DC fault arc state monitoring method and device, and computer program product

Abstract

The invention provides a direct-current fault arc state monitoring method and device and a computer program product, and the method comprises the steps: sampling an electrical signal of a monitored load, and obtaining a current and voltage signal sequence; an electrical characteristic signal sequence is extracted by adopting a wavelet packet decomposition and reconstruction technology, when the load type is unknown, similarity measurement is carried out through electrical characteristic sample sequences of various preset types of loads, and the load type is determined by utilizing a deep belief network model. And when the type of the load is known, fault analysis is carried out through the similarity measurement value, the second deep belief network model is utilized to output a DC fault arc state monitoring result, and whether the load is in normal operation or has a DC fault arc is judged, so that the monitoring accuracy and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method and device for monitoring a DC fault arc state, and a computer program product. Background Art

[0002] With the continuous development of society, the improvement of living standards and the advancement of science and technology are closely related to the development of electricity, but the existence or generation of fault arcs will seriously affect the safe and stable operation of the power system. Fault arcs are divided into DC fault arcs and AC fault arcs according to the current type, both of which are the main factors causing electrical fires. In traditional power systems, AC power accounts for a large proportion of applications, and the monitoring and diagnosis of AC arcs are relatively mature. However, at present, DC fault arcs are not given enough attention, and the research on their diagnostic methods is still in the development stage.

[0003] A DC arc is a very high energy instantaneous current, usually in the form of gas discharge. The main difference between a DC arc and an AC arc is that it does not have a zero crossing phenomenon, so it is difficult for traditional circuit protection devices to play a protective role. In other words, once a DC fault arc occurs, the fault part will remain in a stable arcing state for a long time and will not extinguish, thus causing electrical accidents such as fires. Summary of the invention

[0004] The technical problem to be solved by the embodiments of the present invention is to provide a method and device for monitoring the state of a DC fault arc, and a computer program product, so as to improve the accuracy and efficiency of the monitoring of the state of a DC fault arc.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a DC fault arc state monitoring method, comprising the following steps:

[0006] Sampling the electrical signal of the monitored load according to a preset sampling frequency to obtain an electrical signal sequence, wherein the electrical signal sequence includes a loop current signal sequence and an arc voltage signal sequence;

[0007] Extracting an electrical signal sequence to be analyzed that matches a preset time window from the electrical signal sequence, wherein the electrical signal sequence to be analyzed includes a current signal sequence and a voltage signal sequence to be analyzed;

[0008] Performing wavelet packet decomposition and reconstruction on the electrical signal sequence to be analyzed to obtain an electrical characteristic signal sequence, wherein the electrical characteristic signal sequence includes a current characteristic signal sequence and a voltage characteristic signal sequence;

[0009] When the load type of the monitored load is unknown, a similarity measurement is performed on the electrical characteristic signal sequence and the electrical characteristic sample sequences corresponding to multiple preset types of loads to obtain similarity measurement values ​​of the electrical characteristics between the monitored load and the multiple preset types of loads, and the similarity measurement values ​​of the electrical characteristics between the monitored load and the multiple preset types of loads are input into a pre-trained first deep belief network model to obtain the load type of the monitored load; wherein the electrical characteristic sample sequence corresponding to each preset type of load includes a current characteristic sample sequence and a voltage characteristic sample sequence when the preset type of load is running under various working conditions;

[0010] When the load type of the monitored load is known, a similarity measurement is performed on the electrical characteristic signal sequence and the electrical characteristic sample sequence corresponding to the monitored load to obtain a similarity measurement value for fault analysis, and the similarity measurement value for fault analysis is input into a pre-trained second deep confidence network model to obtain a DC fault arc state monitoring result of the monitored load; the DC fault arc state monitoring result is normal operation or the presence of a DC fault arc.

[0011] Optionally, the method further comprises:

[0012] After performing wavelet packet decomposition and reconstruction on the electrical signal sequence to be analyzed to obtain the electrical characteristic signal sequence, the current characteristic signal sequence and the voltage characteristic signal sequence are divided into sub-segments of equal length, and then the average value of each sub-segment is calculated to represent each sub-segment, thereby converting the current characteristic signal sequence and the voltage characteristic signal sequence into a low-dimensional signal sequence.

[0013] Optionally, the performing similarity measurement on the electrical characteristic signal sequence and electrical characteristic sample sequences corresponding to multiple preset types of loads to obtain similarity measurement values ​​of electrical characteristics between the monitored load and the multiple preset types of loads further includes:

[0014] After similarity measurement is performed on the current characteristic signal sequence and the current characteristic sample sequence corresponding to each preset type of load, a distance matrix is ​​created to store the distance of each pair of points between the current characteristic signal sequence and the current characteristic sample sequence currently undergoing similarity measurement, the distance between each point in the current characteristic signal sequence and each point in the current characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is searched in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the current characteristic signal sequence and each current characteristic sample sequence, and finally the cumulative distance on the path is output as the current similarity measurement value between the current characteristic signal sequence and the current characteristic sample sequence currently undergoing similarity measurement;

[0015] After similarity measurement is performed on the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to each preset type of load, a distance matrix is ​​created to store the distance of each group of point pairs between the voltage characteristic signal sequence and the voltage characteristic sample sequence currently undergoing similarity measurement, the distance between each point in the voltage characteristic signal sequence and each point in the voltage characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is searched in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the voltage characteristic signal sequence and each voltage characteristic sample sequence, and finally the cumulative distance on the path is output as the voltage similarity measurement value between the voltage characteristic signal sequence and the voltage characteristic sample sequence currently undergoing similarity measurement;

[0016] The similarity metrics of the electrical characteristics between the monitored load and the multiple preset types of loads each include a current similarity metric value and a voltage similarity metric value.

[0017] Optionally, the performing similarity measurement on the electrical characteristic signal sequence and the electrical characteristic sample sequence corresponding to the monitored load to obtain a similarity measurement value for fault analysis further includes:

[0018] After similarity measurement is performed on the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load, a distance matrix is ​​created to store the distance of each pair of points between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load, the distance between each point in the current characteristic signal sequence and each point in the current characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is found in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the current characteristic signal sequence and each current characteristic sample sequence, and finally the cumulative distance on the path is output as the current similarity measurement value between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load;

[0019] When measuring the similarity between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load, a distance matrix is ​​created to store the distance between each pair of points between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load, the distance between each point in the voltage characteristic signal sequence and each point in the voltage characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is found in the distance matrix so that the cumulative distance on the path is minimized. The path represents the best alignment between the voltage characteristic signal sequence and each voltage characteristic sample sequence. Finally, the cumulative distance on the path is output as the voltage similarity measurement value between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load.

[0020] The similarity metric value of the electrical characteristics between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load includes a current similarity metric value and a voltage similarity metric value.

[0021] The embodiment of the present invention further provides a DC fault arc state monitoring device, comprising:

[0022] A sampling module, used for sampling the electrical signal of the monitored load according to a preset sampling frequency to obtain an electrical signal sequence, wherein the electrical signal sequence includes a loop current signal sequence and an arc voltage signal sequence;

[0023] A preprocessing module, used for extracting a to-be-analyzed electrical signal sequence that matches a preset time window from the electrical signal sequence, wherein the to-be-analyzed electrical signal sequence includes a to-be-analyzed current signal sequence and a to-be-analyzed voltage signal sequence;

[0024] A feature extraction module, used for performing wavelet packet decomposition and reconstruction on the electrical signal sequence to be analyzed to obtain an electrical feature signal sequence, wherein the electrical feature signal sequence includes a current feature signal sequence and a voltage feature signal sequence;

[0025] A load type identification module, for, when the load type of the monitored load is unknown, performing similarity measurement on the electrical characteristic signal sequence and electrical characteristic sample sequences corresponding to multiple preset types of loads to obtain similarity measurement values ​​of the electrical characteristics between the monitored load and the multiple preset types of loads, and inputting the similarity measurement values ​​of the electrical characteristics between the monitored load and the multiple preset types of loads into a pre-trained first deep belief network model to obtain the load type of the monitored load; wherein the electrical characteristic sample sequence corresponding to each preset type of load includes a current characteristic sample sequence and a voltage characteristic sample sequence when the preset type of load is running under various working conditions;

[0026] A DC fault identification module is used to perform similarity measurement on the electrical characteristic signal sequence and the electrical characteristic sample sequence corresponding to the monitored load to obtain a similarity measurement value for fault analysis when the load type of the monitored load is known, and input the similarity measurement value for fault analysis into a pre-trained second deep confidence network model to obtain a DC fault arc state monitoring result of the monitored load; the DC fault arc state monitoring result is normal operation or the presence of a DC fault arc.

[0027] Optionally, the feature extraction module is further used to, after performing wavelet packet decomposition and reconstruction on the electrical signal sequence to be analyzed to obtain an electrical feature signal sequence, divide the current feature signal sequence and the voltage feature signal sequence into sub-segments of equal length, and then calculate the average value of each sub-segment to represent each sub-segment, thereby converting the current feature signal sequence and the voltage feature signal sequence into low-dimensional signal sequences.

[0028] Optionally, the load type identification module is further used to:

[0029] After similarity measurement is performed on the current characteristic signal sequence and the current characteristic sample sequence corresponding to each preset type of load, a distance matrix is ​​created to store the distance of each pair of points between the current characteristic signal sequence and the current characteristic sample sequence currently undergoing similarity measurement, the distance between each point in the current characteristic signal sequence and each point in the current characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is searched in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the current characteristic signal sequence and each current characteristic sample sequence, and finally the cumulative distance on the path is output as the current similarity measurement value between the current characteristic signal sequence and the current characteristic sample sequence currently undergoing similarity measurement;

[0030] After similarity measurement is performed on the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to each preset type of load, a distance matrix is ​​created to store the distance of each group of point pairs between the voltage characteristic signal sequence and the voltage characteristic sample sequence currently undergoing similarity measurement, the distance between each point in the voltage characteristic signal sequence and each point in the voltage characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is searched in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the voltage characteristic signal sequence and each voltage characteristic sample sequence, and finally the cumulative distance on the path is output as the voltage similarity measurement value between the voltage characteristic signal sequence and the voltage characteristic sample sequence currently undergoing similarity measurement;

[0031] The similarity metrics of the electrical characteristics between the monitored load and the multiple preset types of loads each include a current similarity metric value and a voltage similarity metric value.

[0032] Optionally, the DC fault identification module is further used to:

[0033] After similarity measurement is performed on the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load, a distance matrix is ​​created to store the distance of each pair of points between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load, the distance between each point in the current characteristic signal sequence and each point in the current characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is found in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the current characteristic signal sequence and each current characteristic sample sequence, and finally the cumulative distance on the path is output as the current similarity measurement value between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load;

[0034] When measuring the similarity between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load, a distance matrix is ​​created to store the distance between each pair of points between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load, the distance between each point in the voltage characteristic signal sequence and each point in the voltage characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is found in the distance matrix so that the cumulative distance on the path is minimized. The path represents the best alignment between the voltage characteristic signal sequence and each voltage characteristic sample sequence. Finally, the cumulative distance on the path is output as the voltage similarity measurement value between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load.

[0035] The similarity metric value of the electrical characteristics between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load includes a current similarity metric value and a voltage similarity metric value.

[0036] An embodiment of the present invention further provides a computer program product, including computer program instructions, wherein the computer program instructions instruct a computer device to perform operations corresponding to the above method.

[0037] The above-mentioned DC fault arc state monitoring method and device, and computer program product have the following beneficial effects:

[0038] (1) By performing wavelet packet decomposition and reconstruction on the electrical signal sequence to be analyzed, the current characteristic signal sequence and the voltage characteristic signal sequence are extracted, which effectively captures the subtle characteristics of the DC fault arc and makes the monitoring results more accurate.

[0039] (2) It is able to identify the load type to be monitored. When the load type is unknown, the load type can be intelligently judged by measuring the similarity with the electrical characteristic sample sequences of multiple preset load types, thereby improving the adaptability of the monitoring method to different loads.

[0040] (3) A deep belief network model is used to monitor the fault arc status. Compared with traditional methods, the deep learning model has stronger feature extraction and classification capabilities, can give accurate monitoring results in a short time, and improves the monitoring efficiency.

[0041] (4) By real-time monitoring of the DC arc fault status, potential arc faults can be discovered and handled in a timely manner, effectively reducing the risk of equipment damage, power outages, and electrical fires caused by DC arc faults, ensuring the safe and stable operation of the power system, reducing the cost of equipment repair and replacement caused by arc faults, and reducing the operation and maintenance cost of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying creative labor, other drawings obtained based on these drawings still belong to the scope of the present invention.

[0043] Figure 1 The present invention is a flow chart of a DC fault arc state monitoring method according to an embodiment of the present invention.

[0044] Figure 2 A schematic diagram of a deep belief network model in one embodiment of the present invention.

[0045] Figure 3 The structure diagram of a DC fault arc state monitoring device in one embodiment of the present invention. DETAILED DESCRIPTION

[0046] The detailed description of the drawings is intended as an illustration of the current embodiment of the present application, and is not intended to represent the only form in which the present application can be implemented. It should be understood that the same or equivalent functions can be accomplished by different embodiments intended to be included in the spirit and scope of the present application.

[0047] See also Figure 1An embodiment of the present invention provides a method for monitoring a DC fault arc state, comprising the following steps:

[0048] Step S10, sampling the electrical signal of the monitored load according to a preset sampling frequency to obtain an electrical signal sequence, wherein the electrical signal sequence includes a loop current signal sequence and an arc voltage signal sequence.

[0049] Specifically, the description in step S10 involves the basic process of electrical signal sampling. The sampling frequency refers to the number of samples per unit time, usually in Hertz (Hz). The preset sampling frequency is a parameter set before signal acquisition. It determines the sampling time interval. This frequency needs to be high enough to ensure that the original signal can be accurately restored.

[0050] The electrical signal of the monitored load refers to the electrical signal generated by the monitoring target (ie, the load) during operation, including current and voltage signals. The load can be any device or system that uses direct current.

[0051] Sampling refers to recording the value of an electrical signal at a specific point in time according to a preset sampling frequency. This process is the first step in digitization, converting a continuous analog signal into a discrete digital signal.

[0052] A series of numerical values ​​obtained through sampling are arranged in chronological order to form an electrical signal sequence. In this step, the loop current signal sequence and the arc voltage signal sequence are obtained. The loop current signal sequence refers to the change of current during the operation of the load, which reflects the electrical behavior and power demand of the load; the arc voltage signal sequence refers to the change of voltage measured at both ends of the load, which can reveal the existence and characteristics of the arc, because the generation of the arc will cause specific changes in the voltage signal.

[0053] Step S20: extracting an electrical signal sequence to be analyzed that matches a preset time window from the electrical signal sequence, wherein the electrical signal sequence to be analyzed includes a current signal sequence and a voltage signal sequence to be analyzed.

[0054] Specifically, before the analysis, a specific time window has been set. This time window is determined based on the analysis and monitoring requirements of the fault arc characteristics, and it represents the duration of the signal segment extracted from the electrical signal sequence. The electrical signal sequence to be analyzed refers to the part extracted from the entire electrical signal sequence that matches the preset time window. This part of the signal sequence will be used as the object of analysis to detect the characteristics of the fault arc. The current signal sequence to be analyzed is a part of the electrical signal sequence to be analyzed, which specifically represents the current changes within the preset time window. The voltage signal sequence to be analyzed is a part of the electrical signal sequence to be analyzed, which represents the voltage changes within the preset time window.

[0055] Step S30, performing wavelet packet decomposition and reconstruction on the electrical signal sequence to be analyzed to obtain an electrical characteristic signal sequence, wherein the electrical characteristic signal sequence includes a current characteristic signal sequence and a voltage characteristic signal sequence.

[0056] Specifically, wavelet packet decomposition is a more sophisticated signal processing method that not only decomposes the low-frequency part of the signal, but also further decomposes the high-frequency part. This method can more effectively analyze the details of the signal, especially when detecting transient changes and non-stationary characteristics of the signal. Wavelet packet decomposition decomposes the original signal into multiple sub-bands of different frequencies, which can represent the characteristics of the signal in more detail. After wavelet packet decomposition, certain sub-bands can be selectively reconstructed as needed to extract features that are useful for fault diagnosis. The reconstruction process is to recombine the decomposed sub-band signals to form new signal sequences that highlight specific features in the original signal.

[0057] The new signal sequences obtained by wavelet packet decomposition and reconstruction contain the characteristic information related to fault diagnosis in the original signal. These characteristic signal sequences are usually simpler than the original signal and easier to analyze and identify fault modes. The current characteristic signal sequence is the current signal after wavelet packet decomposition and reconstruction. It contains the fault characteristics related to current, such as current mutation and spike. The voltage characteristic signal sequence is the voltage signal after wavelet packet decomposition and reconstruction. It contains the fault characteristics related to voltage, such as voltage fluctuation and drop.

[0058] Specifically, the operation flow of step S30 is as follows:

[0059] Step 1: Perform wavelet packet decomposition on the current signal sequence and voltage signal sequence to be analyzed respectively.

[0060] Step 2: According to the characteristics of the fault arc, select the appropriate sub-band for reconstruction to extract the characteristics of current and voltage.

[0061] Step 3, reconstruct the current characteristic signal sequence and voltage characteristic signal sequence, which will be used for subsequent fault analysis and monitoring.

[0062] Through this step, features useful for fault arc detection can be effectively extracted from complex electrical signals, providing important data support for subsequent fault diagnosis.

[0063] Step S40, when the load type of the monitored load is unknown, a similarity measurement is performed on the electrical characteristic signal sequence and the electrical characteristic sample sequences corresponding to multiple preset types of loads to obtain similarity measurement values ​​of the electrical characteristics between the monitored load and the multiple preset types of loads, and the similarity measurement values ​​of the electrical characteristics between the monitored load and the multiple preset types of loads are input into a pre-trained first deep confidence network model to obtain the load type of the monitored load; wherein the electrical characteristic sample sequence corresponding to each preset type of load includes a current characteristic sample sequence and a voltage characteristic sample sequence when the preset type of load is operating under various working conditions.

[0064] Specifically, the unknown load type of the monitored load means that during the monitoring process, we do not know what type of equipment or system the monitored load belongs to. Different load types may produce different electrical characteristics. Therefore, it is necessary to determine the load type first in order to more accurately monitor the fault arc.

[0065] The electrical characteristic signal sequence is obtained by wavelet packet decomposition and reconstruction in step S30, and includes a current characteristic signal sequence and a voltage characteristic signal sequence.

[0066] The electrical characteristic sample sequences corresponding to multiple preset types of loads refer to the electrical characteristic samples of different types of loads that have been collected and preprocessed during the system design and training phase. These sample sequences include current characteristic sample sequences and voltage characteristic sample sequences under various working conditions (such as normal operation, different load conditions, etc.).

[0067] Similarity measurement refers to calculating the similarity between the electrical characteristic signal sequence to be analyzed and the electrical characteristic sample sequence of each preset type of load. For example, the similarity measurement can be a statistical method such as Euclidean distance, cosine similarity, correlation coefficient, etc., which is used to evaluate the degree of match between two sequences.

[0068] The first deep belief network model is a pre-trained deep learning model that can predict or classify load types based on the similarity measure of the input. A deep belief network is a neural network with multiple hidden layers suitable for feature learning and classification tasks.

[0069] Step S50, when the load type of the monitored load is known, a similarity measurement is performed on the electrical characteristic signal sequence and the electrical characteristic sample sequence corresponding to the monitored load to obtain a similarity measurement value for fault analysis, and the similarity measurement value for fault analysis is input into a pre-trained second deep confidence network model to obtain a DC fault arc state monitoring result of the monitored load; the DC fault arc state monitoring result is normal operation or the presence of a DC fault arc.

[0070] Specifically, the load type of the monitored load is known, which means that during the monitoring process, we have determined the type of the monitored load. This information is very important for arc fault monitoring, because different types of loads may have different electrical characteristics and failure modes.

[0071] The electrical characteristic signal sequence is the electrical characteristic signal sequence obtained by wavelet packet decomposition and reconstruction in step S30, and includes a current characteristic signal sequence and a voltage characteristic signal sequence.

[0072] The electrical characteristic sample sequence corresponding to the monitored load refers to the electrical characteristic sample sequence collected and prepared in advance for a known load type, and these sample sequences represent the electrical characteristics of this type of load in normal operation and various fault states.

[0073] Similarity measurement refers to calculating the similarity between the electrical characteristic signal sequence to be analyzed and the electrical characteristic sample sequence of the known load type. The similarity measurement value can reflect the matching degree between the current signal and the sample sequence, thus providing a basis for fault analysis.

[0074] The second deep belief network model is a pre-trained deep learning model specifically used to determine whether the load is in a DC fault arc state based on the input similarity metric value. The model has been trained with a large amount of data and can identify the characteristics of the fault arc.

[0075] Specifically, the Deep Belief Network (DBN) is a probabilistic generation model. Compared with the traditional neural network model, DBN establishes a joint distribution between labeled observation data, so it can not only identify features and classify data, but also generate training data according to the maximum probability. The DBN network is composed of many layers of restricted Boltzmann machines (RBM) and a layer of BP. During the training process, the data of each layer of RBM is first trained unsupervised to obtain as much feature information as possible. Its last layer is set up with a BP network, which can receive the previous feature vector. It is a semi-supervised probabilistic prediction model. The structure of DBN is as follows: Figure 2As shown, DBN is composed of multiple layers of neurons, which are divided into explicit and implicit neurons. Explicit neurons receive input signals, while the role of implicit neurons is to extract features. The first deep belief network and the second deep belief network can be trained according to different needs to achieve the purpose of the present invention.

[0076] Furthermore, the step S30 further includes:

[0077] After performing wavelet packet decomposition and reconstruction on the electrical signal sequence to be analyzed to obtain the electrical characteristic signal sequence, the current characteristic signal sequence and the voltage characteristic signal sequence are divided into sub-segments of equal length, and then the average value of each sub-segment is calculated to represent each sub-segment, thereby converting the current characteristic signal sequence and the voltage characteristic signal sequence into a low-dimensional signal sequence.

[0078] Specifically, the above description is a further refinement of step S30, which explains how to process the obtained electrical characteristic signal sequence after completing the wavelet packet decomposition and reconstruction to reduce the dimension of the signal and extract key information; dividing into equal-length sub-segments means dividing the current characteristic signal sequence and the voltage characteristic signal sequence into several equal-length parts; the length of each sub-segment is pre-set, so as to ensure the uniformity and comparability of signal processing; for each segmented sub-segment, the average value of all its values ​​is calculated; this average value will be used as the representative value of the sub-segment, which can reflect the general characteristics of the sub-segment signal; by calculating the average value of each sub-segment, the original current characteristic signal sequence and the voltage characteristic signal sequence are converted into a series of average values, which constitute a new signal sequence with lower dimension; this process is actually a dimensionality reduction process, which simplifies the signal data and makes subsequent analysis and calculation more efficient. Further, in the step S40, the electrical characteristic signal sequence and the electrical characteristic sample sequence corresponding to the multiple preset types of loads are measured for similarity to obtain the similarity measurement value of the electrical characteristics between the monitored load and the multiple preset types of loads, further including:

[0079] Step S401, after similarity measurement is performed on the current characteristic signal sequence and the current characteristic sample sequence corresponding to each preset type of load, a distance matrix is ​​created to store the distance between each group of point pairs between the current characteristic signal sequence and the current characteristic sample sequence currently undergoing similarity measurement, the distance between each point in the current characteristic signal sequence and each point in the current characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is searched in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the current characteristic signal sequence and each current characteristic sample sequence, and finally the cumulative distance on the path is output as the current similarity measurement value between the current characteristic signal sequence and the current characteristic sample sequence currently undergoing similarity measurement;

[0080] Specifically, in step S401, the number of rows and columns of the matrix corresponds to the number of points in the two sequences, respectively. For each point in the current feature signal sequence, the distance between it and each point in the current feature sample sequence is calculated. This distance can be the Euclidean distance, the Manhattan distance, etc., depending on the application requirements. In the distance matrix, a path is found from the upper left corner (representing the beginning of the two sequences) to the lower right corner (representing the end of the two sequences). The cumulative distance on this path is the smallest. This path is not a straight line, it can be curved to find the best alignment of the two time series. In this embodiment, the time series is allowed to stretch and twist on the time axis to find the best match between the two sequences, which means that even if the two sequences are different in time (for example, one sequence may be faster or slower than the other sequence), the correspondence between them can be found; along the found path, the sum of the distances in all cells on the path is calculated. This cumulative distance represents a similarity measure between the two sequences. The smaller the cumulative distance, the more similar the two sequences are.

[0081] Step S402, after similarity measurement is performed on the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to each preset type of load, a distance matrix is ​​created to store the distance of each group of point pairs between the voltage characteristic signal sequence and the voltage characteristic sample sequence currently undergoing similarity measurement, the distance between each point in the voltage characteristic signal sequence and each point in the voltage characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is searched in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the voltage characteristic signal sequence and each voltage characteristic sample sequence, and finally the cumulative distance on the path is output as the voltage similarity measurement value between the voltage characteristic signal sequence and the voltage characteristic sample sequence currently undergoing similarity measurement;

[0082] Specifically, in step S402, the number of rows and columns of the matrix corresponds to the number of points in the two sequences, respectively. For each point in the voltage feature signal sequence, the distance between it and each point in the voltage feature sample sequence is calculated. This distance can be the Euclidean distance, the Manhattan distance, etc., depending on the application requirements. In the distance matrix, a path is found from the upper left corner (representing the beginning of the two sequences) to the lower right corner (representing the end of the two sequences). The cumulative distance on this path is the smallest. This path is not a straight line, it can be curved to find the best alignment of the two time series. In this embodiment, the time series is allowed to stretch and twist on the time axis to find the best match between the two sequences, which means that even if the two sequences are different in time (for example, one sequence may be faster or slower than the other sequence), the corresponding relationship between them can be found; along the found path, the sum of the distances in all cells on the path is calculated. This cumulative distance represents the similarity measure between the two sequences. The smaller the cumulative distance, the more similar the two sequences are.

[0083] Wherein, the similarity metrics of the electrical characteristics between the monitored load and the plurality of preset types of loads each include a current similarity metric value and a voltage similarity metric value.

[0084] Furthermore, the step S50 of performing similarity measurement on the electrical characteristic signal sequence and the electrical characteristic sample sequence corresponding to the monitored load to obtain a similarity measurement value for fault analysis further includes:

[0085] Step S501, after similarity measurement is performed on the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load, a distance matrix is ​​created to store the distance between each group of point pairs between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load, the distance between each point in the current characteristic signal sequence and each point in the current characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is found in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the current characteristic signal sequence and each current characteristic sample sequence, and finally the cumulative distance on the path is output as the current similarity measurement value between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load;

[0086] Specifically, in step S501, the number of rows and columns of the matrix corresponds to the number of points in the two sequences, respectively. For each point in the current characteristic signal sequence, the distance between it and each point in the current characteristic sample sequence is calculated. This distance can be the Euclidean distance, the Manhattan distance, etc., depending on the application requirements. In the distance matrix, a path is found from the upper left corner (representing the beginning of the two sequences) to the lower right corner (representing the end of the two sequences). The cumulative distance on this path is the smallest. This path is not a straight line, it can be curved to find the best alignment of the two time series. In this embodiment, the time series is allowed to stretch and twist on the time axis to find the best match between the two sequences, which means that even if the two sequences are different in time (for example, one sequence may be faster or slower than the other sequence), the corresponding relationship between them can be found; along the found path, the sum of the distances in all cells on the path is calculated. This cumulative distance represents the similarity measure between the two sequences. The smaller the cumulative distance, the more similar the two sequences are.

[0087] Step S502, when measuring the similarity between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load, create a distance matrix for storing the distance between each pair of points between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load, calculate the distance between each point in the voltage characteristic signal sequence and each point in the voltage characteristic sample sequence, and find a path from the upper left corner to the lower right corner in the distance matrix so that the cumulative distance on the path is the smallest. The path represents the best alignment between the voltage characteristic signal sequence and each voltage characteristic sample sequence. Finally, the cumulative distance on the path is output as the voltage similarity measurement value between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load.

[0088] Specifically, in step S502, the number of rows and columns of the matrix corresponds to the number of points in the two sequences, respectively. For each point in the voltage feature signal sequence, the distance between it and each point in the voltage feature sample sequence is calculated. This distance can be the Euclidean distance, the Manhattan distance, etc., depending on the application requirements. In the distance matrix, a path is found from the upper left corner (representing the beginning of the two sequences) to the lower right corner (representing the end of the two sequences). The cumulative distance on this path is the smallest. This path is not a straight line, it can be curved to find the best alignment of the two time series. In this embodiment, the time series is allowed to stretch and twist on the time axis to find the best match between the two sequences, which means that even if the two sequences are different in time (for example, one sequence may be faster or slower than the other sequence), the corresponding relationship between them can be found; along the found path, the sum of the distances in all cells on the path is calculated. This cumulative distance represents the similarity measure between the two sequences. The smaller the cumulative distance, the more similar the two sequences are.

[0089] The similarity measurement value of the electrical characteristics between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load includes a current similarity measurement value and a voltage similarity measurement value.

[0090] See also Figure 3 Another embodiment of the present invention further provides a DC fault arc state monitoring device, comprising:

[0091] A sampling module 1, used for sampling the electrical signal of the monitored load according to a preset sampling frequency to obtain an electrical signal sequence, wherein the electrical signal sequence includes a loop current signal sequence and an arc voltage signal sequence;

[0092] A preprocessing module 2, used for extracting a to-be-analyzed electrical signal sequence that matches a preset time window from the electrical signal sequence, wherein the to-be-analyzed electrical signal sequence includes a to-be-analyzed current signal sequence and a to-be-analyzed voltage signal sequence;

[0093] A feature extraction module 3, used for performing wavelet packet decomposition and reconstruction on the electrical signal sequence to be analyzed to obtain an electrical feature signal sequence, wherein the electrical feature signal sequence includes a current feature signal sequence and a voltage feature signal sequence;

[0094] A load type identification module 4 is used for, when the load type of the monitored load is unknown, performing similarity measurement on the electrical characteristic signal sequence and the electrical characteristic sample sequences corresponding to multiple preset types of loads to obtain similarity measurement values ​​of the electrical characteristics between the monitored load and the multiple preset types of loads, and inputting the similarity measurement values ​​of the electrical characteristics between the monitored load and the multiple preset types of loads into a pre-trained first deep belief network model to obtain the load type of the monitored load; wherein the electrical characteristic sample sequence corresponding to each preset type of load includes a current characteristic sample sequence and a voltage characteristic sample sequence when the preset type of load is running under various working conditions;

[0095] The DC fault identification module 5 is used to perform similarity measurement on the electrical characteristic signal sequence and the electrical characteristic sample sequence corresponding to the monitored load to obtain a similarity measurement value for fault analysis when the load type of the monitored load is known, and input the similarity measurement value for fault analysis into a pre-trained second deep confidence network model to obtain a DC fault arc state monitoring result of the monitored load; the DC fault arc state monitoring result is normal operation or the presence of a DC fault arc.

[0096] Furthermore, the feature extraction module 3 is further used to divide the current characteristic signal sequence and the voltage characteristic signal sequence into sub-segments of equal length after performing wavelet packet decomposition and reconstruction on the electrical signal sequence to be analyzed to obtain the electrical characteristic signal sequence, and then calculate the average value of each sub-segment to represent each sub-segment, thereby converting the current characteristic signal sequence and the voltage characteristic signal sequence into a low-dimensional signal sequence.

[0097] Furthermore, the load type identification module 4 is further used to:

[0098] After similarity measurement is performed on the current characteristic signal sequence and the current characteristic sample sequence corresponding to each preset type of load, a distance matrix is ​​created to store the distance of each pair of points between the current characteristic signal sequence and the current characteristic sample sequence currently undergoing similarity measurement, the distance between each point in the current characteristic signal sequence and each point in the current characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is searched in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the current characteristic signal sequence and each current characteristic sample sequence, and finally the cumulative distance on the path is output as the current similarity measurement value between the current characteristic signal sequence and the current characteristic sample sequence currently undergoing similarity measurement;

[0099] After similarity measurement is performed on the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to each preset type of load, a distance matrix is ​​created to store the distance of each group of point pairs between the voltage characteristic signal sequence and the voltage characteristic sample sequence currently undergoing similarity measurement, the distance between each point in the voltage characteristic signal sequence and each point in the voltage characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is searched in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the voltage characteristic signal sequence and each voltage characteristic sample sequence, and finally the cumulative distance on the path is output as the voltage similarity measurement value between the voltage characteristic signal sequence and the voltage characteristic sample sequence currently undergoing similarity measurement;

[0100] The similarity metrics of the electrical characteristics between the monitored load and the multiple preset types of loads each include a current similarity metric value and a voltage similarity metric value.

[0101] Furthermore, the DC fault identification module 5 is further used for:

[0102] After similarity measurement is performed on the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load, a distance matrix is ​​created to store the distance of each pair of points between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load, the distance between each point in the current characteristic signal sequence and each point in the current characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is found in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the current characteristic signal sequence and each current characteristic sample sequence, and finally the cumulative distance on the path is output as the current similarity measurement value between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load;

[0103] When measuring the similarity between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load, a distance matrix is ​​created to store the distance between each pair of points between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load, the distance between each point in the voltage characteristic signal sequence and each point in the voltage characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is found in the distance matrix so that the cumulative distance on the path is minimized. The path represents the best alignment between the voltage characteristic signal sequence and each voltage characteristic sample sequence. Finally, the cumulative distance on the path is output as the voltage similarity measurement value between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load.

[0104] The similarity metric value of the electrical characteristics between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load includes a current similarity metric value and a voltage similarity metric value.

[0105] It should be noted that the device of this embodiment corresponds to the method of the above embodiment. Therefore, the contents not described in detail in the device of this embodiment can be obtained by referring to the contents of the method of the above embodiment, and will not be repeated in this embodiment.

[0106] An embodiment of the present invention further provides a computer program product, including computer program instructions, wherein the computer program instructions instruct a computer device to perform operations corresponding to the above method.

[0107] Specifically, the computer program product includes a series of computer program instructions, which can instruct a computer device to execute the DC fault arc state monitoring method described in this application. These instructions are codes written in a computer program, which define how to perform specific operations. In this embodiment, these instructions are used to execute the DC fault arc state monitoring method of the above embodiment.

[0108] These program instructions are designed to be loaded onto a computer device and instruct the device to perform specific operations, which are the various steps in the DC fault arc state monitoring method described in the above embodiment.

[0109] In this way, the computer program product provides a complete software solution, which can be run on various computer devices to implement the DC fault arc state monitoring method of the above embodiment.

[0110] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A DC fault arc state monitoring method, characterized in that: The following steps are involved: Sampling the electrical signal of the monitored load according to a preset sampling frequency to obtain an electrical signal sequence, wherein the electrical signal sequence includes a loop current signal sequence and an arc voltage signal sequence; Extracting an electrical signal sequence to be analyzed that matches a preset time window from the electrical signal sequence, wherein the electrical signal sequence to be analyzed includes a current signal sequence and a voltage signal sequence to be analyzed; Performing wavelet packet decomposition and reconstruction on the electrical signal sequence to be analyzed to obtain an electrical characteristic signal sequence, wherein the electrical characteristic signal sequence includes a current characteristic signal sequence and a voltage characteristic signal sequence; When the load type of the monitored load is unknown, a similarity measurement is performed on the electrical characteristic signal sequence and the electrical characteristic sample sequences corresponding to multiple preset types of loads to obtain similarity measurement values ​​of the electrical characteristics between the monitored load and the multiple preset types of loads, and the similarity measurement values ​​of the electrical characteristics between the monitored load and the multiple preset types of loads are input into a pre-trained first deep belief network model to obtain the load type of the monitored load; wherein the electrical characteristic sample sequence corresponding to each preset type of load includes a current characteristic sample sequence and a voltage characteristic sample sequence when the preset type of load is running under various working conditions; When the load type of the monitored load is known, a similarity measurement is performed on the electrical characteristic signal sequence and the electrical characteristic sample sequence corresponding to the monitored load to obtain a similarity measurement value for fault analysis, and the similarity measurement value for fault analysis is input into a pre-trained second deep belief network model to obtain a DC fault arc state monitoring result of the monitored load; The DC fault arc state monitoring result is normal operation or the presence of a DC fault arc.

2. The DC fault arc state monitoring method according to claim 1, characterized in that: The method further comprises: After performing wavelet packet decomposition and reconstruction on the electrical signal sequence to be analyzed to obtain the electrical characteristic signal sequence, the current characteristic signal sequence and the voltage characteristic signal sequence are divided into sub-segments of equal length, and then the average value of each sub-segment is calculated to represent each sub-segment, thereby converting the current characteristic signal sequence and the voltage characteristic signal sequence into a low-dimensional signal sequence.

3. The DC fault arc state monitoring method according to claim 1, characterized in that: The performing similarity measurement on the electrical characteristic signal sequence and the electrical characteristic sample sequences corresponding to the multiple preset types of loads to obtain similarity measurement values ​​of the electrical characteristics between the monitored load and the multiple preset types of loads further includes: After similarity measurement is performed on the current characteristic signal sequence and the current characteristic sample sequence corresponding to each preset type of load, a distance matrix is ​​created to store the distance of each pair of points between the current characteristic signal sequence and the current characteristic sample sequence currently undergoing similarity measurement, the distance between each point in the current characteristic signal sequence and each point in the current characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is searched in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the current characteristic signal sequence and each current characteristic sample sequence, and finally the cumulative distance on the path is output as the current similarity measurement value between the current characteristic signal sequence and the current characteristic sample sequence currently undergoing similarity measurement; After similarity measurement is performed on the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to each preset type of load, a distance matrix is ​​created to store the distance of each group of point pairs between the voltage characteristic signal sequence and the voltage characteristic sample sequence currently undergoing similarity measurement, the distance between each point in the voltage characteristic signal sequence and each point in the voltage characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is searched in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the voltage characteristic signal sequence and each voltage characteristic sample sequence, and finally the cumulative distance on the path is output as the voltage similarity measurement value between the voltage characteristic signal sequence and the voltage characteristic sample sequence currently undergoing similarity measurement; The similarity metrics of the electrical characteristics between the monitored load and the multiple preset types of loads each include a current similarity metric value and a voltage similarity metric value.

4. The DC fault arc state monitoring method according to claim 1, characterized in that: The performing similarity measurement on the electrical characteristic signal sequence and the electrical characteristic sample sequence corresponding to the monitored load to obtain a similarity measurement value for fault analysis further includes: After similarity measurement is performed on the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load, a distance matrix is ​​created to store the distance of each pair of points between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load, the distance between each point in the current characteristic signal sequence and each point in the current characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is found in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the current characteristic signal sequence and each current characteristic sample sequence, and finally the cumulative distance on the path is output as the current similarity measurement value between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load; When measuring the similarity between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load, a distance matrix is ​​created to store the distance between each pair of points between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load, the distance between each point in the voltage characteristic signal sequence and each point in the voltage characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is found in the distance matrix so that the cumulative distance on the path is minimized. The path represents the best alignment between the voltage characteristic signal sequence and each voltage characteristic sample sequence. Finally, the cumulative distance on the path is output as the voltage similarity measurement value between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load. The similarity metric value of the electrical characteristics between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load includes a current similarity metric value and a voltage similarity metric value.

5. A DC fault arc state monitoring device, characterized in that: include: A sampling module, used for sampling the electrical signal of the monitored load according to a preset sampling frequency to obtain an electrical signal sequence, wherein the electrical signal sequence includes a loop current signal sequence and an arc voltage signal sequence; A preprocessing module, used for extracting a to-be-analyzed electrical signal sequence that matches a preset time window from the electrical signal sequence, wherein the to-be-analyzed electrical signal sequence includes a to-be-analyzed current signal sequence and a to-be-analyzed voltage signal sequence; A feature extraction module, used for performing wavelet packet decomposition and reconstruction on the electrical signal sequence to be analyzed to obtain an electrical feature signal sequence, wherein the electrical feature signal sequence includes a current feature signal sequence and a voltage feature signal sequence; A load type identification module, for, when the load type of the monitored load is unknown, performing similarity measurement on the electrical characteristic signal sequence and electrical characteristic sample sequences corresponding to multiple preset types of loads to obtain similarity measurement values ​​of the electrical characteristics between the monitored load and the multiple preset types of loads, and inputting the similarity measurement values ​​of the electrical characteristics between the monitored load and the multiple preset types of loads into a pre-trained first deep belief network model to obtain the load type of the monitored load; wherein the electrical characteristic sample sequence corresponding to each preset type of load includes a current characteristic sample sequence and a voltage characteristic sample sequence when the preset type of load is running under various working conditions; A DC fault identification module is used to perform similarity measurement on the electrical characteristic signal sequence and the electrical characteristic sample sequence corresponding to the monitored load to obtain a similarity measurement value for fault analysis when the load type of the monitored load is known, and input the similarity measurement value for fault analysis into a pre-trained second deep confidence network model to obtain a DC fault arc state monitoring result of the monitored load; the DC fault arc state monitoring result is normal operation or the presence of a DC fault arc.

6. The DC fault arc state monitoring device according to claim 5, characterized in that: The feature extraction module is further used to divide the current characteristic signal sequence and the voltage characteristic signal sequence into sub-segments of equal length after performing wavelet packet decomposition and reconstruction on the electrical signal sequence to be analyzed to obtain the electrical characteristic signal sequence, and then calculate the average value of each sub-segment to represent each sub-segment, thereby converting the current characteristic signal sequence and the voltage characteristic signal sequence into a low-dimensional signal sequence.

7. The DC fault arc state monitoring device according to claim 5, characterized in that: The load type identification module is further used to: After similarity measurement is performed on the current characteristic signal sequence and the current characteristic sample sequence corresponding to each preset type of load, a distance matrix is ​​created to store the distance of each pair of points between the current characteristic signal sequence and the current characteristic sample sequence currently undergoing similarity measurement, the distance between each point in the current characteristic signal sequence and each point in the current characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is searched in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the current characteristic signal sequence and each current characteristic sample sequence, and finally the cumulative distance on the path is output as the current similarity measurement value between the current characteristic signal sequence and the current characteristic sample sequence currently undergoing similarity measurement; After similarity measurement is performed on the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to each preset type of load, a distance matrix is ​​created to store the distance of each group of point pairs between the voltage characteristic signal sequence and the voltage characteristic sample sequence currently undergoing similarity measurement, the distance between each point in the voltage characteristic signal sequence and each point in the voltage characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is searched in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the voltage characteristic signal sequence and each voltage characteristic sample sequence, and finally the cumulative distance on the path is output as the voltage similarity measurement value between the voltage characteristic signal sequence and the voltage characteristic sample sequence currently undergoing similarity measurement; The similarity metrics of the electrical characteristics between the monitored load and the multiple preset types of loads each include a current similarity metric value and a voltage similarity metric value.

8. The DC fault arc state monitoring device according to claim 5, characterized in that: The DC fault identification module is further used for: After similarity measurement is performed on the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load, a distance matrix is ​​created to store the distance of each pair of points between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load, the distance between each point in the current characteristic signal sequence and each point in the current characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is found in the distance matrix so that the cumulative distance on the path is minimized, and the path represents the best alignment between the current characteristic signal sequence and each current characteristic sample sequence, and finally the cumulative distance on the path is output as the current similarity measurement value between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load; When measuring the similarity between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load, a distance matrix is ​​created to store the distance between each pair of points between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load, the distance between each point in the voltage characteristic signal sequence and each point in the voltage characteristic sample sequence is calculated, and a path from the upper left corner to the lower right corner is found in the distance matrix so that the cumulative distance on the path is minimized. The path represents the best alignment between the voltage characteristic signal sequence and each voltage characteristic sample sequence. Finally, the cumulative distance on the path is output as the voltage similarity measurement value between the voltage characteristic signal sequence and the voltage characteristic sample sequence corresponding to the monitored load. The similarity metric value of the electrical characteristics between the current characteristic signal sequence and the current characteristic sample sequence corresponding to the monitored load includes a current similarity metric value and a voltage similarity metric value.

9. A computer program product, characterized in that The method comprises computer program instructions, wherein the computer program instructions instruct a computer device to execute operations corresponding to the method according to any one of claims 1 to 4.

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