Self-adaptive cable grounding resistance detector and self-checking method

Abstract

The invention discloses a self-adaptive cable grounding resistance detector and a self-checking method, the self-adaptive cable grounding resistance detector comprises a shell, the shell is provided with an indicating lamp, a charging socket, a power switch and a single-ended grounding and cross-connected wiring socket, the shell is internally provided with a main control unit, and the algorithm of the main control unit has functions of function calibration, compensation calibration, power supply self-checking and the like. The main control unit is integrated on a circuit board, and can adapt to various grounding detections without replacing hardware by controlling automatic switching of a relay, so that the universality and the economical efficiency are improved. The device is controlled by an integrated circuit chip, has high precision and high environmental adaptability, and is suitable for single-end grounding and cross interconnection grounding resistance detection of a high-voltage cable. According to the invention, the self-checking process is completed before starting up, and through pulse current and standard resistance simulation detection, the measurement accuracy is improved, and personal errors are reduced.

Description

Technical Field:

[0001] The present invention relates to the field of high-voltage cable grounding detection, and particularly to an adaptive cable grounding resistance detector and a self-checking method. Background Art:

[0002] Single-end grounding is a common grounding method for high-voltage cables, especially suitable for cable lines with relatively short distances, usually less than 500 meters. In the single-end grounding method, one end of the cable is grounded through the metal sheath, while the other end remains insulated or is grounded through a sheath protector. This grounding method can reduce the grounding loop current, thereby reducing the energy loss and heating effect during cable operation. Prevent overvoltage, extend the cable life and ensure the safe operation of the system. Single-end grounding is particularly important in high-voltage power transmission systems, which can ensure the stability and reliability of the cable system, especially suitable for scenarios with short lines and high fault voltages.

[0003] Cross bonding is a grounding technique for long-distance high-voltage cables, mainly to solve problems such as cable heating and sheath aging caused by induced voltage and loop current effects. In the cross-bonding grounding method, the multi-point grounding design effectively disperses the electromagnetic induction current at a single point, significantly reducing the loop current intensity at each point and reducing the heating phenomenon of the cable sheath. By reducing the heating and aging of the cable metal sheath, cross bonding extends the service life of the cable and is suitable for long-distance cable lines. Therefore, cross bonding has a wide range of applications in long-distance high-voltage power transmission systems. Especially in complex environments with strong loop current effects, it can significantly reduce the failure rate and ensure the reliable operation of the power transmission system.

[0004] Existing cable grounding resistance detectors have problems that are easily affected by external environmental factors in practical applications, resulting in unstable and inaccurate measurement results, mainly reflected in the following aspects:

[0005] (1) Influence of environmental harmonics: There are inevitably various harmonics in the power system. Especially in industrial areas, harmonics will interfere with the measurement of the cable grounding resistance, resulting in large deviations in the measurement results of the instrument.

[0006] (2) Influence of temperature and humidity fluctuations: Fluctuations in environmental temperature and humidity will also affect the measurement of the cable resistance value, resulting in inconsistent results and making it difficult to ensure the measurement accuracy.

[0007] (3) Influence of power supply voltage fluctuations: Existing instruments mostly rely on battery power supply. If the battery voltage is unstable or insufficient, it will directly affect the reliability of the measurement data. Summary of the Invention:

[0008] The object of the present invention is to solve the above problems, and to provide an adaptive cable grounding resistance detector and a self-checking method, aiming to achieve automatic calibration and stability improvement of the device, enabling the detector to complete the detection preparation before startup and ensuring that the device is always in the best working state. By improving the measurement accuracy and stability, reducing the influence of the external environment on the measurement results, and ultimately achieving more accurate, reliable and efficient grounding resistance measurement.

[0009] The object of the present invention is achieved by the following technical solutions:

[0010] An adaptive cable grounding resistance detector includes a housing, in which a battery, an indicator light, a charging socket, a power switch, and a wiring socket for single-end grounding and cross-interconnection are installed. A main control unit is also installed in the housing. The algorithm of the main control unit has functions of calibration, compensation calibration, and power supply self-check. The main control unit is integrated on a circuit board and can adapt to various grounding plate detections through an automatic relay switching design without replacing hardware.

[0011] A further improvement of the present invention is that before startup, the adaptive cable grounding resistance detector will perform a series of self-checking processes to ensure that the device is calibrated and meets the accuracy requirements before the detection starts. The specific steps are as follows:

[0012] A. Self-check startup:

[0013] When the power switch on the device housing is pressed to turn on the machine, the system first automatically starts the self-checking process, and the main control unit will enter the self-check mode to prepare for the complete calibration of the device and the detection of the system status.

[0014] B. Pulse signal injection:

[0015] In the self-checking process, the system uses pulse current signal injection. The specific steps are as follows:

[0016] The main control unit will generate a set of precisely regulated pulse signals, which are injected into the internal circuit from the wiring sockets for cross-interconnection and single-end grounding to simulate the grounding resistance detection process of the cable. To ensure the standardization of the detection, a standard resistor is provided on the main control unit, and the pulse signals will be transmitted to the measurement channel of the main control unit after passing through this standard resistor to simulate the actual working conditions of the cable system.

[0017] C. Data acquisition:

[0018] During the transmission of the pulse signals through the standard resistor, the indicator light starts to flash, and the measurement channel starts to collect data in real time.

[0019] Voltage and current detection: After the measurement channel collects the voltage and current values passing through the standard resistor, these data are uploaded to the main control unit.

[0020] Multiple Sampling: To ensure data accuracy, the main control unit will perform multiple samplings and take the average of the samples to filter out noise or accidental interferences and ensure data stability.

[0021] D. Data Analysis:

[0022] After the main control unit completes the acquisition of voltage and current data, it immediately performs analysis and processing.

[0023] Calculation of Nominal Resistance Value: Based on a known standard resistance, the main control unit calculates the resistance value obtained from the acquired data.

[0024] Analysis and Deviation Detection: The main control unit compares the calculated resistance value with the standard value to determine the deviation between the actual measurement and the ideal value. For deviations outside the set allowable range, the main control unit will issue a prompt message for the user to perform further calibration or adjustment.

[0025] E. Environmental Harmonic Compensation Calibration:

[0026] If external harmonic interference is detected, the main control unit will use the environmental harmonic compensation calibration function to optimize the detection accuracy:

[0027] Data Fitting: The main control unit calibrates the measurement results through a data fitting function to make them more matched with the actual resistance value. Through the fitted data, the deviation caused by external environmental harmonics to the resistance measurement is compensated.

[0028] Real-time Compensation: According to the fitting function, the main control unit can compensate the resistance data during actual measurement to offset the interference of environmental harmonics and ensure the stability of subsequent detection results.

[0029] F. Evaluation and Output of Self-test Results:

[0030] After completing all data acquisition and analysis, the main control unit will evaluate the self-test results and make the following processing according to the results:

[0031] Qualified Judgment: If the self-test results meet the accuracy range set by the device, the system will mark the device as "calibrated" and prepare to enter the normal detection mode.

[0032] Abnormal Prompt: If the self-test results exceed the allowable error range, the system will prompt the user to perform maintenance or manual calibration. The abnormal prompt message includes an analysis of possible causes to provide guidance for the user to troubleshoot problems.

[0033] G. Power Supply Detection:

[0034] During the self-test process, the main control unit will also detect the battery voltage to ensure power supply stability:

[0035] The main control unit indirectly obtains the actual voltage of the battery by detecting the voltage difference between key resistors to ensure that it is within the normal operating range.

[0036] If it is detected that the battery voltage is too low, the system will issue a reminder to prevent inaccurate measurement due to insufficient power supply.

[0037] H. Self-check process completed:

[0038] When all the above steps are completed and the test results are normal, the system self-check process ends, and the device enters the standby state, ready to measure the external grounding resistance at any time. At this time, the device has completed internal calibration and environmental harmonic compensation to ensure that the instrument is in the best working state before the measurement starts.

[0039] A further improvement of the present invention lies in: in step E, environmental harmonic calibration: specifically describe the method and principle of realizing harmonic compensation through a fitting function to ensure the accuracy of the device in different harmonic environments. Two input variables and one output variable are defined in the model. This system is a five-layer network. The parameters of the first layer are premise parameters, and the fourth layer is conclusion parameters. There are three methods for learning and updating the network parameters: gradient descent method, least squares method, and a hybrid method of the two, which are specifically selected according to adaptability.

[0040] The inference process of the fuzzy inference system can successfully transfer the input variables through calculation to the final output value. This requires a series of transfer processes, which are completed by five-layer mapping functions: starting from mapping the input variables to the input membership function, the input membership function is mapped to the rules, the rules are mapped to the output characteristics, then mapped to the output membership function, and finally mapped to a certain output decision.

[0041] The first layer is the membership layer of the input variables, Q ij is the membership function of the fuzzy set (a 1 , a 2 , b 1 , b 2 ). As shown in the figure, x and y represent the input current and voltage signals respectively. a 1 a 2 b 1 b 2 represent the signal time parameters related to x and y respectively. μ is the membership degree, indicating the degree to which the input variable belongs to a certain fuzzy set. The value range is [0, 1]. The membership function can be selected as a bell-shaped function or a Gaussian function to calculate the dimension membership degree of each input data and the fuzzification operation.

[0042]

[0043] The second layer constructs the rule weights using the results of the membership function. The output of one type of node represents the credibility of one rule, and the output is the product of all input signals, which is used to match the antecedent of the fuzzy rule and calculate the excitation strength of each fuzzy rule:

[0044] Q 2，i = w i = μ ai (x)·μ bi (y), where i = 1, 2(1.15)

[0045] w is the rule weight, which reflects the credibility of the input variable in exciting the rule. Each node corresponds to a fuzzy rule, and the output represents the confidence of that rule.

[0046] The third layer calculates the normalized credibility of each rule. The normalized weight of the i-th rule output is:

[0047] where i = 1, 2(1.16)

[0048] The fourth layer calculates the output of the fuzzy rule. Each node is a node function. This layer shares the antecedent parameters of the entire system, calculates the consequent parameters of all rules, and the output is:

[0049]

[0050] In the formula, i = 1, 2, is the output of the third layer, that is, the normalized calculated value, {l i , m i , n i} is the setting of the consequent node parameters, x 1 and x 2 are the input variables, w is the normalized weight calculated previously, and f i represents the output of the i-th rule, that is, the node function.

[0051] The fifth layer calculates the output of all rules of the entire system:

[0052]

[0053] In the formula, i = 1, 2, z is the weighted sum of the outputs of all rules, and f j is the output of the i-th rule, which comes from the previous layer.

[0054] Aiming at suppressing the noise model and combining self - organizing learning technology to determine fuzzy inference rules. In the self - organizing learning stage, first, use the designed on - site discharge simulation signal to construct training samples, conduct fuzzy inference division on the nodes of the second and fourth layers of the system, and calculate the membership function. Establish fuzzy rules during the normalization of all rule strengths in the third layer. In this stage, the width and center of the membership function are dynamically determined by self - organizing learning technology. The training data is input into the network from both the input and output sides to make the membership function cover the input - output area. The self - organizing learning back - propagation formula is as follows:

[0055]

[0056] where \(Z(t)\) is the expected output, is the actual output, through fuzzy learning inference:

[0057]

[0058] where \(\eta\) is the learning rate, and is to obtain the learning rule \(\omega\) of the transfer process i and \(m\) i , calculate from the fourth layer in reverse as follows:

[0059]

[0060] In the formula, \(u\) i is the confidence of the \(i\) - th rule

[0061] Thus, determine the center parameter:

[0062]

[0063] Width parameter:

[0064]

[0065] Repeat the above steps to determine the parameters of the second layer, obtain the dynamic membership parameters, and finally determine the fuzzy inference rules to adapt to environmental changes and interference, achieving the purpose of suppressing noise signals.

[0066] In specific application implementations, the real measured signal is represented by d(k), the environmental interference signals are represented by n(k) and n(k - 1), the colored noise is obtained by nonlinearly approximating white noise as s(k) = f(n(k), n(k - 1)), the measured signal g(k) is the sum of the real signal d(k) and the colored noise s(k), that is, g(k) = d(k) + s(k). The colored noise s(k)' is approximated, and the output in the training data obtained by reproduction is the approximated colored noise s(k)'. The approximated colored noise is removed from the measured signal to obtain the estimated signal d(k)' = g(k) + s(k)'. The closer the estimated signal d(k)' is to the real signal d(k), the better the function fitting effect is.

[0067] Since the detection signal is oscillatory and decaying, single-exponential decay pulses and double-exponential decay pulses are usually used for simulation, and their mathematical expressions are as shown in Equations (1.11) and (1.12).

[0068]

[0069] Where: a 1 and a 2 are the signal amplitudes, τ 1 and τ 2 are the decay coefficients, f is the oscillation frequency, and 0.20 mV Gaussian white noise is superimposed. Using the proposed adaptive fitting method to process the measured signal can remove the influence of a large number of harmonics, weaken the distortion at the pulse endpoints, and make the waveform closer to the original signal.

[0070] The present invention has the following advantages compared with the prior art:

[0071] Through steps such as pulse signal injection, data acquisition and analysis, harmonic compensation calibration, and power supply detection, the present invention ensures the accuracy and stability of the device from multiple perspectives. This self-check mechanism before startup enables the device to complete self-calibration efficiently without complex manual debugging, meeting the measurement requirements of high precision and high reliability.

[0072] Controlled by an integrated circuit chip, the present invention has high precision and environmental adaptability, and is applicable to the detection of single-end grounding and cross-bonding grounding resistances of high-voltage cables. The present invention completes the self-check process before startup, improves the measurement accuracy through simulated detection with pulse current and standard resistance, and reduces human error. The main control unit effectively reduces the influence of environmental interference through the environmental harmonic compensation function and ensures the stability of the device in different temperature, humidity, and electromagnetic interference environments. In addition, the present invention has a built-in power supply detection function to ensure stable power supply and extend the service life of the device. Brief Description of the Drawings:

[0073] Figure 1This is the overall structural schematic diagram of the adaptive cable grounding resistance detector of the present invention.

[0074] Figure 2 This is the cross-sectional schematic diagram of the adaptive cable grounding resistance detector of the present invention.

[0075] In the figure: 1. Housing; 2. Main control unit; 3. Battery; 4. Wiring socket for cross-bonding and single-end grounding; 5. Power switch; 6. Charging socket; 7. Indicator light.

[0076] Figure 3 This is the denoising effect of the ANFIS noise suppression model of the adaptive fitting mode method proposed by the present invention.

[0077] Figure 4 This is the adaptive fuzzy inference structure of the adaptive fitting mode method proposed by the present invention.

[0078] Figure 5 This is the self-check and calibration function structure diagram of the adaptive fitting mode method proposed by the present invention. Specific implementation method:

[0079] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. The elements and features described in one embodiment of the present invention can be combined with those shown in one or more other embodiments. It should be noted that, for the sake of clarity, the representation and description of components and processes that are irrelevant to the present invention and known to those of ordinary skill in the art are omitted in the description. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0080] The present invention proposes a self-check and calibration function, which reduces the interference of environmental factors in the following ways, thereby improving the accuracy and stability of measurement:

[0081] Self-check function: Perform self-check before the instrument is powered on. Through the detection of pulsed current of simulated resistance values, ensure that the parameters of the instrument are within the normal range, and reduce the errors caused by abnormalities inside the instrument.

[0082] Environmental harmonic compensation and calibration function: Compare the value obtained from self-check with the nominal value, and calibrate the measurement result through a data fitting function to compensate for the deviation caused by environmental harmonics.

[0083] Power supply detection function: It monitors the battery voltage in real time to ensure that the power supply voltage is stable within the range required by the instrument, guaranteeing the continuity and accuracy of the measurement results. Adding self-check and calibration functions is a necessary improvement measure to improve the measurement accuracy and stability of the grounding resistance detector, which can effectively reduce the interference of the external environment on the measurement and make the measurement results more reliable.

[0084] The present invention will be further described below with reference to the accompanying drawings:

[0085] An adaptive cable grounding resistance detector includes a housing 1, on which an indicator light 7, a charging socket 6, a power switch 5, and a wiring socket 4 for single-end grounding and cross-interconnection are installed. A main control unit 2 is installed inside the housing 1. The algorithm of the main control unit 2 has functions such as function calibration, compensation calibration, and power supply self-check. The main control unit 2 is integrated on a circuit board. Through the design of automatic switching of control relays, it can adapt to various grounding plate detections without replacing hardware, improving versatility and economy.

[0086] Self-check method: Before starting up, the adaptive cable grounding resistance detector will perform a series of self-check processes to ensure that the device is calibrated and meets the accuracy requirements before the detection starts. The following is a detailed description of the self-check process:

[0087] 1. Self-check startup

[0088] When the power switch 5 on the device housing 1 is pressed to start up, the system first automatically starts the self-check process. The main control unit 2 will enter the self-check mode and prepare for the complete calibration of the device and the detection of the system status. This step ensures that the internal detection functions and various parameters of the device are in an ideal state before the external detection operation starts.

[0089] 2. Pulse signal injection

[0090] In the self-check process, the system uses pulse current signal injection instead of traditional large direct current. The specific steps are as follows:

[0091] The main control unit 2 will generate a group of precisely regulated pulse signals, which are injected into the internal circuit from the wiring socket 2 for cross-interconnection and single-end grounding to simulate the grounding resistance detection process of the cable.

[0092] To ensure the standardization of the detection, a standard resistance (such as 50 milliohms) is provided on the main control unit 2. The pulse signal will pass through this standard resistance and then be transmitted to the measurement channel of the main control unit 2 to simulate the actual working conditions of the cable system.

[0093] The pulse current is selected to replace the traditional large direct current mainly to improve the detection accuracy, reduce the loss to the system, and obtain more stable measurement results in complex environments. Specifically, the pulse current has the following significant advantages compared with the large direct current and has a positive impact on the stability of the system: Reducing the heat loss of the cable system. The traditional large direct current detection method often brings large power consumption. Especially in the detection of high-voltage cables, continuous large current injection is likely to cause the metal sheath of the cable to heat up, resulting in temperature drift during the measurement process and affecting the stability of the resistance value. The pulse current effectively reduces the cable temperature rise through short-term and intermittent current injection, thus avoiding the influence of temperature on the measurement results and improving the measurement accuracy. Reducing the impact on the system to be measured and prolonging the cable life. The large direct current will bring a large load to the cable sheath and grounding system during the measurement process, and long-term use may have an adverse impact on the cable insulation layer. The pulse current can complete data acquisition in a short time, meeting the measurement requirements while avoiding excessive consumption of cable materials, which helps to prolong the service life of the equipment. Improving the anti-interference ability and being suitable for complex power environments. In the power system, harmonics and electromagnetic interference are common factors affecting the measurement accuracy. Due to the relatively richer frequency characteristics of the pulse current compared with direct current, the harmonic frequency band can be effectively avoided by optimizing the pulse frequency, reducing the influence of environmental interference. In addition, the pulse current can complete sampling in a short time and has a strong suppression ability for transient interference in the power system, making the measurement results more stable and reliable. Reducing energy consumption and improving the working efficiency of the detection equipment. The energy consumption of the pulse current is much lower than that of the continuous large direct current, especially suitable for battery-powered detection equipment. The low-energy consumption design not only prolongs the battery life of the instrument but also improves the overall working efficiency of the equipment, enabling it to perform long-term and high-precision detection more flexibly in the wild and at power construction sites. Using the pulse current signal can significantly improve the stability of the system, enabling the detection instrument to maintain consistent measurement results under different environmental conditions. First, reducing heat loss and the risk of cable damage helps to maintain the integrity of the cable system, thus ensuring long-term stability. Second, by selecting the appropriate pulse frequency to avoid the interference frequency band, the anti-interference ability of the measurement is effectively improved, ensuring the stability and reliability of the results. Finally, the low-power pulse mode can also effectively support the long-term stable operation of the detection instrument in different scenarios and is not easily interrupted or in error due to energy consumption problems.

[0094] 3. Data acquisition

[0095] During the transmission of the pulse signal through the standard resistor, the indicator light 7 starts to flash, and the measurement channel starts to collect data in real time.

[0096] Voltage and current detection: After the measurement channel collects the voltage and current values after passing through the standard resistor, these data are uploaded to the main control unit 2.

[0097] Multiple Sampling: To ensure the accuracy of data, the main control unit 2 will perform multiple samplings and take the average value of the samples to filter out noise or accidental interference and ensure the stability of the data.

[0098] 4. Data Analysis

[0099] After the main control unit 2 completes the acquisition of voltage and current data, it immediately performs analysis and processing:

[0100] Calculate the nominal resistance value: Based on the known standard resistance, the main control unit 2 calculates the resistance value obtained from the acquired data.

[0101] Analysis and Deviation Detection: The main control unit 2 compares the calculated resistance value with the standard value to determine the deviation between the actual measurement and the ideal value. For deviations outside the set allowable range (e.g., ±0.5%), the main control unit 2 will issue a prompt message for the user to perform further calibration or adjustment.

[0102] 5. Environmental Harmonic Compensation Calibration

[0103] If external harmonic interference is detected, the main control unit 2 will use the environmental harmonic compensation calibration function to optimize the detection accuracy:

[0104] Data Fitting: The main control unit 2 calibrates the measurement results through a data fitting function to make them more match the actual resistance value. Through the fitted data, the deviation caused by external environmental harmonics to the resistance measurement is compensated.

[0105] Real-time Compensation: According to the fitting function, the main control unit 2 can compensate the resistance data in actual measurement to offset the interference of environmental harmonics and ensure the stability of subsequent detection results.

[0106] Environmental Harmonic Calibration: Specifically describe the method and principle of realizing harmonic compensation through the fitting function to ensure the accuracy of the equipment in different harmonic environments. Two input variables and one output variable are defined in the model. This system is a five-layer network, as Figure 4 shown. The square represents an adaptive node with variable parameters, and the circle represents a fixed node with no parameters. The parameters of the first layer are premise parameters, and the fourth layer is conclusion parameters. There are three methods for learning and updating the network parameters: gradient descent method, least squares method, and a hybrid method of the two, which are specifically selected according to adaptability.

[0107] The inference process of the fuzzy inference system can successfully transfer the input variables through calculation to the final output value. This requires a series of transfer processes, which are completed by five-layer mapping functions: First, map the input variables to the input membership functions, then map the input membership functions to the rules, map the rules to the output characteristics, then map to the output membership functions, and finally map to a certain output decision.

[0108] The first layer is the membership layer of input variables, Q ij is the membership function of the fuzzy set (a 1 , a 2 , b 1 , b 2 ). As shown in the figure, x and y represent the input current and voltage signals respectively. a 1 a 2 b 1 b 2 represent the signal time parameters related to x and y respectively. μ is the membership degree, indicating the degree to which the input variable belongs to a certain fuzzy set, and its value range is [0, 1]. The membership function can be selected as a bell-shaped function or a Gaussian function to calculate the dimensional membership degree of each input data and the fuzzification operation.

[0109]

[0110] The second layer uses the calculation results of the membership function to construct the rule weights. One kind of node output represents the credibility of one rule, and the output is the product of all input signals, which is used to match the antecedent of the fuzzy rule and calculate the excitation strength of each fuzzy rule:

[0111] Q 2，i = w i = μ ai (x)·μ bi (y), where i = 1, 2(1.27)

[0112] w is the rule weight, reflecting the credibility of the input variable exciting the rule. Each node corresponds to a fuzzy rule, and the output represents the confidence of that rule.

[0113] The third layer calculates the normalized credibility of each rule, and the output of the normalized weight of the i-th rule is:

[0114] where i = 1, 2(1.28)

[0115] The fourth layer calculates the output of the fuzzy rule. Each node is a node function. This layer shares the antecedent parameters of the entire system, calculates the consequent parameters of all rules, and the output is:

[0116]

[0117] In the formula, i = 1, 2, is the output of the third layer, that is, the normalized calculated value, {l i , m i , n i} is the setting of the consequent node parameters, x 1 and x 2 are input variables, w is the normalized weight calculated previously, f i represents the output of the \(i\)-th rule, i.e., the node function.

[0118] The fifth layer calculates the outputs of all the rules of the entire system:

[0119]

[0120] where \(i = 1, 2,\cdots\), \(z\) is the weighted sum of all rule outputs, and \(f\) j is the output of the \(i\)-th rule, originating from the previous layer.

[0121] For the noise suppression model, combined with the self-organizing learning technique to determine the fuzzy inference rules. In the self-organizing learning stage, first, using the designed on-site discharge simulation signal, training samples are constructed, and the nodes of the second and fourth layers of the system are divided by fuzzy inference and the membership functions are calculated. Fuzzy rules are established in the normalization of all rule strengths in the third layer. In this stage, the width and center of the membership function are dynamically determined by the self-organizing learning technique, and the training data is input into the two-input network of the input and output to make the membership function cover the input and output regions. The self-organizing learning backpropagation formula is as follows:

[0122]

[0123] where \(Z(t)\) is the expected output, is the actual output, through fuzzy learning inference:

[0124]

[0125] where \(\eta\) is the learning rate, and is to obtain the learning rule \(\omega\) of the transfer process i and \(m\) i , calculated backward from the fourth layer as follows:

[0126]

[0127] where \(u\) i is the confidence of the \(i\)-th rule

[0128] Thus, the center parameter is determined:

[0129]

[0130] The width parameter:

[0131]

[0132] Repeat the above steps to determine the parameters of the second layer, obtain the dynamic membership parameters, and finally determine the fuzzy inference rules to adapt to environmental changes and interference, achieving the purpose of suppressing noise signals.

[0133] In specific application implementations, the real measured signal is represented by d(k), the environmental interference signals are represented by n(k) and n(k - 1), the colored noise is obtained by non - linear approximation of white noise, s(k)=f(n(k), n(k - 1)), the measured signal g(k) is the sum of the real signal d(k) and the colored noise s(k), that is, g(k)=d(k)+s(k). Approximate the colored noise s(k)', and the output in the training data obtained by reproduction is the approximated colored noise s(k)'. Remove the approximated colored noise from the measured signal to obtain the estimated signal d(k)' = g(k)+s(k)'. The closer the estimated signal d(k)' is to the real signal d(k), the better the function fitting effect is.

[0134] Since the detection signal is oscillatory and decaying, single - exponential decay pulses and double - exponential decay pulses are usually used for simulation, and their mathematical expressions are as shown in Eqs. (1.11) and (1.12).

[0135]

[0136] Where: a 1 and a 2 are the signal amplitudes, τ 1 and τ 2 are the decay coefficients, f is the oscillation frequency, and 0.20 mV Gaussian white noise is superimposed. Using the proposed adaptive fitting method to process the measured signal, as Figure 3 shown, it can remove the influence of a large number of harmonics, weaken the distortion at the pulse endpoints, and the waveform is closer to the original signal.

[0137] 6. Evaluation and Output of Self - test Results

[0138] After completing all data acquisition and analysis, the main control unit 2 will evaluate the self - test results and make the following processing according to the results:

[0139] Qualified determination: If the self - test results meet the accuracy range set by the device, the system will mark the device as "calibrated" and be ready to enter the normal detection mode.

[0140] Abnormal prompt: If the self - test results exceed the allowable error range, the system will prompt the user for maintenance or manual calibration. The abnormal prompt information includes possible cause analysis (such as insufficient battery 3 voltage, abnormal internal resistance, etc.), providing guidance for the user to troubleshoot problems.

[0141] 7. Power Supply Detection

[0142] During the self - test process, the main control unit 2 will also detect the voltage of the battery 3 to ensure power supply stability:

[0143] The main control unit 2 indirectly obtains the actual voltage of the battery by detecting the voltage difference between key resistors (such as R125 and R126) to ensure that it is within the normal operating range.

[0144] If the voltage of battery 3 is detected to be low, the system will issue a reminder to prevent inaccurate measurement due to insufficient power supply.

[0145] 8. Completion of the self-check process

[0146] When all the above steps are completed and the test results are normal, the system self-check process ends, and the device enters the standby state, ready to measure the external ground resistance at any time. At this time, the device has completed internal calibration and environmental harmonic compensation to ensure that the instrument is in the best working state before the measurement starts.

[0147] The main functions of the present invention:

[0148] 1. Single-end grounding / cross-interconnection resistance measurement self-check: Using a pulsed current signal, self-check is performed by simulating a standard resistor to analyze the accuracy of the output result.

[0149] 2. Environmental harmonic compensation calibration: By comparing the self-check measured value with the nominal value, the data fitting method is used to compensate for the influence of environmental harmonics to improve the measurement stability.

[0150] 3. Power supply detection function: Monitor the battery voltage to ensure the stability of the power supply and the reliable operation of the microcontroller.

[0151] The present invention has significant application value in the detection of high-voltage cables and is suitable for use by power companies, cable manufacturers, and power maintenance enterprises. It can significantly reduce the manual debugging cost, shorten the detection time, and improve the safety and reliability of the cable system.

[0152] Finally, it should be noted that: Although the present invention and its advantages have been described in detail above, it should be understood that various changes, substitutions, and transformations can be made without exceeding the spirit and scope of the present invention as defined by the appended claims. Moreover, the scope of the present invention is not limited to the specific embodiments of the processes, devices, means, methods, and steps described in the specification. Those of ordinary skill in the art will readily understand from the disclosure of the present invention that according to the present invention, processes, devices, means, methods, or steps that perform substantially the same functions as the corresponding embodiments described herein or obtain substantially the same results can be used, existing and to be developed in the future. Therefore, the appended claims are intended to cover such processes, devices, means, methods, or steps within their scope.

Claims

1. An adaptive cable grounding resistance detector, characterized in that: The invention comprises a housing (1), wherein a battery (3), an indicator light (7), a charging socket (6), a power switch (5), and a wiring socket (4) with single-end grounding and cross-connection are installed in the housing (1); a main control unit (2) is also installed in the housing (1); the algorithm of the main control unit (2) has function calibration, compensation calibration, and power supply self-check; the main control unit (2) is integrated on a circuit board, and is designed to automatically switch through a control relay, so that it can adapt to various types of grounding sheet detection without replacing hardware.

2. According to claim 1, an adaptive cable grounding resistance detector is characterized in that: Before starting up, the adaptive cable ground resistance tester will perform a series of self-test processes to ensure that the device has been calibrated and meets the accuracy requirements before the test begins. The specific steps are as follows: A. Self-test start: When the power switch (5) on the device housing (1) is pressed to turn on the device, the system will first automatically start the self-test process, and the main control unit (2) will enter the self-test mode to prepare for the complete calibration of the device and the system status detection. B. Pulse signal injection: In the self-test process, the system uses pulse current signal injection, the specific steps are as follows: The main control unit (2) will generate a set of precisely controlled pulse signals, which will be injected into the internal circuit from the cross-connected and single-end grounded wiring sockets to simulate the ground resistance detection process of the cable. In order to ensure the standardization of the detection, a standard resistor is provided on the main control unit (2), and the pulse signal will be transmitted to the measurement channel of the main control unit (2) after passing through the standard resistor, simulating the actual working conditions of the cable system. C. Data Collection: During the transmission of the pulse signal through the standard resistor, the indicator light (7) starts flashing and the measurement channel starts collecting data in real time. Voltage and current detection: The measurement channel collects the voltage and current values ​​after passing through the standard resistor and uploads these data to the main control unit (2). Multiple sampling: To ensure the accuracy of the data, the main control unit (2) will perform multiple sampling and take the average value of the samples to filter out noise or accidental interference and ensure the stability of the data. D. Data Analysis: After collecting the voltage and current data, the main control unit (2) immediately performs analysis and processing: Calculating the nominal resistance value: Based on the known standard resistance, the main control unit (2) calculates the resistance value obtained by collecting data. Analysis and deviation detection: The main control unit (2) compares the calculated resistance value with the standard value to determine the deviation between the actual measurement and the ideal value. For deviations outside the set allowable range, the main control unit (2) will issue a prompt message so that the user can perform further calibration or adjustment. E. Environmental harmonic compensation calibration: If external harmonic interference is detected, the main control unit (2) will use the environmental harmonic compensation calibration function to optimize the detection accuracy: Data fitting: The main control unit (2) calibrates the measurement results through the data fitting function so that it can better match the actual resistance value. The deviation caused by the external environmental harmonics on the resistance measurement is compensated through the fitted data. Real-time compensation: Based on the fitting function, the main control unit (2) can compensate the resistance data in actual measurement to offset the interference of environmental harmonics and ensure the stability of subsequent test results. F. Evaluation and output of self-inspection results: After completing all data collection and analysis, the main control unit (2) will evaluate the self-test results and make the following processing based on the results: Qualification judgment: If the self-test result is within the accuracy range set by the device, the system will mark the device as "calibrated" and prepare to enter normal detection mode. Abnormal prompt: If the self-test result exceeds the allowable error range, the system will prompt the user to perform maintenance or manual calibration. The abnormal prompt information includes possible cause analysis and provides users with guidance for troubleshooting. G. Power supply detection: During the self-test process, the main control unit (2) will also test the voltage of the battery (3) to ensure the stability of the power supply: The main control unit (2) indirectly obtains the actual voltage of the battery by detecting the voltage difference between the key resistors to ensure that it is within the normal working range. If the system detects that the battery (3) voltage is low, it will issue a reminder to prevent inaccurate measurements due to insufficient power supply. H. Self-check process completed: When all the above steps are completed and the test results are normal, the system self-test process ends and the device enters the standby state, ready to perform external ground resistance measurement at any time. At this point, the device has completed internal calibration and environmental harmonic compensation, ensuring that the instrument is in the best working condition before the measurement begins.

3. According to claim 2, an adaptive cable grounding resistance detector is characterized in that: In the step E, environmental harmonic calibration: specifically explain the method and principle of harmonic compensation through fitting function to ensure the accuracy of the equipment in different harmonic environments. Two input variables and one output variable are defined in the model. The system is a 5-layer network. The first layer parameters are premise parameters, and the fourth layer is conclusion parameters. There are three methods for learning and updating network parameters: gradient descent method, least squares method, and a hybrid method of the two. The specific selection is based on adaptability. The reasoning process of the fuzzy inference system can successfully transfer the input variables to the final output value through calculation. This requires a series of transfer processes, which are completed by five layers of mapping functions: first mapping the input variables to the input membership function, the input membership function to the rules, the rules to the output characteristics, then mapped to the output membership function, and finally mapped to a certain output decision. The first layer is the membership layer of the input variables, Q ij It is the membership function of the fuzzy set (a1, a2, b1, b2). As shown in the figure, x and y represent the input current and voltage signals respectively, and a1a2b1b2 represent the signal time parameters related to x and y respectively. μ is the membership, which indicates the degree to which the input variable belongs to a fuzzy set. The value range is [0, 1]. The membership function can use a bell function or a Gaussian function to calculate the dimensional membership and fuzzification operation of each input data. The second layer uses the membership function calculation results to construct the rule weight. A node output represents a rule credibility. The output is the product of all input signals, which is used to match the antecedents of the fuzzy rules and calculate the incentive intensity of each fuzzy rule: Q 2，i =w i =μ ai (x)·m bi (y), among them i=1.2 (1.3) w is the rule weight, which reflects the credibility of the input variable incentive rule. Each node corresponds to a fuzzy rule, and the output represents the confidence of the rule. The third layer calculates the normalized credibility of each rule and outputs the normalized weight of the i-th rule as: where i = 1,2 (1.4) The fourth layer calculates the output of the fuzzy rules. Each node is a node function. This layer shares the antecedent parameters of the entire system and calculates the consequent parameters of all rules. The output is: Where i = 1, 2, is the output of the third layer, that is, the normalized calculated value, {l i ,m i ,n i } is the parameter setting of the subsequent node, x1 and x2 are input variables, w is the normalized weight calculated previously, and f i It represents the output of the i-th rule, i.e., the node function. The fifth layer calculates the output of all rules in the entire system: Where i=1,2,z is the weighted sum of all rule outputs, and f j is the output of the i-th rule, which comes from the previous layer. The noise suppression model is combined with self-organizing learning technology to determine the fuzzy inference rules. In the self-organizing learning stage, the design of the field discharge simulation signal is first used to construct the training sample, the nodes of the second and fourth layers of the system are divided by fuzzy reasoning and the membership function is calculated, and the fuzzy rules are established in the normalization of all rule strengths in the third layer. In this stage, the width and center of the membership function are dynamically determined by the self-organizing learning technology, and the training data input and output are input into the network so that the membership function covers the input and output areas. The self-organizing learning back propagation formula is as follows: Where Z(t) is the expected output, For the actual output, through fuzzy learning reasoning: in, η is the learning rate, and To derive the transfer process learning rule ω i and m i , the reverse calculation from the fourth layer is as follows: Where u i is the confidence of the i-th rule The center parameters are determined as follows: Width parameter: Repeat the above steps to determine the second layer parameters, obtain the dynamic membership parameters, and finally determine the fuzzy inference rules to adapt to environmental changes and interference and achieve the purpose of suppressing noise signals. In the specific application implementation, the real measured signal is represented by d(k), the environmental interference signal is represented by n(k) and n(k-1), the colored noise is white noise obtained by nonlinear approximation s(k)=f(n(k),n(k-1)), the measured signal g(k) is the sum of the real signal d(k) and the colored noise s(k), that is, g(k)=d(k)+s(k), the colored noise s(k)′ is approximated, and the output in the training data is the approximated colored noise s(k)′, the approximated colored noise is removed from the measured signal, and the estimated signal d(k)′=g(k)+s(k)′ is obtained. The closer the estimated signal d(k)′ is to the real signal d(k), the better the function fitting effect is. Since the detection signal is oscillatory and attenuated, it is usually simulated using single exponential decay pulses and double exponential decay pulses, and their mathematical expressions are shown in equations (1.11) and (1.12). Among them: a1 and a2 are signal amplitudes, τ1 and τ2 are attenuation coefficients, f is the oscillation frequency, and Gaussian white noise with a standard deviation of 0.20mV is superimposed. The proposed adaptive fitting method is used to process the measured signal, which can remove a large number of harmonic effects, weaken the distortion of the pulse endpoints, and make the waveform closer to the original signal.

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