and f be the liquid-solid contact area fraction. This is to achieve the apparent contact angle θ using the Cassie-Baxter model. CBTo achieve superoleophobic properties by approaching or exceeding 150°, a systematic optimization of the roughness factor r and contact area fraction f is required. Constructing a nanoscale columnar array to increase surface roughness, with a diameter of 50-100 nm and a height of 200-500 nm, can significantly improve the r value. The surface material of the coating should be a low surface energy substance, such as polytetrafluoroethylene (PTFE) or a fluoropolymer with a surface energy below 20 mJ / m². 2 This material can further reduce the adhesion between carbon particles and the electrode surface, and can also significantly reduce the liquid-solid contact area fraction f. The oleophobic properties of the coating were evaluated using a contact angle meter and a roll-off angle tester. The target value was the contact angle θ. CB >150, roll angle <5°. The specific formula is as follows:

[0078] cosθ CB =φ·cosθ0+(φ-1)

[0079] Where φ is the solid-liquid contact area fraction.

[0080] Liquid-solid contact area fraction f: describes the proportion of the contact area between the liquid and the solid surface from the perspective of the liquid.

[0081] Solid-liquid contact area fraction φ: describes the proportion of the contact area between the solid and the liquid surface from the perspective of the solid.

[0082] The liquid-solid contact area fraction *f* focuses more on the contact between the liquid and the solid, while the solid-liquid contact area fraction *φ* focuses more on the contact between the solid and the liquid. Strictly speaking, they are essentially the same, both used to describe the proportion of contact area between a liquid and a solid surface. In practical applications, *f* and *φ* can be used interchangeably, both aiming to enhance hydrophobic properties by optimizing surface microstructure and material properties to reduce direct contact between the liquid and the solid surface.

[0083] The liquid-solid contact area fraction f or solid-liquid contact area fraction φ can be derived by combining the Cassie-Baxter model. First, the apparent contact angle θ of the coating is measured using a contact angle meter. CB The inherent contact angle of polytetrafluoroethylene (PTFE) is approximately 110°–120°, while the inherent contact angle of fluoropolymers is typically between 90° and 120°, depending on the surface treatment methods of the materials produced by different manufacturers. Based on this, the specific values ​​of the liquid-solid contact area fraction f or solid-liquid contact area fraction φ are calculated using formulas from the Cassie-Baxter model. The specific formulas are as follows:

[0084]

[0085] θ airLet θ be the contact angle between the liquid and air, with a value of 180°, therefore cosθ air =-1. θ Y θ is the material's inherent contact angle, which is the contact angle of the material on an ideal smooth plane, also known as the Young contact angle. CB It is the apparent contact angle, which is the contact angle obtained directly through experiments. It represents the contact angle of the liquid on the actual surface of the coating (which has a micro-rough structure).

[0086] Furthermore, to improve the mechanical stability and durability of the coating, a multi-layer composite structure design is adopted: the bottom layer is an adhesion layer with good bonding to the electrode substrate material (such as silicon or platinum), such as silica or alumina. The middle layer is a functional layer with anti-corrosion and insulating properties, such as graphene oxide. The top layer is an oleophobic layer with low surface energy, such as polytetrafluoroethylene (PTFE) or a fluoropolymer. The signal processing unit integrates a microprocessor and efficient signal processing algorithms, enabling rapid processing and analysis of sensor data to accurately determine the carbon particle concentration in the oil sample. The communication module uses wireless transmission technology to send the processed data to a remote monitoring center in real time. The alarm unit is used to issue an alarm when the carbon particle concentration exceeds the standard, ensuring that operators can respond promptly.

[0087] like Figure 4 As shown, the signal processing unit uses Fourier transform and wavelet transform algorithms to analyze and process the electrical signals in the oil sample. Fourier transform can convert the time-domain signal detected by the sensor into a frequency-domain signal, extracting the characteristic frequencies related to free carbon particles. The specific formula is as follows:

[0088]

[0089] In the formula, X(f) represents the frequency domain characteristics of carbon impurity particles in transformer oil. It describes the periodic changes caused by the vibration of carbon particles in the oil or other physical processes (such as flow and collision), especially the response intensity at a specific frequency. x(t) is the time-varying signal acquired by the sensor, representing the instantaneous signal detected by the sensor (such as an optical or electrical sensor) when carbon particles move, settle, or distribute in the oil. f1 is the frequency, representing the vibration frequency of carbon impurity particles or the rate of other periodic phenomena in transformer oil. This represents a complex exponential function used to convert a time-domain signal to the frequency domain, suitable for analyzing the vibration modes or dynamic behavior of carbon impurity particles. t represents time, indicating the real-time signal recorded by the sensor over time, reflecting the temporal variation of particle behavior.

[0090] It is important to clarify that the signal processing unit does not directly monitor the vibration of carbon particles in the oil. Instead, it analyzes the electrochemical response after particle adsorption via a microelectrode array sensor module. When free carbon particles adsorb onto the surface of the microelectrode array, the electrochemical signals caused by the particles' physical properties and motion behavior are captured by the sensor. These signals contain characteristic information left behind by the particles' movement, vibration, and collisions in the oil before adsorption. Through Fourier transform, the signal processing unit extracts the characteristic frequencies and intensities of particle motion and vibration from the adsorption signals, achieving indirect monitoring of the particles' dynamic behavior.

[0091] Wavelet transform further refines the features in the signal, enabling the differentiation of carbon particles of different sizes. Its formula is:

[0092]

[0093] In the formula, W x (a,b) represents the wavelet coefficients, indicating the characteristics of carbon impurity particles in transformer oil at different scales a and time positions b. It is used to capture the local characteristics of particles changing over time. x(t) is the sensor signal, describing the instantaneous behavior of the carbon particles. The signal processing unit monitors the motion of carbon particles in the oil using optical or electrical sensors: when carbon particles move in the oil, they generate weak disturbance signals along their path, such as changes in light scattering intensity caused by flow, electric field disturbances, or small current fluctuations. The sensors capture these changes and record their position and velocity characteristics. As carbon particles gradually settle to the electrode surface, the sensors reflect the settling rate by monitoring changes in signal intensity during the deposition process. Furthermore, the sensor array can collect signals from different locations, and the distribution of carbon particles within the detection area can be further determined by analyzing the signal distribution characteristics. The signal processing unit inputs these real-time acquired sensor signals into a wavelet transform model, extracting the frequency and time characteristics of the signals through wavelet transform to analyze the specific behavior of the carbon particles.

[0094] ψ a,b (t) represents the wavelet basis function, used to decompose the sensor signal at different scales a (frequency resolution) and displacements b (time position), highlighting the local changes of carbon particles. 'a' represents the scale, indicating the ability to resolve different frequency components of carbon impurity particles. Larger scales are used for low-frequency components, suitable for capturing the slow movement or settling characteristics of larger-diameter carbon particles; smaller scales are used for high-frequency components, suitable for capturing the rapid vibration or drastic changes of smaller-diameter carbon particles. This frequency resolution depends on the influence of the carbon particle size and motion state on the signal frequency characteristics. 'b' represents the time displacement, indicating a local time interval in the signal, used to capture the instantaneous changes of carbon particles in the oil. 't' represents the time, indicating the sensor sampling moment.

[0095] In addition, the signal processing unit also integrates a support vector machine (SVM) algorithm, which determines the type of carbon particles in the oil sample through quantitative analysis of characteristic frequency and intensity parameters.

[0096] The SVM algorithm trains on the collected data to establish an optimal classification model based on frequency domain features. It extracts the resonant frequency offset Δf and the corresponding amplitude intensity I from the sensor's resonant frequency response curve as key classification parameters. Specifically:

[0097] Δf: represents the resonant frequency shift caused by carbon particles adsorbing onto the sensor surface. Its value is related to the equivalent particle size and dielectric constant of the particles (larger or more conductive particles result in a more significant Δf shift); I: represents the resonant signal attenuation intensity (dB) at the characteristic frequency point, reflecting the particle concentration level (higher concentrations lead to more severe signal attenuation).

[0098] A two-dimensional feature space is constructed based on historical data, and the following category boundaries are determined through experimental statistical distribution:

[0099] Category A (low concentration small particles): 0 < Δf ≤ 10 kHz, I ≤ 20 dB (corresponding to non-conductive particles with a diameter d ≤ 5 μm, such as graphite fragments);

[0100] Category B (high-concentration small particles): 0 < Δf ≤ 10 kHz, I > 20 dB (e.g., high-density submicron carbon black agglomerates);

[0101] Category C (large conductive particles): Δf > 10 kHz, ignoring the I range (e.g., metallized carbon fibers, d > 20 μm).

[0102] The SVM algorithm trains an optimal classification model using collected data. The model construction process includes the following steps: First, feature extraction is performed on historical detection data to identify key features affecting the classification results. Then, based on the feature dataset, the classifier is optimized during training by selecting an appropriate kernel function (such as the radial basis function (RBF) or a linear kernel function) and setting suitable parameters (such as the penalty parameter C and the kernel function parameters) to obtain the optimal classification hyperplane or decision boundary. After normalizing Δf and I of the historical dataset, the optimal parameter combination C = 1.5 and γ = 0.8 is determined through grid search and 5-fold cross-validation, enabling the model to achieve a classification accuracy of >95% on the test set. Specific optimizations in this process include adjusting the model using methods such as cross-validation to ensure high classification accuracy and a low misclassification rate. The formula for the classification model is:

[0103]

[0104] In the formula, f(x) represents the category (e.g., particle type or concentration level) of the carbon impurity particles detected by the sensor. The result of this function reflects the classification result corresponding to the particle characteristics (such as particle size, number, and type) in the oil. sign is the sign function, whose output is positive or negative, corresponding to different categories. x=[Δf,I] T K(x) represents the standardized feature vector of the input sample. i ,x)=exp(-γ||x i -x|| 2 ) is the RBF kernel function. When the output f(x) = +1, it can be determined that the carbon particles belong to category B or C (high-risk particles that need to trigger an early warning). When the output f(x) = -1, it can be determined that the carbon particles belong to category A (normal wear particles).

[0105] The classification is based on the relationship between the data features collected by the sensor and the decision boundary in the classification model. The sign function gives the final classification result based on the result position of the input data point (relative to the positive or negative side of the classification boundary). For a weighted sum, α represents a weighted summation over the support vectors. i and y i These represent the weights and class labels of the support vectors, respectively. Support vectors specifically refer to particular sample points located near the classification boundary during the training phase. These sample points play a crucial role in the position and shape of the classification boundary; therefore, during the model prediction phase, these support vectors influence the classification result of new detection data through weighted summation. It reflects the similarity between sensor data and training samples (i.e., historical detection data) and is used to determine the classification of new detection data. The classification boundary is the decision hyperplane or boundary learned by the SVM algorithm from the training data; it is used to distinguish data points of different categories. In this invention, the classification boundary is obtained by optimizing the classification error function in the SVM model and defined based on the characteristics of historical data. K(x) i (x, ) is the kernel function that calculates the relationship between the input sample x and the support vector x. iThe similarity between the data is used to classify new carbon particle detection data using a Support Vector Machine (SVM). The SVM, as the core algorithm of this invention, is mainly used for classifying carbon particle data. Specifically, the SVM finds key support vectors during training and builds a classification model to predict new data. ω represents the bias term, adjusting the classification boundary to ensure the accuracy of the classifier's output. The classifier is a key component in this invention, used to classify carbon particles based on sensor-collected data. Specifically, the classifier is based on an SVM model, combining features from the training data to classify carbon particles in the oil sample. The bias term ω is optimized by minimizing the classification error function during training. By adjusting the value of the bias term ω, the specific position of the classification boundary can be adjusted, thereby optimizing the accuracy of the classification results. This optimization can effectively fine-tune the position of the decision boundary, maximizing classification accuracy in the training sample space and reducing the possibility of misclassification. The decision boundary is the boundary used in the SVM to distinguish different categories of data points. In this invention, the decision boundary is obtained through learning from the training data and is typically represented as a hyperplane in an SVM model, which divides the data space into different regions. This boundary is optimized by adjusting the bias term ω and the weights of the support vectors in the model to better distinguish the concentration and type of carbon particles. The optimization of the bias term is achieved by minimizing the classification error function during training, ensuring that the classifier's decision boundary accurately classifies carbon particle categories in the high-dimensional feature space.

[0106] The communication module adopts the DTM-100 wireless data transmission module, which supports 4G and NB-IoT communication protocols. It can transmit the processed detection data to the remote monitoring platform in real time, ensuring the timeliness and reliability of data transmission.

[0107] This invention also provides a method for detecting free carbon particles in transformer oil based on a microelectrode array. The method is the same as the aforementioned method for detecting free carbon particles in transformer oil based on a microelectrode array, comprising:

[0108] Oil samples flow into the microelectrode array sensor module through the sampling module. The sensor module adsorbs free carbon particles in the oil through its graphene oxide coating. When carbon particles adhere to the electrode surface, they cause changes in conductivity. These changes are analyzed by a signal processing algorithm in the microprocessor, ultimately outputting carbon particle concentration information. The processed data is transmitted to a remote monitoring platform via the communication module for real-time monitoring. When the concentration of carbon particles in the oil exceeds a predetermined threshold, the alarm unit automatically issues an alarm, prompting personnel to perform maintenance operations.

[0109] Example 1

[0110] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0111] This embodiment 1 provides a specific implementation of an online detection device for carbon particles in transformer oil based on a microelectrode array. This device can effectively achieve real-time online monitoring of free carbon particles in transformer oil and has advantages such as high precision, low cost, and easy maintenance. It is preferably suitable for transformer condition monitoring and fault early warning.

[0112] like Figure 1 As shown, Embodiment 1 of the present invention provides an online detection system for carbon particles in transformer oil based on a microelectrode array, specifically including a sampling device, a microelectrode array sensor module, a signal processing unit, a communication module, and an alarm unit.

[0113] The sampling device includes an oil sampling port located in the transformer oil system, which is connected to the microelectrode array sensor 7 via an oil inlet pipe 4. Specifically, the oil inlet pipe 4 is preferably made of oil-resistant material to ensure that the oil sample flows into the sensor without contamination. An electrostatic eliminator and a coarse filter are integrated into the oil inlet pipe 4 to prevent impurities and charges from interfering with the accuracy of the detection. The flow control system of the sampling device includes a flow meter, enabling real-time monitoring of the oil sample flow rate and ensuring flow stability.

[0114] like Figure 2 As shown, the microelectrode array sensor module preferably employs nanoscale electrode technology. Each nanoelectrode 2 has a diameter of 50 nanometers and a spacing of 50-100 nanometers, with its surface coated with graphene oxide. The electrodes are arranged on a sensor substrate and connected to a signal processing unit via leads. The graphene oxide layer effectively enhances the adsorption of free carbon particles in the oil sample, thereby causing a change in conductivity. This change is transmitted to the signal processing unit via the detection signal from the microelectrode sensor.

[0115] Preferably, the nanoelectrode 2 is a nanoscale metal electrode.

[0116] Preferably, the signal processing unit mainly includes Fourier transform and wavelet transform algorithm modules for analyzing the frequency characteristics of the sensor signal. The Fourier transform converts the conductivity signal from the time domain to the frequency domain, extracting characteristic frequency information, while the wavelet transform further distinguishes carbon particles of different sizes and filters out signal noise. The data output by the signal processing unit is transmitted to a remote monitoring platform via a communication module.

[0117] Preferably, the communication module supports 4G and NB-IoT communication protocols for real-time transmission of carbon particle concentration data to the host computer, ensuring the timeliness and accuracy of the detection information. When the detected carbon particle concentration exceeds a preset threshold, the alarm unit issues an alarm to remind the operator to maintain the transformer oil.

[0118] Specifically, the microelectrode array sensor module works as follows: when carbon particles in the transformer oil enter the sensor module, they are adsorbed onto the surface of the microelectrodes coated with graphene oxide, causing a change in conductivity. By detecting this conductivity change in real time, the sensor can quickly and accurately determine the concentration of carbon particles in the oil sample. The signal processing unit analyzes and processes the detection signal, ultimately outputting the carbon particle concentration data. The entire detection process does not require stopping the transformer's operation, enabling online detection and significantly improving the efficiency and accuracy of carbon particle monitoring in the oil.

[0119] The detection system of this invention was tested in a transformer oil circulation system. Experimental results show that the system can accurately detect carbon particles in the oil within a concentration range of 1-10 mg / L, and can operate stably even at concentrations as low as 0.1 mg / L. Furthermore, by receiving and processing detection data in real time via a host computer, the system can continuously monitor changes in the concentration of carbon particles in the transformer oil and promptly detect potential fault risks.

[0120] The detection system of the present invention has a compact structure and is easy to install. It is preferably suitable for monitoring the condition of various types of transformer oil, and has significant advantages, especially in high-precision detection of low concentration carbon particles in oil.

[0121] Example 2:

[0122] This embodiment 2 provides an improved design of an online detection device for carbon particles in transformer oil based on a microelectrode array. Preferably, based on embodiment 1, the detection sensitivity, anti-interference ability and remote monitoring function of the device are further improved. The specific implementation method is as follows.

[0123] like Figure 3 As shown, the sampling device in this embodiment preferably employs a multi-stage filtration system to ensure higher purity of the oil sample entering the sensor module, thereby improving detection accuracy. Specifically, the sampling device includes a filter. The primary filter preferably uses a metal mesh structure, which can effectively remove larger particulate impurities in the oil sample and reduce interference from large-particle impurities on detection. Preferably, the oil inlet pipe 4 is made of a high-polymer oil-resistant material and has high-temperature and corrosion-resistant properties, enabling it to remain stable for a long time in the high-temperature operating environment of the transformer.

[0124] Preferably, the static elimination device is further optimized, and an electrostatic shielding layer is preferably used to wrap the oil inlet pipe 4 to prevent the accumulation of static electricity during the flow of the oil sample from affecting the accuracy of the detection signal. The oil sample flow rate is monitored by a flow meter throughout the sampling process to ensure that the flow rate remains stable for each sampling, thereby improving the repeatability and accuracy of the detection.

[0125] Preferably, the sensor electrode diameter is reduced to 30 nanometers, and the electrode spacing is reduced to the range of 40-80 nanometers, in order to respond more sensitively to tiny carbon particles in transformer oil. The graphene oxide material coated on the electrode surface undergoes chemical modification treatment, which can significantly improve its adsorption capacity for carbon particles of different sizes, especially when detecting low concentrations (less than 0.05 mg / L) of carbon particles, exhibiting a more significant increase in sensitivity. The number of microelectrodes in the sensor array is also appropriately increased, preferably several hundred independent electrodes, arranged on a large-area substrate, to improve the detection coverage area and detection efficiency.

[0126] Preferably, the signal processing unit integrates a more complex signal processing algorithm module, preferably including a triple algorithm of adaptive filtering, Fourier transform, and wavelet transform. Specifically, the adaptive filtering algorithm is used to eliminate noise interference in the detected signal in real time. The Fourier transform converts the conductivity signal detected by the sensor from the time domain to the frequency domain, extracting the characteristic signals of carbon particles at different frequencies. The wavelet transform is further used for fine analysis of the detected signal, preferably capable of distinguishing the characteristic frequency signals of carbon particles of different sizes, and accurately filtering out environmental noise interference through algorithms to ensure the accuracy of the detection results.

[0127] The signal processing unit preferably employs a high-performance processing chip, capable of real-time analysis and processing of massive amounts of signal data, and transmitting the analysis results to the remote monitoring platform in the form of data packets via the communication module. Unlike Embodiment 1, in this embodiment, the communication module preferably supports 5G and NB-IoT communication protocols. Specifically, 5G communication is used for large-volume data transmission in high-bandwidth scenarios, while NB-IoT is suitable for stable data transmission in low-power, low-bandwidth scenarios, ensuring timely and reliable transmission of detection data in various scenarios.

[0128] Preferably, the alarm unit in this embodiment incorporates a dynamic alarm mechanism. Specifically, the alarm unit dynamically adjusts the alarm threshold based on the detected carbon particle concentration. This dynamic adjustment mechanism is based on a comprehensive analysis of historical and real-time detection data. By setting a basic alarm threshold and combining it with the real-time trend of the detection data, an algorithm dynamically calculates the adjusted alarm threshold. When the rate of change of carbon particle concentration is high (indicating a rapid increase in particle concentration), the system automatically lowers the alarm threshold, thereby issuing an early warning signal to remind operators to make necessary maintenance preparations. When the carbon particle concentration approaches the threshold, the system will preferably issue an early warning signal to remind operators to make necessary maintenance preparations. When the concentration exceeds the set alarm threshold, the system will trigger a forced alarm, requiring operators to take immediate maintenance measures to prevent transformer oil deterioration from causing serious faults. The core of the dynamic alarm mechanism lies in dynamically adjusting the threshold by combining the absolute value and rate of change of carbon particle concentration, thereby improving the accuracy and timeliness of the alarm.

[0129] Preferably, this embodiment also includes a cloud-based data analysis platform capable of long-term storage and analysis of historical detection data, specifically including the prediction of carbon particle concentration change trends and the establishment of fault early warning models. Specifically, the cloud platform first performs data cleaning and feature extraction on the uploaded historical and real-time detection data, extracting key features related to carbon particle concentration (such as concentration change rate, fluctuation amplitude, and detection environment temperature). Based on the extracted features, the platform utilizes big data analytics and machine learning algorithms (such as time series prediction models, regression analysis algorithms, or Long Short-Term Memory networks (LSTM)) to establish a trend model of carbon particle concentration changes. Through dynamic analysis of real-time and historical data, the cloud platform can predict the carbon particle concentration change trend over a certain period and generate prediction reports in real time, providing crucial early warning information. When the prediction report indicates that the carbon particle concentration may continue to rise and approach or exceed the alarm threshold in the future, the system will proactively send an early warning to the operator, helping them to assess potential risks to the transformer oil condition and take necessary countermeasures. Meanwhile, the cloud platform can also establish a fault early warning model based on long-term data analysis. By discovering the correlation between changes in carbon particle concentration and actual equipment failures, it can further optimize the dynamic adjustment mechanism of the alarm unit, ensuring more accurate and efficient monitoring and prediction.

[0130] Experimental verification shows that the detection system of this embodiment has significant detection advantages over a wider range of carbon particle concentrations (0.05 mg / L to 10 mg / L), especially at low concentrations (e.g., 0.05 mg / L), it can still achieve accurate carbon particle concentration monitoring, demonstrating higher detection sensitivity and reliability. Furthermore, the system's dynamic alarm and cloud data analysis functions greatly enhance its intelligence and reliability in practical applications. The following is a tabular example of the experimental data, covering the carbon particle concentration range (0.05 mg / L to 10 mg / L) and its detection results, verifying the system's sensitivity and reliability:

[0131]

[0132]

[0133] In the table above, carbon particle concentration (mg / L): the actual carbon particle concentration set in the experiment. Detected value (mg / L): the carbon particle concentration value detected by the system. Detection error (%): the relative error between the system's detected value and the actual concentration, reflecting the system's detection accuracy. Sensitivity (mg / L): the minimum resolution capability of the system at the corresponding concentration. Reliability (%): the stability of the system's detection results, based on the reliability percentage calculated from multiple experiments. Preferably, this embodiment further optimizes the sampling device, microelectrode array sensor module, signal processing unit, and communication and alarm module based on Embodiment 1, possessing higher detection sensitivity and data transmission capabilities. It is particularly suitable for real-time online detection of low-concentration carbon particles in transformer oil, and enhances the remote monitoring and fault early warning capabilities of transformer operating status through a cloud analysis platform.

[0134] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0135] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0136] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0137] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. An online detection system for free carbon particles in transformer oil based on a microelectrode array, comprising: The system comprises a sampling module, a microelectrode array sensor module, a signal processing unit, a communication module, a remote monitoring platform, and an alarm unit; its features are as follows: The sampling module includes an oil inlet pipe, a filter device, a servo motor, a micro pump, and valves. The oil sample is transported from the transformer tank to the microelectrode array sensor module through the oil inlet pipe. The filter device is located inside the oil inlet pipe and contains a filter membrane to remove impurities from the oil sample. The valves, micro pump, and servo motor are all located outside the electrostatic shielding layer, which covers the outside of the oil inlet pipe. The micro pump is used to drive the oil sample flow, the valve is used to regulate the oil sample flow rate, and the servo motor is used to control the valve opening to regulate the oil sample flow rate. The microelectrode array sensor module includes a microelectrode array sensor, a temperature sensor, and a pressure sensor. The microelectrode array sensor adopts a hexagonal honeycomb arrangement, with nanoelectrodes evenly spaced on a square silicon substrate with a side length of c. From the first row of nanoelectrodes to the row with the largest number of nanoelectrodes, the distance from the leftmost and rightmost nanoelectrodes to the edge of the silicon substrate decreases row by row by a decrease of Δd. The distance from the leftmost and rightmost nanoelectrodes in the row with the largest number of nanoelectrodes to the edge of the silicon substrate is... The number of nanoelectrodes increases from the row with the largest number to the last row. The distance between the leftmost and rightmost nanoelectrodes and the edge of the silicon substrate increases row by row, with an increase of Δd. The surface of the microelectrode array sensor is coated with graphene oxide to adsorb free carbon particles in the oil sample. When free carbon particles are adsorbed onto the surface of the microelectrode array sensor, an adsorption signal is generated. The microelectrode array sensor, temperature sensor, and pressure sensor are all located in the oil detection chamber. The oil outlet pipe is connected to the oil detection chamber. The pressure sensor is connected to the servo motor via a servo motor control line. The signal processing unit is used to extract the characteristic frequencies and intensities of carbon particle vibrations in the adsorption signal, and to classify the carbon particles according to their characteristic frequencies and intensities.

2. A method for online detection of free carbon particles in transformer oil based on a microelectrode array, implemented using the online detection system for carbon particles in transformer oil based on a microelectrode array as described in claim 1, comprising: The oil sample acquired by the sampling module flows into the microelectrode array sensor module through the oil inlet pipe; The pressure and temperature of the oil sample are monitored in real time by the pressure and temperature sensors of the microelectrode array sensor module. The micro pump and valve of the sampling module are adjusted according to the real-time changes in the pressure and temperature of the oil sample to regulate the flow rate and velocity of the oil sample. After adjusting the flow rate and velocity of the oil sample, the microelectrode array sensor generates an adsorption signal when it adsorbs free carbon particles in the oil sample. The signal processing unit extracts the characteristic frequencies and intensities of particle vibrations from the adsorption signal and classifies the carbon particles based on their characteristic frequencies and intensities.

3. The online detection method for free carbon particles in transformer oil based on microelectrode array according to claim 2, characterized in that: The micropump in the sampling module is adjusted based on the real-time changes in the pressure and temperature of the oil sample. The adjusted flow rate of the micropump satisfies the following relationship: In the formula, This is the flow rate after adjustment by the micro pump, expressed in m³ / s. This is the first proportionality coefficient, with a value of 1.2; The target pressure is expressed in MPa. The pressure of the oil sample is the actual pressure collected, in MPa. This is the viscosity of the oil sample, measured in Pa·s, and is determined by temperature. T The viscosity influence coefficient is determined. It is 0.8; The pressure change of the oil sample, in MPa, and its influence coefficient. It is 0.5; C0 is the carbon particle concentration in the oil sample, in mg / L; C0 is the initial carbon particle concentration, in mg / L. and These are the linear and nonlinear influence coefficients of particle concentration on flow regulation, respectively. This refers to the particle size of the carbon particles, expressed in μm. It is a coefficient related to viscosity and particle size; ζ is the adjustment coefficient for the effect of particle size on viscosity; ζ is the nonlinear effect coefficient of particle concentration on flow rate regulation.

4. The online detection method for free carbon particles in transformer oil based on microelectrode array according to claim 3, characterized in that: The valves of the sampling module are adjusted based on the real-time changes in the pressure and temperature of the oil sample. The adjusted valve openings satisfy the following relationship: In the formula, The adjusted valve opening, in degrees (°). Initial valve opening, unit: °. The coefficient representing the effect of temperature on valve opening is 0.

3. Temperature change, unit: °C; The coefficient representing the effect of pressure change on valve opening is 0.

4. The pressure change of the oil sample is expressed in MPa. This refers to the concentration of carbon particles in the oil sample, in mg / L. C 0 represents the initial carbon particle concentration, in mg / L. The coefficient representing the influence of particle concentration on valve opening is 0.

5. The nonlinear response coefficient for particle concentration is 1.2; The flow rate of the micro pump after adjustment, in m³ / s. Q max Maximum flow rate, unit: m³ / s, refers to the flow rate when the valve opening is adjusted to the maximum. This is the coefficient representing the secondary influence of flow rate on valve opening. D particle The particle size of the carbon particles is expressed in μm. The coefficient representing the influence of particle size on valve opening. is the response coefficient of particle concentration to valve opening. k This is the adjustment coefficient for the Sigmoid function.

5. The online detection method for free carbon particles in transformer oil based on microelectrode array according to claim 4, characterized in that: Adjusting the flow rate and volume of the oil sample also includes: By combining PID control and machine learning algorithms for adjustment, the control output satisfies the following relationship: in, u ( t This refers to the adjustment amount of the micro pump flow rate or valve opening to control the output; e ( t The error signal is the difference between the target pressure and the actual pressure. This refers to the oil sample testing time. K c This is the second proportionality coefficient, with a value of 0.8; K i This is the integral coefficient, with a value of 0.5; K d is the differential coefficient, with a value of 0.3; This is the adjustment coefficient for the flow rate in relation to the PID feedback; Q actual Actual flow rate, unit: m³ / s Q max Maximum flow rate, unit: m³ / s; This is the adjustment coefficient for particle concentration on PID feedback; C part This refers to the concentration of carbon particles in the oil sample, in mg / L. C 0 represents the initial carbon particle concentration, in mg / L; denoted as the nonlinear response coefficient of particle concentration to detection sensitivity.

6. The method for online detection of free carbon particles in transformer oil based on a microelectrode array according to claim 5, characterized in that: The signal processing unit uses Fourier transform and wavelet transform algorithms to analyze and process the adsorption signal, converting the time-domain signal detected by the microelectrode array sensor into a frequency-domain signal, and extracting the characteristic frequency and intensity of the free carbon particles. The characteristic frequency includes the resonant frequency offset of the carbon particles, and the intensity includes the resonant signal attenuation intensity of the carbon particles. The wavelet transform algorithm is used to distinguish carbon particles of different sizes, and the SVM algorithm is used to classify the data and determine the carbon particle content in the oil sample.

7. The method for online detection of free carbon particles in transformer oil based on a microelectrode array according to claim 6, characterized in that: The signal processing unit classifies carbon particles based on their characteristic frequencies and intensities, including: Based on the resonant frequency offset Δ of the adsorption signal And classify according to the attenuation intensity I of the resonant signal, when 0 < Δ When ≤10kHz and I≤20 dB, carbon particles are classified as low-concentration small particles; when 0<Δ ≤10 kHz and When the value is >20 dB, carbon particles are classified as high-concentration small particles; when Δ At >10 kHz, carbon particles are classified as large-sized conductive particles.

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