A high-voltage field strength detection method for oil fume purifier based on electric variable measurement

Abstract

This invention relates to the field of high-voltage electrostatic parameter measurement and circuit detection and analysis technology, and discloses a method for detecting high-voltage field strength in an oil fume purifier based on electrical variable measurement. The method includes: acquiring real-time discharge current and output voltage data sequences of the tested electrostatic load; determining dynamic electrical stiffness parameters based on the response gradient; extracting the DC component of the current to determine real-time leakage conductance parameters; establishing a coupling characteristic between the electrical stiffness increment and the leakage conductance change rate, and determining impedance boundary displacement when there is a feature mismatch; calculating the virtual displacement on the electrode surface using the discrete curvature of the electrical stiffness evolution trajectory; and using the virtual displacement to correct the equivalent geometric depth and reconstruct the true field strength. This invention achieves dynamic field strength compensation by sensing changes in charge migration paths, eliminating calculation errors caused by dielectric accumulation.

Description

Technical Field

[0001] This invention belongs to the field of high voltage electrostatic parameter measurement and circuit detection and analysis technology, and particularly relates to a method for detecting the high voltage field strength of an oil fume purifier based on electrical variable measurement. Background Technology

[0002] The current mainstream solution in the industry is to use a high-voltage DC power supply to generate an electrostatic field to charge oil fume particles and migrate them to the electrode plates. Existing monitoring schemes use sampling circuits to obtain voltage and current readings at the power supply output and evaluate the field strength between the electrode plates based on a predetermined static electrical parameter model. During long-term operation, a layer of grease with dielectric properties will inevitably accumulate on the surface of the collecting electrode plates. This grease layer has insulating properties and occupies the discharge gap in physical space, causing physical displacement of the effective electric field boundary between the electrode plates. At this time, the output voltage obtained by the sampling circuit includes the additional voltage drop of the grease layer, resulting in a nonlinear offset between the observed electrical reading and the true value of the field strength actually acting on the oil fume particles. This spatial field contraction caused by the accumulation of the medium constitutes measurement noise that cannot be effectively eliminated in the existing monitoring model.

[0003] To mitigate interference, existing technologies focus on improving hardware structures such as roller shape, physical filtration, or multi-stage interception. For example, the utility model patent with authorization announcement number CN216368418U discloses an oil fume purifier with a filter groove plate electrostatic field at the end of the wire mesh interception. By setting up physical interception structures such as wire mesh oil vapor interception plate, plate filter groove, and perforated air distribution plate, it reduces the impact of large oil droplets and water vapor on the electrostatic field from the front end, and lowers the frequency of tip arcing discharge. Regarding the above interference, the method of directly measuring the grease thickness with physical sensors is prone to performance degradation under the internal working conditions of high temperature, high humidity, and strong oil contamination. If a strategy of increasing the output voltage is used for linear compensation, the electric field energy will exceed the safety threshold, resulting in frequent arcing failures.

[0004] Therefore, the technical problem to be solved by this invention is how to identify the evolution of the electric field boundary caused by dielectric intrusion by utilizing the characteristics of loop electrical variables under complex non-stationary load conditions, and to obtain the true value of the effective field strength. Summary of the Invention

[0005] This invention provides a method for detecting the high-voltage field strength of an oil fume purifier based on electrical variable measurement, comprising the following steps:

[0006] Step S1: Obtain the real-time discharge current data sequence and the real-time output voltage data sequence of the electrostatic load under test, wherein the electrostatic load under test includes a discharge electrode and a sampling electrode that are set opposite to each other.

[0007] Step S2: Calculate the dynamic electrical stiffness parameter based on the response gradient of the real-time discharge current data sequence relative to the real-time output voltage data sequence.

[0008] Step S3: Extract the DC component from the real-time discharge current data sequence, and determine the real-time leakage conductance parameter of the tested electrostatic load based on the ratio of the DC component to the real-time output voltage data sequence.

[0009] Step S4: Calculate the correlation deviation between the increment of the dynamic electric stiffness parameter and the rate of change of the real-time leakage conductivity parameter. When the correlation deviation exceeds the preset mismatch threshold, it is determined that the impedance boundary of the tested electrostatic load has shifted.

[0010] Step S5: Perform discrete curvature calculation with respect to the time period to extract the nonlinear evolution trajectory of the dynamic electrical stiffness parameter, and calculate the virtual displacement of the electrode surface based on the nonlinear evolution trajectory.

[0011] Step S6: The equivalent geometric depth of the tested electrostatic load is corrected using virtual displacement, and the real field strength data inside the tested electrostatic load is calculated by combining the real-time output voltage data sequence to perform a pre-breakdown risk warning.

[0012] Preferably, step S1 specifically includes: acquiring the original current analog signal by means of a sampling resistor set at the sampling terminal, and synchronously acquiring the output voltage analog signal using a voltage divider circuit; performing high-frequency component filtering and impedance matching processing on the original current analog signal to generate a current sampling sequence; and performing synchronous analog-to-digital conversion on the current sampling sequence and the output voltage analog signal to output a real-time discharge current data sequence and a real-time output voltage data sequence aligned in the time dimension.

[0013] Preferably, the calculation rule for the dynamic electric stiffness parameter in step S2 is as follows: within the preset disturbance voltage range, a first-order difference operation is performed on the real-time discharge current data sequence, and the ratio of the current change to the voltage change is defined as the dynamic electric stiffness parameter.

[0014] Preferably, step S4 specifically includes: recording the first fluctuation variance of the dynamic electric stiffness parameter in a continuous sampling period in real time, and the second fluctuation variance of the leakage conductivity parameter in the same sampling period in real time; when the first fluctuation variance shows a monotonically increasing trend and the growth slope of the second fluctuation variance is lower than a preset slope threshold, it is determined that the reduction in the effective field strength depth is caused by the accumulation of non-conductive deposition medium inside the tested electrostatic field load.

[0015] Preferably, in step S5, when extracting the nonlinear evolution trajectory using discrete curvature calculation, the evolution coefficients characterizing the load of the measured electrostatic field are determined using the following rules. : ,in, Evolution coefficient, unit: ; It represents the difference in dynamic electrical stiffness parameters within adjacent sampling periods; The preset sampling period step size, in units of ; This is the real-time leakage conductivity parameter.

[0016] Preferably, step S6 specifically includes: substituting the virtual displacement into the field strength mapping equation, and obtaining the actual electric force vector value acting on the suspended particles inside the tested electrostatic load by subtracting the potential drop caused by the dielectric properties of the non-conductive deposition medium.

[0017] Preferably, the hysteresis phase angle of the real-time discharge current data sequence relative to the real-time output voltage data sequence is extracted, and the offset of the hysteresis phase angle is used to characterize the viscosity coefficient of the non-conductive deposition medium.

[0018] Preferably, the early warning includes: calculating the difference between the actual field strength data and the preset critical field strength threshold; when the difference is lower than the preset safety margin threshold, generating a current limiting control command or adjusting the duty cycle of the drive power supply so that the electric field distribution state returns to the linear characteristic range.

[0019] Preferably, the distribution characteristics of the random fluctuation residual signal in the real-time discharge current data sequence are statistically analyzed, and the uniformity of the scale distribution on the surface of the discharge electrode and the sampling electrode is evaluated based on the distribution variance of the residual signal.

[0020] Preferably, when the actual field strength data is detected to be continuously decreasing and reaching a preset contamination accumulation threshold, a maintenance command is output indicating that a cleaning operation should be performed on the tested electrostatic load.

[0021] Compared with existing technologies, the high-voltage field strength detection method for oil fume purifiers based on electrical variable measurement in this invention has the following advantages:

[0022] 1. In the high-voltage field strength detection circuit of the fume purifier, the accuracy of electrical variable measurement and the anti-drift capability of the reference under complex nonlinear loads are improved. By superimposing a high-frequency pulse width modulation disturbance signal on the high-voltage DC output reference and simultaneously extracting the current response fluctuation and voltage response fluctuation in the circuit, a dynamic response operator that can characterize the sensitivity of the electric field space to external disturbances is constructed. This changes the limitation of traditional technology that relies solely on quasi-static voltage readings for field strength estimation. By utilizing the feedback characteristics generated by the disturbance signal, measurement noise caused by grease buildup on the electrode surface can be identified and eliminated without the need for additional external physical sensors. This compensation measurement method driven by the microscopic characteristics of electrical signals effectively solves the problem of deviation in obtaining the true value of field strength due to changes in dielectric properties.

[0023] 2. Achieving dynamic compensation for spatial boundary evolution based on electrical characteristic mapping: This invention utilizes the coupling relationship between the dynamic response operator and the leakage conductance component to establish an identification mechanism reflecting the reduction in the effective depth of the electric field space. When the thickness of the grease layer on the electrode surface changes, by analyzing the step gradient and phase shift characteristics of the current signal, the physical displacement of the effective electric field boundary caused by grease accumulation is deduced in reverse. The originally invisible gap contraction is transformed into a calculable geometric correction parameter, which is then substituted into the equivalent physical model for field strength calculation. This electrical measurement method, which inverses the spatial geometric state by monitoring changes in the charge migration path of the circuit, enables the detection circuit to track the encroachment of the electric field boundary, thereby avoiding calculation distortion caused by excessively narrow gaps and improving the operational reliability of the detection system under heavily polluted conditions.

[0024] 3. Establish a pre-breakdown risk identification and self-avoidance closed loop based on electrical signal evolution logic. By extracting the hysteresis phase angle of the current response relative to voltage disturbance and the distribution characteristics of random fluctuation residual signals, an identification criterion for the precursors of electric field stability collapse is established. The phase offset is used to characterize the viscosity of the oil fume medium, and the uniformity of scale distribution on the electrode surface is evaluated by combining the disorder of the residual signal. When the nonlinear gradient is detected to exceed the preset safe linear range, the system automatically identifies that the electric field has entered the metastable state of pre-breakdown and actively triggers current limiting or duty cycle adjustment to bring the electric field state back to the linear characteristic range. This feedback control based on the micro-characteristic analysis of electrical variables transforms the existing post-fault protection into proactive avoidance of pre-operation logic, reducing the risk of frequent arcing caused by local field strength distortion. Attached Figure Description

[0025] Figure 1 This is a flowchart of the impedance boundary determination and field strength reconstruction logic for the electrical variable characteristic analysis of this invention;

[0026] Figure 2 This is a diagram of the multi-dimensional support architecture for the physical evolution and algorithmic inversion of the high-precision field strength reconstruction of this invention. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0028] It should be noted that all directional and positional terms used in this invention, such as: up, down, left, right, front, back, vertical, horizontal, inner, outer, top, bottom, transverse, longitudinal, center, etc., are only used to explain the relative positional relationship and connection between components in a specific state (as shown in the accompanying drawings). They are only for the convenience of describing this invention and do not require that this invention be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the descriptions of "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0029] In the description of this invention, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to the internal connection of two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0030] In the description of this specification, references to the terms "an embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0031] This invention provides a method for detecting high-voltage field strength in an oil fume purifier based on electrostatic variable measurement. By monitoring the fluctuations in electrostatic field under minute disturbances, the physical evolution state of the dielectric layer on the electrode surface is identified. The method involves a high-frequency duty cycle disturbance signal superimposed on the output DC high-voltage reference by the high-voltage power supply control unit. The frequency of the high-frequency duty cycle disturbance signal is set to be higher than twice the power supply pulse width modulation carrier frequency to avoid the switching noise frequency band generated by the power supply topology. Under the influence of the disturbance signal, a small voltage fluctuation is generated at the high-voltage output terminal. The synchronous sampling unit obtains the corresponding current change through a precision resistor set at the sampling end of the load circuit. The sampling circuit performs impedance matching and high-frequency component filtering on the original current signal to ensure that the current sampling sequence and the output voltage sequence are aligned on the time axis. To address the impedance boundary displacement problem caused by grease buildup on the electrode surface, this method utilizes the response gradient to decouple the structural characteristic parameters of the electric field. The control unit performs first-order difference operations on the real-time acquired voltage and current data sequences to calculate the dynamic electric stiffness parameters. The calculation formula is as follows: ,in, It is a dynamic electric stiffness parameter used to characterize the sensitivity of the electric field space to external voltage disturbances; This refers to the change in loop current. The output voltage change is represented by the DC component extracted from the discharge current sequence, and the real-time leakage conductance parameter is calculated. The calculation formula is as follows: ,in, Real-time leakage conductivity parameters; This represents the DC component in the current sequence. For real-time output voltage; dynamic electrical stiffness parameters It maps the total equivalent electrical stiffness, including air gaps and grease layers, while It maps the conductivity loss characteristics of the grease layer.

[0032] To quantify the degree of contraction of the effective electric field boundary between the plates, this method performs discrete curvature calculations with respect to the time period, and the control unit monitors the dynamic electric stiffness parameters. Incremental and real-time leakage conductance parameters The degree of deviation between the rates of change; when The first fluctuation variance shows a monotonically increasing trend and The second volatility variance growth slope is lower than the preset value. When the threshold is reached, it is determined that the electric field impedance boundary has shifted; at this point, the system extracts the dynamic electric stiffness parameters. The nonlinear evolution trajectory is used to calculate the evolution coefficient. The calculation formula is as follows: ,in, Evolution coefficient, unit: ; It represents the difference in dynamic electrical stiffness parameters within adjacent sampling periods; This is the sampling period step size, in units of ; For real-time leakage conductivity parameters, based on evolution coefficients The integral path determines the virtual displacement on the electrode surface. Virtual displacement The physical reduction in the effective electric field spatial depth caused by the accumulation of grease layers is mapped; a geometric mapping operator is introduced. During the prototype testing phase, the physical displacement of the sampling electrode relative to the discharge electrode was adjusted, and the evolution coefficient was recorded. Integral trajectory determination operator, system for evolution coefficients sampling period step Inner numerical integration, the result is compared with the geometric mapping operator. Multiplication transforms the frequency-domain characteristic electric variable evolution intensity into a physical length quantity that effectively reduces the depth of the electric field, thus enabling the real field strength data to be analyzed. Reconstruction is based on the mapping of electrical signal characteristics to geometric space.

[0033] In obtaining virtual displacement Then, this method reconstructs the true field strength data inside the electric field. The computational logic adopts the following modified model: ,in, This is real field strength data; For real-time output voltage; This is the loop current; The voltage drop constant is the internal resistance of the power supply. This represents the equivalent pressure drop across the grease layer. Initial geometric gap designed for the electrode plates; This refers to the virtual displacement; the equivalent pressure drop of the grease layer. according to and The linear combination is determined; the control unit will calculate the result. As a reference quantity for closed-loop feedback; if If the difference between the actual field strength and the preset critical breakdown field strength threshold is less than the safety margin, the control unit automatically adjusts the pulse width modulation duty cycle to reduce the output energy and bring the electric field state back to the linear characteristic range. The specific execution logic of the instruction structure image is as follows: when the difference between the actual field strength data and the preset critical breakdown field strength threshold is less than 0.8kV / cm, the microcontroller generates a subtraction compensation amount, decreasing the pulse width modulation duty cycle of the current drive power supply in increments of 2.0% in each control cycle until the actual field strength data rises back to the safety margin of 1.2kV / cm or higher. If the field strength still cannot return to the linear range after 50 consecutive duty cycle reductions, it is determined that heavy fouling has caused impedance failure. The controller directly sends a shutdown protection command with a logic value of 1 to the actuator. At the same time, the system corrects the detection deviation caused by the viscosity characteristics of the medium by extracting the hysteresis phase angle of the current response relative to the voltage disturbance, and evaluates the uniformity of the fouling distribution based on the variance of the random fluctuation residual signal. When the temperature continues to drop to the maintenance threshold, the system outputs a cleaning warning command.

[0034] Example 1: Under the continuous operation of a high-load commercial kitchen fume purification system, a non-uniform grease layer is deposited on the surface of the dust collection plate due to the continuous impact of high-viscosity grease particles. This physically reduces the discharge gap between the plates and introduces an equivalent impedance that varies non-linearly with the grease thickness. A high-frequency duty cycle perturbation signal superimposed on a high-voltage DC reference is used to detect the dielectric evolution state on the plate surface. A synchronous sampling unit acquires the current change corresponding to the perturbation signal. and voltage change And according to the calculation formula Determine dynamic electric stiffness parameters ;in, For dynamic electrical stiffness parameters; This refers to the change in loop current. Output voltage change; dynamic electrical stiffness parameter This provides intermediate features reflecting the spatial response characteristics of the electric field for subsequent identification of physical intrusion caused by the grease layer, while also combining real-time leakage conductivity parameters. Criteria for determining interference from sedimentation media were established.

[0035] The system monitors dynamic electrical stiffness parameters. Incremental and real-time leakage conductance parameters The degree of deviation between the rates of change, when The first fluctuation variance shows a monotonically increasing trend and The second fluctuation variance growth slope is lower than At the threshold, the reduction in effective field strength depth is determined to originate from the accumulation of non-conductive deposition medium; the system's dynamic electrical stiffness parameters... Execution regarding time period Discrete curvature calculations are performed to extract evolution coefficients. ;in, Evolution coefficient; It represents the difference in dynamic electrical stiffness parameters within adjacent sampling periods; This is the sampling period step size; For real-time leakage conductivity parameters; based on the evolution coefficient The integral trajectory determines the virtual displacement. The virtual displacement Used to correct true field strength data When the grease layer thickness reaches Under the test node, the reconstructed real field strength data This indicates that the effective gap has shrunk to its initial value. ; through field strength correction model Through calculation, the system identifies field strength drops hidden beneath fixed voltage readings and, based on the actual field strength data... The feedback reduces the power supply duty cycle, causing the electric field distribution to return to the preset linear characteristic range; among which, This is real field strength data; For real-time output voltage; This is the loop current; The voltage drop constant is the internal resistance of the power supply. This represents the equivalent pressure drop across the grease layer. The initial geometric gap designed for the electrode plates; this mechanism, which binds the ionization response gradient with the spatial geometric correction depth, enables the system to compensate for field strength distortion caused by the grease layer in real time online, thus improving the detection reliability of the purification equipment in polluted environments.

[0036] Example 2: In the field strength detection accuracy verification test simulating a heavy oil fume emission environment in a commercial kitchen and bathroom, the test platform adopted a gap of... Parallel plate electrostatic field device, high voltage DC power supply provides 0 to The adjustable output voltage has a measurement resolution of not less than [specified value]. The sampling frequency is set to To meet the requirement of capturing the response characteristics of dynamic electrical variables; and to simulate industrial electromagnetic interference environments, a signal-to-noise ratio of [value missing] is actively superimposed in the sampling loop. Gaussian white noise and frequency of Power frequency harmonic interference; the experiment set a grease thickness gradient for the tested electrostatic field load, respectively covering... That is, the baseline operating condition, That is, mild pollution That is, moderate pollution, That is, the boundary of severe pollution and This includes five test nodes such as over-limit operating conditions; and the core parameter duty cycle disturbance signal. The intensity setting needs to balance the sensitivity of signal extraction with the stability of power supply output. When the disturbance amplitude is set at... At that time, the system generates a current fluctuation sufficient to be detected by the precision sampling resistor without triggering the hardware protection of the drive power supply. The sample group of this invention adopts the field strength reconstruction method in the aforementioned specific embodiments, while the control group directly calculates the field strength data based on the readings of the real-time output voltage data sequence.

[0037] Referring to Table 1, which records the key monitoring data and reconstruction errors of the sample group and control group under different grease deposition thicknesses, as the grease thickness increased from 0.0 mm to 3.0 mm, the control group, unable to identify the spurious voltage drop caused by the dielectric properties of the grease layer, had its detected field strength data stabilized around 15.2 kV / cm due to voltage closed-loop maintenance. However, because the grease layer occupied part of the inter-electrode potential, the actual effective field strength dropped to 12.1 kV / cm, causing the detection error to show a monotonically increasing trend with the increase of contamination degree. In contrast, the sample group of this invention identified the physical displacement of the impedance boundary by extracting the dynamic electrical stiffness parameter K. When the grease thickness was 2.0 mm, the calculated dynamic electrical stiffness parameter K was... Extracted virtual displacement The actual field strength data after reconstruction is 2.12 mm. The value was 12.45 kV / cm, with a deviation of 2.8% from the reference value. When the grease thickness reached an excessive 4.0 mm, the dynamic electric stiffness parameter K underwent nonlinear saturation because the grease layer thickness exceeded the linear response region of the inter-plate ionization electric field, leading to a discrepancy in the actual field strength data. The reconstruction error suddenly increased from 3.1% at the heavily contaminated boundary to 13.5%, thereby determining the detection compensation range defined by the present invention.

[0038] Table 1: Comparison of Field Strength Detection Accuracy Verification

[0039] ;

[0040] To address the synergistic effect observed in experiments, the present invention utilizes the change in current generated by high-frequency perturbation excitation. For dynamic electric stiffness parameters The computation provides input, while The nonlinear evolution trajectory provides a physical basis for correcting the effective geometric depth; if the duty cycle perturbation signal is removed and static sampling is used, the system cannot obtain the electrical stiffness increment and real-time leakage conductance parameters. The degree of deviation between the rates of change leads to the virtual displacement. Unable to be quantified; in comparisons with partially missing control groups, if only real-time leakage conductance parameters are used... Perform resistive compensation while ignoring The mapped charge storage characteristics cannot resolve the contradiction of spatial field contraction caused by the grease medium; with a grease thickness of 2.0 mm, the field strength detection error of the pure resistive compensation scheme reaches 9.2%, which is higher than the 2.8% of the complete scheme of this invention; the method of this invention can suppress measurement interference in complex noise backgrounds, and correct the calculation deviation caused by the interference of the deposition medium on the true value of field strength detection in the field of fume purification by binding the ionization response gradient with the spatial geometric correction depth.

[0041] Example 3: This example combines Figures 1 to 2 This document describes a method for detecting the high-voltage field strength of an oil fume purifier based on electrical variable measurements. Figure 1As shown, the detection logic begins with the raw signal acquisition and sequence alignment stage. This step establishes a basic data stream by acquiring real-time discharge current and output voltage data sequences. The process is divided into two parallel paths: one path performs dynamic electrical stiffness parameter calculation, the core of which is to calculate the current-to-voltage response gradient; the other path performs real-time leakage conductance parameter determination. This process involves extracting the DC component of the current and the voltage ratio. The two parameters converge to the impedance boundary displacement judgment module, where the correlation deviation between the electrical stiffness increment and the conductance change rate is calculated and it is determined whether it exceeds the mismatch threshold. When the system determines that there is a characteristic mismatch, the logic flow enters the stage of calculating the virtual displacement on the plate surface. By extracting the nonlinear evolution trajectory of the dynamic electrical stiffness parameters and performing discrete curvature calculation to quantify the displacement, combined with the input of the real-time output voltage data sequence, the process enters the real field strength data reconstruction stage. The virtual displacement is used to correct the equivalent geometric depth. Finally, the system completes the pre-breakdown risk warning based on the closed-loop feedback of the real field strength data, thereby realizing a complete detection closed loop.

[0042] like Figure 2 As shown, the physical load evolution dimension at the top includes the causes of three physical levels: non-conductive deposition medium accumulation, impedance boundary physical displacement, and medium polarization effect. The corresponding algorithm inversion model dimension uses dynamic electrical stiffness coupling characteristics, evolution trajectory discrete curvature, and virtual displacement quantization to mathematically represent the physical phenomena. The dynamic variable perception dimension at the bottom covers signal acquisition methods such as real-time leakage conductance parameters, current hysteresis phase angle extraction, and high-frequency pulse width modulation disturbance. These perception data ultimately serve the closed-loop correction mechanism dimension, which specifically includes active avoidance control commands, safety margin threshold assessment, and equivalent geometric depth correction. The synergistic effect of the above four dimensions supports the realization of the field strength reconstruction target.

[0043] Example 4: In application scenarios where changes in ambient temperature cause parameter drift in the high-voltage power supply drive circuit components, and the measured electrostatic load is accompanied by non-stationary electromagnetic interference noise, the system's sensitivity in distinguishing between sensor hardware drift and the evolution trajectory of grease layer electrical variables is limited; to establish a deterministic operator execution path for the correlation deviation, the control unit processes the collected data... Dynamic electrical stiffness parameters of a group of continuous sampling periods The initial state definition is executed, and the original sequence is preprocessed using a moving average operator to filter out random pulse interference caused by power supply ripple. At the low-level execution layer of signal processing, a circular buffer with a depth of 1024 sampling points is configured, and the real-time sampling frequency is forcibly set to 32kHz to ensure the capture of high-frequency pulse width modulation disturbance responses above 10kHz. The length of the sliding sampling window is set to 200 data points. A statistical operation is triggered every time the buffer is updated with 50 new sampling points, meaning the overlap rate of the sampling window is fixed at 75.0%. Through this high-overlap sliding window design, the system calculates the first and second fluctuation variances within a 6.25ms update cycle, ensuring the real-time and unique extraction of electrical variable response characteristics under non-stationary electromagnetic interference. The process then enters the process judgment and quantization stage. The specific algorithm execution flow is as follows: [The text abruptly ends here, likely due to an incomplete sentence or missing information.] The sliding sampling window, using the formula The real-time mean within the calculation window is where This represents the average value of the dynamic electrical stiffness parameter within the window. For window length, For the first in the window Observed values ​​at each sampling point; calculate the dynamic electric stiffness parameters within the current window. The discrete variance is defined as the first fluctuation variance. ,in The degree of dispersion of the spatial impedance fluctuation of the mapped electric field; for real-time leakage conductance parameters The second fluctuation variance is obtained by performing the same second-order statistical operation. ,in Here, the discrete variance of the real-time leakage conductivity parameter over the same sampling period is... The value is 50. The value is 10.

[0044] To eliminate the preset mismatch threshold To address the inherent uncertainties, the system employs an adaptive calibration procedure based on a baseline noise level. During the no-load startup phase, the system monitors and records the initial residual variance in real time under conditions free of grease deposits. And according to the formula Determine the preset mismatch threshold ,in This is the baseline fluctuation of electrical variables under no-load conditions. The sensitivity gain coefficient is set to a constant between 1.5 and 2.0 under standard operating conditions for industrial air purifiers; the calculated first fluctuation variance... With the second fluctuation variance The ratio exceeded the preset mismatch threshold for five consecutive sampling periods. At that time, the logic unit determines that the physical electrode boundary has shifted; the system calls the weighted model of equivalent pressure drop in the grease layer, according to the formula... Calculate the potential drop generated by the dielectric layer, where The weighting coefficient represents the equivalent pressure drop across the grease layer. and Pre-calibrated using offline short-circuit impedance experiments, the contribution weights of dielectric polarization effect and resistive loss to the total voltage drop are mapped respectively; a mismatch threshold is preset. Based on the background noise statistics of the equipment under no-load conditions, it was determined that the control unit continuously [performs noise] during the system startup self-test cycle. The variance of the dynamic electrical stiffness parameter sequence is calculated to obtain the reference variance reflecting the zero-point fluctuation of the hardware circuit. A sensitivity coefficient of 1.5 to 2.0 was selected. Compared with the benchmark variance The product is used as a preset mismatch threshold. The distributed capacitance of the shielding transformer or the noise from the power supply topology switching can cause numerical jumps, and non-conductive deposited media can be identified.

[0045] During the equipment calibration phase, standard polytetrafluoroethylene (PTFE) dielectric sheets with thicknesses of 1.0 mm, 2.0 mm, and 3.0 mm were placed into the electrostatic field load under test, and dynamic electrical stiffness parameters corresponding to each thickness gradient were collected simultaneously. With real-time leakage conductivity parameters By fitting the correlation between the measured voltage drop and the reciprocal of the electrical parameters using the least squares method, the weighting coefficients of the polarization effect of the mapping medium are determined. With mapping transmission loss weighting coefficient The weighting coefficients are stored in the non-volatile memory of the control unit to reconstruct the true field strength data. The equivalent pressure drop of the grease layer is calculated based on the measured electrical parameters. After acquiring the correction parameters, the system performs field strength reconstruction calculations to achieve active obstacle avoidance control. When the real-time output voltage... The virtual displacement is 15000V and identified through the above procedures. When the field strength is 1.85 mm, the field strength mapping equation outputs the true field strength data. It is 12.8 kV / cm; if the actual field strength data With a safety margin of less than 0.5 kV / cm between the difference from the preset metastable field strength threshold, the controller reduces the duty cycle of the power supply pulse width modulation signal to 8.5%, thereby removing the effective field strength within the tested electrostatic load from the breakdown risk zone. This method resolves the technical contradiction between frequent arcing caused by dielectric accumulation in high-voltage electric fields and the drop in effective purification capacity by deconstructing the correlation deviation into a statistical operator and combining it with threshold calibration based on no-load reference, thus ensuring the uniqueness of the field strength detection results under complex interference environments.

[0046] Example 5: Under the pre-calibration conditions of the newly installed fume purification equipment, the control unit drives the high-voltage DC power supply to generate a voltage scan sequence that linearly rises from 0V to 10000V in the initial state environment where the electrode plates are free of grease deposits and the ambient humidity is within the range of 45% to 55%. The synchronous sampling unit collects the residual current data of the circuit with a fixed step size of 1ms. The system uses a first-order differential algorithm to solve the voltage and current sequence to obtain the unloaded electrode reference of the tested electrostatic load. This reference is recorded in the storage unit as the zero offset of the hardware system and is used to correct the parasitic impedance deviation caused by the difference in the installation environment.

[0047] When the system faces application scenarios where the electrical response characteristics of different batches of plates fluctuate, the control unit determines the correction parameters through an offline calibration procedure. The procedure involves sequentially filling the gap between the plates with standard dielectric grease layers of 0.5 mm and 1.5 mm thickness in a controlled test chamber, and simultaneously recording the dynamic electrical stiffness parameters generated under the action of a high-frequency duty cycle disturbance signal. and real-time leakage conductivity parameters The weighting coefficients are determined by solving a system of linear equations concerning the pressure drop loss of the medium. With weighting coefficients The weighting parameters are then embedded into the parameter table of the field strength reconstruction module, enabling the system to output true field strength data that conforms to the constraints of physical laws based on real-time electrical variables during the dynamic evolution of the oil layer thickness. .

[0048] Example 6: Under the condition of performing standardized impedance characteristic calibration on the acquisition electrodes for different hardware batches, the control unit simulates the dielectric response characteristics generated by the grease layer by sequentially placing polytetrafluoroethylene standard dielectric sheets with thicknesses of 1.0 mm, 2.0 mm, and 3.0 mm into the electrostatic field load under test, and simultaneously records the dynamic electrical stiffness parameters obtained at a sampling frequency of 10 kHz. With real-time leakage conductivity parameters The system uses a multiple linear regression algorithm to calculate the relationship between dielectric pressure drop loss and dynamic electrical parameters and determines the weighting coefficients. With weighting coefficients The calculation formula is as follows: ,in, This represents the equivalent pressure drop across the grease layer. For dynamic electrical stiffness parameters; For real-time leakage conductivity parameters, weighting coefficients The weighting coefficients are used to map the potential drop components caused by polarization within the lipid layer. The potential drop component generated by the conduction loss of the grease layer is mapped, and the resulting correction parameter matrix is ​​stored in a non-volatile memory cell.

[0049] When the system calculates virtual displacement during operation At that time, the control unit uses a discrete accumulation algorithm based on a finite set of sampling points to extract the evolution coefficients within each calculation step of 10ms. First-order difference slope and time period The specific procedure for calculating virtual displacement during numerical integration is as follows: ,in, This is a virtual displacement; For the first Evolution coefficients within each sampling step; This is the sampling period step size, in units of The system monitors virtual displacement in real time. The monotonic cumulative amount generated as running time increases, in the virtual displacement amount Reaching the initial geometric gap When the ratio reaches 15%, the logic unit triggers the boundary displacement compensation mechanism, utilizing the corrected virtual displacement. Reconstructing real field strength data .

[0050] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A method for detecting the high-voltage field strength of an oil fume purifier based on electrical variable measurement, characterized in that, Includes the following steps: A high-frequency duty cycle disturbance signal is superimposed on the output DC high-voltage reference by the high-voltage power supply control unit; Step S1: Obtain the real-time discharge current data sequence and the real-time output voltage data sequence of the electrostatic load under test, wherein the electrostatic load under test includes a discharge electrode and a sampling electrode that are set opposite to each other. Step S2: Calculate the dynamic electrical stiffness parameter based on the response gradient of the real-time discharge current data sequence relative to the real-time output voltage data sequence. Step S3: Extract the DC component from the real-time discharge current data sequence, and determine the real-time leakage conductance parameter of the tested electrostatic load based on the ratio of the DC component to the real-time output voltage data sequence. Step S4: Calculate the correlation deviation between the increment of the dynamic electrical stiffness parameter and the rate of change of the real-time leakage conductivity parameter. When the correlation deviation exceeds a preset mismatch threshold, it is determined that the impedance boundary of the tested electrostatic load has shifted. Specifically, step S4 includes: recording the first fluctuation variance of the dynamic electrical stiffness parameter in a continuous sampling period and the second fluctuation variance of the real-time leakage conductivity parameter in the same sampling period; calculating the ratio of the first fluctuation variance to the second fluctuation variance; when the ratio exceeds a preset mismatch threshold in a continuous sampling period, it is determined that the impedance boundary of the tested electrostatic load has shifted; when the first fluctuation variance shows a monotonically increasing trend and the growth slope of the second fluctuation variance is lower than a preset slope threshold, it is determined that the reduction in the effective field strength depth is caused by the accumulation of non-conductive deposited medium inside the tested electrostatic load. Step S5: Perform discrete curvature calculation with respect to the time period to extract the nonlinear evolution trajectory of the dynamic electric stiffness parameter, and calculate the virtual displacement on the electrode surface based on the nonlinear evolution trajectory. The evolution coefficient characterizing the load of the measured electrostatic field is determined using the following rules. : ,in, Evolution coefficient, unit: ; It represents the difference in dynamic electrical stiffness parameters within adjacent sampling periods; The preset sampling period step size, in units of ; The specific procedure for calculating the virtual displacement is as follows: (To obtain real-time leakage conductivity parameters) ,in, This is a virtual displacement. For the first Evolution coefficients within a sampling step size This is the sampling period step size; Step S6: The equivalent geometric depth of the tested electrostatic load is corrected using virtual displacement, and the real field strength data inside the tested electrostatic load is calculated by combining the real-time output voltage data sequence to perform a pre-breakdown risk warning.

2. The method for detecting high-voltage field strength in an oil fume purifier based on electrical variable measurement according to claim 1, characterized in that, Step S1 specifically includes: acquiring the original current analog signal by means of the sampling resistor set at the sampling terminal, and synchronously acquiring the output voltage analog signal by means of the voltage divider circuit; performing high frequency component filtering and impedance matching processing on the original current analog signal to generate a current sampling sequence; and performing synchronous analog-to-digital conversion on the current sampling sequence and the output voltage analog signal to output a real-time discharge current data sequence and a real-time output voltage data sequence aligned in the time dimension.

3. The method for detecting high-voltage field strength in an oil fume purifier based on electrical variable measurement according to claim 1, characterized in that, The calculation rule for the dynamic electric stiffness parameter in step S2 is as follows: within the preset disturbance voltage range, perform a first-order difference operation on the real-time discharge current data sequence, and define the ratio of the current change to the voltage change as the dynamic electric stiffness parameter.

4. The method for detecting high-voltage field strength in an oil fume purifier based on electrical variable measurement according to claim 1, characterized in that, Step S6 specifically includes: substituting the virtual displacement into the field strength mapping equation, and obtaining the actual electric force vector value acting on the suspended particles inside the tested electrostatic load by subtracting the potential drop caused by the dielectric properties of the non-conductive deposition medium.

5. The method for detecting high-voltage field strength in an oil fume purifier based on electrical variable measurement according to claim 1, characterized in that, The hysteresis phase angle of the real-time discharge current data sequence relative to the real-time output voltage data sequence is extracted, and the offset of the hysteresis phase angle is used to characterize the viscosity coefficient of the non-conductive deposition medium.

6. The method for detecting high-voltage field strength in an oil fume purifier based on electrical variable measurement according to claim 1, characterized in that, The warning includes: The difference between the actual field strength data and the preset critical field strength threshold is calculated. When the difference is lower than the preset safety margin threshold, a current limiting control command is generated or the duty cycle of the drive power supply is adjusted so that the electric field distribution returns to the linear characteristic range.

7. The method for detecting high-voltage field strength in an oil fume purifier based on electrical variable measurement according to claim 1, characterized in that, The distribution characteristics of random fluctuation residual signals in real-time discharge current data sequences are statistically analyzed, and the uniformity of scale distribution on the surface of discharge electrodes and sampling electrodes is evaluated based on the distribution variance of the residual signals.

8. The method for detecting high-voltage field strength in an oil fume purifier based on electrical variable measurement according to claim 1, characterized in that, When the actual field strength data is detected to be continuously decreasing and reaching the preset contamination accumulation threshold, a maintenance command is output indicating that a cleaning operation should be performed on the tested electrostatic load.

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