An arc grading early warning method for charging line faults of electric bicycles

Abstract

The present invention discloses a method for hierarchical early warning of fault arcing in an electric bicycle charging circuit, belonging to the technical field of fault arcing diagnosis for electric bicycle charging circuits. The present invention estimates the burning energy of the fault arcing to distinguish the disaster-causing possibility of the fault arcing, and uses this as the basis for reducing the false alarm rate and leakage protection rate of traditional fault arcing detection methods. It is more scientific and applicable, and can better assist the electric bicycle charging pile to complete the decision-making of charging fault protection, and effectively contain the electrical fire caused by fault arcing on the premise of ensuring the charging safety of electric bicycles as much as possible.

Description

Technical Field

[0001] The present invention relates to the technical field of electric bicycle charging line fault arc diagnosis, and particularly relates to a method for grading and warning of electric bicycle charging line fault arcs. Background Art

[0002] Research and analysis show that the main inducements of electrical fires during the charging process of electric bicycles include faults in electric bicycle batteries and chargers themselves, mismatches between the two, wire aging, unauthorized wiring, poor contact, etc. Among them, battery faults and fault arcs caused by wire aging and poor contact are the main disaster-causing hidden dangers (fault arcs themselves can also damage electric bicycle batteries and their chargers).

[0003] To cope with possible fire accidents during the charging process of electric bicycles, the industry has successively developed a large number of fault arc detection devices that can be used for electric bicycle charging piles. However, due to the uneven quality of chargers during the charging process of electric bicycles, a large amount of high-frequency harmonics are contained in the charging line, resulting in a large number of misoperations of existing fault arc detection devices. In addition, even if there is a fault arc in the electric bicycle charging line, due to the occasional nature of the fault arc and the different degrees of combustion, not all fault arcs have the possibility of causing disasters. Disconnecting the circuit for such fault arcs seriously interferes with the normal charging behavior of electric bicycles. Therefore, a method for grading and warning of electric bicycle charging line fault arcs is proposed. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: how to solve the technical problems that existing fault arc detection devices have low accuracy in identifying fault arcs and cannot evaluate the disaster-causing possibility of fault arcs, and provides a method for grading and warning of electric bicycle charging line fault arcs.

[0005] As Figure 1 shown, the present invention solves the above technical problems through the following technical solutions. The present invention includes the following steps:

[0006] S1: Collect the electric bicycle charging current signal, and obtain the electric bicycle charging line fault arc arcing voltage signal based on the collected electric bicycle charging current signal, the basic transformation relationship of circuit equations, and the fault arc equivalent model;

[0007] S2: Calculate the arc fault characteristics of the collected electric bicycle charging current signal, and determine the arcing time of the electric bicycle charging line fault arc based on the change law of the electric bicycle charging current arc fault characteristics;

[0008] S3: Based on the collected electric bicycle charging current signal, the obtained faulty arc ignition voltage signal, and the determined burning time of the faulty arc, calculate the burning energy of the faulty arc.

[0009] S4: Based on the burning energy of the faulty arc, implement hierarchical early warning for the faulty arc in the electric bicycle charging circuit.

[0010] Furthermore, in the step S1, the process of collecting the electric bicycle charging current signal is as follows:

[0011] S11: Connect a manganese copper resistor with a set resistance value in series in the electric bicycle charging circuit;

[0012] S12: Collect the voltage signal across the manganese copper resistor;

[0013] S13: Through inverse transformation of the voltage signal by a set ratio, obtain the electric bicycle charging current signal.

[0014] Furthermore, in the step S1, the faulty arc equivalent model is the Cassie arc model, and the basic transformation relationship of the circuit equation is the reciprocal relationship between conductance and current. Then, obtain the faulty arc ignition voltage signal of the electric bicycle charging circuit as follows:

[0015] ;

[0016] where is the voltage constant of the Cassie arc model; is the time constant of the Cassie arc model; is the time derivative of the electric bicycle charging current signal sequence, is the electric bicycle charging current signal.

[0017] Furthermore, in the step S2, the specific process is as follows:

[0018] S21: Divide the electric bicycle charging current signal into M equal parts, such that each part of the current signal has the length of a current half-wave period. Perform differential transformation on the electric bicycle charging current signal for each current half-wave period to obtain the electric bicycle differential charging current signal , where M is a positive integer;

[0019] S22: Calculate the arc fault feature of the differential charging current signal :

[0020] ;

[0021] wherein, N is the length of the charging current signal of the electric bicycle and its differential charging current signal in a half-wave cycle of the current; is the discrete sequence of the charging current signal of the electric bicycle, is the current signal sequence after differential processing of is the mean value of

[0022] S23: Combine the arc fault features of M current signals calculated per second into the fault feature vector of the charging current signal of the electric bicycle ;

[0023] S24: Input the fault feature vector of the charging current signal of the electric bicycle into the fault arc detection algorithm to priori judge whether the charging current signal of the electric bicycle carries fault arc information, and determine the burning time of the line fault arc within an observation window according to the variation law of the arc fault feature in different half-wave cycles of the current .

[0024] Furthermore, in the step S24, record the number of arc fault features exceeding the preset arc fault feature threshold per second, and determine the burning time of the line fault arc within an observation window based on the half-wave current duration .

[0025] Furthermore, in the step S3, the calculation formula of the burning energy of the fault arc is as follows:

[0026]

[0027] wherein, is the arc ignition voltage signal sequence of the fault arc.

[0028] Furthermore, in the step S4, the specific processing process is as follows:

[0029] S41: Normalize the burning energy of the fault arc based on the maximum value that can appear to obtain the arc burning energy factor :

[0030] ;

[0031] S42: Calculate the arc burning energy factor of the charging current signal of the electric bicycle in real time, according to The magnitude of the value classifies the fault arc of the electric bicycle charging line into three levels for alarm.

[0032] Furthermore, in the step S42, indicates that the electric bicycle charging line is in good condition, indicates that there is a weak fault arc in the electric bicycle line, indicates that there is a severe fault arc in the electric bicycle line that may cause a disaster.

[0033] The present invention has the following advantages compared with the prior art: The method for grading and warning of fault arcs in the electric bicycle charging line completes the calculation of the arcing voltage and arcing time of potential fault arcs carried in the electric bicycle charging current signal to be processed through the reverse voltage transformation of the electric bicycle charging current signal to be processed and the detection and processing of arc fault characteristics, and then performs an integral operation on the product of the arcing voltage and the arc current of the fault arc within the arcing period to obtain the energy integral of the fault arc of the electric bicycle charging line, and completes the grading and warning of the fault arc of the electric bicycle charging line based on the burning energy of the fault arc. Compared with the traditional fault arc detection algorithm method, the present invention has the following beneficial technical effects: This method resolves the disaster-causing possibility of the fault arc by estimating the burning energy of the fault arc, and uses this as the basis for reducing the false alarm rate and leakage protection rate of the traditional fault arc detection method. It is more scientific and applicable, and can better assist the electric bicycle charging pile to complete the decision-making of charging fault protection, and effectively contain the electrical fire caused by the fault arc on the premise of ensuring the charging safety of the electric bicycle as much as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a schematic flow chart of the method for grading and warning of fault arcs in the electric bicycle charging line of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0035] The following will describe the embodiments of the present invention in detail. The embodiments are implemented on the premise of the technical solution of the present invention, and detailed implementation manners and specific operation processes are given. However, the protection scope of the present invention is not limited to the following embodiments.

[0036] This embodiment provides a technical solution: A method for grading and warning of fault arcs in the electric bicycle charging line includes the following steps:

[0037] Step 1: Use the GD32F103 chip to collect the voltage signal at both ends of the manganin resistor, and obtain the electric bicycle charging current through the inversion of a certain ratio of the voltage signal, and generate a sequence of electric bicycle charging current signals to be processed ;

[0038] In this embodiment, the resistance value of the manganin resistor is 10 It is connected to the electric bicycle charging circuit in a series connection. In addition, for the fault arc grading warning method of the electric bicycle charging circuit, the fault arc diagnosis is generally carried out in seconds. And due to the cost and performance limitations of the embedded software and hardware, the trunk current signal sequence refers to the current signal sequence of a certain second in this embodiment. And according to the response characteristics of the electric bicycle charger and battery to the circuit voltage, the current signal sampling frequency adopted in this embodiment is 40 kHz, that is, the trunk current signal sequence has a length of 40k.

[0039] Step 2: Use the basic transformation relationship of the circuit equation and the fault arc equivalent model to perform an inverse transformation on the electric bicycle charging current signal to obtain the fault arc ignition voltage signal of the electric bicycle charging circuit .

[0040] In this embodiment, the fault arc equivalent model is the Cassie arc model, and the specific expression is . The basic transformation relationship of the circuit equation is the reciprocal relationship between conductance and current. Using the above relationship, the equivalent transformation of the Cassie arc model can be obtained, that is, the fault arc ignition voltage signal of the electric bicycle charging circuit:

[0041] ;

[0042] Among them, is the voltage constant of the Cassie arc model, and it can be specifically characterized by the arc fault characteristics of the electric bicycle charging current signal ; is the time constant of the Cassie arc model; is the time derivative of the electric bicycle charging current signal sequence.

[0043] Step 3: Calculate the arc fault characteristics of the electric bicycle charging current signal , and its specific operation steps are as follows:

[0044] According to the requirements for fault arc detection in GB14287.4, the electric bicycle charging current signal is evenly divided into 100 parts so that each current signal is exactly the length of a half cycle. Perform a difference transformation on the electric bicycle charging current signal for each current half-wave cycle to obtain the electric bicycle differential charging current signal ;

[0045] Obtain the arc fault characteristics of the differential charging current signal , specifically, its expression is:

[0046] ;

[0047] Wherein, N is the length of the charging current signal of the electric bicycle and its differential charging current signal in a half cycle of the current. It should be noted that according to the preset sampling rate of 40 kHz of the current signal in this embodiment, the value of N here is 400, that is, the length of the charging current signal of the electric bicycle in a half cycle of the current is 400.

[0048] It should be noted that the arc fault feature is an intermediate feature constructed by the above mathematical transformation in the method of the present invention and has good performance in the subsequent processing of the method of the present invention.

[0049] Step 4: Combine the arc fault features of 100 current signals calculated per second into a fault feature vector of the charging current signal of the electric bicycle .

[0050] Step 5: Input the fault feature vector of the charging current signal of the electric bicycle into the fault arc detection algorithm to preliminarily judge whether the charging current signal of the electric bicycle carries fault arc information, and determine the burning time of the line fault arc within an observation window according to its variation law in different half cycles of the current .

[0051] It should be noted that the burning time of the fault arc in this embodiment is the actual continuous time of the fault arc.

[0052] In this embodiment, the fault arc detection algorithm judges based on the distortion degree of the current signal to obtain whether the current signal carries fault arc information.

[0053] Step 6: Calculate the energy integral of the fault arc of the electric bicycle charging line during the arcing period to obtain the burning energy of the fault arc , and its specific expression is:

[0054] .

[0055] In this embodiment, the burning energy of the fault arc is the energy released by the fault arc calculated based on the equivalent circuit model, that is, the time integral of voltage and current.

[0056] Step 7: Normalize the burning energy of the fault arc based on the maximum value that can occur to obtain the arc burning energy factor :

[0057] 。

[0058] Step 8: Real-time calculate the arc ignition energy factor of the electric bicycle charging current signal, and classify the fault arcs of the electric bicycle charging line into 3 levels for alarm according to the magnitude of the value.

[0059] In this embodiment, indicates that the electric bicycle charging line is in good condition, indicates that there is a weak fault arc in the electric bicycle line, indicates that there is a severe fault arc in the electric bicycle line that may cause a disaster.

[0060] It should be noted that in this embodiment, the energy integral of the current signal and the arc voltage signal during the fault arc ignition time is increased. By calculating the correlation between the arc voltage and the arc current, the interference of external harmonics can be better filtered out. Without disturbing the sensitivity of the original fault arc detection algorithm, the false alarm probability of the original fault arc detection algorithm can be better reduced, ensuring the reliability and anti-interference ability of the fault arc grading and early warning method for the electric bicycle charging line proposed by the present invention, and making it more practical.

[0061] In summary, the fault arc grading and early warning method for the electric bicycle charging line in the above embodiment only needs to perform reverse voltage transformation and energy integral on the electric bicycle charging current signal to realize the monitoring and evaluation of potential fault arcs in the electric bicycle charging line, and can effectively solve the industry problem that it is very difficult or even impossible for the existing fault arc detection technology to complete the evaluation of the disaster-causing possibility of the fault arcs in the electric bicycle charging line, and then it is impossible to accurately screen out accidental arcs or disaster-causing arcs. Moreover, the implementation of the overall scheme has low requirements for the hardware circuit and is convenient for popularization and application.

[0062] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for early warning of arc faults in an electric bicycle charging line, characterized in that: The following steps are involved: S1: Collecting the charging current signal of the electric bicycle, and obtaining the arc voltage signal of the fault arc of the electric bicycle charging line based on the collected charging current signal of the electric bicycle, the basic transformation relationship of the circuit equation and the fault arc equivalent model; S2: Calculate the arc fault characteristics of the collected electric bicycle charging current signal, and determine the burning time of the fault arc of the electric bicycle charging line based on the change law of the arc fault characteristics of the electric bicycle charging current; In step S2, the specific process is as follows: S21: Divide the charging current signal of the electric bicycle into M parts, so that each current signal has a length of a current half-wave cycle, and perform differential transformation on the charging current signal of the electric bicycle in each current half-wave cycle to obtain a differential charging current signal of the electric bicycle. , M is a positive integer; S22: Calculate the differential charging current signal Arc fault characteristics : ; Wherein, N is the length of a current half-wave cycle of the electric bicycle charging current signal and its differential charging current signal; is the discrete sequence of the electric bicycle charging current signal, For The current signal sequence after differential processing, for The mean of S23: Arc fault characteristics of M current signals calculated every second Combined into the fault feature vector of the electric bicycle charging current signal ; S24: The fault feature vector of the electric bicycle charging current signal Input to the fault arc detection algorithm to determine whether the electric bicycle charging current signal carries fault arc information, and then detect the fault arc according to the arc fault characteristics. The changing rules in different current half-wave cycles determine the burning time of the line fault arc within an observation window ; S3: based on the collected charging current signal of the electric bicycle, the obtained arcing voltage signal of the fault arc, and the determined burning time of the fault arc, the burning energy of the fault arc is obtained; S4: A graded warning of the fault arc in the electric bicycle charging line is realized based on the burning energy of the fault arc.

2. The electric bicycle charging line fault arc classification warning method according to claim 1 is characterized in that: In step S1, the process of collecting the charging current signal of the electric bicycle is as follows: S11: Connecting a manganese copper resistor with a set resistance in series to a charging circuit of the electric bicycle; S12: collecting voltage signals at both ends of the manganese copper resistor; S13: The voltage signal is inverted at a set ratio to obtain a charging current signal of the electric bicycle.

3. The electric bicycle charging line fault arc classification warning method according to claim 2 is characterized in that: In step S1, the fault arc equivalent model is the Cassie arc model, and the basic transformation relationship of the circuit equation is the inverse relationship between conductance and current, thereby obtaining the arc voltage signal of the electric bicycle charging line fault arc as follows: ； in, is the voltage constant of the Cassie arc model; is the time constant of the Cassie arc model; is the time derivative of the electric bicycle charging current signal sequence, Charging current signal for electric bicycles.

4. The electric bicycle charging line fault arc classification warning method according to claim 1 is characterized in that: In step S24, the arc fault characteristics exceeding the preset arc fault characteristic threshold value are recorded every second. The number of half-wave current durations determines the burning time of the line fault arc within an observation window. .

5. The electric bicycle charging line fault arc classification warning method according to claim 3 is characterized in that: In step S3, the burning energy of the fault arc The calculation formula is as follows: ;in, It is the arcing voltage signal sequence of the fault arc.

6. The electric bicycle charging line fault arc classification warning method according to claim 5 is characterized in that: In step S4, the specific processing process is as follows: S41: The burning energy of the fault arc based on The maximum value that can appear is normalized to obtain the arc burning energy factor : ; S42: Real-time calculation of arc burning energy factor of electric bicycle charging current signal ,according to The value divides the electric bicycle charging line fault arc into three levels for alarm.

7. The electric bicycle charging line fault arc classification warning method according to claim 6 is characterized in that: In step S42, It indicates that the charging circuit of the electric bicycle is in good condition. It indicates that there is a weak fault arc in the electric bicycle line. It indicates that there is a severe fault arc in the electric bicycle line that may cause a disaster.

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