Low-voltage direct-current power distribution network small-current grounding fault identification method based on active injection and phase characteristic analysis and related device

Abstract

The invention provides a low-voltage direct-current power distribution network small-current grounding fault identification method based on active injection and phase characteristic analysis and a related device, and the method comprises the steps: responding to a starting criterion of fault detection, and triggering an active signal injection strategy; collecting the positive and negative electrode current of the head end of each feeder line and extracting the zero-mode current of the head end of each feeder line; calculating the total zero mode current of the system; aligning the zero-mode current of each feeder line with the total zero-mode current; extracting instantaneous phases of the zero-mode current of each feeder line and the total zero-mode current by adopting a wavelet transform method; calculating an average phase difference between the zero-mode current of each feeder line and the total zero-mode current; and judging whether the cosine value of the average phase difference is smaller than a set value, and if so, judging that the section to which the corresponding feeder belongs has a fault. According to the invention, the fault features are enhanced by using the active injection detection signal, and the criterion is constructed through zero-mode current processing and phase analysis, so that the fault identification capability is improved under the condition of not adding an additional injection device.

Description

Technical Field

[0001] The present invention belongs to the technical field of low-voltage DC distribution network fault detection, and particularly relates to a low-voltage DC distribution network small current grounding fault identification method and related devices based on active injection and phase characteristic analysis. Background Art

[0002] With the influx of new loads like distributed power sources, electric vehicles, and energy storage, traditional AC distribution systems are gradually exposing limitations in terms of energy efficiency, control flexibility, and equipment compatibility. Low-voltage DC distribution networks (LVDC), on the other hand, offer advantages such as a simple structure, fewer power conversion steps, high power quality, and ease of coordinated source-load control. In recent years, LVDC networks based on flexible DC technology have experienced rapid development and have become a key technology enabler for integrating distributed renewable energy.

[0003] To ensure personal safety, low-voltage DC distribution networks operate in an ungrounded mode. Under this grounding method, the fault current is low after a single-pole ground fault occurs, allowing for a period of operation with the fault. However, a ground fault can cause voltage imbalance between poles, potentially causing further damage to equipment. Therefore, it is still necessary to locate the faulty line and remove it as soon as possible.

[0004] Existing DC distribution network line selection schemes are divided into passive detection schemes and active detection schemes. Passive detection schemes mainly select lines based on fault characteristics generated by the discharge of converter shunt capacitance or line-to-ground capacitance to the fault branch during a fault. However, due to the short line lengths, brief transient fault processes, and rapid transient current decay in low-voltage DC distribution networks, the sampling frequency requirements for detection equipment are high and the reliability is low. If active detection schemes require the addition of additional injection equipment, the economic efficiency is greatly reduced. Currently, the method of injecting detection signals by adding additional control to existing power electronic commutation equipment has certain application prospects. However, the existing method of identifying faulty feeders by the difference in the direction of sudden changes in positive and negative transient currents has high sampling frequency requirements, and the stability and reliability of the judgment criteria are affected by the rapid oscillation and decay of transient currents. Existing methods based on electromagnetic time reversal theory and graph analysis have high sampling frequency and accuracy requirements for measurement devices, and the actual application judgment performance is easily limited by equipment performance. Summary of the Invention

[0005] In view of this, the present invention provides a low-voltage DC distribution network small current grounding fault identification method and related devices based on active injection and phase feature analysis, aiming to effectively improve the sensitivity and reliability of small current grounding fault identification without the need for additional signal injection devices.

[0006] In order to achieve the above object, the technical solution provided by the present invention is as follows:

[0007] In a first aspect, the present invention provides a method for identifying a small current ground fault in a low-voltage DC distribution network based on active injection and phase characteristic analysis, comprising the following steps:

[0008] In response to the start-up criteria of the fault detection, an active signal injection strategy is triggered; the active signal injection strategy is to inject a detection signal into the low-voltage DC distribution network system to be identified;

[0009] Collect the positive and negative currents at the head end of each feeder, and extract the zero-mode current at the head end of each feeder based on the positive and negative currents;

[0010] Calculate the total zero-mode current of the system based on the zero-mode current at the head end of each feeder;

[0011] Align the zero-mode current at the head end of each feeder with the total zero-mode current in the time domain;

[0012] Based on the detection frequency of the detection signal, the instantaneous phase of the zero-mode current at the head end of each feeder and the total zero-mode current is extracted using the wavelet transform method.

[0013] Based on the instantaneous phase of the zero-mode current at the head end of each feeder and the total zero-mode current, the average phase difference between the zero-mode current of each feeder and the total zero-mode current is calculated;

[0014] It is determined whether the cosine value of the average phase difference is less than a set value. If so, it is determined that a fault occurs in the section to which the corresponding feeder belongs. If not, it is determined that no fault occurs in the section to which the corresponding feeder belongs.

[0015] Furthermore, the starting criteria for fault detection are as follows:

[0016]

[0017] Where, and Respectively represent the voltage amplitude of the positive DC bus to ground and the voltage amplitude of the negative DC bus to ground, Indicates the DC rated voltage.

[0018] Furthermore, the zero-mode current at the head end of each feeder is extracted according to the following formula:

[0019]

[0020] Where, and are the single-mode current value and zero-mode current value observed by the first end of line k within a calculation data window length, and are the positive current and negative current at the head end of each feeder respectively.

[0021] Furthermore, the total zero-mode current is calculated as follows:

[0022]

[0023] Where, is the total zero-mode current, is the zero-mode current at the head end of the kth feeder, and n is the total number of feeders.

[0024] Furthermore, the zero-mode current at the head end of each feeder is aligned with the total zero-mode current in the time domain, including:

[0025] The zero-mode current at the head end of each feeder is cross-correlated with the total zero-mode current as follows:

[0026]

[0027] Where, is the cross-correlation function between the zero-mode current at the head end of the kth feeder and the total zero-mode current, is the time delay variable, and are the instantaneous value of the zero-mode current at the head end of the kth feeder at time t and the time delay after time t. The instantaneous value of the total zero-mode current after

[0028] Obtaining the optimal alignment delay between each set of current signals;

[0029]

[0030] Where, The time delay when the cross-correlation function value reaches the maximum value is the optimal alignment delay;

[0031] Based on the optimal alignment delay, the zero-mode current at the head end of each feeder is aligned with the total zero-mode current in the time domain as follows:

[0032]

[0033] Where, is the signal after zero-mode current alignment at the head end of the kth feeder.

[0034] Furthermore, the wavelet transform method is Morlet wavelet, and the wavelet transform scale of Morlet wavelet is determined according to the following formula:

[0035]

[0036] Where, represents the scale factor of wavelet transform, is the center frequency of the Morlet wavelet, is the frequency of the detection signal, is the sampling period.

[0037] Furthermore, the average phase difference between the zero-mode current of each feeder and the total zero-mode current is calculated according to the following formula:

[0038]

[0039] Where, is the average phase difference between the zero-mode current at the head end of the kth feeder and the total zero-mode current, is the number of sampling points, is the sampling point index, and are the instantaneous phase of the zero-mode current at the head end of the kth feeder and the instantaneous phase of the total zero-mode current, respectively.

[0040] In a second aspect, the present invention provides a low-voltage DC distribution network small current grounding fault identification device based on active injection and phase characteristic analysis, comprising:

[0041] A startup module, configured to respond to a startup criterion of a fault detection and trigger an active signal injection strategy; the active signal injection strategy is to inject a detection signal into the low-voltage DC distribution network system to be identified;

[0042] The first calculation module is used to collect the positive and negative currents at the head end of each feeder, and extract the zero-mode current at the head end of each feeder based on the positive and negative currents;

[0043] A second calculation module is used to calculate the total zero-mode current of the system based on the zero-mode current at the head end of each feeder;

[0044] A third calculation module is used to align the zero-mode current at the head end of each feeder with the total zero-mode current in the time domain;

[0045] a fourth calculation module, configured to extract the instantaneous phase of the zero-mode current at the head end of each feeder and the total zero-mode current by using a wavelet transform method based on the detection frequency of the detection signal;

[0046] a fifth calculation module, configured to calculate an average phase difference between the zero mode current of each feeder and the total zero mode current based on the instantaneous phase of the zero mode current at the head end of each feeder and the total zero mode current;

[0047] The fault identification module is used to determine whether the cosine value of the average phase difference is less than a set value. If so, it is determined that a fault has occurred in the section to which the corresponding feeder belongs; if not, it is determined that no fault has occurred in the section to which the corresponding feeder belongs.

[0048] In a third aspect, the present invention provides a computer device, comprising a processor and a memory:

[0049] The memory is used to store computer programs and send instructions of the computer programs to the processor;

[0050] The processor executes the method for identifying small current grounding faults in a low-voltage direct current distribution network based on active injection and phase characteristic analysis according to the instructions of the computer program as described in the first aspect.

[0051] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a method for identifying small current grounding faults in a low-voltage DC distribution network based on active injection and phase characteristic analysis as described in the first aspect is implemented.

[0052] In summary, the present invention provides a method and related device for identifying small current grounding faults in a low-voltage DC distribution network based on active injection and phase feature analysis, including a starting criterion that responds to fault detection and triggers an active signal injection strategy; the active signal injection strategy is to inject a detection signal into the low-voltage DC distribution network system to be identified; the positive and negative currents at the head end of each feeder are collected, and the zero-mode current at the head end of each feeder is extracted based on the positive and negative currents; the total zero-mode current of the system is calculated based on the zero-mode current at the head end of each feeder; the zero-mode current at the head end of each feeder is aligned with the total zero-mode current in the time domain; based on the detection frequency of the detection signal, a wavelet transform method is used to extract the instantaneous phase of the zero-mode current at the head end of each feeder and the total zero-mode current; based on the instantaneous phase of the zero-mode current at the head end of each feeder and the total zero-mode current, the average phase difference between the zero-mode current of each feeder and the total zero-mode current is calculated; it is judged whether the cosine value of the average phase difference is less than a set value. If so, it is determined that a fault has occurred in the section to which the corresponding feeder belongs; if not, it is determined that no fault has occurred in the section to which the corresponding feeder belongs. The present invention enhances fault characteristics by actively injecting detection signals and constructs judgment criteria through zero-mode current processing and phase analysis, thereby overcoming the limitations of the existing technology on sampling frequency, accuracy and equipment performance, and improving fault identification capabilities without adding additional injection devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 A flowchart of a method for identifying small current grounding faults in a low-voltage DC distribution network based on active injection and phase characteristic analysis provided by an embodiment of the present invention;

[0055] Figure 2 A block diagram of a low-voltage DC distribution network small current grounding fault identification device based on active injection and phase characteristic analysis provided by an embodiment of the present invention;

[0056] Figure 3 A block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purposes, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0058] An embodiment of the present invention provides a method for identifying low-current grounding faults in a low-voltage DC distribution network based on active injection and phase characteristic analysis, comprising the following steps:

[0059] S1: responding to the start criterion of fault detection and triggering the active signal injection strategy; the active signal injection strategy is to inject a detection signal into the low-voltage DC distribution network system to be identified.

[0060] It should be noted that the starting criteria for fault detection are pre-set conditions for determining whether a system fault may occur, such as abnormal fluctuation thresholds of electrical quantities such as voltage and current. When the electrical quantity meets the threshold, it is considered that a fault may occur.

[0061] The active signal injection strategy is to actively inject a specific signal into the distribution network system to assist in fault identification. Here it is a detection signal, and its parameters such as frequency and amplitude can be controlled.

[0062] The detection signal is a special signal injected. Its frequency, waveform and other characteristics can be designed according to the fault identification requirements, and is used to generate identifiable fault-related responses in the system.

[0063] This step monitors the electrical parameters of the low-voltage DC distribution network in real time. When the monitored parameters meet the fault detection start criteria, it is considered that the system may have a fault. At this time, the active signal injection device is triggered to inject a detection signal into the system, so that the fault can subsequently produce easier-to-identify characteristics under the action of the detection signal.

[0064] S2: Collect the positive and negative currents at the head end of each feeder, and extract the zero-mode current at the head end of each feeder based on the positive and negative currents.

[0065] It should be noted that the feeder head end refers to the end point of each feeder close to the power supply side, which is the location where the current signal is collected.

[0066] Positive and negative current refers to the current on the positive and negative conductors in the low-voltage DC distribution network.

[0067] Zero-mode current refers to the current component obtained by mathematically transforming the positive and negative currents, reflecting the common-mode characteristics of the positive and negative currents. When a small current grounding fault occurs, the zero-mode current will change significantly.

[0068] This step uses current sensors to collect current signals at the positive and negative poles of each feeder head end. Then, through mathematical transformation, the zero-mode current is extracted from the positive and negative currents. This is because when a small current grounding fault occurs, the fault current loop will cause characteristic changes in the zero-mode current, facilitating subsequent fault analysis based on the zero-mode current.

[0069] S3: Calculate the total zero-mode current of the system based on the zero-mode current at the head end of each feeder.

[0070] It should be noted that the total zero-mode current is the current obtained by synthesizing the zero-mode current extracted from the head end of each feeder, reflecting the overall zero-mode current situation of the entire low-voltage DC distribution network system.

[0071] In this step, the zero-mode current at the head end of each feeder obtained in step S2 can be calculated to obtain the total zero-mode current of the system, which is used for subsequent comparison with the zero-mode current of each feeder to analyze the fault.

[0072] S4: Align the zero-mode current at the head end of each feeder with the total zero-mode current in the time domain.

[0073] It should be noted that due to differences in the zero-mode current transmission paths and acquisition times of each feeder, there will be time asynchrony. Time domain alignment is to adjust the time coordinates of the zero-mode current of each feeder so that they are synchronized on the time axis.

[0074] This step uses a signal processing algorithm to calculate the time delay between the zero-mode current at the head end of each feeder and the total zero-mode current. Then, based on this delay, a time shift operation is performed on the zero-mode current of each feeder so that they are in the same time reference frame in the time domain, ensuring the accuracy of subsequent phase analysis.

[0075] S5: Based on the detection frequency of the detection signal, the instantaneous phase of the zero-mode current at the head end of each feeder and the total zero-mode current is extracted using the wavelet transform method.

[0076] It should be noted that wavelet transform is a time-frequency analysis method that can decompose the signal into different frequency bands. For example, Morlet wavelet (complex-valued wavelet with good time-frequency localization characteristics) can be used to process the signal.

[0077] The detection frequency is the frequency of the actively injected detection signal.

[0078] The instantaneous phase is the phase value of the signal at a certain moment, reflecting the phase state of the signal at that moment. After wavelet transform processing, the instantaneous phase corresponding to a specific frequency (detection frequency) can be extracted.

[0079] This step determines the scale of the wavelet transform based on the detection frequency of the detection signal. Then, the wavelet transform is used to transform the zero-mode current and the total zero-mode current at the head end of each feeder after time domain alignment, and the wavelet coefficient corresponding to the detection frequency is extracted to obtain the instantaneous phase, highlighting the phase characteristics of the fault-related frequency components.

[0080] S6: Calculate an average phase difference between the zero mode current of each feeder and the total zero mode current based on the instantaneous phase of the zero mode current at the head end of each feeder and the total zero mode current.

[0081] It should be noted that the average phase difference is the value obtained by calculating the phase difference between the zero-mode current at the head end of each feeder and the total zero-mode current at multiple moments (sampling points) and then averaging the calculated value.

[0082] In this step, at multiple sampling moments, the difference between the instantaneous phase of the zero-mode current at the head end of each feeder and the instantaneous phase of the total zero-mode current is calculated respectively. These differences are summed and divided by the number of sampling moments to obtain the average phase difference, making the phase difference feature more representative and facilitating fault identification.

[0083] S7: Determine whether the cosine value of the average phase difference is less than a set value. If so, determine that a fault occurs in the section to which the corresponding feeder belongs. If not, determine that no fault occurs in the section to which the corresponding feeder belongs.

[0084] It should be noted that the cosine value of the average phase difference is a value obtained by performing a cosine operation on the average phase difference, and is used to convert the phase difference into a value that is easier to compare with a set threshold.

[0085] The set value is a threshold value for distinguishing the cosine value range corresponding to the fault state and the normal state.

[0086] This step calculates the cosine of the average phase difference and compares it with a set value. If it is less than the set value, it indicates that the phase difference between the corresponding feeder zero-mode current and the total zero-mode current is large, which meets the phase characteristics of a fault. Therefore, the feeder section to which the fault belongs is determined to be faulty. Otherwise, it is determined that no fault has occurred, and the fault location is achieved by utilizing the sudden change in current phase during a fault.

[0087] This embodiment provides a method for identifying small current grounding faults in low-voltage DC distribution networks based on active injection and phase feature analysis. This method integrates active signal injection and phase feature analysis technologies. By actively injecting detection signals into the low-voltage DC distribution network, it addresses the problem of weak inherent characteristics of small current grounding faults that are difficult to identify. The method utilizes the sensitivity of zero-mode current to small current grounding faults, combines time domain alignment to eliminate signal asynchrony interference, and uses wavelet transform to accurately extract the instantaneous phase at the detection frequency. Fault identification is then achieved through comparison of average phase difference and cosine value. This method overcomes the limitation of traditional reliance on natural fault characteristics that are susceptible to noise and system operating conditions. It can complete full-line fault detection using only single-ended measurement (feeder head-end current), eliminating the need for complex two-end synchronous communication and large-scale monitoring point deployment. While improving the sensitivity and accuracy of small current grounding fault identification, it also reduces implementation costs and system complexity. The method is adaptable to complex low-voltage DC distribution network topologies with multiple lines and branches, and has good versatility and engineering practicality. Moreover, the proposed method can enhance the current response characteristics under high-resistance faults, has strong anti-noise capability, and has low sampling rate requirements, which can effectively improve the reliability of single-pole grounding fault detection.

[0088] See also Figure 1 , Figure 1 This is an implementation process of a method for identifying low-current ground faults in a low-voltage DC distribution network based on active injection and phase characteristic analysis, designed based on the above embodiment. The implementation process is described below in conjunction with some other embodiments of the present invention. The implementation process includes the following steps:

[0089] Step 1: Use DC inter-pole voltage imbalance as the starting criterion for fault detection and trigger the active signal injection strategy.

[0090] In one embodiment of the present invention, the starting criteria for fault detection are as follows:

[0091] (1)

[0092] Where, and Respectively represent the voltage amplitude of the positive DC bus to ground and the voltage amplitude of the negative DC bus to ground, Indicates the DC rated voltage.

[0093] Step 2: Collect the positive and negative currents i at the head end of each feeder Pk 、i Nk (k=1,2,…,n), the positive and negative currents are decoupled by Karrenbauer transformation in matrix form, and the zero-mode component of each feeder is extracted.

[0094] In one embodiment of the present invention, the zero-mode current at the head end of each feeder is extracted according to the following formula:

[0095] (2)

[0096] Where, and are the single-mode current value and zero-mode current value observed by the first end of line k within a calculation data window length, and are the positive current and negative current at the head end of each feeder respectively.

[0097] Step 3: Considering that the phase angle judgment in fault identification needs to be based on a unified reference, the total zero-mode current i of the system needs to be calculated first. 0_total .

[0098] In one embodiment of the present invention, the total zero-mode current is calculated according to the following formula:

[0099] (3)

[0100] Where, is the total zero-mode current, is the zero-mode current at the head end of the kth feeder, and n is the total number of feeders.

[0101] In one embodiment of the present invention, aligning the zero-mode current at the head end of each feeder with the total zero-mode current in the time domain includes the following steps 4 and 5.

[0102] Step 4: Perform cross-correlation processing on the zero-mode current at the head end of each feeder and the total zero-mode current, as follows:

[0103] (4)

[0104] Where, is the cross-correlation function between the zero-mode current at the head end of the kth feeder and the total zero-mode current, is the time delay variable, and are the instantaneous value of the zero-mode current at the head end of the kth feeder at time t and the time delay after time t. The instantaneous value of the total zero-mode current after

[0105] Get the optimal alignment delay between each set of current signals, that is, by analyzing R k The absolute value of (τ) is the maximum point, and the corresponding time delay τ is obtained k , that is, the optimal alignment delay between the two signals:

[0106] (5)

[0107] Where, The time delay when the cross-correlation function value reaches the maximum value is the optimal alignment delay;

[0108] Step 5: Align the zero-mode current at each feeder headend with the total zero-mode current in the time domain based on the optimal alignment delay, as follows:

[0109] (6)

[0110] Where, is the signal after zero-mode current alignment at the head end of the kth feeder.

[0111] In one embodiment of the present invention, the zero-mode current signal of each feeder is The Morlet wavelet is used to perform wavelet transform near the injected specific detection frequency, and the instantaneous phase of the wavelet coefficient of the frequency component is extracted and recorded as φ k_head0R The scale of wavelet transform is chosen as:

[0112] (7)

[0113] Where, represents the scale factor of wavelet transform, is the center frequency of the Morlet wavelet, is the frequency of the detection signal, is the sampling period.

[0114] Step 6: For the total zero-mode current i 0_total The Morlet wavelet is used to perform wavelet transform near the injected specific detection frequency, and the instantaneous phase of the wavelet coefficient of the frequency component is extracted and recorded as φ 0_total .

[0115] Step 7: Calculate the average phase difference between each feeder and the reference signal under the optimal time delay.

[0116] In one embodiment of the present invention, the average phase difference between the zero-mode current of each feeder and the total zero-mode current is calculated according to the following formula:

[0117] (8)

[0118] Where, is the average phase difference between the zero-mode current at the head end of the kth feeder and the total zero-mode current, is the number of sampling points, is the sampling point index, and are the instantaneous phase of the zero-mode current at the head end of the kth feeder and the instantaneous phase of the total zero-mode current, respectively.

[0119] Step 8: Construct a line selection and identification criterion based on the phase difference cosine value. The line selection and identification criterion is:

[0120] (9)

[0121] Among them, ξ set In order to protect the reliability coefficient of the criterion, the present invention takes -0.3.

[0122] The above-mentioned embodiment uses the zero-mode current response characteristics based on active signal injection and phase difference analysis as the core criterion for fault identification. Additional control is introduced into the voltage source converter (VSC) to actively inject a detection signal of a specific frequency to enhance the ground fault characteristics. Cross-correlation is then used to achieve optimal time alignment of the waveforms of each feeder, and the phase difference cosine value is calculated to identify the fault line. At the same time, through cross-correlation analysis of the head end of each feeder and the total zero-mode current, the optimal time alignment delay is obtained, overcoming the problem of traditional phase judgment criteria being prone to jitter in noisy environments. Based on the waveform time alignment, a judgment criterion based on the phase difference cosine value is further proposed to improve the accuracy and response speed of fault detection.

[0123] Based on the above embodiments, it can be seen that the present invention has the following advantages:

[0124] (1) This invention enhances the zero-mode current characteristics of the system response by actively injecting a detection signal of a specific frequency after the fault is initiated. Waveform alignment is then achieved through cross-correlation analysis. The line selection criterion is constructed by combining the phase difference cosine value, effectively solving the problem of poor sensitivity in traditional transient-based line selection methods. Cross-correlation processing enhances the method's robustness to noise and signal offset, while subsequent phase difference analysis ensures the directionality and distinguishability of the fault characteristics, thereby achieving more reliable line selection.

[0125] (2) Compared with traditional methods that rely on high sampling rates or communication coordination, the present invention does not require the deployment of additional sensors or communication devices, and only relies on local measurement and control strategies at the head end of the feeder. It has the advantages of low implementation cost, simple configuration, and strong robustness. It is particularly suitable for low-voltage DC distribution systems with flexible structures and variable loads.

[0126] Based on the same inventive concept, an embodiment of the present application further provides a low-voltage DC distribution network low-current grounding fault identification device based on active injection and phase feature analysis, which is used to implement the aforementioned low-voltage DC distribution network low-current grounding fault identification method based on active injection and phase feature analysis. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of the embodiments of the low-voltage DC distribution network low-current grounding fault identification device based on active injection and phase feature analysis provided below can be found in the above-mentioned limitations of the low-voltage DC distribution network low-current grounding fault identification method based on active injection and phase feature analysis, and will not be repeated here.

[0127] See also Figure 2 The present invention provides a low-voltage DC distribution network small current grounding fault identification device based on active injection and phase characteristic analysis, comprising:

[0128] A startup module, configured to respond to a startup criterion of a fault detection and trigger an active signal injection strategy; the active signal injection strategy is to inject a detection signal into the low-voltage DC distribution network system to be identified;

[0129] The first calculation module is used to collect the positive and negative currents at the head end of each feeder, and extract the zero-mode current at the head end of each feeder based on the positive and negative currents;

[0130] A second calculation module is used to calculate the total zero-mode current of the system based on the zero-mode current at the head end of each feeder;

[0131] A third calculation module is used to align the zero-mode current at the head end of each feeder with the total zero-mode current in the time domain;

[0132] a fourth calculation module, configured to extract the instantaneous phase of the zero-mode current at the head end of each feeder and the total zero-mode current by using a wavelet transform method based on the detection frequency of the detection signal;

[0133] a fifth calculation module, configured to calculate an average phase difference between the zero mode current of each feeder and the total zero mode current based on the instantaneous phase of the zero mode current at the head end of each feeder and the total zero mode current;

[0134] The fault identification module is used to determine whether the cosine value of the average phase difference is less than a set value. If so, it is determined that a fault has occurred in the section to which the corresponding feeder belongs; if not, it is determined that no fault has occurred in the section to which the corresponding feeder belongs.

[0135] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0136] Reference Figure 3An embodiment of the present invention also provides a computer device, including: a memory and a processor and a computer program stored in the memory. When the computer program is executed on the processor, it implements the low-voltage DC distribution network small current grounding fault identification method based on active injection and phase feature analysis as described in any one of the above methods.

[0137] The computer device may be a desktop computer, notebook computer, PDA, cloud server or other computing device. The computer device may include, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that Figure 3 The computer device is merely an example and does not constitute a limitation on the computer device. The computer device may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, etc.

[0138] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0139] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped with the computer device. Furthermore, the memory may include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory may also be used to temporarily store data that has been output or is about to be output.

[0140] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for identifying low-current grounding faults in a low-voltage DC distribution network based on active injection and phase characteristic analysis as described in any one of the above methods is implemented.

[0141] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0142] An embodiment of the present invention provides a computer program product, including a computer program. When the computer program is executed by a processor, the computer program implements the low-voltage DC distribution network small current grounding fault identification method based on active injection and phase characteristic analysis as described in any one of the above methods.

[0143] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0144] Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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.

[0145] In the embodiments disclosed in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0146] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for identifying small current grounding faults in low-voltage DC distribution networks based on active injection and phase characteristic analysis is characterized by: The steps include: In response to the start-up criteria of the fault detection, an active signal injection strategy is triggered; the active signal injection strategy is to inject a detection signal into the low-voltage DC distribution network system to be identified; Collecting the positive and negative currents at the head end of each feeder, and extracting the zero-mode current at the head end of each feeder based on the positive and negative currents; Calculate the total zero-mode current of the system based on the zero-mode current at the head end of each feeder; Aligning the zero-mode current at the head end of each feeder with the total zero-mode current in the time domain; Based on the detection frequency of the detection signal, a wavelet transform method is used to extract the instantaneous phase of the zero-mode current at the head end of each feeder and the total zero-mode current; Calculating an average phase difference between the zero mode current of each feeder and the total zero mode current based on the instantaneous phase of the zero mode current at the head end of each feeder and the total zero mode current; It is determined whether the cosine value of the average phase difference is less than a set value. If so, it is determined that a fault occurs in the section to which the corresponding feeder belongs; if not, it is determined that no fault occurs in the section to which the corresponding feeder belongs.

2. The method for identifying small current grounding faults in low-voltage DC distribution networks based on active injection and phase characteristic analysis according to claim 1 is characterized in that: The starting criteria for the fault detection are as follows: Where, and Respectively represent the voltage amplitude of the positive DC bus to ground and the voltage amplitude of the negative DC bus to ground, Indicates the DC rated voltage.

3. The method for identifying small current grounding faults in low-voltage DC distribution networks based on active injection and phase characteristic analysis according to claim 1 is characterized in that: The zero-mode current at the head end of each feeder is extracted according to the following formula: Where, and are the single-mode current value and zero-mode current value observed by the first end of line k within a calculation data window length, and are the positive current and negative current at the head end of each feeder respectively.

4. The method for identifying small current grounding faults in low-voltage DC distribution networks based on active injection and phase characteristic analysis according to claim 1 is characterized in that: The total zero-mode current is calculated according to the following formula: Where, is the total zero-mode current, is the zero-mode current at the head end of the kth feeder, and n is the total number of feeders.

5. The method for identifying low-current grounding faults in low-voltage DC distribution networks based on active injection and phase characteristic analysis according to claim 1 is characterized in that: Aligning the zero-mode current at the head end of each feeder with the total zero-mode current in the time domain includes: The zero-mode current at the head end of each feeder is cross-correlated with the total zero-mode current as follows: Where, is the cross-correlation function value between the zero-mode current at the head end of the k-th feeder and the total zero-mode current, is the time delay variable, and are the instantaneous value of the zero-mode current at the head end of the kth feeder at time t and the time delay after time t. The instantaneous value of the total zero-mode current after Obtaining the optimal alignment delay between each set of current signals; Where, The time delay when the cross-correlation function value reaches the maximum value, that is, the optimal alignment delay; Based on the optimal alignment delay, the zero-mode current at the head end of each feeder is aligned with the total zero-mode current in the time domain as follows: Where, is the signal after zero-mode current alignment at the head end of the k-th feeder.

6. The method for identifying small current grounding faults in low-voltage DC distribution networks based on active injection and phase characteristic analysis according to claim 1, characterized in that: The wavelet transform method is Morlet wavelet, and the wavelet transform scale of Morlet wavelet is determined according to the following formula: Where, represents the scale factor of wavelet transform, is the center frequency of the Morlet wavelet, is the frequency of the detection signal, is the sampling period.

7. The method for identifying small current grounding faults in low-voltage DC distribution networks based on active injection and phase characteristic analysis according to claim 1 is characterized in that: The average phase difference between the zero-mode current of each feeder and the total zero-mode current is calculated according to the following formula: Where, is the average phase difference between the zero-mode current at the head end of the kth feeder and the total zero-mode current, is the number of sampling points, is the sampling point index, and They are respectively the instantaneous phase of the zero-mode current at the head end of the kth feeder and the instantaneous phase of the total zero-mode current.

8. A low-voltage DC distribution network small current grounding fault identification device based on active injection and phase characteristic analysis, characterized in that: include: A startup module, configured to respond to a startup criterion of a fault detection and trigger an active signal injection strategy; The active signal injection strategy is to inject a detection signal into the low-voltage DC distribution network system to be identified; A first calculation module is used to collect the positive and negative currents at the head end of each feeder, and extract the zero mode current at the head end of each feeder based on the positive and negative currents; A second calculation module is used to calculate the total zero-mode current of the system based on the zero-mode current at the head end of each feeder; A third calculation module is used to align the zero mode current at the head end of each feeder with the total zero mode current in the time domain; a fourth calculation module, configured to extract, based on the detection frequency of the detection signal, the instantaneous phase of the zero-mode current at the head end of each feeder and the total zero-mode current by using a wavelet transform method; a fifth calculation module, configured to calculate an average phase difference between the zero mode current of each feeder and the total zero mode current based on the instantaneous phase of the zero mode current at the head end of each feeder and the total zero mode current; The fault identification module is used to determine whether the cosine value of the average phase difference is less than a set value. If so, it is determined that a fault occurs in the section to which the corresponding feeder belongs; if not, it is determined that no fault occurs in the section to which the corresponding feeder belongs.

9. A computer device, characterized in that: The device includes a processor and a memory: The memory is used to store the computer program and send instructions of the computer program to the processor; The processor executes the method for identifying small current grounding faults in a low-voltage direct current distribution network based on active injection and phase characteristic analysis according to any one of claims 1 to 7 according to the instructions of the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for identifying small current grounding faults in a low-voltage DC distribution network based on active injection and phase characteristic analysis according to any one of claims 1 to 7 is implemented.

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