DC fault arc detection method and device, storage medium and program product

Abstract

The invention relates to a DC fault arc detection method and device, a storage medium and a program product. The direct-current fault arc detection method comprises the following steps: acquiring a vibration signal of an electric signal of a direct-current power system; according to the vibration signal, determining the changed signal entropy of the vibration signal and the duration of the signal entropy; and according to the signal entropy and the duration, determining whether the DC power system has a DC fault arc. The method can accurately identify and detect the DC fault arc, can be applied to an open DC power system, and is no longer limited to a single system circuit.

Description

Technical Field

[0001] The present disclosure relates to the technical field of DC fault arc detection, and in particular to a DC fault arc detection method and device, a storage medium, and a program product. Background Art

[0002] In recent years, the technology behind new power system infrastructure and DC power distribution systems has advanced rapidly. The increasing capacity of DC systems, the complexity of cable networks, and the diversity of load types have undoubtedly placed new demands on the safety and reliability of DC power distribution systems. Power lines have been in operation for a long time and are experiencing increasing aging. The system's load performance is diverse, and line transmission distances are long. Inadequate maintenance and inspection can easily lead to safety incidents. Long-term power lines are prone to arc faults and other problems. Arc faults are a type of gas ionized discharge, also known as plasma. They are characterized by high temperatures, low currents, and short durations. Once a breakdown point is reached, they frequently recur. When an arc fault occurs, it releases significant heat, potentially igniting nearby flammable and explosive materials, causing fires or even explosions.

[0003] DC fault arc current has no zero-crossing point, so it is more likely to self-sustain combustion than AC arc. In the absence of human detection and protection, it is very easy to cause electrical fires, thus seriously threatening the safety and stability of the DC system. Summary of the Invention

[0004] Through research, the inventors discovered that in order to accurately predict and diagnose DC arc faults, an effective method is needed to collect and analyze arc fault signals. However, related technologies cannot fully detect the occurrence of arc faults and cannot be widely used in open DC power systems.

[0005] In view of at least one of the above technical problems, the present disclosure provides a DC fault arc detection method and device, storage medium and program product, which can accurately identify and detect DC fault arcs and can be applied to open DC power systems, no longer limited to a single system circuit.

[0006] According to one aspect of the present disclosure, a DC arc fault detection method is provided, comprising:

[0007] Acquiring vibration signals of electrical signals of a DC power system;

[0008] determining, according to the vibration signal, a signal entropy of a change in the vibration signal and a duration of the signal entropy;

[0009] It is determined whether a DC arc fault exists in the DC power system according to the signal entropy and the duration.

[0010] In some embodiments of the present disclosure, determining, based on the vibration signal, the signal entropy of the vibration signal change and the duration of the signal entropy includes:

[0011] Comparing each sampling value of the vibration signal with the previous sampling value and the next sampling value;

[0012] Taking each sampling value of the vibration signal as the current sampling value;

[0013] When the current sampling value is less than the previous sampling value and the next sampling value, or when the current sampling value is greater than the previous sampling value and the next sampling value, the current sampling value is added to the first array;

[0014] The difference between every two adjacent values in the first array is calculated as the signal entropy of the vibration signal change.

[0015] In some embodiments of the present disclosure, determining, based on the vibration signal, the signal entropy of the vibration signal change and the duration of the signal entropy further includes:

[0016] constructing a scatter plot according to the signal entropy of the vibration signal change;

[0017] A duration of the signal entropy is determined based on the scatter plot.

[0018] In some embodiments of the present disclosure, constructing a scatter plot based on the signal entropy of the vibration signal includes:

[0019] performing a normalization operation on the signal entropy of the vibration signal change;

[0020] A scatter plot is constructed based on the normalized data.

[0021] In some embodiments of the present disclosure, determining whether a DC arc fault exists in the DC power system according to the signal entropy and the duration includes:

[0022] When the signal entropy is greater than a predetermined change signal threshold and the duration is greater than a predetermined time length, it is determined that the electrical signal is an abnormal electrical signal and that a DC fault arc exists in the DC power system.

[0023] In some embodiments of the present disclosure, determining whether a DC arc fault exists in the DC power system according to the signal entropy and the duration includes:

[0024] When the signal entropy is greater than a predetermined change signal threshold and the duration is no greater than a predetermined time length, it is determined that the electrical signal is a first normal electrical signal and that a safety arc exists in the DC power system.

[0025] In some embodiments of the present disclosure, determining whether a DC arc fault exists in the DC power system according to the signal entropy and the duration includes:

[0026] When the signal entropy is not greater than a predetermined change signal threshold, it is determined that the electrical signal is a second normal electrical signal, and it is determined that no arc exists in the DC power system.

[0027] In some embodiments of the present disclosure, determining whether a DC arc fault exists in the DC power system according to the signal entropy and the duration includes:

[0028] The electrical signal is classified using a classification model according to the signal entropy and the duration to determine whether a DC arc fault exists in the DC power system.

[0029] In some embodiments of the present disclosure, classifying the electrical signal using a classification model based on the signal entropy and the duration to determine whether a DC arc fault exists in the DC power system includes at least one of the following steps:

[0030] When the signal entropy is greater than a predetermined change signal threshold and the duration is greater than a predetermined time length, classifying the electrical signal as an abnormal electrical signal and determining that a DC fault arc exists in the DC power system;

[0031] When the signal entropy is greater than a predetermined change signal threshold and the duration is no greater than a predetermined time length, classifying the electrical signal as a first normal electrical signal and determining that a safety arc exists in the DC power system;

[0032] When the signal entropy is not greater than a predetermined change signal threshold, the electrical signal is classified as a second normal electrical signal, and it is determined that no arc exists in the DC power system.

[0033] In some embodiments of the present disclosure, the electrical signal is at least one of a voltage signal and a current signal.

[0034] In some embodiments of the present disclosure, the vibration signal is at least one of an amplitude and a mean value.

[0035] According to another aspect of the present disclosure, there is provided a DC arc fault detection device, comprising:

[0036] a vibration signal acquisition module configured to acquire a vibration signal of an electrical signal of a DC power system;

[0037] a signal entropy acquisition module, configured to determine, based on the vibration signal, a signal entropy of a change in the vibration signal and a duration of the signal entropy;

[0038] The judgment module is configured to judge whether a DC arc fault exists in the DC power system according to the signal entropy and the duration.

[0039] According to another aspect of the present disclosure, there is provided a DC arc fault detection device, comprising:

[0040] a memory configured to store instructions; and

[0041] The processor is configured to execute the instructions so that the DC fault arc detection device implements the DC fault arc detection method as described in any of the above embodiments.

[0042] According to another aspect of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the DC fault arc detection method as described in any of the above embodiments is implemented.

[0043] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the method for detecting a DC arc fault as described in any one of the above embodiments is implemented.

[0044] The present disclosure can accurately identify and detect DC fault arcs and can be applied to open DC power systems, no longer limited to a single system circuit. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present disclosure 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 disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0046] Figure 1 Schematic diagram of some embodiments of the DC arc fault detection method disclosed in the present invention.

[0047] Figure 2 This is a diagram of the entropy distribution of the changes in the amplitude of the electrical signal in some embodiments of the present disclosure.

[0048] Figure 3 Schematic diagram of the structure of some embodiments of the DC fault arc detection device disclosed in the present invention.

[0049] Figure 4 Schematic diagram of the structure of other embodiments of the DC fault arc detection device disclosed in the present invention.

[0050] Figure 5This is a schematic diagram of the installation of a DC fault arc detection device in some embodiments of the present disclosure. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is in no way intended to limit the present disclosure and its application or use. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0052] Unless specifically stated otherwise, the relative arrangement of components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure.

[0053] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0054] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0055] In all examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0056] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0057] Through research, the inventors discovered that progress in related art analysis, identification, and detection of DC arc fault signals has been unsatisfactory. Common DC arc fault signal analysis methods include time domain analysis, frequency domain analysis, power spectrum analysis, wavelet transform, and neural network analysis. However, due to varying load characteristics, most of these methods are unable to fully detect arc faults. Consequently, they are not widely applicable in open DC power systems, and therefore cannot guarantee power supply security. Consequently, they have not been widely adopted in practice.

[0058] In related technologies, characteristic spectrum DC arc fault detectors directly analyze electrical signals in the time or frequency domain to form a single characteristic spectrum for identification. However, due to factors such as noise, switching frequency, and different load properties, the detection rate of DC arc faults is very low, and it is often impossible to accurately analyze and identify electrical signals in open power systems, which brings difficulties to practical applications.

[0059] In related technologies, training a large number of feature spectrum models requires a lot of hardware resources, resulting in high costs.

[0060] In view of at least one of the above technical problems, the present disclosure provides a DC arc fault detection method and device, a storage medium, and a program product. The present disclosure is described below through specific embodiments.

[0061] Figure 1 Schematic diagram of some embodiments of the DC arc fault detection method disclosed in the present invention. Figure 1 The embodiment can be executed by the photovoltaic community system of the present disclosure or the DC fault arc detection device of the present disclosure. Figure 1 As shown, Figure 1 The method of the embodiment may include at least one of steps 1 to 3.

[0062] In step 1, a vibration signal of an electric signal of a DC power system is obtained.

[0063] In some embodiments of the present disclosure, a DC power system may include a DC power source and a load.

[0064] In some embodiments of the present disclosure, the electrical signal may be at least one of a voltage signal and a current signal.

[0065] In some embodiments of the present disclosure, the vibration signal may be at least one of an amplitude and a mean value.

[0066] In step 2, the signal entropy of the vibration signal change and the duration of the signal entropy are determined according to the vibration signal.

[0067] In some embodiments of the present disclosure, the signal entropy of the vibration signal change of the electrical signal is the signal entropy of the vibration signal.

[0068] In some embodiments of the present disclosure, the signal entropy of the amplitude change of the electrical signal is the amplitude signal entropy.

[0069] In some embodiments of the present disclosure, the signal entropy of the mean change of the electrical signal is the mean signal entropy.

[0070] The above-mentioned embodiments of the present disclosure study the intrinsic characteristics UI of electrical signals and explore the entropy changes in the voltage and current amplitudes of electrical signals and their continuity. Therefore, the above-mentioned embodiments of the present disclosure can comprehensively reflect the noise change ratio of the device operating status and the intrinsic characteristics of objects.

[0071] The DC fault arc detection and identification method disclosed in the above-mentioned embodiment of the present invention can intuitively reflect the operating status of the equipment by analyzing the degree of chaos of the signal entropy of the amplitude change and the continuity of the chaos degree of the collected voltage and current electrical signals. The degree of chaos (the size of the signal entropy) represents the strength of the arc, and the continuity of the chaos degree (the duration of the signal entropy) represents the occurrence time of the arc.

[0072] In the above embodiments of the present disclosure, various states in the DC power system are directly fed back in the electrical signal UI, and the voltage signal and current signal are collected at high speed in the continuously changing time domain, and their amplitude or frequency changes continuously with time. The above embodiments of the present disclosure study and analyze the characteristics of normal electrical signals and arc electrical signals by studying the electrical signal amplitude time domain waveform.

[0073] In some embodiments of the present disclosure, step 2 may include at least one of steps 21 to 23.

[0074] In step 21, each sampling value of the vibration signal is compared with the previous sampling value and the next sampling value.

[0075] In step 22, each sampling value of the vibration signal is taken as the current sampling value; when the current sampling value is less than the previous sampling value and the next sampling value at the same time, or the current sampling value is greater than the previous sampling value and the next sampling value at the same time, the current sampling value is added to the first array.

[0076] In some embodiments of the present disclosure, steps 21 to 22 may include: setting a counter and an empty array (first array) A; comparing each sampling value with the valid value of the previous sampling and the valid value of the next sampling; if the current sampling value is less than or greater than the previous sampling value and the next sampling value at the same time, determining that the current sampling value is an abnormal sampling value, adding the value to the first array A, and increasing the counter by 1; if the condition is not met, the counter is cleared, and this rotation detection ends.

[0077] In step 23, the difference between every two adjacent values in the first array is calculated as the signal entropy of the vibration signal change.

[0078] Step 22 of the present disclosure first detects the abnormally large or small electrical signal value. By taking the difference between adjacent abnormal sampling values, the above-mentioned embodiment 23 of the present disclosure can study the essential characteristics of the electrical signal based on electrical situation awareness, conduct a year-on-year analysis of state data (year-on-year analysis of current data from the same detection terminal and data from the previous acquisition moment), and study the signal entropy value of the amplitude change of the electrical signal, thereby reflecting the electrical signal state. As a result, the above-mentioned embodiment of the present disclosure can achieve accurate detection and diagnosis of DC fault arcs.

[0079] In some embodiments of the present disclosure, step 2 may further include at least one of steps 24 to 25.

[0080] In step 24, a scatter plot is constructed based on the signal entropy of the vibration signal change.

[0081] Figure 2 The entropy distribution diagram of the change of the amplitude of the electrical signal in some embodiments of the present disclosure is shown in FIG. Figure 2 As shown, three arcs 11, 12 and 13 are detected in this case.

[0082] In some embodiments of the present disclosure, step 25 may include: performing a normalization operation on the differenced data (the signal entropy of the vibration signal change); and constructing a scatter plot based on the normalized data.

[0083] The above embodiments of the present disclosure improve the readability of data by normalizing the data.

[0084] In some embodiments of the present disclosure, step 23 and step 24 may include: setting an empty array (second array) B, determining the abnormal sampling values in the first array A of this rotation detection, taking the difference between two adjacent values in the first array A as the signal entropy of the amplitude change, normalizing the difference, and adding the normalized data to the second array B, and constructing a scatter plot for the data in the second array B.

[0085] In step 25 , the duration of the signal entropy is determined based on the scatter plot.

[0086] The above-mentioned embodiments of the present disclosure use the changing signal entropy of the vibration signal to indicate the strength of the arc, and the duration of the changing signal entropy represents the arc occurrence time. In a stable DC power system, any abnormalities in the equipment's operating status can be reflected in the most primitive electrical changes. Therefore, the above-mentioned embodiments of the present disclosure can use the changing characteristics of the original electrical signal to indicate the equipment's operating status and its noise changes, thereby achieving accurate detection and diagnosis of DC fault arcs.

[0087] In some embodiments of the present disclosure, Figure 2As shown, the duration of the signal entropy corresponding to arc 12 is longer, while the duration of the signal entropy corresponding to arcs 11 and 13 is shorter.

[0088] In step 3, it is determined whether a DC arc fault exists in the DC power system according to the signal entropy and the duration.

[0089] The above-mentioned embodiments of the present disclosure perform coupled analysis of electrical signals by studying and analyzing the signal entropy of their amplitude variations. Entropy is a measure of the degree of disorder or chaos in a system; greater entropy indicates a greater diversity of microscopic states within the system and a greater degree of disorder. The above-mentioned embodiments of the present disclosure introduce the concept of entropy primarily to study the degree of chaos in electrical signals, thereby comprehensively reflecting the operating status of equipment and the intrinsic characteristics of objects. Consequently, the above-mentioned embodiments of the present disclosure can accurately identify and detect DC arc faults and are applicable to open DC power systems, no longer limited to single system circuits.

[0090] In some embodiments of the present disclosure, a single system circuit refers to a DC power system consisting of a DC power supply and a specific load (e.g., an air conditioner). An open DC power system refers to a DC power system consisting of a DC power supply and any load of unspecified type.

[0091] The above-mentioned embodiments of the present disclosure collect original electrical signals in real time, analyze the electrical signals in real time, and output the added detection results of the analyzed electrical signals, which can realize the hazard detection of DC arc, reduce the occurrence of hazardous safety accidents, and reduce property losses caused by DC arc disasters.

[0092] In some embodiments of the present disclosure, step 3 may include at least one of steps 31 to 33 .

[0093] In step 31 , when the signal entropy is greater than a predetermined change signal threshold and the duration is greater than a predetermined time length, it is determined that the electrical signal is an abnormal electrical signal and that a DC arc fault exists in the DC power system.

[0094] In some embodiments of the present disclosure, the magnitude of the predetermined change signal threshold and the predetermined duration are related to the magnitude of the normal electrical signal.

[0095] In some embodiments of the present disclosure, when the normal electrical signal is 20V, the predetermined change signal threshold may be 50-80V, and the predetermined time length may be 15-100ms.

[0096] In some embodiments of the present disclosure, the DC arc detection method further includes: shutting down the DC power system when it is determined that a DC fault arc exists in the DC power system.

[0097] The above-mentioned embodiments of the present disclosure can accurately identify and detect the status of the system, and can shut down the abnormal system when the system is abnormal, thereby ensuring the safety of the system.

[0098] When a dangerous DC arc fault occurs in the above-mentioned embodiments of the present disclosure, the signal entropy of its amplitude changes greatly and irregularly, with a continuous irregular change. A larger signal entropy value indicates a stronger arc, and a large signal entropy value that remains relatively large over consecutive cycles indicates a dangerous arc fault. Therefore, the above-mentioned embodiments of the present disclosure can determine that a DC arc fault occurs when the changing signal entropy is greater than a predetermined value and the duration is long. Therefore, the above-mentioned embodiments of the present disclosure can accurately identify and detect DC arc faults.

[0099] In some embodiments of the present disclosure, Figure 2 In the entropy distribution diagram of the electric signal amplitude change shown in the figure, the distribution of normal electric signals and abnormal electric signals can be clearly identified. When a DC arc occurs, the electric signal fluctuates violently. The larger the entropy of the signal amplitude and the more continuous it is. Figure 2 As shown, the signal entropy corresponding to the arc 12 is greater than the predetermined change signal threshold, and the duration is greater than the predetermined time length. Therefore, it can be known that the arc 12 is a DC fault arc.

[0100] The above-mentioned embodiments of the present disclosure can effectively monitor the unstable electrical signals in the electrical storage signal.

[0101] In step 32 , when the signal entropy is greater than a predetermined change signal threshold and the duration is not greater than a predetermined time length, it is determined that the electrical signal is a first normal electrical signal and that a safety arc exists in the DC power system.

[0102] In some embodiments of the present disclosure, the signal entropy corresponding to arc 11 and arc 13 is greater than the predetermined change signal threshold, and the duration is not greater than the predetermined time length. Figure 2 The arcs 11 and 13 shown are safety arcs.

[0103] The above-mentioned embodiments of the present disclosure can determine that the situation where the entropy of the changing signal is greater than a predetermined value and the continuous time is short is a safe arc, thereby accurately identifying and detecting the safe arc. Therefore, the above-mentioned embodiments of the present disclosure can accurately distinguish between the safe arc and the fault arc, thereby improving the recognition accuracy of the DC fault arc.

[0104] In step 33 , when the signal entropy is not greater than a predetermined change signal threshold, it is determined that the electrical signal is a second normal electrical signal, and it is determined that no arc exists in the DC power system.

[0105] In some embodiments of the present disclosure, Figure 2As shown, except for arcs 11, 12 and 13, the electrical signal of the DC power system is a second normal electrical signal, and it is determined that there is no arc in the DC power system.

[0106] In the above-mentioned embodiment of the present disclosure, in a stable DC power system, the intrinsic characteristic UI of the DC signal is a change in a stable state. In the absence of a DC fault, the signal entropy of the amplitude change remains stable within a continuous cycle. Therefore, when the entropy of the changing signal is not greater than the predetermined changing signal threshold, it can be accurately determined that there is no arc, thereby further improving the recognition accuracy of the DC fault arc.

[0107] The above-mentioned embodiments of the present disclosure can effectively identify DC arc faults (dangerous arc faults), safe arcs, and no arcs.

[0108] In some embodiments of the present disclosure, step 3 may include: classifying the electrical signal into a first normal electrical signal (presence of a safe arc), a second normal electrical signal (no arc), and an abnormal electrical signal (presence of a DC fault arc) through a classification algorithm (e.g., a KNN (K-nearest neighbor) classification algorithm), thereby realizing DC fault arc detection.

[0109] In some embodiments of the present disclosure, step 3 may include: classifying the electrical signal using a classification model (such as a KNN classification algorithm) based on the signal entropy and the duration, and determining whether a DC fault arc exists in the DC power system.

[0110] The above embodiments of the present disclosure provide a DC fault arc detection method based on the time domain characteristic signal entropy of the electrical signal and a classification model, which, combined with a KNN classification model, can achieve accurate identification and detection of DC system fault arcs.

[0111] In some embodiments of the present disclosure, the step of classifying the electrical signal using a classification model based on the signal entropy and the duration to determine whether a DC fault arc exists in the DC power system may include at least one of steps 34 to 36.

[0112] In step 34 , when the signal entropy is greater than a predetermined change signal threshold and the duration is greater than a predetermined time length, the electrical signal is classified as an abnormal electrical signal, and it is determined that a DC arc fault exists in the DC power system.

[0113] In step 35 , when the signal entropy is greater than a predetermined change signal threshold and the duration is not greater than a predetermined time length, the electrical signal is classified as a first normal electrical signal, and it is determined that a safety arc exists in the DC power system.

[0114] In step 36 , when the signal entropy is not greater than a predetermined change signal threshold, the electrical signal is classified as a second normal electrical signal, and it is determined that no arc exists in the DC power system.

[0115] The above embodiments of the present disclosure use a suitable classifier (such as a KNN classifier) to further accurately and quickly identify and classify identification signals, thereby realizing DC arc fault detection more accurately and quickly.

[0116] In some embodiments of the present disclosure, step 3 may include: using a KNN classifier to classify the data into normal and abnormal states. To make the data more readable, the output data is normalized so that the output data is controlled between -1 and 1. The output corresponding to a normal signal is mapped to -1, and the output corresponding to an arc signal is mapped to 1.

[0117] The above-mentioned embodiments of the present disclosure provide a DC arc fault detection method based on time-domain signal entropy and KNN classification. Combined with a KNN classification model, this method can identify and detect DC arc faults. The above-mentioned embodiments of the present disclosure utilize an appropriate KNN classifier to further accurately identify and classify signals, enabling more precise DC arc fault detection.

[0118] Figure 3 Schematic diagram of the structure of some embodiments of the DC fault arc detection device disclosed in the present invention. Figure 3 As shown, the DC fault arc detection device disclosed in the present invention may include a vibration signal acquisition module 31 , a signal entropy acquisition module 32 and a judgment module 33 .

[0119] The vibration signal acquisition module 31 is configured to acquire a vibration signal of an electrical signal of a DC power system.

[0120] In some embodiments of the present disclosure, a DC power system may include a DC power source and a load.

[0121] In some embodiments of the present disclosure, the electrical signal may be at least one of a voltage signal and a current signal.

[0122] In some embodiments of the present disclosure, the vibration signal may be at least one of an amplitude and a mean value.

[0123] The signal entropy acquisition module 32 is configured to determine the signal entropy of the vibration signal change and the duration of the signal entropy according to the vibration signal.

[0124] In some embodiments of the present disclosure, the signal entropy of the vibration signal change of the electrical signal is the signal entropy of the vibration signal.

[0125] In some embodiments of the present disclosure, the signal entropy of the amplitude change of the electrical signal is the amplitude signal entropy.

[0126] In some embodiments of the present disclosure, the signal entropy of the mean change of the electrical signal is the mean signal entropy.

[0127] The above-mentioned embodiments of the present disclosure study the intrinsic characteristics UI of electrical signals and explore the entropy changes in the voltage and current amplitudes of electrical signals and their continuity. Therefore, the above-mentioned embodiments of the present disclosure can comprehensively reflect the noise change ratio of the device operating status and the intrinsic characteristics of objects.

[0128] The DC fault arc detection and identification method disclosed in the above-mentioned embodiment of the present invention can intuitively reflect the operating status of the equipment by analyzing the degree of chaos of the signal entropy of the amplitude change and the continuity of the chaos degree of the collected voltage and current electrical signals. The degree of chaos (the size of the signal entropy) represents the strength of the arc, and the continuity of the chaos degree (the duration of the signal entropy) represents the occurrence time of the arc.

[0129] In the above embodiments of the present disclosure, various states in the DC power system are directly fed back in the electrical signal UI, and the voltage signal and current signal are collected at high speed in the continuously changing time domain, and their amplitude or frequency changes continuously with time. The above embodiments of the present disclosure study and analyze the characteristics of normal electrical signals and arc electrical signals by studying the electrical signal amplitude time domain waveform.

[0130] In some embodiments of the present disclosure, the signal entropy acquisition module 32 is configured to compare each sampling value of the vibration signal with the previous sampling value and the next sampling value; take each sampling value of the vibration signal as the current sampling value; when the current sampling value is less than the previous sampling value and the next sampling value at the same time, or the current sampling value is greater than the previous sampling value and the next sampling value at the same time, add the current sampling value to the first array; and calculate the difference between each two adjacent values in the first array as the signal entropy of the vibration signal change.

[0131] The above-described embodiments of the present disclosure first detect abnormally large or small electrical signal values. By subtracting adjacent abnormal sampling values, the above-described embodiments of the present disclosure can analyze the essential characteristics of the electrical signal based on electrical situational awareness, conduct year-on-year analysis of state data (year-on-year analysis of current data from the same detection terminal and data from the previous acquisition moment), and analyze the signal entropy value of the amplitude change of the electrical signal to reflect the electrical signal state. As a result, the above-described embodiments of the present disclosure can achieve accurate detection and diagnosis of DC arc faults.

[0132] In some embodiments of the present disclosure, the signal entropy acquisition module 32 is further configured to construct a scatter plot according to the signal entropy of the vibration signal change; and determine the duration of the signal entropy according to the scatter plot.

[0133] The above-mentioned embodiments of the present disclosure use the changing signal entropy of the vibration signal to indicate the strength of the arc, and the duration of the changing signal entropy represents the arc occurrence time. In a stable DC power system, any abnormalities in the equipment's operating status can be reflected in the most primitive electrical changes. Therefore, the above-mentioned embodiments of the present disclosure can use the changing characteristics of the original electrical signal to indicate the equipment's operating status and its noise changes, thereby achieving accurate detection and diagnosis of DC fault arcs.

[0134] In some embodiments of the present disclosure, when constructing a scatter plot based on the signal entropy of the vibration signal change, the signal entropy acquisition module 32 can be configured to perform a normalization operation on the signal entropy of the vibration signal change; and construct a scatter plot based on the normalized data.

[0135] The above embodiments of the present disclosure improve the readability of data by normalizing the data.

[0136] In some embodiments of the present disclosure, the signal entropy acquisition module 32 can be configured to set a counter and an empty array (first array) A; compare each sampling value with the effective value of the previous sampling and the effective value of the next sampling; if the current sampling value is less than or greater than the previous sampling value and the next sampling value at the same time, the current sampling value is determined to be an abnormal sampling value, and the value is added to the first array A, and the counter is incremented by 1; if the condition is not met, the counter is cleared and the rotation detection ends; set an empty array (second array) B, determine the abnormal sampling value in the first array A of this rotation detection, take the difference between two adjacent values in the first array A as the signal entropy of the amplitude change, perform normalization operation, and add the normalized data to the second array B, and construct a scatter plot for the data in the second array B.

[0137] The judgment module 33 is configured to judge whether a DC arc fault exists in the DC power system according to the signal entropy and the duration.

[0138] The above-mentioned embodiments of the present disclosure perform coupled analysis of electrical signals by studying and analyzing the signal entropy of their amplitude variations. Entropy is a measure of the degree of disorder or chaos in a system; greater entropy indicates a greater diversity of microscopic states within the system and a greater degree of disorder. The above-mentioned embodiments of the present disclosure introduce the concept of entropy primarily to study the degree of chaos in electrical signals, thereby comprehensively reflecting the operating status of equipment and the intrinsic characteristics of objects. Consequently, the above-mentioned embodiments of the present disclosure can accurately identify and detect DC arc faults and are applicable to open DC power systems, no longer limited to single system circuits.

[0139] In some embodiments of the present disclosure, the judgment module 33 can be configured to determine that the electrical signal is an abnormal electrical signal and that a DC fault arc exists in the DC power system when the signal entropy is greater than a predetermined change signal threshold and the duration is greater than a predetermined time length.

[0140] In some embodiments of the present disclosure, the magnitude of the predetermined change signal threshold and the predetermined duration are related to the magnitude of the normal electrical signal.

[0141] In some embodiments of the present disclosure, when the normal electrical signal is 20V, the predetermined change signal threshold may be 50-80V, and the predetermined time length may be 15-100ms.

[0142] In some embodiments of the present disclosure, the judgment module 33 may be further configured to shut down the DC power system if it is determined that a DC arc fault exists in the DC power system.

[0143] The above-mentioned embodiments of the present disclosure can accurately identify and detect the status of the system, and can shut down the abnormal system when the system is abnormal, thereby ensuring the safety of the system.

[0144] When a dangerous DC arc fault occurs in the above-mentioned embodiments of the present disclosure, the signal entropy of its amplitude changes greatly and irregularly, with a continuous irregular change. A larger signal entropy value indicates a stronger arc, and a large signal entropy value that remains relatively large over consecutive cycles indicates a dangerous arc fault. Therefore, the above-mentioned embodiments of the present disclosure can determine that a DC arc fault occurs when the changing signal entropy is greater than a predetermined value and the duration is long. Therefore, the above-mentioned embodiments of the present disclosure can accurately identify and detect DC arc faults.

[0145] In some embodiments of the present disclosure, Figure 2 In the entropy distribution diagram of the electric signal amplitude change shown in the figure, the distribution of normal electric signals and abnormal electric signals can be clearly identified. When a DC arc occurs, the electric signal fluctuates violently. The larger the entropy of the signal amplitude and the more continuous it is. Figure 2 As shown, the signal entropy corresponding to the arc 12 is greater than the predetermined change signal threshold, and the duration is greater than the predetermined time length. Therefore, it can be known that the arc 12 is a DC fault arc.

[0146] The above-mentioned embodiments of the present disclosure can effectively monitor the unstable electrical signals in the electrical storage signal.

[0147] In some embodiments of the present disclosure, the judgment module 33 can be configured to determine that the electrical signal is a first normal electrical signal and that a safety arc exists in the DC power system when the signal entropy is greater than a predetermined change signal threshold and the duration is no greater than a predetermined time length.

[0148] The above-mentioned embodiments of the present disclosure can determine that the situation where the entropy of the changing signal is greater than a predetermined value and the continuous time is short is a safe arc, thereby accurately identifying and detecting the safe arc. Therefore, the above-mentioned embodiments of the present disclosure can accurately distinguish between the safe arc and the fault arc, thereby improving the recognition accuracy of the DC fault arc.

[0149] In some embodiments of the present disclosure, the judgment module 33 may be configured to determine that the electrical signal is a second normal electrical signal and that no arc exists in the DC power system when the signal entropy is not greater than a predetermined change signal threshold.

[0150] In the above-mentioned embodiment of the present disclosure, in a stable DC power system, the intrinsic characteristic UI of the DC signal is a change in a stable state. In the absence of a DC fault, the signal entropy of the amplitude change remains stable within a continuous cycle. Therefore, when the entropy of the changing signal is not greater than the predetermined changing signal threshold, it can be accurately determined that there is no arc, thereby further improving the recognition accuracy of the DC fault arc.

[0151] The above-mentioned embodiments of the present disclosure can effectively identify DC arc faults (dangerous arc faults), safe arcs, and no arcs.

[0152] In some embodiments of the present disclosure, the judgment module 33 can be configured to classify the electrical signal into a first normal electrical signal (presence of a safe arc), a second normal electrical signal (no arc), and an abnormal electrical signal (presence of a DC fault arc) through a classification algorithm (e.g., a KNN classification algorithm), thereby realizing DC fault arc detection.

[0153] In some embodiments of the present disclosure, the judgment module 33 may be configured to classify the electrical signal based on the signal entropy and the duration using a classification model (e.g., a KNN classification algorithm) to determine whether a DC arc fault exists in the DC power system.

[0154] In some embodiments of the present disclosure, the judgment module 33 can be configured to perform at least one of the following operations when, based on the signal entropy and the duration, the classification model is used to classify the electrical signal to determine whether a DC fault arc exists in the DC power system: when the signal entropy is greater than a predetermined change signal threshold and the duration is greater than a predetermined duration, the electrical signal is classified as an abnormal electrical signal, and it is determined that a DC fault arc exists in the DC power system; when the signal entropy is greater than a predetermined change signal threshold and the duration is not greater than a predetermined duration, the electrical signal is classified as a first normal electrical signal, and it is determined that a safe arc exists in the DC power system; when the signal entropy is not greater than a predetermined change signal threshold, the electrical signal is classified as a second normal electrical signal, and it is determined that no arc exists in the DC power system.

[0155] In some embodiments of the present disclosure, a single system circuit refers to a DC power system consisting of a DC power supply and a specific load (e.g., an air conditioner). An open DC power system refers to a DC power system consisting of a DC power supply and any load of unspecified type.

[0156] The above-mentioned embodiments of the present disclosure collect original electrical signals in real time, analyze the electrical signals in real time, and output the added detection results of the analyzed electrical signals, which can realize the hazard detection of DC arc, reduce the occurrence of hazardous safety accidents, and reduce property losses caused by DC arc disasters.

[0157] The above-mentioned embodiments of the present disclosure provide a DC arc fault detection method based on time-domain signal entropy and KNN classification. Combined with a KNN classification model, this method can identify and detect DC arc faults. The above-mentioned embodiments of the present disclosure utilize an appropriate KNN classifier to further accurately identify and classify signals, enabling more precise DC arc fault detection.

[0158] In some embodiments of the present disclosure, the DC arc fault detection device of the present disclosure may be configured to implement the DC arc fault detection method as described in any of the above embodiments.

[0159] Figure 4 Schematic diagram of the structure of some other embodiments of the DC fault arc detection device disclosed in the present invention. Figure 4 As shown, the DC fault arc detection device disclosed herein may include a memory 41 and a processor 42 .

[0160] The memory 41 is used to store instructions. The processor 42 is coupled to the memory 41 . The processor 42 is configured to execute the DC fault arc detection method according to the above embodiment based on the instructions stored in the memory.

[0161] like Figure 4As shown, the DC arc fault detection device further includes a communication interface 43 for exchanging information with other devices. Furthermore, the DC arc fault detection device further includes a bus 44 through which the processor 42, the communication interface 43, and the memory 41 communicate with each other.

[0162] Memory 41 may include high-speed RAM memory or non-volatile memory, such as at least one disk storage device. Memory 41 may also be a memory array. Memory 41 may also be divided into blocks, and the blocks may be combined into virtual volumes according to certain rules.

[0163] Furthermore, the processor 42 may be a central processing unit (CPU), or may be an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the present disclosure.

[0164] Figure 5 This is a schematic diagram of the installation of a DC fault arc detection device in some embodiments of the present disclosure. Connected in series to the loop of the DC power system, Figure 5 The DC power system of the embodiment includes a DC power supply and multiple loads, where loads 1 to N can be any load. Therefore, the DC arc fault detection device disclosed herein can be applied to an open DC power system, i.e., a DC power system that does not restrict load type. The DC arc fault detection device disclosed herein can collect voltage and current signals from the DC power system at high speed and high precision, mine and analyze the electrical signal data, and comprehensively monitor the operating status of the DC power system in real time.

[0165] The inventors have experimentally verified the effectiveness and stability of the arc fault identification method used by the DC arc fault detection device in the above-mentioned embodiment of the present disclosure, achieving DC fault identification and detection accuracy exceeding 99.999%. The above-mentioned embodiment of the present disclosure is applicable to a wide range of scenarios and is suitable for complex DC power systems. The above-mentioned embodiment of the present disclosure enables arc fault detection in open DC power systems.

[0166] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the method for detecting a DC arc fault as described in any one of the above embodiments is implemented.

[0167] According to another aspect of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the DC fault arc detection method as described in any of the above embodiments is implemented.

[0168] The computer-readable storage medium of the present disclosure may be implemented as a non-transitory computer-readable storage medium.

[0169] Those skilled in the art will appreciate that embodiments of the present disclosure may be provided as methods, apparatuses, or computer program products. Thus, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product implemented on one or more computer-usable, non-transitory storage media (including, but not limited to, magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0170] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0171] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0172] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0173] The DC fault arc detection device, vibration signal acquisition module, signal entropy acquisition module and judgment module described above can be implemented as a general-purpose processor, a programmable logic controller, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component or any appropriate combination thereof for performing the functions described in the present disclosure.

[0174] Those skilled in the art will appreciate that all or part of the steps of the above-described embodiment methods of the present disclosure may be accomplished by hardware, and the hardware may be implemented as a general-purpose processor, a programmable logic controller, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or any appropriate combination thereof for executing the methods described in the present disclosure.

[0175] The present disclosure has been described in detail so far. To avoid obscuring the concept of the present disclosure, some details known in the art have not been described. Based on the above description, those skilled in the art can fully understand how to implement the technical solutions disclosed herein.

[0176] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or may be accomplished by instructing the relevant hardware through a program, and the program may be stored in a non-transitory computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0177] The description of the present disclosure is provided for purposes of illustration and description and is not intended to be exhaustive or to limit the disclosure to the disclosed form. Many modifications and variations will be apparent to those skilled in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present disclosure and to enable those skilled in the art to understand the present disclosure and design various embodiments with various modifications suitable for specific applications.

Claims

1. A DC arc fault detection method, comprising: Acquiring vibration signals of electrical signals of a DC power system; determining, according to the vibration signal, a signal entropy of a change in the vibration signal and a duration of the signal entropy; It is determined whether a DC arc fault exists in the DC power system according to the signal entropy and the duration.

2. The DC arc fault detection method according to claim 1, wherein: Determining, according to the vibration signal, the signal entropy of the vibration signal change and the duration of the signal entropy includes: Comparing each sampling value of the vibration signal with the previous sampling value and the next sampling value; Taking each sampling value of the vibration signal as the current sampling value; When the current sampling value is less than the previous sampling value and the next sampling value, or when the current sampling value is greater than the previous sampling value and the next sampling value, the current sampling value is added to the first array; The difference between every two adjacent values in the first array is calculated as the signal entropy of the vibration signal change.

3. The DC arc fault detection method according to claim 2, wherein: Determining, based on the vibration signal, the signal entropy of the vibration signal change and the duration of the signal entropy further includes: constructing a scatter plot according to the signal entropy of the vibration signal change; A duration of the signal entropy is determined based on the scatter plot.

4. The DC arc fault detection method according to claim 3, wherein: The constructing a scatter plot according to the signal entropy of the vibration signal includes: performing a normalization operation on the signal entropy of the vibration signal change; A scatter plot is constructed based on the normalized data.

5. The DC arc fault detection method according to any one of claims 1 to 4, wherein: The determining, based on the signal entropy and the duration, whether a DC arc fault exists in the DC power system includes: When the signal entropy is greater than a predetermined change signal threshold and the duration is greater than a predetermined time length, it is determined that the electrical signal is an abnormal electrical signal and that a DC fault arc exists in the DC power system.

6. The DC arc fault detection method according to any one of claims 1 to 4, wherein: The determining, based on the signal entropy and the duration, whether a DC arc fault exists in the DC power system includes: When the signal entropy is greater than a predetermined change signal threshold and the duration is no greater than a predetermined time length, it is determined that the electrical signal is a first normal electrical signal and that a safety arc exists in the DC power system.

7. The DC arc fault detection method according to any one of claims 1 to 3, wherein: The determining, based on the signal entropy and the duration, whether a DC arc fault exists in the DC power system includes: When the signal entropy is not greater than a predetermined change signal threshold, it is determined that the electrical signal is a second normal electrical signal, and it is determined that no arc exists in the DC power system.

8. The DC arc fault detection method according to any one of claims 1 to 3, wherein: The determining, based on the signal entropy and the duration, whether a DC arc fault exists in the DC power system includes: The electrical signal is classified using a classification model according to the signal entropy and the duration to determine whether a DC arc fault exists in the DC power system.

9. The DC arc fault detection method according to claim 8, wherein: The classifying the electrical signal using a classification model according to the signal entropy and the duration to determine whether a DC arc fault exists in the DC power system comprises at least one of the following steps: When the signal entropy is greater than a predetermined change signal threshold and the duration is greater than a predetermined time length, classifying the electrical signal as an abnormal electrical signal and determining that a DC fault arc exists in the DC power system; When the signal entropy is greater than a predetermined change signal threshold and the duration is no greater than a predetermined time length, classifying the electrical signal as a first normal electrical signal and determining that a safety arc exists in the DC power system; When the signal entropy is not greater than a predetermined change signal threshold, the electrical signal is classified as a second normal electrical signal, and it is determined that no arc exists in the DC power system.

10. The DC arc fault detection method according to any one of claims 1 to 3, wherein: The electrical signal is at least one of a voltage signal and a current signal; The vibration signal is at least one of an amplitude and a mean value.

11. A DC fault arc detection device, comprising: a vibration signal acquisition module configured to acquire a vibration signal of an electrical signal of a DC power system; a signal entropy acquisition module, configured to determine, based on the vibration signal, a signal entropy of a change in the vibration signal and a duration of the signal entropy; The judgment module is configured to judge whether a DC arc fault exists in the DC power system according to the signal entropy and the duration.

12. A DC fault arc detection device, comprising: a memory configured to store instructions; and The processor is configured to execute the instruction so that the DC arc fault detection device implements the DC arc fault detection method according to any one of claims 1 to 10.

13. A computer-readable storage medium, wherein: The computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the DC arc fault detection method according to any one of claims 1 to 10 is implemented.

14. A computer program product comprising a computer program, wherein When the computer program is executed by a processor, the DC arc fault detection method according to any one of claims 1 to 10 is implemented.

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