Series fault arc detection method, device, equipment and storage medium

Abstract

The present invention relates to the field of circuit detection, and discloses a series fault arc detection method, device, equipment and storage medium. The method obtains a load working current signal of a DC power system at preset intervals; performs empirical mode decomposition on the load working current signal to obtain a multi-layer mode sequence of the load working current signal; obtains a fault feature enhancement coefficient, and determines the fault arc indication feature of the load working current signal based on the fault feature enhancement coefficient and the multi-layer mode sequence; obtains the auxiliary judgment feature of the load working current signal, performs fault arc judgment based on the fault arc indication feature and the auxiliary judgment feature, and obtains a fault arc detection result. The present invention performs fault arc judgment based on the enhanced fault arc indication feature and the auxiliary judgment feature, so as to accurately detect the fault arc when the circuit load is small.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit detection, and in particular to a series fault arc detection method, device, equipment and storage medium. Background Art

[0002] Arc is a form of gas discharge. Insulated electrical lines usually do not have arcs. Due to long-term aging and damage of the line, or looseness of the electrical connection interface, fault arcs may occur. The temperature of the arc can usually reach 4000-50000K, releasing a large amount of heat in a short period of time, igniting surrounding objects and causing fires.

[0003] In DC circuit systems such as photovoltaic power stations and electric vehicle charging stations, compared with the periodic "zero-rest" phenomenon of AC arcs, once a DC arc is generated, it will continue to discharge stably, making it more difficult to extinguish the arc, releasing more heat and causing greater harm.

[0004] At present, the research on DC fault arc detection is mostly focused on the detection of fault current changes. However, when the loop load is small, the current signal does not change significantly. The current distortion caused by the fault arc is easily affected by the current intensity or background noise, resulting in reduced detection accuracy.

[0005] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0006] The main purpose of the present invention is to provide a series fault arc detection method, device, equipment and storage medium, aiming to solve the technical problem of low fault arc detection accuracy in the prior art.

[0007] To achieve the above object, the present invention provides a series fault arc detection method, the method comprising the following steps:

[0008] Obtaining load working current signals of a DC power system at preset intervals;

[0009] Obtaining a fault characteristic enhancement coefficient of the load working current signal;

[0010] Performing empirical mode decomposition on the load working current signal to obtain a multi-layer mode sequence of the load working current signal;

[0011] Determining a fault arc indication feature of the load operating current signal based on the fault feature enhancement coefficient and the multi-layer modal sequence;

[0012] The auxiliary judgment feature of the load working current signal is obtained, and the fault arc judgment is performed based on the fault arc indication feature and the auxiliary judgment feature to obtain the fault arc detection result.

[0013] Optionally, the step of obtaining a fault feature enhancement coefficient of the load working current signal includes:

[0014] Dividing the load working current signal into a signal sequence of a preset number of groups;

[0015] The fault feature enhancement coefficient of the load working current signal is determined according to the number of signal samples and the mean value of the signal samples in each group of signal sequences.

[0016] Optionally, the step of performing empirical mode decomposition on the load working current signal to obtain a multi-layer mode sequence of the load working current signal includes:

[0017] Performing empirical mode decomposition on the load working current signal in each of the signal sequences to obtain a multi-layer mode sequence of the signal sequence.

[0018] Optionally, the step of determining the fault arc indication feature of the load working current signal based on the fault feature enhancement coefficient and the multi-layer modal sequence includes:

[0019] Determine a signal sequence component of the signal sequence according to the signal sample number and the multi-layer modal sequence, the signal sequence component comprising: a first signal sequence component and a second signal sequence component;

[0020] The fault arc indication feature of the load working current signal is determined according to the fault feature enhancement coefficient and the signal sequence component, and the fault arc indication feature includes: a first fault arc indication feature and a second fault arc indication feature.

[0021] Optionally, the step of obtaining the auxiliary judgment feature of the load working current signal includes:

[0022] Performing time-frequency transformation on the load working current signal to obtain a frequency coefficient of the load working current signal;

[0023] The frequency band energy of a preset frequency band is determined based on the frequency coefficient, and the frequency band energy is used as an auxiliary judgment feature of the load working current signal.

[0024] Optionally, the step of performing arc fault judgment based on the arc fault indication feature and the auxiliary judgment feature to obtain an arc fault detection result includes:

[0025] Comparing the first arc fault indication feature with a preset first fault feature threshold, and comparing the second arc fault indication feature with a preset second fault feature threshold;

[0026] When the first fault arc indication feature is greater than the first fault feature threshold and the second fault arc indication feature is greater than the second fault feature threshold, increasing a count value;

[0027] When the count value is greater than the count threshold, the auxiliary judgment feature is compared with a preset auxiliary feature threshold to obtain a fault arc detection result.

[0028] Optionally, the step of comparing the auxiliary judgment feature with a preset auxiliary feature threshold to obtain a fault arc detection result includes:

[0029] If the auxiliary judgment feature is greater than a preset auxiliary feature threshold, the arc judgment flag is normalized to determine that a fault arc exists in the DC power system;

[0030] If the auxiliary judgment feature is not greater than a preset auxiliary feature threshold, the arc judgment flag is reset to zero to determine that there is no fault arc in the DC power system.

[0031] In addition, to achieve the above-mentioned purpose, the present invention also proposes a series fault arc detection device, the series fault arc detection device comprising:

[0032] A signal acquisition module, used to acquire a load working current signal of a DC power system at preset intervals;

[0033] A coefficient acquisition module, used to acquire a fault characteristic enhancement coefficient of the load working current signal;

[0034] A signal decomposition module, used for performing empirical mode decomposition on the load working current signal to obtain a multi-layer mode sequence of the load working current signal;

[0035] A feature enhancement module, used to determine the fault arc indication feature of the load working current signal based on the fault feature enhancement coefficient and the multi-layer modal sequence;

[0036] The fault judgment module is used to obtain the auxiliary judgment feature of the load working current signal, perform fault arc judgment based on the fault arc indication feature and the auxiliary judgment feature, and obtain a fault arc detection result.

[0037] In addition, to achieve the above-mentioned objectives, the present invention also proposes a series fault arc detection device, which includes: a memory, a processor, and a series fault arc detection program stored in the memory and executable on the processor, wherein the series fault arc detection program is configured to implement the steps of the series fault arc detection method described above.

[0038] In addition, to achieve the above objectives, the present invention further proposes a storage medium, on which a series fault arc detection program is stored, and when the series fault arc detection program is executed by a processor, the steps of the series fault arc detection method described above are implemented.

[0039] The present invention obtains the load working current signal of the DC power system at preset intervals; performs empirical mode decomposition on the load working current signal to obtain a multi-layer mode sequence of the load working current signal; obtains the fault feature enhancement coefficient, and determines the fault arc indication feature of the load working current signal based on the fault feature enhancement coefficient and the multi-layer mode sequence; obtains the auxiliary judgment feature of the load working current signal, performs fault arc judgment based on the fault arc indication feature and the auxiliary judgment feature, and obtains the fault arc detection result. Since the multi-layer mode sequence of the load working current signal is enhanced by the fault feature enhancement coefficient, and the fault arc judgment is performed based on the enhanced fault arc indication feature and the auxiliary judgment feature of the load working current signal, real-time detection of the DC power system is realized. Compared with the existing method, this scheme combines the fault feature enhancement coefficient and the auxiliary judgment feature, and has a higher detection accuracy when the loop load is small. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a structural schematic diagram of a series fault arc detection device in a hardware operating environment involved in an embodiment of the present invention;

[0041] Figure 2 It is a flow chart of the first embodiment of the series fault arc detection method of the present invention;

[0042] Figure 3 It is a flow chart of a second embodiment of a series fault arc detection method of the present invention;

[0043] Figure 4 It is a flow chart of a third embodiment of a series fault arc detection method of the present invention;

[0044] Figure 5 A schematic diagram of a scenario flow in one implementation of series fault arc detection of the present invention;

[0045] Figure 6 It is a structural block diagram of the first embodiment of the series fault arc detection device of the present invention.

[0046] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0048] Reference Figure 1 , Figure 1 It is a schematic diagram of the structure of a series fault arc detection device in the hardware operating environment involved in the embodiment of the present invention.

[0049] like Figure 1 As shown, the series fault arc detection device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM), or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0050] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the series fault arc detection device, and may include more or less components than those shown in the figure, or combine certain components, or arrange the components differently.

[0051] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a series fault arc detection program.

[0052] exist Figure 1In the series fault arc detection device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the series fault arc detection device of the present invention can be set in the series fault arc detection device, and the series fault arc detection device calls the series fault arc detection program stored in the memory 1005 through the processor 1001, and executes the series fault arc detection method provided by the embodiment of the present invention.

[0053] The embodiment of the present invention provides a series fault arc detection method, referring to Figure 2 , Figure 2 It is a flow chart of the first embodiment of the series fault arc detection method of the present invention.

[0054] In this embodiment, the series fault arc detection method includes the following steps:

[0055] Step S10: acquiring a load operating current signal of a DC power system at preset time intervals.

[0056] It should be noted that the execution subject of the method of this embodiment can be a terminal device with signal processing, data analysis and program running functions, such as a personal computer, an industrial computer, a microcomputer, etc., or an electronic device with the same or similar functions, such as the above-mentioned series fault arc detection device. The following takes the series fault arc detection device (hereinafter referred to as the detection device) as an example to illustrate this embodiment and the following embodiments.

[0057] It is understandable that a DC power system is an electrical system powered by direct current. In a DC power system, the electric energy generated by the power supply is supplied to various devices and loads in the form of direct current. In daily life, DC power systems are widely used, for example, solar power generation systems, electric vehicles, aerospace fields, high-speed rail power supply systems, large equipment and industrial systems. Since extremely high temperatures are caused when a fault arc is generated, it is easy to cause hazards such as fire, equipment burning, melting and even explosion. In addition, due to its suddenness, the fault arc is also easy to cause electric shocks and burns to the contact personnel, and cause circuit overcurrent and short circuit. Therefore, it is necessary to detect the fault arc in the DC current system in a timely manner to avoid the hazards caused by the fault arc.

[0058] In the present invention, the detection device can be connected to a data acquisition device for load working current signal, such as a current transformer, an inductive current sensor, a load current detection module, a current sensor, etc. By collecting signals from the DC power system through these data acquisition devices, the detection device can obtain the load working current signal of the DC power system at preset intervals.

[0059] It is understood that the load current signal refers to the current signal generated by the load in the DC power system when it is running. By real-time detection and analysis of the load working current signal, parameters such as the current size, waveform characteristics, and frequency in the current can be obtained.

[0060] It should be noted that when a fault arc occurs, the current waveform of the load current signal will be significantly distorted, generating characteristic high-frequency components and irregular pulse signals. By real-time detection and analysis of the load working current signal, abnormal changes in the current signal can be detected and identified, so that the fault arc circuit can be discovered, diagnosed and maintained in a timely manner.

[0061] It should be understood that the above-mentioned preset time can be any preset time, such as 0.5s, 1s, 1ms, 10ms, etc., and the embodiment of the present invention is not limited to this.

[0062] In a specific implementation, the detection device obtains the load working current signal of the DC power system at preset intervals, thereby realizing real-time detection of the DC power system, thereby promptly discovering the occurrence of fault arcs.

[0063] Step S20: obtaining the fault characteristic enhancement coefficient of the load working current signal.

[0064] It should be noted that the fault feature enhancement coefficient is a parameter that can enhance the above-mentioned multi-layer modal sequence, such as signal peak value, signal duration, signal waveform characteristics, etc., and the embodiments of the present invention are not limited to this.

[0065] In a specific implementation, the detection device can obtain the fault characteristic enhancement coefficient of the load working current signal.

[0066] Step S30: performing empirical mode decomposition on the load working current signal to obtain a multi-layer mode sequence of the load working current signal.

[0067] It should be noted that Empirical Mode Decomposition (EMD) is a method for signal processing and time-frequency analysis of load working current signals, which can decompose complex nonlinear and non-stationary signals into a set of signal components of intrinsic mode functions (IMF). Empirical mode decomposition can effectively process non-stationary signals and propose local characteristics of signals. By decomposing the load working current signal into a multi-layer modal sequence, the time-frequency characteristics, vibration modes and components of different scales of the signal can be better understood and analyzed, thereby realizing fault arc analysis of the load working current signal.

[0068] It should be understood that the modal sequence is the intrinsic mode function produced by empirical mode decomposition. By performing empirical mode decomposition on the load working current signal, a multi-layer modal sequence corresponding to the load working current signal can be obtained. Specifically, the detection device can use the load working current signal as the input of the first layer, perform an empirical mode decomposition, and obtain the modal sequence of the first layer, which includes a set of intrinsic mode functions and a residual term; by further performing empirical mode decomposition on the residual term, the modal sequence of the second layer is obtained, which also includes a set of intrinsic mode functions and a residual term; by repeating the steps of empirical mode decomposition, a multi-layer modal sequence of the load working current signal can be obtained. Each layer corresponds to a set of intrinsic mode functions and a residual term. The residual term can be regarded as the low-frequency part or trend component of the overall signal, while the intrinsic mode function represents the local oscillation characteristics of the signal at different frequencies and scales.

[0069] It should be noted that since empirical mode decomposition can be decomposed according to the local characteristics of the signal and does not rely on any prior knowledge or mathematical model, it has strong adaptability to non-stationary and nonlinear fault arc signals; since empirical mode decomposition can decompose the signal into a series of intrinsic mode functions, each intrinsic mode function can represent the local vibration mode of the signal at different frequencies and scales, so that it has a higher resolution in time-frequency analysis and can better capture the dynamic characteristics of the fault arc signal; since empirical mode decomposition can directly observe and analyze the decomposed intrinsic mode functions, each IMF represents the local component of the signal in different frequency ranges, so that detection personnel can more deeply analyze the physical meaning of each intrinsic mode function and understand the fault characteristics therein for further fault diagnosis and analysis; since empirical mode decomposition can perform multi-layer decomposition to obtain intrinsic mode functions in different frequency ranges, it can realize multi-scale analysis of fault arc signals, thereby extracting fault characteristics in different frequency ranges, which is helpful to deeply understand the nature of the fault. Therefore, compared with traditional methods such as wavelet transform and Fourier transform, empirical mode decomposition can more comprehensively and effectively obtain the time-frequency characteristics of the load working current signal, thereby achieving more convenient and accurate fault current analysis.

[0070] In a specific implementation, the detection device performs empirical mode decomposition on the load working current signal to obtain a multi-layer mode sequence of the load working current.

[0071] Step S40: determining the fault arc indication feature of the load operating current signal based on the fault feature enhancement coefficient and the multi-layer modal sequence.

[0072] It can be understood that by obtaining the fault feature enhancement coefficient of the load working current signal and enhancing the features of the multi-layer modal sequence by the fault feature enhancement coefficient, the fault arc indication feature of the load working current signal can be obtained. By enhancing the multi-layer modal sequence by the fault feature enhancement coefficient, the abnormal signal can be more significantly represented in the multi-layer modal sequence, thereby realizing the detection and positioning of the fault arc and improving the accuracy of the fault arc.

[0073] In the specific implementation, the detection device obtains the fault feature enhancement coefficient of the load working current signal, and enhances the features of the multi-layer modal sequence based on the fault feature enhancement coefficient, thereby obtaining the fault arc indication feature. The fault arc indication feature after feature enhancement is more significant than the multi-layer modal sequence before enhancement, thereby improving the accuracy of fault arc detection.

[0074] Step S50: Acquire the auxiliary judgment feature of the load working current signal, perform arc fault judgment based on the arc fault indication feature and the auxiliary judgment feature, and obtain an arc fault detection result.

[0075] It should be noted that the auxiliary judgment feature is a feature that can assist in judging the load working current signal, such as signal frequency, signal energy, etc., and the embodiment of the present invention is not limited to this.

[0076] It can be understood that by obtaining the auxiliary judgment feature of the load working current signal and performing a fault arc judgment on the load working current signal based on the auxiliary judgment feature and the fault arc indication feature, the fault arc detection result can be determined. Specifically, the fault arc indication feature can be compared with a preset indication feature threshold, the auxiliary judgment feature can be compared with a preset auxiliary feature threshold, and the fault arc detection result can be determined based on the two comparison results.

[0077] In a specific implementation, the detection device obtains the auxiliary judgment feature of the load working current signal, and performs fault arc detection based on the fault arc indication feature and the auxiliary judgment feature to obtain the fault arc detection result, so that the fault arc detection result is more accurate.

[0078] The embodiment of the present invention obtains the load working current signal of the DC power system at preset intervals; performs empirical mode decomposition on the load working current signal to obtain a multi-layer mode sequence of the load working current signal; obtains the fault feature enhancement coefficient, and determines the fault arc indication feature of the load working current signal based on the fault feature enhancement coefficient and the multi-layer mode sequence; obtains the auxiliary judgment feature of the load working current signal, performs fault arc judgment based on the fault arc indication feature and the auxiliary judgment feature, and obtains the fault arc detection result. Since the multi-layer mode sequence of the load working current signal is enhanced by the fault feature enhancement coefficient, and the fault arc judgment is performed based on the enhanced fault arc indication feature and the auxiliary judgment feature of the load working current signal, real-time detection of the DC power system is achieved. Compared with the existing method, this scheme combines the fault feature enhancement coefficient and the auxiliary judgment feature, and has a higher detection accuracy when the loop load is small.

[0079] refer to Figure 3 , Figure 3 It is a flow chart of the second embodiment of the series fault arc detection method of the present invention.

[0080] Based on the above first embodiment, in this embodiment, in order to determine the fault feature enhancement coefficient, the step of obtaining the fault feature enhancement coefficient of the load working current signal may include:

[0081] Step S21: Divide the load working current signal into signal sequences of a preset number of groups.

[0082] It should be noted that, in order to obtain the fault feature enhancement coefficient, one method that can be used is to obtain the load working current signal collected within a preset period, and divide the load working current signal to obtain a preset number of groups T of signal sequences.

[0083] It should be understood that the above-mentioned preset period and the number of groups T of the signal sequence can be selected according to the acquisition frequency (that is, the preset time of the interval) in the actual application, for example, the acquisition of 1s is divided into 50 groups, the acquisition of 0.5s is divided into 20 groups, etc. The embodiment of the present invention is not limited to this.

[0084] It should be understood that by grouping the load working current signals collected within a preset period, multiple groups of signal sequences can be obtained, and each group of signal sequences can include up to N signal samples (that is, each group of signal sequences can include N load working current signals). The number of signal samples N in each group of signal sequences can be the same or different, and the embodiment of the present invention is not limited to this.

[0085] Step S22: determining the fault feature enhancement coefficient of the load working current signal according to the number of signal samples and the mean value of the signal samples in each group of signal sequences.

[0086] It should be understood that the number of signal samples is the number of load working current signals contained in the signal sequence, and the mean of the signal samples is the characteristic mean of the load working current signals contained in the signal sequence. The characteristic mean may be a frequency characteristic mean, an energy characteristic mean, a comprehensive characteristic mean or other characteristic mean, which is not limited in the embodiment of the present invention.

[0087] In one implementation, the number of signal samples N in each group of signal sequences is the same. When the signal sequence division is completed, the signal sample mean of the load working current signal in each group of signal sequences is determined, and the fault feature enhancement coefficient of the group of signal sequences is calculated according to the number of signal samples and the signal sample mean in the signal sequence. Specifically, the calculation formula is as follows:

[0088]

[0089] Where Ef is the fault feature enhancement factor, x i represents the characteristic value of the i-th signal sample in a signal sequence, N is the number of signal samples in the signal sequence, is the signal sample mean of each group of signal sequences.

[0090] Through the above formula, the fault feature enhancement coefficient of each signal sequence can be calculated.

[0091] It can be understood that, since the load working current signal is divided into a signal sequence of a preset number of groups, accordingly, the step of performing empirical mode decomposition on the load working current signal to obtain a multi-layer mode sequence of the load working current signal includes:

[0092] Step S30 ′: performing empirical mode decomposition on the load working current signal in each of the signal sequences to obtain a multi-layer mode sequence of the signal sequence.

[0093] For example, empirical mode decomposition is performed on the load working current signal in the signal sequence to determine the modal sequences Imf1, Imf2 and Imf3 of the first three layers.

[0094] In the specific implementation, the load working current signal is divided into signal sequences of a preset number of groups; the fault feature enhancement coefficient of the load working current signal is determined according to the number of signal samples and the mean value of the signal samples in each group of signal sequences; the load working current signal in each signal sequence is subjected to empirical mode decomposition to obtain a multi-layer mode sequence of the signal sequence. By dividing the load working current signal into signal sequences of a preset number of groups and detecting these signal sequences, the accuracy of fault arc detection is improved, and it can be used for accurate detection when the current signal change is not obvious, while reducing the influence of current intensity, background noise and other conditions.

[0095] Further, the step of determining the fault arc indication feature of the load working current signal based on the fault feature enhancement coefficient and the multi-layer modal sequence includes:

[0096] Step S41: determining a signal sequence component of the signal sequence according to the number of signal samples and the multi-layer modal sequence, wherein the signal sequence component includes: a first signal sequence component and a second signal sequence component.

[0097] It can be understood that when determining the fault feature enhancement coefficient of each signal sequence and the multi-layer modal sequence of each signal sequence, the signal sequence component of the signal sequence can be determined according to the fault enhancement coefficient and the multi-layer modal sequence.

[0098] Specifically, the signal sequence component may include a first signal sequence component and a second signal sequence component.

[0099] The calculation formula of the first signal sequence component Th1 is as follows:

[0100]

[0101] The calculation formula of the second signal sequence component Th2 is as follows:

[0102]

[0103] Among them, y i represents the modal sequence of each group of signal sequences, N is the number of signal samples of each group of signal sequences, is the modal sequence average of each group of signal sequences.

[0104] Step S42: determining the fault arc indication feature of the load working current signal according to the fault feature enhancement coefficient and the signal sequence component, wherein the fault arc indication feature includes: a first fault arc indication feature and a second fault arc indication feature.

[0105] It can be understood that by enhancing the characteristics of the signal sequence components through the fault characteristic enhancement coefficient, the fault arc indication characteristics of the load working current signal can be determined. Specifically, the result of Ef*Th1 can be used as the first fault arc indication characteristic of the signal sequence, and the result of Ef*Th2 can be used as the second fault arc indication characteristic of the signal sequence, and then a judgment can be made based on the first fault arc indication characteristic and the second fault arc indication characteristic to determine whether there is a fault arc in the signal sequence.

[0106] In a specific implementation, the detection device determines the signal sequence components of the signal sequence according to the number of signal samples and the multi-layer modal sequence, and the signal sequence components include: a first signal sequence component and a second signal sequence component; the fault arc indication characteristics of the load working current signal are determined according to the fault feature enhancement coefficient and the signal sequence components, and the fault arc indication characteristics include: a first fault arc indication characteristic and a second fault arc indication characteristic. By obtaining the first fault arc indication characteristic and the second fault arc indication characteristic of the signal sequence, a comprehensive judgment can be made based on the first fault arc indication characteristic and the second fault arc indication characteristic, thereby improving the detection accuracy of the fault arc.

[0107] Furthermore, in order to obtain the auxiliary judgment feature, in one embodiment, the step of obtaining the auxiliary judgment feature of the load working current signal includes:

[0108] Step S51: performing time-frequency transformation on the load working current signal to obtain a frequency coefficient of the load working current signal.

[0109] It can be understood that, for each group of divided signal sequences, time-frequency transformation can be performed to obtain the time-frequency characteristics of each signal sequence, and then auxiliary judgment characteristics can be determined according to the time-frequency characteristics to further judge the signal sequence.

[0110] It should be noted that the above-mentioned time-frequency transformation method can be wavelet transformation, Fourier transformation or other transformation methods, which is not limited in the embodiment of the present invention. The above-mentioned time-frequency features can be frequency coefficients, wavelet variances, wavelet absolute mean values, etc., which are not limited in the embodiment of the present invention.

[0111] In one implementation, the frequency coefficient may be used as a time-frequency feature, and the frequency band energy of the signal sequence in a preset frequency band may be determined according to the time-frequency feature.

[0112] Specifically, step S52: determining the frequency band energy of a preset frequency band based on the frequency coefficient, and using the frequency band energy as an auxiliary determination feature of the load working current signal.

[0113] It should be noted that the above-mentioned preset frequency band is a high-frequency range for fault arc judgment, such as 3kHz-10kHz, 9kHz-15kHz, 3kHz-15Khz, etc., and the embodiment of the present invention is not limited to this.

[0114] It can be understood that by determining the frequency band energy E of the signal sequence in the preset frequency band and using the frequency band energy E as an auxiliary judgment feature, more accurate fault arc detection can be achieved.

[0115] In a specific implementation, the detection device performs time-frequency transformation on the load working current signal to obtain the frequency coefficient of the load working current signal; the frequency band energy of the preset frequency band is determined based on the frequency coefficient, and the frequency band energy is used as an auxiliary judgment feature of the load working current signal. By using the frequency band energy in the preset frequency band as an auxiliary judgment feature of fault arc detection, the accuracy of fault arc detection is improved.

[0116] The embodiment of the present invention divides the load working current signal into signal sequences of preset number of groups; determines the fault feature enhancement coefficient of the load working current signal according to the number of signal samples and the mean value of the signal samples in each group of signal sequences; performs empirical mode decomposition on the load working current signal in each signal sequence to obtain a multi-layer mode sequence of the signal sequence; determines the signal sequence component of the signal sequence according to the number of signal samples and the multi-layer mode sequence, and the signal sequence component includes: a first signal sequence component and a second signal sequence component; determines the fault arc indication feature of the load working current signal according to the fault feature enhancement coefficient and the signal sequence component, and the fault arc indication feature includes: a first fault arc indication feature and a second fault arc indication feature; performs time-frequency transformation on the load working current signal to obtain the frequency coefficient of the load working current signal; determines the frequency band energy of the preset frequency band based on the frequency coefficient, and uses the frequency band energy as the auxiliary judgment feature of the load working current signal. Since the load working current signal is divided into signal sequences of preset number of groups, and the arc fault indication feature and the auxiliary judgment feature of each signal sequence are determined, and whether each signal sequence has a fault arc is determined according to the arc fault indication feature and the auxiliary judgment feature, the fine granularity of the fault arc detection is improved, and the fault arc detection is more accurate.

[0117] Based on the above embodiments, in order to obtain the fault arc detection result, a third embodiment of the series fault arc detection method of the present invention is proposed, referring to Figure 4 , Figure 4 It is a flow chart of the third embodiment of the series fault arc detection method of the present invention.

[0118] In this embodiment, the step of performing arc fault judgment based on the arc fault indication feature and the auxiliary judgment feature to obtain an arc fault detection result includes:

[0119] Step S53: comparing the first arc fault indication feature with a preset first fault feature threshold, and comparing the second arc fault indication feature with a preset second fault feature threshold;

[0120] Step S54: when the first fault arc indication feature is greater than the first fault feature threshold and the second fault arc indication feature is greater than the second fault feature threshold, increase the count value;

[0121] Step S55: When the count value is greater than the count threshold, the auxiliary judgment feature is compared with a preset auxiliary feature threshold to obtain a fault arc detection result.

[0122] It is understandable that, in order to perform arc fault detection, a fault feature threshold may be preset, and the arc fault indication feature may be compared with the fault feature threshold. Specifically, the first arc fault indication feature may be compared with the preset first fault feature threshold, and the second arc fault indication feature may be compared with the preset second fault feature threshold; when the first arc fault indication feature is greater than the first fault feature threshold and the second arc fault indication feature is greater than the second fault feature threshold, the count value is increased.

[0123] It should be understood that when the first fault arc indication feature is greater than the first fault feature threshold and the second fault arc indication feature is greater than the second fault feature threshold, it can be indicated that the signal sequence is abnormal, and the count value of the counter is further processed. When the count value within the preset period is greater than the preset count threshold, it can be determined that there is a fault arc.

[0124] In one embodiment, the accuracy of detection can be further improved based on the auxiliary judgment feature. Specifically, the step of comparing the auxiliary judgment feature with a preset auxiliary feature threshold to obtain a fault arc detection result includes:

[0125] If the auxiliary judgment feature is greater than a preset auxiliary feature threshold, the arc judgment flag is normalized to determine that a fault arc exists in the DC power system;

[0126] If the auxiliary judgment feature is not greater than a preset auxiliary feature threshold, the arc judgment flag is reset to zero to determine that there is no fault arc in the DC power system.

[0127] It should be noted that the above-mentioned first fault feature threshold, second fault feature threshold and preset auxiliary feature threshold are all preset thresholds, and their specific values ​​or range values ​​can be determined according to actual applications, and the embodiments of the present invention are not limited to this.

[0128] like Figure 5 As shown, Figure 5 A schematic diagram of a scenario flow in one implementation of series fault arc detection of the present invention.

[0129] Reference Figure 5 When the detection device obtains the load working current signal, it can perform empirical mode decomposition on it to obtain the modal sequence of the first three layers; at the same time, the fault feature enhancement coefficient is calculated based on the load working current signal; in addition, Fourier transform can be performed based on the load working current signal to obtain the frequency coefficient of the load working current signal, and the auxiliary judgment feature of the fault arc can be calculated based on the frequency coefficient.

[0130] Furthermore, the detection device can calculate the fault arc indication characteristics of the load working current signal based on the modal sequence and the fault characteristic enhancement coefficient, and determine whether the fault arc indication characteristics meet the threshold judgment conditions (that is, whether they are greater than the preset fault characteristic threshold); if not, wait for subsequent signal judgment; if so, add one to the count value of the counter.

[0131] Furthermore, when the count value of the counter is greater than the count threshold, if the auxiliary judgment feature is greater than the preset auxiliary feature threshold, the flag bit can be normalized and it is judged that a fault arc exists; if the auxiliary judgment feature is not greater than the preset auxiliary feature threshold, the flag bit is cleared and it is judged that there is no fault arc.

[0132] The embodiment of the present invention compares the first fault arc indication feature with the preset first fault feature threshold, and compares the second fault arc indication feature with the preset second fault feature threshold; when the first fault arc indication feature is greater than the first fault feature threshold and the second fault arc indication feature is greater than the second fault feature threshold, the count value is increased; when the count value is greater than the count threshold, if the auxiliary judgment feature is greater than the preset auxiliary feature threshold, the arc judgment flag is normalized to determine that there is a fault arc in the DC power system; if the auxiliary judgment feature is not greater than the preset auxiliary feature threshold, the arc judgment flag is reset to zero to determine that there is no fault arc in the DC power system. Since the fault feature threshold and the auxiliary judgment feature are combined for comprehensive judgment, the accuracy of fault arc detection is improved.

[0133] In addition, an embodiment of the present invention further provides a storage medium, on which a series fault arc detection program is stored. When the series fault arc detection program is executed by a processor, the steps of the series fault arc detection method described above are implemented.

[0134] Based on the first embodiment of the series fault arc detection method of the present invention, the first embodiment of the series fault arc detection device of the present invention is proposed, referring to Figure 6 , Figure 6 It is a structural block diagram of the first embodiment of the series fault arc detection device of the present invention.

[0135] like Figure 6 As shown, the series fault arc detection device proposed in the embodiment of the present invention includes:

[0136] The signal acquisition module 601 is used to acquire the load working current signal of the DC power system at preset intervals;

[0137] A coefficient acquisition module 602 is used to acquire a fault characteristic enhancement coefficient of the load working current signal;

[0138] A signal decomposition module 603 is used to perform empirical mode decomposition on the load working current signal to obtain a multi-layer mode sequence of the load working current signal;

[0139] A feature enhancement module 604, configured to determine a fault arc indication feature of the load operating current signal based on the fault feature enhancement coefficient and the multi-layer modal sequence;

[0140] The fault judgment module 605 is used to obtain the auxiliary judgment feature of the load working current signal, perform fault arc judgment based on the fault arc indication feature and the auxiliary judgment feature, and obtain a fault arc detection result.

[0141] The embodiment of the present invention obtains the load working current signal of the DC power system at preset intervals; performs empirical mode decomposition on the load working current signal to obtain a multi-layer mode sequence of the load working current signal; obtains the fault feature enhancement coefficient, and determines the fault arc indication feature of the load working current signal based on the fault feature enhancement coefficient and the multi-layer mode sequence; obtains the auxiliary judgment feature of the load working current signal, performs fault arc judgment based on the fault arc indication feature and the auxiliary judgment feature, and obtains the fault arc detection result. Since the multi-layer mode sequence of the load working current signal is enhanced by the fault feature enhancement coefficient, and the fault arc judgment is performed based on the enhanced fault arc indication feature and the auxiliary judgment feature of the load working current signal, real-time detection of the DC power system is achieved. Compared with the existing method, this scheme combines the fault feature enhancement coefficient and the auxiliary judgment feature, and has a higher detection accuracy when the loop load is small.

[0142] The coefficient acquisition module 602 is further used to divide the load working current signal into a preset number of signal sequences; and determine the fault feature enhancement coefficient of the load working current signal according to the number of signal samples and the signal sample mean in each group of signal sequences.

[0143] The signal decomposition module 603 is further used to perform empirical mode decomposition on the load working current signal in each of the signal sequences to obtain a multi-layer mode sequence of the signal sequence.

[0144] The feature enhancement module 604 is further used to determine the signal sequence components of the signal sequence according to the number of signal samples and the multi-layer modal sequence, and the signal sequence components include: a first signal sequence component and a second signal sequence component; determine the fault arc indication feature of the load working current signal according to the fault feature enhancement coefficient and the signal sequence component, and the fault arc indication feature includes: a first fault arc indication feature and a second fault arc indication feature.

[0145] The fault judgment module 605 is also used to perform time-frequency transformation on the load working current signal to obtain the frequency coefficient of the load working current signal; determine the frequency band energy of the preset frequency band based on the frequency coefficient, and use the frequency band energy as an auxiliary judgment feature of the load working current signal.

[0146] The fault judgment module 605 is also used to compare the first fault arc indication feature with a preset first fault feature threshold, and to compare the second fault arc indication feature with a preset second fault feature threshold; when the first fault arc indication feature is greater than the first fault feature threshold and the second fault arc indication feature is greater than the second fault feature threshold, increase the count value; when the count value is greater than the count threshold, compare the auxiliary judgment feature with the preset auxiliary feature threshold to obtain a fault arc detection result.

[0147] The fault judgment module 605 is also used to normalize the arc judgment flag bit to one if the auxiliary judgment feature is greater than a preset auxiliary feature threshold, and determine that there is a fault arc in the DC power system; if the auxiliary judgment feature is not greater than the preset auxiliary feature threshold, reset the arc judgment flag bit to zero, and determine that there is no fault arc in the DC power system.

[0148] Other embodiments or specific implementations of the series fault arc detection device of the present invention can refer to the above-mentioned method embodiments, which will not be described in detail here.

[0149] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0150] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0151] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0152] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for detecting a series fault arc, characterized in that: The method comprises: Obtaining load working current signals of a DC power system at preset intervals; Obtaining a fault characteristic enhancement coefficient of the load working current signal; Performing empirical mode decomposition on the load working current signal to obtain a multi-layer mode sequence of the load working current signal; Determining a fault arc indication feature of the load operating current signal based on the fault feature enhancement coefficient and the multi-layer modal sequence; Acquire the auxiliary judgment feature of the load working current signal, perform fault arc judgment based on the fault arc indication feature and the auxiliary judgment feature, and obtain a fault arc detection result; The calculation formula of the fault feature enhancement coefficient is as follows: ； in, is the fault feature enhancement factor, represents the characteristic value of the i-th signal sample in a signal sequence, is the number of signal samples in the signal sequence, is the signal sample mean of each group of signal sequences; The step of performing empirical mode decomposition on the load working current signal to obtain a multi-layer mode sequence of the load working current signal includes: Performing empirical mode decomposition on the load working current signal in each of the signal sequences to obtain a multi-layer mode sequence of the signal sequence; The step of determining the fault arc indication feature of the load working current signal based on the fault feature enhancement coefficient and the multi-layer modal sequence comprises: Determine a signal sequence component of the signal sequence according to the signal sample number and the multi-layer modal sequence, the signal sequence component comprising: a first signal sequence component and a second signal sequence component; Determine the fault arc indication feature of the load working current signal according to the fault feature enhancement coefficient and the signal sequence component, wherein the fault arc indication feature includes: a first fault arc indication feature and a second fault arc indication feature; The calculation formula of the first signal sequence component is as follows: ； The calculation formula of the second signal sequence component is as follows: ； in, represents the modal sequence of each group of signal sequences, Represents the average value of the modal sequence of each group of signal sequences.

2. The method for detecting a series fault arc according to claim 1, characterized in that: The step of obtaining the fault characteristic enhancement coefficient of the load working current signal includes: Dividing the load working current signal into a signal sequence of a preset number of groups; The fault feature enhancement coefficient of the load working current signal is determined according to the number of signal samples and the mean value of the signal samples in each group of signal sequences.

3. The method for detecting a series fault arc according to claim 1, characterized in that: The step of obtaining the auxiliary judgment feature of the load working current signal includes: Performing time-frequency transformation on the load working current signal to obtain a frequency coefficient of the load working current signal; The frequency band energy of a preset frequency band is determined based on the frequency coefficient, and the frequency band energy is used as an auxiliary judgment feature of the load working current signal.

4. The method for detecting a series fault arc according to claim 1, characterized in that: The step of performing arc fault judgment based on the arc fault indication feature and the auxiliary judgment feature to obtain an arc fault detection result comprises: Comparing the first arc fault indication feature with a preset first fault feature threshold, and comparing the second arc fault indication feature with a preset second fault feature threshold; When the first fault arc indication feature is greater than the first fault feature threshold and the second fault arc indication feature is greater than the second fault feature threshold, increasing a count value; When the count value is greater than the count threshold, the auxiliary judgment feature is compared with a preset auxiliary feature threshold to obtain a fault arc detection result.

5. The method for detecting a series fault arc as claimed in claim 4, characterized in that: The step of comparing the auxiliary judgment feature with a preset auxiliary feature threshold to obtain a fault arc detection result includes: If the auxiliary judgment feature is greater than a preset auxiliary feature threshold, the arc judgment flag is normalized to determine that a fault arc exists in the DC power system; If the auxiliary judgment feature is not greater than a preset auxiliary feature threshold, the arc judgment flag is reset to zero to determine that there is no fault arc in the DC power system.

6. A series fault arc detection device, characterized in that: The series fault arc detection device comprises: A signal acquisition module, used to acquire a load working current signal of a DC power system at preset intervals; A coefficient acquisition module, used to acquire a fault characteristic enhancement coefficient of the load working current signal; A signal decomposition module, used for performing empirical mode decomposition on the load working current signal to obtain a multi-layer mode sequence of the load working current signal; A feature enhancement module, used to determine the fault arc indication feature of the load working current signal based on the fault feature enhancement coefficient and the multi-layer modal sequence; A fault judgment module, used for obtaining the auxiliary judgment feature of the load working current signal, performing fault arc judgment based on the fault arc indication feature and the auxiliary judgment feature, and obtaining a fault arc detection result; The calculation formula of the fault feature enhancement coefficient is as follows: ； in, is the fault feature enhancement factor, represents the characteristic value of the i-th signal sample in a signal sequence, is the number of signal samples in the signal sequence, is the signal sample mean of each group of signal sequences; The operation of performing empirical mode decomposition on the load working current signal to obtain a multi-layer mode sequence of the load working current signal includes: Performing empirical mode decomposition on the load working current signal in each of the signal sequences to obtain a multi-layer mode sequence of the signal sequence; The operation of determining the fault arc indication feature of the load working current signal based on the fault feature enhancement coefficient and the multi-layer modal sequence includes: Determine a signal sequence component of the signal sequence according to the signal sample number and the multi-layer modal sequence, the signal sequence component comprising: a first signal sequence component and a second signal sequence component; Determine the fault arc indication feature of the load working current signal according to the fault feature enhancement coefficient and the signal sequence component, wherein the fault arc indication feature includes: a first fault arc indication feature and a second fault arc indication feature; The calculation formula of the first signal sequence component is as follows: ； The calculation formula of the second signal sequence component is as follows: ； in, represents the modal sequence of each group of signal sequences, Represents the average value of the modal sequence of each group of signal sequences.

7. A series fault arc detection device, characterized in that: The device comprises: a memory, a processor, and a series fault arc detection program stored in the memory and executable on the processor, wherein the series fault arc detection program is configured to implement the steps of the series fault arc detection method according to any one of claims 1 to 5.

8. A storage medium, characterized in that: The storage medium stores a series fault arc detection program, and when the series fault arc detection program is executed by the processor, the steps of the series fault arc detection method according to any one of claims 1 to 5 are implemented.

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