Foreign matter external damage fault identification method, device and equipment and storage medium

Abstract

The invention discloses a foreign matter external damage fault identification method, device and equipment and a storage medium. The method comprises the following steps: acquiring fault data of a power transmission line; if it is determined that the fault key factors in the fault factor library exist in the fault data, it is determined that the power transmission line has a foreign matter external damage fault; otherwise, analyzing the fault data based on a fault factor library and a pre-trained foreign matter external damage identification model to obtain a fault identification result of the power transmission line, wherein the fault factor library comprises a fault key factor, a fault coupling factor and a fault removal factor. According to the method, the fault data in the power transmission line is analyzed, so that the accuracy of identifying the foreign matter external damage fault can be improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of fault type identification, and in particular, to a method, device, equipment and storage medium for identifying a foreign object damage fault. Background Art

[0002] As the "blood vessels" of the power system, the stable operation of transmission lines is crucial to ensuring the safety of the power grid. In recent years, with the acceleration of urbanization and changes in the natural environment, foreign object damage failures on transmission lines have occurred frequently, which not only threatens the stability of power supply, but also may cause significant economic losses and even casualties. Therefore, the research on foreign object damage fault diagnosis technology for transmission lines is of extremely important practical significance.

[0003] Although a variety of technical means have been applied to the fault diagnosis of foreign objects breaking power lines, there are still some problems and shortcomings. Existing diagnostic methods often rely on manual inspections, which are inefficient and easily affected by human factors. Although some advanced diagnostic technologies such as drone inspections and infrared thermal imaging have improved the accuracy and efficiency of detection, the equipment is expensive and has limited popularity. In addition, for tiny foreign objects or highly concealed faults in complex environments, it is difficult for existing technologies to achieve accurate identification, which can easily lead to missed detections or misjudgments. Summary of the invention

[0004] The present invention provides a foreign object external damage fault identification method, device, equipment and storage medium to solve the problem that the foreign object external damage fault in the power transmission line cannot be accurately identified in the prior art.

[0005] According to one aspect of the present invention, a method for identifying a foreign body damage fault is provided, the method comprising:

[0006] Obtaining fault data of transmission lines;

[0007] If it is determined that the fault data contains a key fault factor in the fault factor library, it is determined that the transmission line has a foreign object external damage fault; otherwise, the fault data is analyzed based on the fault factor library and a pre-trained foreign object external damage recognition model to obtain a fault recognition result of the transmission line;

[0008] The fault factor library includes fault critical factors, fault coupling factors and fault elimination factors.

[0009] According to another aspect of the present invention, a foreign body external damage fault identification device is provided, the device comprising:

[0010] An acquisition module, used for acquiring fault data of a transmission line;

[0011] A determination module, configured to determine that a foreign object external damage fault exists in the power transmission line if it is determined that the fault data contains a key fault factor in a fault factor library; otherwise, the fault data is analyzed based on the fault factor library and a pre-trained foreign object external damage recognition model to obtain a fault recognition result of the transmission line;

[0012] The fault factor library includes fault critical factors, fault coupling factors and fault elimination factors.

[0013] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising: at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the foreign object external damage fault identification method described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the foreign object external damage fault identification method described in any embodiment of the present invention when executed.

[0017] A method, device, equipment and storage medium for identifying a foreign object external damage fault according to an embodiment of the present invention, the method comprises: obtaining fault data of a transmission line; if it is determined that a critical fault factor in a fault factor library exists in the fault data, then determining that a foreign object external damage fault exists in the transmission line; otherwise, analyzing the fault data based on the fault factor library and a pre-trained foreign object external damage identification model to obtain a fault identification result of the transmission line; wherein the fault factor library includes critical fault factors, fault coupling factors and fault elimination factors. The method can improve the accuracy of identifying foreign object external damage faults by analyzing the fault data in the transmission line, solving the problem that the prior art cannot accurately identify foreign object external damage faults in the transmission line.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A schematic diagram of a flow chart of a method for identifying a foreign body external damage fault provided in the first embodiment of the present invention;

[0021] Figure 2 A schematic flow chart of a method for identifying a foreign body external damage fault provided in the second embodiment of the present invention;

[0022] Figure 3 A schematic flow chart of a method for identifying a foreign body external damage fault provided by an embodiment of the present invention;

[0023] Figure 4 A schematic diagram of the structure of a foreign body damage fault identification device provided in Embodiment 3 of the present invention;

[0024] Figure 5 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only an embodiment of a part of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work should belong to the scope of protection of the present invention. It should be understood that the various steps recorded in the method implementation of the present invention can be performed in different orders and / or in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0026] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, any variations of the terms "including" and "having" are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0029] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

[0030] Embodiment 1

[0031] Figure 1 A flow chart of a foreign object breakage fault identification method provided in Embodiment 1 of the present invention is applicable to identifying foreign object breakage faults in power transmission lines. The method can be performed by a foreign object breakage fault identification device, wherein the device can be implemented by software and / or hardware and is generally integrated on an electronic device. In this embodiment, the electronic device includes but is not limited to: computers and other devices.

[0032] like Figure 1 As shown, a foreign body damage fault identification method provided in Embodiment 1 of the present invention includes the following steps:

[0033] S110: Acquire fault data of the power transmission line.

[0034] The transmission line may be a power transmission system connecting a power plant, a substation and a user. The fault data may be a transmission line. The fault data may include basic fault information, electrical quantity data, environmental data and equipment status data of the transmission line.

[0035] In this embodiment, the fault data of the power transmission line may be obtained, and this embodiment does not limit what data is included in the fault data.

[0036] S120. If it is determined that the fault data contains a key fault factor in the fault factor library, it is determined that a foreign object damage fault exists in the transmission line; otherwise, the fault data is analyzed based on the fault factor library and a pre-trained foreign object damage identification model to obtain a fault identification result of the transmission line.

[0037] The fault factor library includes fault critical factors, fault coupling factors and fault elimination factors.

[0038] The fault factor library may be a pre-built database of factors related to whether a fault occurs. The critical fault factor may be understood as a factor that will definitely cause a transmission line fault.

[0039] In this embodiment, the fault data can be analyzed. If it is determined that the fault data contains the key fault factors in the fault factor library, it can be considered that the transmission line has a foreign object damage fault and no subsequent judgment is required. Otherwise, it is necessary to continue to analyze the fault data based on the fault factor library and the pre-trained foreign object damage recognition model to obtain the fault recognition result of the transmission line.

[0040] A method for identifying a foreign object damage fault provided in a first embodiment of the present invention includes: acquiring fault data of a transmission line; if it is determined that a critical fault factor in a fault factor library exists in the fault data, determining that a foreign object damage fault exists in the transmission line; otherwise, analyzing the fault data based on the fault factor library and a pre-trained foreign object damage identification model to obtain a fault identification result of the transmission line; wherein the fault factor library includes critical fault factors, fault coupling factors, and fault elimination factors. This method can improve the accuracy of identifying foreign object damage faults by analyzing the fault data in the transmission line, solving the problem that the prior art cannot accurately identify foreign object damage faults in the transmission line.

[0041] Based on the above embodiment, a variant embodiment of the above embodiment is proposed. It should be noted that in order to make the description concise, only the differences from the above embodiment are described in the variant embodiment.

[0042] In one embodiment, the critical fault factors include at least line image information and meteorological disaster warnings; accordingly, determining whether the critical fault factors in the fault factor library exist in the fault data includes: determining whether there are foreign objects hanging on the transmission line based on the line image information in the fault data, and whether there is a meteorological disaster warning within a preset distance of the transmission line; if so, it is considered that the fault data includes the critical fault factors in the fault factor library.

[0043] The line image information may be a line image collected by a ground monitoring camera. The meteorological disaster warning may be a disaster warning such as a rainstorm, a typhoon, a gale, a heavy snow, an earthquake, or a severe convection, which is not limited in this embodiment. The preset distance may be set according to actual conditions, such as within 8 kilometers of the transmission line.

[0044] In this embodiment, the line image information in the fault data can be analyzed to determine whether there is a foreign object hanging on the transmission line, and whether there is a meteorological disaster warning within a preset distance of the transmission line; if so, it can be considered that the fault data includes all the key fault factors, and it is determined that there is a foreign object external damage fault in the transmission line; if the fault data only includes one key fault factor, it is not directly considered that there is a foreign object external damage fault in the transmission line. For example, if the fault data includes an image of the transmission line, it can be determined by the image whether there is a foreign object hanging on the transmission line, and the environmental data around the transmission line can be obtained from the fault data, and the presence of a meteorological disaster warning can be determined by the environmental data.

[0045] In one embodiment, the training of the foreign body external damage recognition model includes:

[0046] Establish a fault transient waveform data set based on historical fault data of the transmission line;

[0047] Extracting characteristic values ​​of each fault transient waveform in the fault transient waveform data set;

[0048] Based on the eigenvalues, the model is trained using a traditional binary classification method, a deep learning method combined with a synthetic minority oversampling technique to obtain a trained foreign body external damage recognition model.

[0049] Among them, the historical fault data can be the fault data that has occurred in the transmission line. The fault transient waveform data set can include multiple fault transient waveforms. The fault transient waveform refers to the waveform of certain physical quantities (such as voltage, current, power, etc.) in the system that change with time at the moment of the fault of the power system, electronic equipment or other physical system and in the short time thereafter. The characteristic value can be a feature extracted from the fault transient waveform. The traditional binary classification method can be a technology that divides data into two different categories based on statistical learning theory or machine learning algorithm. Common ones include logistic regression, support vector machine, decision tree, etc. The deep learning method can be a type of machine learning technology based on artificial neural network. By constructing a neural network model containing multiple hidden layers, the computer automatically learns features and patterns from a large amount of data to achieve tasks such as data classification and prediction. The synthetic minority over-sampling technique (Synthetic Minority Over-sampling Technique, SMOTE) can be used to solve the problem of too few minority class samples in the data set. By analyzing the minority class samples, new minority class samples are generated in the feature space, the number of minority class samples is increased, and the category distribution of the data set is more balanced, reducing the impact of data imbalance on model training.

[0050] In this embodiment, a fault transient waveform data set can be established based on the fault data of historical lines. For example, the total number of fault traveling waves is 3415, of which 994 are lightning strikes and 2421 are non-lightning strikes. Feature extraction is performed on the fault transient waveform, and classification and identification research is performed through traditional binary classification methods, deep learning methods and combined SMOTE methods. For unbalanced sample data, a fault waveform foreign body external damage identification model based on deep forest and SMOTE algorithms is proposed.

[0051] In one embodiment, extracting the characteristic value of each fault transient waveform in the fault transient waveform data set includes:

[0052] For each fault transient waveform in the fault transient waveform data set, extract time-frequency domain features to obtain characteristic values;

[0053] The characteristic values ​​include 16 time domain features, 13 frequency domain features, 6 empirical wavelet transform (EWF) component information entropies, weighted energy entropy, EWF4 sample entropy and EWF1 root mean square value.

[0054] Among them, the time-frequency domain feature can be a feature quantity used to describe the characteristics of the signal in two dimensions of time and frequency. The Empirical Wavelet Transform (EWF) component information entropy can be a measure of uncertainty based on the information contained in each component after EWF decomposition. The weighted energy entropy can be an entropy measurement method that combines the concepts of weight and energy. The weighted energy entropy combines the two factors of weight and energy, and is used to measure the uniformity of energy distribution in the system and the influence of weight on this distribution, reflecting the overall uncertainty and order of the system. The EWF4 sample entropy is the sample entropy calculated based on the fourth component after EWF transformation. The sample entropy is an indicator used to measure the complexity of a time series. It evaluates the randomness and complexity of a sequence by calculating the probability of repetition of a pattern in the sequence. The EWF1 root mean square value is the root mean square value calculated for the first component after EWF decomposition. The root mean square value refers to the value obtained by dividing the square root of a set of data by the number of data.

[0055] In this embodiment, time-frequency domain feature extraction can be performed on the fault transient waveform to obtain a total of 38 characteristic values ​​including 16 time domain features, 13 frequency domain features, 6 EWF component information entropies, weighted energy entropy, EWF4 sample entropy and EWF1 root mean square value.

[0056] Embodiment 2

[0057] Figure 2 This is a flow chart of a method for identifying a foreign body external damage fault provided by the second embodiment of the present invention. The second embodiment is optimized on the basis of the above embodiments. For details not yet provided in this embodiment, please refer to the first embodiment.

[0058] like Figure 2 As shown, a foreign body damage fault identification method provided by the second embodiment of the present invention includes the following steps:

[0059] S210: Acquire fault data of the power transmission line.

[0060] S220: If it is determined that the fault data contains a key fault factor in a fault factor library, it is determined that a foreign object external damage fault exists in the transmission line.

[0061] S230: Otherwise, the fault data is input into a pre-trained foreign body damage recognition model.

[0062] In this embodiment, the fault data may be input into a pre-trained foreign object damage recognition model, and the fault recognition result of the power transmission line may be determined by the foreign object damage recognition model.

[0063] S240: Determine a first probability of a foreign object damage fault on the transmission line based on the fault data, a fault coupling factor and a fault elimination factor in a fault factor library.

[0064] In this embodiment, if the fault data does not include all the key fault factors in the fault factor library, the first probability of the transmission line foreign body damage fault can be calculated based on the fault data, the fault coupling factor and the fault elimination factor in the fault factor library.

[0065] In one embodiment, determining the first probability of the transmission line foreign body damage fault according to the fault data, the fault coupling factor and the fault elimination factor in the fault factor library includes:

[0066] When the fault data contains a fault coupling factor in the fault factor library, calculating a third probability of a transmission line foreign body external damage fault based on weights corresponding to each fault coupling factor in the fault data;

[0067] When the fault data contains a fault elimination factor in a fault factor library, determining a first probability of the transmission line foreign body external damage fault based on a weight corresponding to the fault elimination factor in the fault data and the third probability;

[0068] Among them, the fault coupling factor is a factor that can cause a foreign object external damage fault in the transmission line, and the fault elimination factor is a factor that will not cause a foreign object external damage fault in the transmission line.

[0069] In this embodiment, if there is a fault coupling factor in the fault data, the third probability of the transmission line foreign object external damage fault can be calculated based on the weight corresponding to the fault coupling factor in the fault data; if there is a fault exclusion factor in the fault data, the first probability of the transmission line foreign object external damage fault is determined by the weight corresponding to the fault exclusion factor and the third probability.

[0070] Exemplarily, more than 90 kinds of correlation factors related to the fault type are sorted out, and a typical fault factor library is established for the foreign body external damage fault type, and the correlation factors are divided into key factors, coupling factors and exclusion factors of the fault, and the weight of each factor is determined. For example, the fault coupling factor can at least include the presence of foreign objects hanging on the transmission line (80%), disaster warning: typhoon, strong wind, strong convection (30%), micro-meteorological wind speed reaches 15m / s (20%), phase-to-phase fault and no mountain fire warning, no ice warning (60%), metallic grounding fault (15%), and the presence of a greenhouse or dust net under the hidden danger line (15%), etc., where the value in the brackets is the weight corresponding to the factor. If the fault data includes the fault coupling factors "there are foreign objects hanging on the transmission line", "micro-meteorological wind speed reaches 15m / s" and "there are greenhouses or dust nets under the hidden danger line", the third probability of the fault data is 80%+20%+15%=115%.

[0071] S250. Extract characteristic values ​​of the fault transient waveform from the fault data, and determine a second probability of the transmission line foreign object damage fault based on the characteristic values.

[0072] In this embodiment, the second probability can be obtained by identifying a fault transient waveform model, and the fault transient waveform model can be a model built into the foreign object damage identification model. Specifically, the second probability of the transmission line foreign object damage fault can be obtained by extracting the characteristic value of the fault transient waveform and processing the characteristic value.

[0073] S260. Determine a probability of a foreign object damage failure of the transmission line based on the first probability and the second probability.

[0074] In this embodiment, the probability of a foreign object damage failure of the transmission line may be calculated using the first probability and the second probability, for example, by a weighted method.

[0075] S270. Determine a fault identification result of the transmission line according to the probability.

[0076] In this embodiment, the probability may be analyzed to determine the fault identification result of the power transmission line. For example, if the probability is greater than a preset threshold, it is considered that the fault identification result is that a foreign object damage fault has been identified.

[0077] A method for identifying a foreign object damage fault provided in the second embodiment of the present invention specifically analyzes the fault data based on a fault factor library and a pre-trained foreign object damage identification model to obtain a fault identification result of the transmission line, including: inputting the fault data into a pre-trained foreign object damage identification model; determining a first probability of the foreign object damage fault of the transmission line according to the fault data, the fault coupling factor and the fault elimination factor in the fault factor library; extracting characteristic values ​​of the fault transient waveform from the fault data, and determining a second probability of the foreign object damage fault of the transmission line based on the characteristic values; determining the probability of the foreign object damage fault of the transmission line based on the first probability and the second probability; and determining the fault identification result of the transmission line according to the probability. This method can improve the accuracy of identifying foreign object damage faults by analyzing the fault data in the transmission line, and solves the problem that the foreign object damage fault in the transmission line cannot be accurately identified in the prior art.

[0078] In one embodiment, determining the first probability of the transmission line foreign object damage fault based on the weight corresponding to the fault elimination factor in the fault data and the third probability includes:

[0079] Obtaining weights corresponding to all fault elimination factors in the fault data, wherein the weights corresponding to the fault elimination factors are negative weights;

[0080] The sum of the weight corresponding to the fault elimination factor and the third probability is used as the first probability of the foreign object external damage fault of the transmission line.

[0081] In this embodiment, the weight corresponding to the fault exclusion factor may be a negative weight. After determining that there is a fault exclusion factor in the fault data, the weight corresponding to the fault exclusion factor may be obtained, and the weight corresponding to the fault exclusion factor may be added to the third probability to obtain the first probability of the transmission line foreign body external damage fault. For example, if there is a fault exclusion factor in the fault data, and the weight of the fault exclusion factor is -30%, the third probability may be added to -30% to obtain the first probability.

[0082] For example, Figure 3 A flow chart of a method for identifying a foreign body damage fault provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the fault coupling factor can include not only the foreign body external damage regularity factor but also the manually entered fault factor, among which the fault exclusion factor is the foreign body external damage negation factor.

[0083] The foreign object external breakage fault identification method of the present embodiment integrates the fault waveform characteristics, reclosing, meteorology, fault phase, terrain environment, fault time and season, historical faults, hidden dangers and other characteristics, and diagnoses the fault in combination with the fault transient traveling wave characteristics. By collecting historical fault information, a line fault transient waveform database is established, and 38 characteristic parameters of the fault traveling wave are extracted from the time domain, frequency domain and time-frequency domain. A foreign object external breakage fault identification model is established to obtain the foreign object external breakage fault waveform characteristic data. Combined with the foreign object external breakage correlation factor of the similar fault type, the cause of the line fault can be accurately analyzed and located.

[0084] Embodiment 3

[0085] Figure 4 This is a schematic diagram of the structure of a foreign object breakage fault identification device provided in Embodiment 3 of the present invention. The device can be used to identify foreign object breakage faults in power transmission lines, wherein the device can be implemented by software and / or hardware and is generally integrated on an electronic device.

[0086] like Figure 4 As shown, the device comprises:

[0087] An acquisition module 310 is used to acquire fault data of a power transmission line;

[0088] The determination module 320 is used to determine that a foreign object external damage fault exists in the power transmission line if it is determined that the fault data contains a key fault factor in the fault factor library; otherwise, the fault data is analyzed based on the fault factor library and a pre-trained foreign object external damage recognition model to obtain a fault recognition result of the power transmission line;

[0089] The fault factor library includes fault critical factors, fault coupling factors and fault elimination factors.

[0090] This embodiment provides a device for identifying a foreign object external damage fault, including: an acquisition module for acquiring fault data of a transmission line; a determination module for determining that a foreign object external damage fault exists in the transmission line if it is determined that the fault data contains a key fault factor in a fault factor library; otherwise, the fault data is analyzed based on the fault factor library and a pre-trained foreign object external damage identification model to obtain a fault identification result of the transmission line; wherein the fault factor library includes a key fault factor, a fault coupling factor, and a fault elimination factor. By analyzing the fault data in the transmission line, the accuracy of identifying a foreign object external damage fault can be improved, solving the problem that the prior art cannot accurately identify a foreign object external damage fault in the transmission line.

[0091] Furthermore, the critical fault factors include at least line image information and meteorological disaster warning; accordingly, the determination module 320 is specifically used to:

[0092] Determine whether there is a foreign object hanging on the transmission line based on the line image information in the fault data, and whether there is a meteorological disaster warning within a preset distance of the transmission line;

[0093] If so, it is considered that the fault data includes the key fault factor in the fault factor library.

[0094] Furthermore, the determination module 320 is specifically configured to:

[0095] Inputting the fault data into a pre-trained foreign body external damage recognition model;

[0096] Determine a first probability of a foreign body external damage fault of the transmission line according to the fault data, a fault coupling factor and a fault elimination factor in a fault factor library;

[0097] Extracting a characteristic value of a fault transient waveform from the fault data, and determining a second probability of a foreign object damage fault of the transmission line based on the characteristic value;

[0098] Determine a probability of a foreign object failure of the transmission line based on the first probability and the second probability;

[0099] A fault identification result of the power transmission line is determined according to the probability.

[0100] Further, the determining of the first probability of the transmission line foreign body damage fault according to the fault data, the fault coupling factor and the fault elimination factor in the fault factor library includes:

[0101] When the fault data contains a fault coupling factor in the fault factor library, calculating a third probability of a transmission line foreign body external damage fault based on weights corresponding to each fault coupling factor in the fault data;

[0102] When the fault data contains a fault elimination factor in a fault factor library, determining a first probability of the transmission line foreign body external damage fault based on a weight corresponding to the fault elimination factor in the fault data and the third probability;

[0103] Among them, the fault coupling factor is a factor that can cause a foreign object external damage fault in the transmission line, and the fault elimination factor is a factor that will not cause a foreign object external damage fault in the transmission line.

[0104] Further, the determining of the first probability of the transmission line foreign body damage fault based on the weight corresponding to the fault elimination factor in the fault data and the third probability includes:

[0105] Obtaining weights corresponding to all fault elimination factors in the fault data, wherein the weights corresponding to the fault elimination factors are negative weights;

[0106] The sum of the weight corresponding to the fault elimination factor and the third probability is used as the first probability of the foreign object external damage fault of the transmission line.

[0107] Furthermore, the training of the foreign body external damage recognition model includes:

[0108] Establish a fault transient waveform data set based on historical fault data of the transmission line;

[0109] Extracting characteristic values ​​of each fault transient waveform in the fault transient waveform data set;

[0110] Based on the eigenvalues, the model is trained using a traditional binary classification method, a deep learning method combined with a synthetic minority oversampling technique to obtain a trained foreign body external damage recognition model.

[0111] Further, the extracting the characteristic value of each fault transient waveform in the fault transient waveform data set includes:

[0112] For each fault transient waveform in the fault transient waveform data set, extracting time-frequency domain features to obtain characteristic values;

[0113] The characteristic values ​​include 16 time domain features, 13 frequency domain features, 6 empirical wavelet transform (EWF) component information entropies, weighted energy entropy, EWF4 sample entropy and EWF1 root mean square value.

[0114] The above-mentioned foreign object external damage fault identification device can execute the foreign object external damage fault identification method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0115] Embodiment 4

[0116] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0117] like Figure 5As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0118] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0119] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a foreign object external damage fault identification method.

[0120] In some embodiments, the foreign object external damage fault identification method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the foreign object external damage fault identification method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the foreign object external damage fault identification method in any other appropriate manner (for example, by means of firmware).

[0121] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0122] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0123] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0124] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0125] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0126] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0127] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0128] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for identifying a foreign body external damage fault, characterized in that: The method comprises: Obtaining fault data of transmission lines; If it is determined that the fault data contains a key fault factor in the fault factor library, it is determined that the transmission line has a foreign object damage fault; otherwise, the fault data is analyzed based on the fault factor library and a pre-trained foreign object damage recognition model to obtain a fault recognition result of the transmission line; The fault factor library includes fault critical factors, fault coupling factors and fault elimination factors.

2. The method according to claim 1, characterized in that The critical fault factors include at least line image information and meteorological disaster warning; accordingly, determining that the critical fault factors in the fault factor library exist in the fault data includes: Determine whether there is a foreign object hanging on the transmission line based on the line image information in the fault data, and whether there is a meteorological disaster warning within a preset distance of the transmission line; If so, it is considered that the fault data includes the key fault factor in the fault factor library.

3. The method according to claim 1, characterized in that The fault data is analyzed based on the fault factor library and the pre-trained foreign body damage recognition model to obtain the fault recognition result of the transmission line, including: Inputting the fault data into a pre-trained foreign body external damage recognition model; Determine a first probability of a foreign body external damage fault of the transmission line according to the fault data, a fault coupling factor and a fault elimination factor in a fault factor library; Extracting a characteristic value of a fault transient waveform from the fault data, and determining a second probability of a foreign object damage fault of the transmission line based on the characteristic value; Determine a probability of a foreign object failure of the transmission line based on the first probability and the second probability; A fault identification result of the power transmission line is determined according to the probability.

4. The method according to claim 3, characterized in that The determining, according to the fault data, the fault coupling factor and the fault elimination factor in the fault factor library, a first probability of the transmission line foreign body external damage fault comprises: When the fault coupling factors in the fault factor library exist in the fault data, a third probability of a transmission line foreign body external damage fault is calculated based on the weights corresponding to the fault coupling factors in the fault data; When the fault data contains a fault elimination factor in a fault factor library, determining a first probability of the transmission line foreign body external damage fault based on a weight corresponding to the fault elimination factor in the fault data and the third probability; Among them, the fault coupling factor is a factor that can cause a foreign object external damage fault in the transmission line, and the fault elimination factor is a factor that will not cause a foreign object external damage fault in the transmission line.

5. The method according to claim 4, characterized in that The determining, based on the weight corresponding to the fault elimination factor in the fault data and the third probability, the first probability of the transmission line foreign object external damage fault comprises: Obtaining weights corresponding to all fault elimination factors in the fault data, wherein the weights corresponding to the fault elimination factors are negative weights; The sum of the weight corresponding to the fault elimination factor and the third probability is used as the first probability of the foreign object external damage fault of the transmission line.

6. The method according to claim 1, characterized in that The training of the foreign body external damage recognition model includes: Establish a fault transient waveform data set based on historical fault data of the transmission line; Extracting characteristic values ​​of each fault transient waveform in the fault transient waveform data set; Based on the eigenvalues, the model is trained using a traditional binary classification method, a deep learning method combined with a synthetic minority oversampling technique to obtain a trained foreign body external damage recognition model.

7. The method according to claim 6, characterized in that The extracting the characteristic value of each fault transient waveform in the fault transient waveform data set includes: For each fault transient waveform in the fault transient waveform data set, extracting time-frequency domain features to obtain characteristic values; The characteristic values ​​include 16 time domain features, 13 frequency domain features, 6 empirical wavelet transform (EWF) component information entropies, weighted energy entropy, EWF4 sample entropy and EWF1 root mean square value.

8. A device for identifying a foreign body external damage fault, characterized in that: The device comprises: An acquisition module, used for acquiring fault data of a transmission line; A determination module, configured to determine that a foreign object external damage fault exists in the power transmission line if it is determined that the fault data contains a key fault factor in a fault factor library; otherwise, the fault data is analyzed based on the fault factor library and a pre-trained foreign object external damage recognition model to obtain a fault recognition result of the transmission line; The fault factor library includes fault critical factors, fault coupling factors and fault elimination factors.

9. An electronic device, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the foreign object external damage fault identification method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the foreign object external damage fault identification method according to any one of claims 1 to 7 when executed.

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