Mobile transformation ratio direct current resistance detection device

Abstract

The invention discloses a mobile transformation ratio direct current resistance detection device, which belongs to the technical field of detection and comprises a direct current resistance detection reliability analysis system. The current stability analysis module obtains a current stability coefficient based on the current output average value and the current fluctuation value; the environment stability analysis module obtains an environment stability coefficient based on the environment temperature, the environment temperature fluctuation value and the environment humidity; the resistance-temperature collaboration analysis module is used for acquiring resistance-temperature collaboration based on the current stability coefficient, the resistance gradient under the environment stability coefficient and the winding temperature; the reliability analysis module is used for acquiring the reliability of a detection result based on the resistance-temperature collaboration, the current stability coefficient and the environment stability coefficient; the judgment module automatically judges the result reliability through threshold comparison; the method significantly improves the field detection anti-interference capability and the result credibility, and is suitable for the complex environment of power operation and maintenance.

Description

Technical Field

[0001] The invention belongs to the field of detection technology, and in particular relates to a mobile ratio direct resistance detection device. Background Art

[0002] In the field of transformer testing, accurate measurement of the turns ratio and DC resistance (DC resistance) is crucial for equipment status assessment. Although traditional mobile testing devices are convenient for on-site operations, they have significant drawbacks:

[0003] Environmental sensitivity: Fluctuations in temperature and humidity can easily cause direct resistance measurement drift, and existing equipment lacks a quantitative assessment mechanism for environmental stability;

[0004] Insufficient current stability: Test current fluctuations will directly affect the accuracy of DC resistance, but traditional methods do not establish a dynamic analysis model for current stability;

[0005] Lack of temperature-resistance synergy: Differences in the temperature coefficients of winding materials are not systematically incorporated into resistance gradient analysis, making it difficult to identify abnormal data.

[0006] Delayed reliability judgment: Detection results rely on manual experience and judgment, and lack a real-time reliability assessment system based on multi-parameter fusion.

[0007] The above problems lead to low credibility of on-site inspection results, which may cause equipment misjudgment or maintenance delays. Summary of the Invention

[0008] In view of the deficiencies in the prior art, the present invention provides a mobile ratio direct resistance detection device to solve the above problems.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: a mobile transformation ratio direct resistance detection device, including a transformation ratio detector and a direct resistance detector installed on a box, and also including:

[0010] The DC resistance detection reliability analysis system is used to judge the detection reliability of the DC resistance detector, including:

[0011] A data acquisition module is used to obtain the current output average value, current fluctuation value, ambient temperature, ambient temperature fluctuation value, ambient humidity, resistance gradient and winding temperature;

[0012] The current stability analysis module builds a current stability model based on the current output average value and current fluctuation value to output the current stability coefficient;

[0013] The environmental stability analysis module builds an environmental temperature model based on the ambient temperature, ambient temperature fluctuation value and ambient humidity to output the environmental stability coefficient;

[0014] The resistance-temperature synergy analysis module constructs a gradient-temperature synergy model based on the resistance gradient under the current stability coefficient and the environmental stability coefficient and the winding temperature to output the resistance-temperature synergy.

[0015] The reliability analysis module constructs a reliability analysis model based on the resistance-temperature synergy, the current stability coefficient and the environmental stability coefficient to output the detection result reliability.

[0016] The judgment module compares the obtained detection result reliability with the preset reliability threshold value, and if the detection result reliability is within the reliability threshold value, it indicates that the detection result is reliable, otherwise, it indicates that the detection result is unreliable.

[0017] On the basis of the above technical solutions, the application further provides the following optional technical solutions:

[0018] The further technical solution is that the reliability analysis model is represented as:

[0019] K = w1M + w2tanh (δK I,cur K E,cur )

[0020] Wherein, K represents the detection result reliability, M represents the resistance-temperature synergy, K I,cur represents the current current stability coefficient, K E,cur represents the current environmental stability coefficient, δ represents the stability product adjustment coefficient, w i represents the weight coefficient and ∑w i = 1, K ∈ [0, 1] and the greater the value, the more reliable the detection result.

[0021] The further technical solution is that the working steps of the resistance-temperature synergy analysis module are:

[0022] The gradient deviation model is constructed based on the resistance gradient and the reference resistance to output the gradient deviation coefficient, and the gradient deviation model is represented as:

[0023]

[0024] Wherein, D represents the gradient deviation, represents the resistance gradient, β represents the gear gradient coefficient, α (T w ) represents the temperature coefficient, R base represents the reference resistance;

[0025] The gradient-temperature synergy model is constructed based on the gradient deviation, the current stability coefficient and the environmental stability coefficient to output the resistance-temperature synergy, and the gradient-temperature synergy model is represented as:

[0026]

[0027] wherein M represents resistance-temperature synergy, D represents gradient deviation, D max represents maximum allowable gradient deviation, K I,cur represents current current stability coefficient, K E,cur represents current environmental stability coefficient, μ represents gradient deviation penalty coefficient, and the M∈[0,1] and the greater the value, the better the synergy.

[0028] Further technical solutions: the α(T w ) is obtained in the following way:

[0029] The winding temperature is introduced into the temperature coefficient function to obtain the temperature coefficient, and the temperature coefficient function is represented as:

[0030]

[0031] wherein α(T w ) represents temperature coefficient, and T w represents winding temperature.

[0032] Further technical solutions: the reference resistance is obtained in the following way:

[0033]

[0034] wherein R base represents reference resistance, R i represents the resistance of the i-th gear of the transformer, and n represents the number of gears.

[0035] Further technical solutions: the working steps of the current stability analysis module are as follows:

[0036] A current stability model is constructed according to the current output average value and the current fluctuation value, and the current stability model is represented as:

[0037]

[0038] wherein K I represents current stability coefficient, ΔI represents current fluctuation value, I represents current output average value, k1 represents current fluctuation sensitivity coefficient, k2 represents adjustment sensitivity, I0 represents reference current value, and K I ∈[0,1] and the greater the value, the more stable the current is;

[0039] The current current stability coefficient is output by introducing the current current output average value and the current current fluctuation value into the current stability model;

[0040] The current current stability coefficient is compared with the preset current stability coefficient threshold value, and if the current current stability coefficient is not within the current stability coefficient threshold value, it indicates that the direct resistance detector is inaccurate.

[0041] Further technical solutions: the working steps of the environmental stability analysis module are:

[0042] An environmental temperature model is constructed based on the environmental temperature, the environmental temperature fluctuation value and the environmental humidity, and the environmental temperature model is expressed as:

[0043] The environmental temperature is introduced into the formula to obtain an environmental temperature index, γ represents a temperature nonlinear coefficient, T opt represents the optimal test temperature, T e represents the environmental temperature;

[0044] The temperature fluctuation value is processed by ratio with the reference temperature to obtain a temperature fluctuation value index;

[0045] The absolute difference between the environmental humidity and the reference humidity is processed by ratio with the reference humidity to obtain an environmental humidity index;

[0046] An environmental stability model is constructed based on the environmental temperature index, the temperature fluctuation index and the environmental humidity index, and the environmental stability model is expressed as:

[0047]

[0048] wherein, K E represents an environmental stability coefficient, T ind represents the environmental temperature index, ΔT ind represents the temperature fluctuation index, H ind represents the environmental humidity index, a1 represents a temperature fluctuation sensitive coefficient, a2 represents a humidity deviation penalty factor, and the K E ∈[0,1] and the greater the value, the more stable the environment;

[0049] The current environmental temperature index, the current temperature fluctuation index and the current environmental humidity index are introduced into the environmental temperature model to output the current environmental stability coefficient;

[0050] The current environmental stability coefficient is compared with a preset environmental stability coefficient threshold value, and if the current environmental stability coefficient is not within the environmental stability coefficient threshold value, it indicates that the direct resistance detector is inaccurate.

[0051] Further technical solutions: the resistance gradient can be obtained through a gradient model, and the gradient model is expressed as:

[0052]

[0053] wherein, represents the resistance gradient of the i-th gear of the transformer decomposition switch, R i represents the resistance of the i-th gear of the transformer, Ni Indicates the number of turns corresponding to the i-th gear.

[0054] Further technical solution: The current fluctuation value can be obtained through a current fluctuation model, and the current fluctuation model is expressed as:

[0055]

[0056] Among them, ΔI represents the current fluctuation value, I k Indicates the instantaneous value of the current at sampling time k, I avg It represents the average current value in a test cycle, N represents the number of sampling points and N>1.

[0057] Further technical solution: The ambient temperature fluctuation value can be obtained through an ambient temperature fluctuation model, and the ambient temperature fluctuation model is expressed as:

[0058]

[0059] Where, ΔT e Indicates the ambient temperature fluctuation value, T e (t) represents the ambient temperature at time t, and T represents the total test duration.

[0060] The present invention provides a mobile ratio direct resistance detection device, which has the following advantages compared with the prior art:

[0061] 1. This invention constructs an environmental temperature model through an environmental stability analysis module, quantifies the interference of ambient temperature, humidity, and their fluctuations on test results, and dynamically outputs an environmental stability coefficient. This design effectively resolves measurement deviations caused by changes in environmental parameters during on-site testing, ensuring that test results remain highly reliable under different environmental conditions.

[0062] 2. The current stability analysis module, a unique feature of this invention, constructs a stability model based on the average current output and fluctuation values, assesses the stability of the test current in real time, and outputs the current stability coefficient. This mechanism can proactively identify abnormal current fluctuations, avoid misjudgments caused by unstable power supply or poor contact, and significantly improve detection accuracy.

[0063] 3. This invention uses a resistance-temperature synergy analysis module, combining winding temperature data with resistance gradient deviation to construct a gradient-temperature synergy model to quantify the degree of matching between material thermal properties and resistance changes. This design eliminates resistance analysis errors caused by material differences and can accurately identify winding anomalies or measurement inaccuracies.

[0064] 4、The application fuses current stability, environmental stability and resistance-temperature cooperativity parameters through a reliability analysis module, constructs a multi-factor reliability model, and sets a preset threshold for automatic comparison. The mechanism can output quantitative reliability indicators in real time, automatically alarms when the results exceed the threshold, and realizes objective and efficient determination of the detection results. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 A flowchart of the direct resistance detection reliability analysis system of the application. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical scheme and advantages of the application clearer and more apparent, the application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application and do not limit the application.

[0067] The specific implementation of the application is described in detail below in combination with specific examples.

[0068] Please refer to Figure 1 For an embodiment of the application, a mobile variable ratio direct resistance detection device is provided, which comprises a variable ratio detector and a direct resistance detector mounted on a box body, and further comprises:

[0069] The direct resistance detection reliability analysis system is used for judging the detection reliability of the direct resistance detector, and comprises:

[0070] The data acquisition module is used for acquiring the current output average value, the current fluctuation value, the environmental temperature, the environmental temperature fluctuation value, the environmental humidity, the resistance gradient and the winding temperature.

[0071] The resistance gradient can be obtained through a gradient model, and the gradient model is represented as:

[0072]

[0073] Wherein, represents the resistance gradient of the i-th gear of the transformer decomposition switch, R i represents the resistance of the i-th gear of the transformer, N i represents the number of turns corresponding to the i-th gear;

[0074] The current fluctuation value can be obtained through a current fluctuation model, and the current fluctuation model is represented as:

[0075]

[0076] Wherein, ΔI represents the current fluctuation value, I k represents the current instantaneous value at sampling time k, I avgrepresents an average value of the current in a test period, N represents a number of sampling points and N>1;

[0077] wherein the environmental temperature fluctuation value can be obtained by an environmental temperature fluctuation model, and the environmental temperature fluctuation model is represented as:

[0078]

[0079] wherein ΔT e represents an environmental temperature fluctuation value, T e (t) represents an environmental temperature at time t, and T represents a total test duration;

[0080] a current stability analysis module, which constructs a current stability model based on the current output average value and the current fluctuation value, and outputs a current stability coefficient;

[0081] an environmental stability analysis module, which constructs an environmental temperature model based on the environmental temperature, the environmental temperature fluctuation value and the environmental humidity, and outputs an environmental stability coefficient;

[0082] a resistance-temperature synergy analysis module, which constructs a gradient-temperature synergy model based on the resistance gradient under the current stability coefficient and the environmental stability coefficient, and the winding temperature, and outputs a resistance-temperature synergy;

[0083] a reliability analysis module, which constructs a reliability analysis model based on the resistance-temperature synergy, the current stability coefficient and the environmental stability coefficient, and outputs a detection result reliability;

[0084] a judgment module, which compares the obtained detection result reliability with a preset reliability threshold value, and if the detection result reliability is within the reliability threshold value, it indicates that the detection result is reliable, otherwise, it indicates that the detection result is unreliable.

[0085] wherein the gradient model refers to reflecting the resistance gradient of the transformer tap changer at different positions by calculating the resistance change rate in sections, and can be specifically implemented by using a discrete difference algorithm, for example, the adjacent difference is calculated for the first and last positions, and the maximum adjacent difference is calculated for the intermediate positions, so as to solve the problem that the traditional single calculation method cannot capture the dynamic gradient. The current fluctuation model refers to calculating the dispersion degree of the current instantaneous value in the test period by using the standard deviation, and can be specifically implemented by using a sliding window sampling method, for example, 100 current samples are collected every 10 seconds to calculate the fluctuation value, so as to overcome the defect that the traditional method ignores the instantaneous fluctuation. The environmental temperature fluctuation model refers to calculating the temperature change amplitude in the test period by using the range, and can be specifically implemented by using a temperature sensor to continuously monitor and record the difference between the maximum value and the minimum value, so as to realize the accurate quantification of the dynamic change of the environmental temperature. The resistance-temperature synergy analysis module refers to calculating the synergy by combining the temperature coefficient of the winding material and the resistance gradient deviation, and can be specifically implemented by using the temperature coefficient function of copper, aluminum or amorphous alloy to calibrate the reference resistance, so as to solve the problem of resistance drift caused by material difference.

[0086] Specifically, the data acquisition module first collects the current output average value, ambient temperature, ambient humidity and winding temperature, and then calculates the resistance value gradient of each gear through the gradient model, and generates the current fluctuation value and temperature fluctuation value respectively by using the current fluctuation model and the ambient temperature fluctuation model. The current stability analysis module inputs the current average value and the fluctuation value into the exponential function model, and outputs the coefficient reflecting the current stability degree, for example, the coefficient significantly decreases when the fluctuation value exceeds the threshold value. The environmental stability analysis module inputs the temperature, temperature fluctuation and humidity parameters into the composite attenuation model, and outputs the environmental stability coefficient, for example, the coefficient attenuates with the increase of fluctuation in high temperature and high humidity environment. The resistance-temperature cooperativity analysis module selects the temperature coefficient function according to the winding material type, calculates the gradient deviation after temperature calibration, and combines the current and environmental stability coefficient to generate the resistance-temperature cooperativity index. The reliability analysis module weights and fuses the cooperativity index and the stability coefficient, and outputs the reliability value in the range of 0 to 1, and the judgment module determines whether the detection result is reliable by comparing the preset threshold value.

[0087] Compared with the prior art, the traditional device only records the absolute value of the resistance value without analyzing the gradient change trend, and the application identifies the abnormal gear difference through the dynamic gradient model. The existing device does not quantify the influence of current fluctuation on measurement accuracy, and the application uses the standard deviation model to evaluate the current stability in real time. The conventional method does not systematically analyze the synergistic effect of environmental temperature fluctuation and material temperature coefficient, and the application realizes multi-factor coupling analysis through temperature calibration and gradient deviation calculation. The traditional reliability judgment relies on artificial experience, and the application constructs a mathematical model to automatically output quantitative evaluation results.

[0088] Through the above technical solutions, the application effectively solves the measurement drift problem caused by environmental sensitivity, and improves the data calibration accuracy by dynamically quantifying the influence of environmental temperature fluctuation and humidity. The current stability model can identify abnormal current fluctuation in real time, avoiding the accumulation of measurement errors caused by transient interference. The resistance-temperature cooperativity analysis can accurately reflect the matching degree of winding material temperature characteristics and resistance value change, and identify abnormal gradient deviation. The reliability evaluation system of multi-parameter fusion realizes real-time automatic judgment of detection results, reduces the dependence on artificial experience, and significantly improves the credibility of on-site detection results

[0089] Preferably, the working steps of the current stability analysis module are:

[0090] A current stability model is constructed according to the current output average value and the current fluctuation value, and the current stability model is expressed as:

[0091]

[0092] wherein K Irepresents a current stability coefficient, ΔI represents a current fluctuation value, I represents a current output average value, k1 represents a current fluctuation sensitivity coefficient, k2 represents an adjustment sensitivity, I0 represents a reference current value, K I ∈[0,1] and the greater the value, the more stable the current is;

[0093] The current current output average value and the current current fluctuation value are introduced into the current stability model to output a current current stability coefficient;

[0094] The current current stability coefficient is compared with a preset current stability coefficient threshold value, and if the current current stability coefficient is not within the current stability coefficient threshold value, it indicates that the direct resistance detector is inaccurate.

[0095] The current fluctuation sensitivity coefficient is a parameter for quantifying the influence of current fluctuation on stability, which can be determined by expert experience calibration or experimental calibration combined with gradient descent algorithm optimization, and is used to adjust the sensitivity of the model to the current fluctuation rate.

[0096] The adjustment sensitivity is a parameter that represents the response strength of the control model to the deviation of the current from the reference value, which can be calibrated by expert experience or by fitting the stability characteristics of different current intervals through a piecewise function, to realize the nonlinear change of the model output when the current deviates from the reference.

[0097] The reference current value is the theoretical output current of the test system under standard working conditions, which can be determined by the rated current of the device or the sliding average value of the historical detection data, and is used as a reference for the model to judge the stability of the current.

[0098] Specifically, the current stability model couples the current fluctuation rate and the reference current deviation through a composite exponential function. When the current output average value is close to the reference current value, the sensitivity of the model to the current fluctuation is dynamically adjusted according to the adjustment sensitivity parameter; when the actual current deviates from the reference value, the logical function term in the exponential function will enhance the punishment of the model to the fluctuation. The current data collected in real time is calculated by the model to generate a normalized stability coefficient, which is used to judge the stability of the current through a preset threshold interval, and an abnormal detection alarm is automatically triggered when the coefficient exceeds the threshold range.

[0099] Compared with the prior art, the traditional detection method only qualitatively judges the stability of the current by manually observing the swing amplitude of the ammeter pointer, while the present scheme realizes dynamic evaluation of the stability of the current by establishing a quantitative mathematical model. The prior art cannot distinguish the stability characteristics under different current conditions, and the present scheme can adaptively adjust the stability judgment standard of different current intervals through the reference current deviation compensation mechanism.

[0100] By the technical solution, the application effectively solves the problem of measurement accuracy reduction caused by current fluctuation, and realizes real-time quantitative evaluation of current stability. The dynamic adjustment exponential model accurately captures the current fluctuation characteristics, avoids the hysteresis of artificial experience judgment, ensures timely suspension of the detection process when the current stability is insufficient, and prevents the generation of false data.

[0101] Preferably, the working steps of the environmental stability analysis module are:

[0102] An environmental temperature model is constructed based on the environmental temperature, the environmental temperature fluctuation value and the environmental humidity, and the environmental temperature model is expressed as:

[0103] The environmental temperature is introduced into the formula to obtain an environmental temperature index, γ represents a temperature nonlinear coefficient, T opt represents the optimal test temperature, T e represents the environmental temperature;

[0104] The temperature fluctuation value is processed by ratio with the reference temperature to obtain a temperature fluctuation value index;

[0105] The absolute difference between the environmental humidity and the reference humidity is processed by ratio with the reference humidity to obtain an environmental humidity index;

[0106] An environmental stability model is constructed based on the environmental temperature index, the temperature fluctuation index and the environmental humidity index, and the environmental stability model is expressed as:

[0107]

[0108] wherein, K E represents an environmental stability coefficient, T ind represents the environmental temperature index, ΔT ind represents the temperature fluctuation index, H ind represents the environmental humidity index, a1 represents a temperature fluctuation sensitivity coefficient, a2 represents a humidity deviation penalty factor, and the K E ∈[0,1] and the greater the value, the more stable the environment;

[0109] The current environmental temperature index, the current temperature fluctuation index and the current environmental humidity index are introduced into the environmental temperature model to output a current environmental stability coefficient;

[0110] The current environmental stability coefficient is compared with a preset environmental stability coefficient threshold value, and if the current environmental stability coefficient is not within the environmental stability coefficient threshold value, it indicates that the direct resistance detector is inaccurate.

[0111] The environmental temperature index is a quantitative index calculated by matching the actual temperature with the optimal test temperature through a nonlinear function, which can be realized by using a Sigmoid function, and is used to reflect the degree of deviation of the test environment temperature from the ideal condition.

[0112] The temperature fluctuation index is a relative value obtained by normalizing the temperature fluctuation value with respect to the reference temperature, which can be realized by using a ratio operation, and is used to eliminate the influence of the dimensional difference of temperature variation in different test scenarios on the evaluation result.

[0113] The environmental humidity index is a relative deviation value of the environmental humidity from the reference humidity, which can be realized by calculating the ratio of the absolute difference to the reference humidity, and is used to quantify the interference strength of humidity anomalies on the detection system.

[0114] The environmental stability model is a multi-parameter evaluation function that integrates the temperature index, the temperature fluctuation index, and the humidity index, which can be realized by combining an exponential decay function with a penalty factor, and dynamically balances the influence weight of each environmental factor on the detection reliability by adjusting the sensitivity coefficient.

[0115] Specifically, when the current temperature data is collected by the environmental temperature sensor, it is converted into a standardized value in the range of 0-1 through the temperature index function. The closer this value is to 1, the closer the temperature condition is to the optimal test state. The temperature fluctuation index eliminates the dimensional difference in different test environments by dividing the maximum temperature difference in the test period by the preset reference temperature. The humidity index represents the potential interference of humidity anomalies on the detection system by calculating the relative deviation of the actual humidity from the standard humidity. The above three indexes are integrated through the exponential decay term and the penalty factor in the environmental stability model, where the temperature fluctuation sensitivity coefficient can adjust the sensitivity of temperature mutation to stability evaluation, and the humidity deviation penalty factor can control the inhibition strength of humidity anomalies on the final coefficient. The environmental stability coefficient output by this can dynamically reflect the influence of the comprehensive environmental condition on the detection accuracy.

[0116] Compared with the prior art, the traditional method only uses a single temperature threshold or humidity threshold for environmental judgment, which cannot quantify the cumulative influence of temperature fluctuation on the detection system, and lacks a multi-parameter collaborative evaluation mechanism. The present scheme constructs a dynamic calculation model of temperature index, temperature fluctuation index, and humidity index, converts the time-varying characteristics of environmental parameters into a quantifiable stability coefficient, and solves the problem of single evaluation dimension and lagging judgment in the prior art.

[0117] By the technical solution, the environmental temperature fluctuation amplitude and humidity deviation degree can be monitored in real time, the test environment stability is dynamically evaluated through multi-parameter fusion calculation, the detection result unreliable warning is automatically triggered when the environment stability coefficient is lower than the preset threshold, the direct resistance measurement error accumulation caused by environmental mutation is avoided, and the credibility and timeliness of the on-site detection result are effectively improved.

[0118] Preferably, the working steps of the resistance-temperature synergy analysis module are:

[0119] The winding temperature is introduced into a temperature coefficient function to obtain a temperature coefficient, and the temperature coefficient function is represented as:

[0120]

[0121] wherein α(T w ) represents a temperature coefficient, T w represents a winding temperature;

[0122] A gradient deviation model is constructed based on the resistance gradient and the reference resistance to output a gradient deviation coefficient, and the gradient deviation model is represented as:

[0123]

[0124]

[0125] wherein D represents a gradient deviation, R represents a resistance gradient, β represents a gear gradient coefficient, α(T w ) represents a temperature coefficient, R base represents a reference resistance, and R i represents the resistance of the i-th gear of the transformer, and n represents the number of gears.

[0126] A gradient-temperature synergy model is constructed based on the gradient deviation, the current stability coefficient and the environment stability coefficient to output a resistance-temperature synergy, and the gradient-temperature synergy model is represented as:

[0127]

[0128] wherein M represents a resistance-temperature synergy, D represents a gradient deviation, D max represents a maximum allowed gradient deviation, K I,cur represents a current current stability coefficient, K E,cur represents a current environment stability coefficient, and μ represents a gradient deviation penalty coefficient, and M∈[0,1] and the greater the value, the better the synergy.

[0129] The temperature coefficient function refers to a mathematical model that converts the winding temperature into a temperature coefficient related to the material properties. It can be implemented in the form of a piecewise function. For example, the temperature coefficient function corresponding to the copper winding is (235+T w ) / 255, aluminum winding is (225+T w ) / 245, amorphous alloy is (180+T w ) / 200, by distinguishing the temperature sensitivity differences of different materials, the influence of the thermal expansion characteristics of the materials on the resistance gradient is eliminated. The gradient deviation model is a mathematical model that calculates the degree of deviation between the measured resistance gradient and the theoretical gradient. It can be implemented in the form of absolute difference, for example By introducing the gear gradient coefficient β to correct the tapping gear effect, combined with the reference resistance R base Establish a standardized reference benchmark. The gradient-temperature synergy model refers to a synergy index that comprehensively evaluates the gradient deviation and stability factors. It can be implemented in the form of a fractional product, for example The deviation tolerance is adjusted by the gradient deviation penalty coefficient μ, dynamically balancing the contribution of current and environmental stability to the evaluation.

[0130] Specifically, after obtaining the winding temperature, the temperature coefficient is calculated by selecting the corresponding temperature coefficient function according to the material type. For example, the temperature coefficient of the copper winding at 50°C is (235+50) / 255≈1.1176. The calculated temperature coefficient is multiplied by the gear gradient coefficient and the reference resistance to generate the theoretical gradient value. For example, when β is 0.95 and R base When the resistance value is 10Ω, the theoretical gradient is 0.95×1.1176×10≈10.62Ω / step. By comparing the absolute deviation between the measured resistance gradient and the theoretical gradient, the gradient deviation coefficient is obtained. For example, when the measured gradient is 11.5Ω / step, the deviation D=|11.5-10.62|=0.88Ω / step. The ratio of the deviation coefficient to the maximum allowable deviation is combined with the current and environmental stability coefficient, and the resistance-temperature synergy is calculated by fractional product. For example, when D max =2Ω / gear, μ=0.5, K I,cur =0.8, K E,cur When =0.7, M=(1 / (1+0.5×0.88 / 2))×(0.8+0.7) / 2≈0.89×0.75≈0.67.

[0131] Compared with the prior art, the traditional method only calculates the resistance gradient with a fixed temperature coefficient, without considering the difference in temperature sensitivity of different winding materials, such as the difference in thermal expansion coefficient of copper and aluminum materials, which can be more than 20%, resulting in cumulative error in gradient analysis. The prior art lacks a cooperative correction mechanism for the gear gradient coefficient and the reference resistance, and it is difficult to maintain the consistency of the gradient reference when testing different gears of the tap switch. The prior art does not establish a dynamic coupling model of gradient deviation and stability coefficient, and cannot accurately evaluate the resistance-temperature cooperativity when the environmental temperature fluctuates or the current is unstable.

[0132] Through the above technical solutions, the present application effectively solves the problem of resistance gradient calculation deviation caused by the difference in temperature characteristics of different materials, and improves the temperature compensation accuracy through the temperature coefficient function of material classification. The established gradient deviation model realizes the dynamic matching of the tap gear gradient and the reference resistance, eliminating the systematic error caused by different test gears. The constructed gradient-temperature cooperative model can adaptively adjust the influence of environmental and current fluctuations on matching degree, for example, when the current fluctuation leads to K I,cur decreases, the reliability of the cooperative evaluation is maintained through the weight balance mechanism.

[0133] Preferably, the reliability analysis model is represented as:

[0134] K=w1M+w2tanh(δK I,cur K E,cur )

[0135] wherein K represents the reliability of the detection result, M represents the resistance-temperature cooperativity, K I,cur represents the current stability coefficient, K E,cur represents the current environmental stability coefficient, δ represents the stability product adjustment coefficient, w i represents the weight coefficient and ∑w i =1, K∈[0,1] and the greater the value, the more reliable the detection result.

[0136] wherein the reliability of the detection result refers to the quantitative evaluation of the credibility of the detection data through mathematical modeling, which can be realized by using normalized weighted calculation method, and is used to comprehensively reflect the coupling influence of environment, current and resistance-temperature cooperativity.

[0137] wherein the resistance-temperature cooperativity refers to the matching degree between the resistance gradient and the winding temperature change, which can be realized by cooperative calculation of gradient deviation and temperature coefficient, and is used to evaluate whether the resistance change conforms to the material thermal characteristics.

[0138] wherein the current stability coefficient is a quantitative index of the fluctuation degree of the test current, which can be realized by using the ratio of the average value and the fluctuation value of the current through the exponential function, and is used to represent the stability of the current output in the test process.

[0139] wherein the current environmental stability coefficient refers to a comprehensive evaluation value of the influence of environmental temperature and humidity on the detection result, and can be calculated by using a nonlinear combination of the temperature index and the humidity index, for quantifying the interference degree of environmental parameter fluctuation on the detection accuracy.

[0140] wherein the stability product adjustment coefficient refers to a sensitivity adjustment parameter of the product of the current and the environmental stability, and can be realized by using a scaling factor of the hyperbolic tangent function, for avoiding the excessive influence of the high stability product value on the reliability model.

[0141] wherein the weight coefficient refers to a contribution proportion parameter of different items in the model, and can be realized by using a normalized weight distribution method, for balancing the contribution weight of the resistance-temperature synergy and the environmental current stability to the reliability.

[0142] Specifically, the reliability analysis model directly reflects the influence of the resistance gradient and the winding temperature matching degree on the detection result by taking the resistance-temperature synergy as the basic item, and introduces the product of the current stability coefficient and the environmental stability coefficient, and uses the hyperbolic tangent function for nonlinear mapping to suppress the interference of extreme stability conditions on the reliability evaluation. The stability product adjustment coefficient can dynamically adjust the sensitivity of the stability product item, for example, when the environmental temperature fluctuates sharply, the direct influence of the fluctuation on the reliability can be reduced by reducing the value of δ. The weight coefficient is normalized to ensure that the contribution proportions of the resistance-temperature synergy and the stability product item are reasonably distributed. Therefore, the model can real-time fuse multiple-dimensional parameters such as the resistance gradient, the temperature characteristic, the current stability and the environmental stability to generate a dynamic quantitative reliability index, replacing the traditional artificial experience judgment.

[0143] Compared with the prior art, the traditional method relies on the subjective experience of the operator to evaluate the reliability of the detection data, and lacks quantitative analysis of the correlation between the current fluctuation, the environmental temperature and humidity change and the resistance gradient. The present scheme realizes dynamic identification of abnormal factors in the detection process by establishing a mathematical model of multi-parameter fusion, nonlinearly combining the gradient-temperature matching degree and the real-time environmental current stability. For example, when the winding temperature and the resistance gradient deviate from the material thermal expansion characteristic, the model automatically reduces the reliability score through gradient deviation calculation without waiting for artificial review.

[0144] By the technical scheme, the application solves the problem of reliability evaluation lag of the traditional detection device, and realizes real-time dynamic quantification of the credibility of the detection result. For example, in the position test of the transformer tap switch, when the resistance gradient is abnormal due to the sudden increase of the ambient temperature, the model can immediately identify the data deviation through the resistance-temperature cooperativity calculation, and judge whether the abnormality is caused by the test current fluctuation combined with the current stability coefficient, so as to quickly generate the reliability score. The scheme effectively avoids the misjudgment risk caused by the insufficient experience of the artificial, and improves the credibility of the on-site detection data.

[0145] It should be noted that, in this document, the terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device.

[0146] Although the embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A mobile transformation ratio and direct resistance detection device, comprising a transformation ratio detector and a direct resistance detector mounted on a box, characterized in that: Also includes: The DC resistance detection reliability analysis system is used to judge the detection reliability of the DC resistance detector, including: A data acquisition module is used to obtain the current output average value, current fluctuation value, ambient temperature, ambient temperature fluctuation value, ambient humidity, resistance gradient and winding temperature; The current stability analysis module builds a current stability model based on the current output average value and current fluctuation value to output the current stability coefficient; The environmental stability analysis module builds an environmental temperature model based on the ambient temperature, ambient temperature fluctuation value and ambient humidity to output the environmental stability coefficient; The resistance-temperature synergy analysis module builds a gradient-temperature synergy model based on the resistance gradient under the current stability coefficient and environmental stability coefficient and the winding temperature to output the resistance-temperature synergy; Reliability analysis module, which builds a reliability analysis model based on resistance-temperature synergy, current stability coefficient, and environmental stability coefficient to output the reliability of the test results; The judgment module compares the reliability of the obtained detection result with the preset reliability threshold. If the reliability of the detection result is within the reliability threshold, it indicates that the detection result is reliable. Conversely, if the reliability of the detection result is not within the reliability threshold, it indicates that the detection result is unreliable.

2. The mobile type ratio direct resistance detection device according to claim 1, characterized in that: The reliability analysis model is expressed as: K=w1M+w2tanh(δK I,cur K E,cur ) Among them, K represents the reliability of the test results, M represents the resistance-temperature synergy, and K I,cur Indicates the current stability coefficient, K E,cur represents the current environmental stability coefficient, δ represents the stability product adjustment coefficient, w i represents the weight coefficient and ∑w i =1, K∈[0,1] and the larger the value, the more reliable the detection result.

3. The mobile transformation ratio direct resistance detection device according to claim 1 or 2, characterized in that: The working steps of the resistance-temperature synergy analysis module are as follows: A gradient deviation model is constructed based on the resistance gradient and the reference resistance to output a gradient deviation coefficient. The gradient deviation model is expressed as: Where D represents the gradient deviation, represents the resistance gradient, β represents the gear gradient coefficient, α(T w ) represents the temperature coefficient, R base Indicates the reference resistance; Based on the gradient deviation, current stability coefficient and environmental stability coefficient, a gradient-temperature synergy model is constructed to output resistance-temperature synergy. The gradient-temperature synergy model is expressed as: Among them, M represents the resistance-temperature synergy, D represents the gradient deviation, and D max Indicates the maximum allowed gradient deviation, K I,cur Indicates the current stability coefficient, K E,cur Represents the current environment stability coefficient, μ represents the gradient deviation penalty coefficient, and M∈[0,1] and the larger the value, the better the coordination.

4. The mobile type ratio direct resistance detection device according to claim 3, characterized in that: The α(T w ) is obtained as follows: The winding temperature is introduced into the temperature coefficient function to obtain the temperature coefficient. The temperature coefficient function is expressed as: Among them, α(T w ) represents the temperature coefficient, T w Indicates the winding temperature.

5. The mobile type ratio direct resistance detection device according to claim 3, characterized in that: The reference resistance is obtained as follows: Among them, R base Represents the reference resistance, R i It represents the resistance value of the transformer in the i-th gear, and n represents the number of gears.

6. The mobile type ratio direct resistance detection device according to claim 3, characterized in that: The working steps of the current stability analysis module are: A current stability model is constructed based on the current output average value and the current fluctuation value. The current stability model is expressed as: Among them, K I represents the current stability coefficient, ΔI represents the current fluctuation value, I represents the current output average value, k1 represents the current fluctuation sensitivity coefficient, k2 represents the adjustment sensitivity, I0 represents the reference current value, K I ∈[0,1] and the larger the value, the more stable the current; Importing the current output average value and the current current fluctuation value into the current stability model to output the current current stability coefficient; The current current stability coefficient is compared with a preset current stability coefficient threshold. If the current current stability coefficient is not within the current stability coefficient threshold, it indicates that the DC resistance tester is not accurate.

7. The mobile type ratio direct resistance detection device according to claim 3, characterized in that: The working steps of the environmental stability analysis module are: An ambient temperature model is constructed based on the ambient temperature, ambient temperature fluctuation value, and ambient humidity. The ambient temperature model is expressed as: Importing the ambient temperature into the formula The ambient temperature index is obtained from the equation, γ represents the temperature nonlinear coefficient, T opt Indicates the optimal test temperature, T e Indicates the ambient temperature; The temperature fluctuation value is compared with the reference temperature to obtain a temperature fluctuation value index; The absolute difference between the ambient humidity and the reference humidity is compared with the reference humidity to obtain the ambient humidity index; An environmental stability model is constructed based on the ambient temperature index, the temperature fluctuation index, and the ambient humidity index. The environmental stability model is expressed as: Among them, K E represents the environmental stability coefficient, T ind Indicates the ambient temperature index, ΔT ind Indicates the temperature fluctuation index, H ind represents the environmental humidity index, a1 represents the temperature fluctuation sensitivity coefficient, a2 represents the humidity deviation penalty factor, and K E ∈[0,1] and the larger the value, the more stable the environment; Import the current ambient temperature index, the current temperature fluctuation index and the current ambient humidity index into the ambient temperature model to output the current ambient stability coefficient; The current environmental stability coefficient is compared with the preset environmental stability coefficient threshold. If the current environmental stability coefficient is not within the environmental stability coefficient threshold, it indicates that the direct resistance tester is not accurate.

8. The mobile type ratio direct resistance detection device according to claim 3, characterized in that: The resistance gradient can be obtained through a gradient model, which is expressed as: in, Represents the resistance gradient of the transformer decomposition switch in the i-th gear, R i Indicates the resistance value of the transformer in the i-th gear, N i Indicates the number of turns corresponding to the i-th gear.

9. The mobile type ratio direct resistance detection device according to claim 3, characterized in that: The current fluctuation value can be obtained through a current fluctuation model, and the current fluctuation model is expressed as: Among them, ΔI represents the current fluctuation value, I k Indicates the instantaneous value of the current at sampling time k, I avg It represents the average current value in a test cycle, N represents the number of sampling points and N>1.

10. The mobile type ratio direct resistance detection device according to claim 3, characterized in that: The ambient temperature fluctuation value can be obtained through an ambient temperature fluctuation model, and the ambient temperature fluctuation model is expressed as: Where, ΔT e Indicates the ambient temperature fluctuation value, T e (t) represents the ambient temperature at time t, and T represents the total test duration.

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