A selective leakage current protection method for mines with arc detection function

Abstract

This invention discloses a selective leakage current protection method for mining equipment with arc detection function, relating to the field of arc detection technology. It aims to improve the problem of missed arc detection due to the timeliness of instantaneous arcs. The method includes monitoring leakage current when the mining equipment is powered on, acquiring the zero-sequence voltage and zero-sequence current under basic harmonics, constructing a ridge regression model based on the zero-sequence voltage and zero-sequence current to calculate the power reference index of the mining equipment and setting a leakage current threshold, calculating the leakage current coefficient at different time points during leakage current monitoring based on the power reference index and leakage current threshold, analyzing the instantaneous leakage current at different time points using fuzzy inference by combining the leakage current coefficient and leakage time interval, selecting whether to collect the zero-sequence current under high-frequency harmonics based on the analysis results, comparing the zero-sequence current at each marked time point with a preset alarm threshold to obtain the level of each marked time point, and determining whether a leakage current alarm needs to be issued based on the level of each marked time point.

Description

Technical Field

[0001] This invention relates to the field of arc detection technology, and more specifically, to a selective leakage current protection method for mines with arc detection function. Background Technology

[0002] Arc detection technology is used to identify and monitor electric arc phenomena. In power systems, arc detection technology reduces the risk of equipment damage. Applying arc detection technology to selective leakage current in mines can reduce mine fires and other safety hazards caused by electric arcs.

[0003] The existing technology has the following shortcomings:

[0004] Previous arc detection technologies used fundamental harmonics to detect zero-sequence current and analyze leakage arcs to determine if the zero-sequence current was abnormal. However, they did not take into account the time sensitivity of instantaneous arcs. When leakage arcs are highly transient, the zero-sequence current collected by fundamental harmonics may not be identified in time, which can damage mining equipment and cause safety hazards. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a selective leakage current protection method for mines with arc detection function. By analyzing the zero-sequence current and zero-sequence voltage under the basic harmonics and setting a threshold for judging leakage current, the leakage current situation of mining equipment at different time points is determined and screened and classified. Based on the level of each time point, it is considered whether to select high-frequency harmonics to detect the zero-sequence current and issue an alarm to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A selective leakage current protection method for mines with arc detection function includes the following steps:

[0008] Step S1: Connect the mining equipment, power on the mining equipment for leakage current monitoring and set the leakage current monitoring time. Use the zero-sequence current transformer to obtain the zero-sequence current and zero-sequence voltage of the mining equipment under the basic harmonic in real time.

[0009] Step S2: Construct a ridge regression model based on zero-sequence voltage and zero-sequence current to calculate the power benchmark index at different time points and set the leakage threshold of mining equipment. Compare the power benchmark index with the leakage threshold in real time, calculate the leakage arc quantification value at different time points, and filter and mark each time point.

[0010] Step S3: Obtain the leakage time interval within the leakage monitoring time based on the marked time points, calculate the leakage coefficient at each marked time point using the leakage arc quantization value, and analyze the leakage instantaneity at each marked time point using fuzzy inference by combining the leakage coefficient and the leakage time interval.

[0011] Step S4: Based on the transient leakage current analysis results at each marked time point, select whether to collect the zero-sequence current under high-frequency harmonics. After obtaining the zero-sequence current under high-frequency harmonics at each marked time point, compare it with the preset alarm threshold. Classify each marked time point according to the comparison results, and determine whether leakage current alarm is required based on the classification results.

[0012] In a preferred embodiment, when the zero-sequence voltage and zero-sequence current of the incoming mining equipment under the basic harmonics are acquired in real time, a period of time is selected as the analysis time. Within the analysis time, n time points are set, and the zero-sequence voltage and zero-sequence current of the mining equipment at each time point are acquired and merged into voltage dataset and current dataset, respectively.

[0013] In a preferred embodiment, a ridge regression model is constructed by combining voltage and current datasets to calculate the power benchmark index for mining equipment. The specific steps are as follows:

[0014] Standardization features: The data in the voltage and current datasets are standardized using a standardization formula: x nore = x - μ / σ, where x is the data in the voltage or current dataset, μ is the average of the data in the voltage or current dataset, σ is the standard deviation of the data in the voltage or current dataset, and x nore The standardized values ​​of data from voltage or current datasets;

[0015] Data matching: Mark the zero-sequence voltage or zero-sequence current at the same time point in the voltage dataset and the current dataset with the same label;

[0016] Model Construction: A ridge regression model is constructed using data with the same labels from both the voltage and current datasets: L(β) = ||y - Xβ|| 2 +λ||β|| 2 Where, ||y-Xβ|| 2 The loss value obtained by least squares calculation, λ||β|| 2 y is the L2 regularization term used to control model complexity, X is the feature matrix composed of zero-sequence voltages and zero-sequence currents with the same label, and y is the preset calibration parameter.

[0017] Selecting regularization parameters: K-fold cross-validation is used to select regularization parameters, and the obtained regularization parameters are imported into the ridge regression model;

[0018] Calculate the electricity benchmark index: Use the result L(β) calculated by the ridge regression model as the electricity benchmark index at each time point;

[0019] Set leakage threshold: Take the average value of the power reference index at each time point and label it as d. Set the leakage threshold: L(d) = cd, where c is the mapping quantity and L(d) is the leakage threshold.

[0020] In a preferred embodiment, the power reference index and the leakage threshold are compared in real time at different time points. The difference between the power reference index and the leakage threshold is used as the leakage arc quantification value. Different time points are assigned values ​​in ascending order according to time sequence. The first time point is assigned a value of 1, and subsequent time points are incremented by 1. When the leakage arc quantification value of the corresponding time point is positive, it is filtered out and marked.

[0021] In a preferred embodiment, in the marked time points, time points with a sequence number difference of 1 are merged to obtain a merged time point, and the time interval between the two time points is obtained as the leakage time interval. After merging the two time points, they have the sequence number characteristics of the two time points. The marked time points are traversed and the merged time point and leakage time interval are obtained until all the marked time points are traversed to obtain multiple leakage time intervals.

[0022] The ratio of the quantified value of the leakage arc calculated at each marked time point to the standard judgment arc is used as the leakage coefficient for the corresponding marked time point.

[0023] In a preferred embodiment, fuzzy inference analysis is used to analyze the transient nature of leakage current in mining equipment, taking into account both the leakage current coefficient and the leakage current time interval. The specific steps are as follows:

[0024] Input preprocessing: The leakage time interval of the merging time points is processed using the Max-Min normalization algorithm to obtain the time coefficient of the corresponding merging time points;

[0025] Define the input: use the time coefficient of the merged time point as the time coefficient of all the marked time points contained therein, define the leakage coefficient and time coefficient of each marked time point as input variables, and divide them into different fuzzy sets;

[0026] Define the output: Define the transient nature of leakage current at the marked time point as the output variable;

[0027] Rule formulation: Formulate fuzzy rules to describe the impact of different input variables on the output variable;

[0028] Perform fuzzy reasoning: classify the transient nature of leakage current at each marked time point as high or low according to fuzzy rules.

[0029] In a preferred embodiment, after receiving the leakage transients at each marked time point, statistics are performed, and the ratio of the number of marked time points with high leakage transients to the number of marked time points with low leakage transients is used as the harmonic selection coefficient. When the harmonic selection coefficient exceeds 1, the zero-sequence current under high-frequency harmonics is collected within the merged time point where the corresponding marked time point is located.

[0030] In a preferred embodiment, if the harmonic selectivity of the marked time point is less than 1, the zero-sequence current of the corresponding marked time point under the basic harmonic is compared with the alarm threshold; if the harmonic selectivity of the marked time point is greater than 1, the zero-sequence current of the corresponding marked time point under the high-frequency harmonic is compared with the alarm threshold.

[0031] In a preferred embodiment, the difference between the zero-sequence current and the alarm threshold at each marked time point is used as the classification level. When the classification level of the marked time point exceeds the difference between the average zero-sequence current of the marked time point under all basic harmonics and the alarm threshold, the marked time point is set as a high-level alarm point; otherwise, the marked time point is set as a low-level alarm point.

[0032] When the marked time point is a low-level alarm point, no leakage current alarm will be triggered; when the marked time point is a high-level alarm point, a leakage current alarm will be triggered.

[0033] The technical effects and advantages of the selective leakage current protection method for mines with arc detection function of the present invention are as follows:

[0034] This invention monitors leakage current in mining equipment by capturing the zero-sequence voltage and current under basic harmonics. Based on the zero-sequence voltage and current, it calculates the power reference index and sets a leakage threshold. The power reference index is compared with the leakage threshold to calculate the leakage arc. Adding a zero-sequence voltage factor improves the accuracy of the leakage arc calculation. Furthermore, it calculates the leakage coefficient at different time points based on the leakage arc. By combining the leakage coefficient and leakage time interval, it analyzes the transient nature of leakage at different time points, effectively locking down each monitoring time point. Based on the analysis results, it selects whether to collect the zero-sequence current under high-frequency harmonics. The zero-sequence current at each marked time point is compared with a preset alarm threshold to obtain the level of each marked time point. Based on the level of each marked time point, it determines whether a leakage alarm is needed, thus improving the safety of mining equipment in the event of transient leakage. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of a selective leakage current protection method for mines with arc detection function according to the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] This invention monitors leakage current in mining equipment by capturing the zero-sequence voltage and current under basic harmonics. It calculates a power reference index based on the zero-sequence voltage and current and sets a leakage threshold. The power reference index is compared with the leakage threshold to calculate the leakage arc, and the leakage coefficient at different time points is calculated based on the leakage arc. The transient nature of leakage at different time points is analyzed by combining the leakage coefficient and leakage time interval. Based on the analysis results, it selects whether to collect the zero-sequence current under high-frequency harmonics. The zero-sequence current at each marked time point is compared with a preset alarm threshold to obtain the level of each marked time point. Based on the level of each marked time point, it determines whether a leakage alarm is needed, thereby improving the safety of mining equipment in the event of transient leakage.

[0038] Example: A selective leakage current protection method for mines with arc detection function, such as... Figure 1 As shown, it includes the following steps:

[0039] Step S1: Connect the mining equipment, power on the mining equipment for leakage current monitoring and set the leakage current monitoring time. Use the zero-sequence current transformer to obtain the zero-sequence current and zero-sequence voltage of the mining equipment under the basic harmonic in real time.

[0040] Step S2: Construct a ridge regression model based on zero-sequence voltage and zero-sequence current to calculate the power benchmark index at different time points and set the leakage threshold of mining equipment. Compare the power benchmark index with the leakage threshold in real time, calculate the leakage arc quantification value at different time points, and filter and mark each time point.

[0041] Step S3: Obtain the leakage time interval within the leakage monitoring time based on the marked time points, calculate the leakage coefficient at each marked time point using the leakage arc quantization value, and analyze the leakage instantaneity at each marked time point using fuzzy inference by combining the leakage coefficient and the leakage time interval.

[0042] Step S4: Based on the transient leakage current analysis results at each marked time point, select whether to collect the zero-sequence current under high-frequency harmonics. After obtaining the zero-sequence current under high-frequency harmonics at each marked time point, compare it with the preset alarm threshold. Classify each marked time point according to the comparison results, and determine whether leakage current alarm is required based on the classification results.

[0043] It should be noted that the zero-sequence current includes the leakage arc of mining equipment. The leakage arc is estimated by comparing the zero-sequence current with the leakage threshold. The zero-sequence voltage is added to calculate the power reference index to improve the accuracy of the leakage arc estimation.

[0044] In step S1, when the mining equipment is powered on for a working test, the voltage supplied to the mining equipment is first converted using a PT voltage transformer, and then the zero-sequence voltage and zero-sequence current passing through the mining equipment are collected using a zero-sequence transformer.

[0045] It should be noted that a PT voltage transformer is a conversion device in a power system used to convert high voltage to low voltage for measurement and monitoring. In this example, a PT voltage transformer is used to convert the voltage supplied to the mining equipment. A zero-sequence transformer is a device for detecting and measuring zero-sequence current and zero-sequence voltage in a power system, mainly used for monitoring unbalanced current and voltage.

[0046] In step S2, the zero-sequence voltage and zero-sequence current of the mining equipment under the basic harmonic are received in real time. A period of time is selected as the analysis time. During the analysis time, n time points are set. The zero-sequence voltage and zero-sequence current of the mining equipment at each time point are obtained and merged into voltage dataset and current dataset respectively.

[0047] A ridge regression model is constructed by combining voltage and current datasets to calculate the power benchmark index for mining equipment. The specific steps are as follows:

[0048] Standardization features: The data in the voltage and current datasets are standardized using a standardization formula: x nore = x - μ / σ, where x is the data in the voltage or current dataset, μ is the average of the data in the voltage or current dataset, σ is the standard deviation of the data in the voltage or current dataset, and x nore The standardized values ​​of data from voltage or current datasets;

[0049] Data matching: Mark the zero-sequence voltage or zero-sequence current at the same time point in the voltage dataset and the current dataset with the same label;

[0050] Model Construction: Construct a ridge regression model using data with the same labels from both the voltage and current datasets: L(β) = ||y - Xβ||| 2 +λ||β|| 2 Where, ||y-Xβ|| 2 The loss value obtained by least squares calculation, λ||β|| 2 y is the L2 regularization term used to control model complexity, X is the feature matrix composed of zero-sequence voltages and zero-sequence currents with the same label, and y is the preset calibration parameter.

[0051] Selecting regularization parameters: K-fold cross-validation is used to select regularization parameters, and the obtained regularization parameters are imported into the ridge regression model;

[0052] Calculate the electricity benchmark index: Use the result L(β) calculated by the ridge regression model as the electricity benchmark index at each time point;

[0053] Set leakage threshold: Take the average value of the power reference index at each time point and label it as d. Set the leakage threshold: L(d) = cd, where c is the mapping quantity and L(d) is the leakage threshold.

[0054] It should be explained that the above-mentioned standardization features process the data in the voltage and current datasets to make their mean 0 and variance 1, thereby improving the stability of the model. The leakage threshold is set using the power benchmark index at different time points, which further improves the accuracy of subsequent leakage judgment. K-fold cross-validation is a model evaluation technique that reduces randomness and bias in model evaluation by dividing the dataset into K subsets.

[0055] The power reference index and leakage threshold are compared in real time at different time points. The difference between the power reference index and the leakage threshold is used as the leakage arc quantification value. Different time points are assigned values ​​in ascending order according to time sequence. The first time point is assigned a value of 1, and subsequent time points are incremented by 1. When the leakage arc quantification value of the corresponding time point is positive, it is filtered out and marked.

[0056] In step S3, for the marked time points, the time points with a sequence number difference of 1 are merged to obtain the merged time point, and the time interval between the two time points is obtained as the leakage time interval. In addition, the merged time point has the sequence number characteristics of the two time points. The marked time points are traversed and the merged time point and leakage time interval are obtained until all the marked time points are traversed and multiple leakage time intervals are obtained.

[0057] The ratio of the quantified value of the leakage arc calculated at each marked time point to the standard judgment arc is used as the leakage coefficient for the corresponding marked time point.

[0058] It should be noted that the larger the leakage coefficient of the marked time current, the larger the leakage arc quantification value, the more serious the leakage situation, the shorter the leakage time interval, and the higher the leakage instantaneity at the corresponding merged time point. The standard judgment arc is set by professionals in this field based on the acceptable leakage amount, which will not be elaborated here.

[0059] The transient nature of leakage current in mining equipment is analyzed using fuzzy inference based on the leakage current coefficient and leakage current time interval. The specific steps are as follows:

[0060] Input preprocessing: The leakage time interval of the merged time points is processed using the Max-Min normalization algorithm: x nore =xx min / x max -x min Where x is the leakage time interval at the merged time point, x min x is the minimum leakage time interval across all merged time points. max x represents the maximum leakage time interval across all merged time points. nore The standardized result of the leakage time at the corresponding merging time point is used as the time coefficient for the corresponding merging time point.

[0061] Input definition: The time coefficient of the merged time point is used as the time coefficient of all the marked time points contained within it. The leakage coefficient and time coefficient of each marked time point are defined as input variables, and they are divided into different fuzzy sets. For example, "High" and "Low" correspond to the leakage coefficient of the marked time point, and "Long" and "Short" correspond to the time coefficient of the marked time point.

[0062] Define the output: Define the transient leakage current at the marked time point as the output variable. For example, assign "Strength" and "Weakness" to the transient leakage current at the marked time point.

[0063] Rule formulation: Formulate fuzzy rules to describe the influence of different input variables on the output variable. For example, if the leakage coefficient at the marked time point is labeled L, the time coefficient at the marked time point is labeled S, and the leakage instantaneity at the corresponding marked time point is labeled E, then fuzzy rules can be defined.

[0064] Rule1: IF(L is High)AND(S is Short)THEN(E is Strength)

[0065] Rule2: IF(L is Low)AND(S is Long)THEN(E is Weakness) ...

[0067] Perform fuzzy reasoning: when the output result is "Strength", the leakage transient rate at the corresponding marked time point is determined to be high; when the output result is "Weakness", the leakage transient rate at the corresponding marked time point is determined to be low.

[0068] It should be noted that the classification of fuzzy sets can be adjusted according to the actual situation to improve the accuracy of harmonic selection. This example uses two fuzzy sets, but three or more fuzzy sets can also be used for classification. In addition, the level of the input variables in this example can be determined by setting the precision threshold. For example, when the leakage coefficient at the marked time point exceeds 1, the leakage coefficient at the marked time point is labeled as "High". When the time coefficient at the marked time point is lower than the average time coefficient of all marked time points, it is labeled as "Short", etc. This will not be analyzed in detail here.

[0069] In step S4, after receiving the leakage transients at each marked time point, statistics are performed. The ratio of the number of marked time points with high leakage transients to the number of marked time points with low leakage transients is used as the harmonic selection coefficient. When the harmonic selection coefficient exceeds 1, the zero-sequence current under high-frequency harmonics is collected within the merged time point where the corresponding marked time point is located.

[0070] When collecting zero-sequence current under high-frequency harmonics, the transient 40th harmonic differential current principle is used for detection. The specific steps are as follows:

[0071] Step 1: Use a high sampling rate analog-to-digital converter to acquire data at frequencies exceeding 51.2 kHz;

[0072] Step 2: Connect the zero-sequence current sensor and install it on the neutral point or grounding wire of the transformer to monitor the zero-sequence current in real time;

[0073] Step 3: Use an ADC to acquire the zero-sequence current signal in real time, measure the transformer's input current and output current respectively, and calculate the zero-sequence current using the following formula: I0 = I in +I out +I neutral Where I0 is the zero-sequence current, I in For the incoming line current, I out For the outgoing line current, I neutral Neutral current;

[0074] Step 4: Collect the zero-sequence current signal at each marked time point, as well as the amplitude and phase of the fortieth harmonic. Use Fast Fourier Transform to perform matching analysis on the collected zero-sequence current information to obtain the zero-sequence current at each marked time point.

[0075] If the harmonic selectivity coefficient of the marked time point is less than 1, the zero-sequence current under the basic harmonic at the corresponding marked time point is compared with the alarm threshold. If the harmonic selectivity coefficient of the marked time point is greater than 1, the zero-sequence current under the high-frequency harmonic at the corresponding marked time point is compared with the alarm threshold.

[0076] It should be noted that in this example, the fundamental harmonic is 50 Hz and the high-frequency harmonic is 2000 Hz. The acquisition efficiency of the high-frequency harmonic is much higher than that of the fundamental harmonic, which improves the accuracy of data acquisition and detection processes as well as the anti-interference capability.

[0077] The difference between the zero-sequence current and the alarm threshold at each marked time point is used as the classification level. When the classification level of the marked time point exceeds the difference between the average zero-sequence current of the marked time point under all basic harmonics and the alarm threshold, the marked time point is set as a high-level alarm point; otherwise, the marked time point is set as a low-level alarm point.

[0078] When the marked time point is a low-level alarm point, a leakage current alarm is triggered; when the marked time point is a high-level alarm point, a leakage current alarm is triggered.

[0079] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0080] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0081] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0082] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0083] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A selective leakage current protection method for mines with arc detection function, characterized in that, Includes the following steps: Step S1: Connect the mining equipment, power on the mining equipment for leakage current monitoring and set the leakage current monitoring time. Use the zero-sequence current transformer to obtain the zero-sequence current and zero-sequence voltage of the mining equipment under the basic harmonic in real time. Step S2: Construct a ridge regression model based on zero-sequence voltage and zero-sequence current to calculate the power benchmark index at different time points and set the leakage threshold of mining equipment. Compare the power benchmark index with the leakage threshold in real time, calculate the leakage arc quantification value at different time points, and filter and mark each time point. Step S3: Obtain the leakage time interval within the leakage monitoring time based on the marked time points, calculate the leakage coefficient at each marked time point using the leakage arc quantization value, and analyze the leakage instantaneity at each marked time point using fuzzy inference by combining the leakage coefficient and the leakage time interval. Step S4: Based on the transient leakage current analysis results at each marked time point, select whether to collect the zero-sequence current under high-frequency harmonics. After obtaining the zero-sequence current under high-frequency harmonics at each marked time point, compare it with the preset alarm threshold. Classify each marked time point according to the comparison results, and determine whether leakage current alarm is required based on the classification results. A ridge regression model is constructed by combining voltage and current datasets to calculate the power benchmark index for mining equipment. The specific steps are as follows: Standardization features: The data in the voltage and current datasets are standardized using a standardization formula. ,in, For data in voltage or current datasets, The average value of the data in the voltage or current dataset. The standard deviation of the data in the voltage or current dataset. The standardized values ​​of data from voltage or current datasets; Data matching: Mark the zero-sequence voltage or zero-sequence current at the same time point in the voltage dataset and the current dataset with the same label; Model Construction: Construct a ridge regression model using data with the same labels from both the voltage and current datasets. ,in, The loss amount obtained by the least squares method. This is an L2 regularization term used to control model complexity. The characteristic matrix is ​​composed of zero-sequence voltages and zero-sequence currents with the same label. These are the preset calibration parameters; Selecting regularization parameters: K-fold cross-validation is used to select regularization parameters, and the obtained regularization parameters are imported into the ridge regression model; Calculating the Electricity Benchmark Index: Results from Ridge Regression Model As a benchmark index for electricity at various points in time; Setting a leakage current threshold: Take the average value of the power reference index at various time points and label it as d. Set the leakage current threshold as follows: Where c is the mapping quantity. Leakage threshold; The transient nature of leakage current in mining equipment is analyzed using fuzzy inference based on the leakage current coefficient and leakage current time interval. The specific steps are as follows: Input preprocessing: The leakage time interval of the merging time points is processed using the Max-Min normalization algorithm to obtain the time coefficient of the corresponding merging time points; Define the input: use the time coefficient of the merged time point as the time coefficient of all the marked time points contained therein, define the leakage coefficient and time coefficient of each marked time point as input variables, and divide them into different fuzzy sets; Define the output: Define the transient nature of leakage current at the marked time point as the output variable; Rule formulation: Formulate fuzzy rules to describe the impact of different input variables on the output variable; Perform fuzzy reasoning: classify the transient nature of leakage current at each marked time point as high or low according to fuzzy rules.

2. The selective leakage current protection method for mines with arc detection function according to claim 1, characterized in that: When acquiring the zero-sequence voltage and zero-sequence current of the incoming mining equipment under the basic harmonics in real time, a period of time is selected as the analysis time. Within the analysis time, n time points are set, and the zero-sequence voltage and zero-sequence current of the mining equipment at each time point are acquired and merged into voltage dataset and current dataset, respectively.

3. The selective leakage current protection method for mines with arc detection function according to claim 1, characterized in that: The power reference index and leakage threshold are compared in real time at different time points. The difference between the power reference index and the leakage threshold is used as the leakage arc quantification value. Different time points are assigned values ​​in ascending order according to time sequence. The first time point is assigned a value of 1, and subsequent time points are incremented by 1. When the leakage arc quantification value of the corresponding time point is positive, it is filtered out and marked.

4. A selective leakage current protection method for mines with arc detection function according to claim 3, characterized in that: In the marked time points, time points with a sequence number difference of 1 are merged to obtain a merged time point. The time interval between the two time points is obtained as the leakage time interval. After merging the two time points, they have the sequence number characteristics of the two time points. Continue to traverse the marked time points and obtain the merged time point and the leakage time interval until all the marked time points are traversed to obtain multiple leakage time intervals. The ratio of the quantified value of the leakage arc calculated at each marked time point to the standard judgment arc is used as the leakage coefficient for the corresponding marked time point.

5. A selective leakage current protection method for mines with arc detection function according to claim 4, characterized in that: After receiving the leakage transients at each marked time point, statistics are performed. The ratio of the number of marked time points with high leakage transients to the number of marked time points with low leakage transients is used as the harmonic selection coefficient. When the harmonic selection coefficient exceeds 1, the zero-sequence current under high-frequency harmonics is collected within the merged time point where the corresponding marked time point is located.

6. A selective leakage current protection method for mines with arc detection function according to claim 5, characterized in that: If the harmonic selectivity coefficient of the marked time point is less than 1, the zero-sequence current under the basic harmonic at the corresponding marked time point is compared with the alarm threshold. If the harmonic selectivity coefficient of the marked time point is greater than 1, the zero-sequence current under the high-frequency harmonic at the corresponding marked time point is compared with the alarm threshold.

7. A selective leakage current protection method for mines with arc detection function according to claim 6, characterized in that: The difference between the zero-sequence current and the alarm threshold at each marked time point is used as the classification level. When the classification level of the marked time point exceeds the difference between the average zero-sequence current of the marked time point under all basic harmonics and the alarm threshold, the marked time point is set as an advanced alarm point. Otherwise, set the marked time point as a low-level alarm point; When the marked time point is a low-level alarm point, no leakage current alarm will be triggered; When the marked time point is an advanced alarm point, a leakage current alarm will be triggered.

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