Fault monitoring method and device for power transmission line, medium and equipment

Abstract

The invention discloses a fault monitoring method and device for a power transmission line, a medium and equipment, and the method comprises the steps: collecting current and electric field data in real time, calculating the root-mean-square value of the current and electric field data, and calling a calculation model to generate a similarity vector of the current and electric field when the root-mean-square value continuously decreases to be greater than or equal to a preset threshold value; then, the similarity confidence coefficient is calculated by matching the degrees of the two similarity vectors; and if the confidence coefficient is greater than or equal to a preset threshold value, judging that the line has a fault. According to the method, the change of the current and electric field signals is comprehensively analyzed, so that faults can be more accurately detected, false alarms are reduced, and the accuracy and efficiency of fault identification are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission lines, and in particular to a method, device, medium and equipment for fault monitoring of power transmission lines. Background Art

[0002] Transmission lines face an increasingly severe threat of wildfires, including frequent natural wildfires caused by climate change, fires caused by grid failures and improper human operations, and changes in the external environment that exacerbate fire risks. These factors may lead to line damage, large-scale power outages and safety hazards, forming a vicious cycle of fire and line failure. However, existing wildfire monitoring technologies have shortcomings such as untimely monitoring, low accuracy and lack of intelligent analysis methods, and cannot effectively respond to these threats. Summary of the invention

[0003] Based on this, it is necessary to provide a fault monitoring method, device, medium and equipment for transmission lines to solve the problems of existing wildfire monitoring technology such as untimely monitoring, low accuracy and lack of intelligent analysis means.

[0004] A method for monitoring a fault of a power transmission line, the method comprising:

[0005] Collecting the current value and electric field value of the transmission line in real time, calculating a plurality of first root mean square values ​​of the current value and a plurality of second root mean square values ​​of the electric field value within a preset number of cycles;

[0006] If the multiple first root mean square values ​​continue to decrease and the maximum decrease amplitude is greater than or equal to the preset first decrease amplitude, and the multiple second root mean square values ​​continue to decrease and the maximum decrease amplitude is greater than or equal to the preset second decrease amplitude, then the current calculation model is called to calculate the current similarity vector according to the multiple first root mean square values, and the electric field calculation model is called to calculate the electric field similarity vector according to the multiple second root mean square values;

[0007] A similarity confidence is calculated according to the matching degree between the current similarity vector and the electric field similarity vector. If the similarity confidence is greater than or equal to a preset similarity threshold, it is determined that a fault currently exists in the power transmission line.

[0008] In one embodiment, the method further comprises:

[0009] Collect the acceleration value of the transmission line in real time, and calculate multiple third root mean square values ​​of the current value within a preset number of cycles;

[0010] Calling the acceleration calculation model to calculate the acceleration similarity vector according to the plurality of third root mean square values;

[0011] If a first distance between the acceleration similarity vector and the current similarity vector, and a second distance between the acceleration similarity vector and the electric field similarity vector are both smaller than a preset distance threshold, a preset floating confidence is added based on the calculated similarity confidence.

[0012] In one embodiment, the method further comprises:

[0013] Segmenting the current value and the electric field value to obtain a plurality of electrical segments;

[0014] The total harmonic distortion and the root mean square value of each electrical segment are calculated, and among the plurality of electrical segments, electrical segments having a total harmonic distortion greater than or equal to a preset first critical value and / or a root mean square value greater than or equal to a preset second critical value are eliminated.

[0015] In one embodiment, the method further comprises:

[0016] Obtain sample electric field data and sample current data as a training set; wherein each data is annotated with a sample label;

[0017] Extracting the electric field rise rate and the electric field drop rate as electric field features from the training set, and extracting the current rise rate, the current drop rate and the surge current as current features;

[0018] Inputting the electric field feature into a first classifier based on a decision tree, and inputting the current feature into a second classifier based on a decision tree, to obtain an output prediction label;

[0019] The first classifier and the second classifier are adjusted according to the difference between the sample label and the predicted label until the first classifier and the second classifier converge, and the first classifier is used as an electric field calculation model and the second classifier is used as a current calculation model.

[0020] A fault monitoring device, comprising:

[0021] A root mean square value calculation module, used for collecting the current value and the electric field value of the transmission line in real time, and calculating a plurality of first root mean square values ​​of the current value and a plurality of second root mean square values ​​of the electric field value within a preset number of cycles;

[0022] A similarity vector calculation module, configured to call a current calculation model to calculate a current similarity vector according to the multiple first root mean square values, and call an electric field calculation model to calculate an electric field similarity vector according to the multiple second root mean square values, if the multiple first root mean square values ​​continue to decrease and the maximum decrease amplitude is greater than or equal to the preset first decrease amplitude, and the multiple second root mean square values ​​continue to decrease and the maximum decrease amplitude is greater than or equal to the preset second decrease amplitude;

[0023] The fault judgment module is used to calculate the similarity confidence according to the matching degree between the current similarity vector and the electric field similarity vector, and if the similarity confidence is greater than or equal to a preset similarity threshold, it is determined that there is a fault in the transmission line.

[0024] In one embodiment, the fault monitoring device further comprises:

[0025] The confidence adjustment module is used to collect the acceleration value of the transmission line in real time, calculate multiple third root mean square values ​​of the current value within a preset number of cycles; call the acceleration calculation model to calculate the acceleration similarity vector according to the multiple third root mean square values; if the first distance between the acceleration similarity vector and the current similarity vector, and the second distance between the acceleration similarity vector and the electric field similarity vector are both less than a preset distance threshold, then add a preset floating confidence on the basis of the calculated similarity confidence.

[0026] In one embodiment, the fault monitoring device further comprises:

[0027] The filtering module is used to segment the current value and the electric field value to obtain multiple electrical segments; calculate the total harmonic distortion and the root mean square value of each electrical segment, and eliminate the electrical segments whose total harmonic distortion is greater than or equal to a preset first critical value and / or whose root mean square value is greater than or equal to a preset second critical value from the multiple electrical segments.

[0028] In one embodiment, the fault monitoring device further comprises:

[0029] A model training module is used to obtain sample electric field data and sample current data as a training set; wherein each data is annotated by a sample label; extracting the electric field rise rate and the electric field drop rate from the training set as electric field features, and extracting the current rise rate, the current drop rate and the surge current as current features; inputting the electric field features into a first classifier based on a decision tree, and inputting the current features into a second classifier based on a decision tree to obtain output prediction labels; adjusting the first classifier and the second classifier according to the difference between the sample label and the prediction label until the first classifier and the second classifier converge, using the first classifier as an electric field calculation model, and using the second classifier as a current calculation model.

[0030] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the steps of the above-mentioned transmission line fault monitoring method.

[0031] A terminal device comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above-mentioned transmission line fault monitoring method.

[0032] The present invention provides a fault monitoring method, device, medium and equipment for a power transmission line. By collecting current and electric field data in real time, the root mean square values ​​are calculated respectively, and when the root mean square value continues to drop to be greater than or equal to a preset threshold, a calculation model is called to generate a similarity vector of the current and the electric field. Then, by matching the degree of the two similarity vectors, the similarity confidence is calculated. If the confidence is greater than or equal to the preset threshold, it is determined that there is a fault in the line. This method can detect faults more accurately, reduce false alarms, and improve the accuracy and efficiency of fault identification by comprehensively analyzing the changes in current and electric field signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] 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, other drawings can be obtained based on these drawings without paying creative work.

[0034] in:

[0035] Figure 1 A schematic diagram of a flow chart of a method for monitoring a fault of a power transmission line;

[0036] Figure 2 It is a structural schematic diagram of a fault monitoring device for a power transmission line;

[0037] Figure 3 This is a structural block diagram of the terminal equipment. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof 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 limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0040] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0041] like Figure 1 As shown, Figure 1 FIG. 1 is a flow chart of a method for monitoring a fault of a power transmission line in an embodiment. The method for monitoring a fault of a power transmission line in this embodiment provides the following steps:

[0042] S101, collecting the current value and the electric field value of the transmission line in real time, and calculating multiple first root mean square values ​​of the current value and multiple second root mean square values ​​of the electric field value within a preset number of cycles.

[0043] Among them, current refers to the current intensity in the transmission line. Changes in current can reflect line load, short circuit or other abnormalities. The electric field value refers to the electric field intensity around the transmission line. Abnormal changes in the electric field indicate insulation failure or other problems in the line. The preset number of cycles refers to the time window when the system collects data and calculates the root mean square value. The root mean square value (RMS) is the effective value of the current or electric field waveform, which represents the average power of the AC signal in one cycle. For a set of continuous discrete data (such as current, voltage, etc.), the calculation formula for its root mean square value is:

[0044]

[0045] Where N is the total number of data points; x i is the value of the ith data point.

[0046] Optionally, a fixed number of cycles, such as 5 cycles, each cycle lasting 30 seconds, is preset to perform continuous data acquisition and calculation. At the end of each cycle, the corresponding root mean square value of the current and electric field is calculated.

[0047] Optionally, the number of monitoring cycles can be adaptively adjusted according to the load or operating status of the line. For example, the system can increase the number of monitoring cycles from the initial 5 cycles to 10 cycles when the load changes greatly, so as to better capture fluctuations.

[0048] S102, if multiple first root mean square values ​​continue to decrease and the maximum decrease amplitude is greater than or equal to the preset first decrease amplitude, and multiple second root mean square values ​​continue to decrease and the maximum decrease amplitude is greater than or equal to the preset second decrease amplitude, then call the current calculation model to calculate the current similarity vector according to the multiple first root mean square values, and call the electric field calculation model to calculate the electric field similarity vector according to the multiple second root mean square values.

[0049] The first drop amplitude and the second drop amplitude are reference standards for the drop of the root mean square value of the current and the electric field set by the system, which are used to judge the possibility of the occurrence of a fault. In this embodiment, the first drop amplitude can be set to 60%, and the second drop amplitude can be set to 50%. The current similarity vector and the electric field similarity vector are feature vectors generated by the calculation model based on the root mean square value of the current and the electric field, which are used to quantify and analyze the similarity between the currently monitored signal characteristics and the standard fault characteristics.

[0050] For example, it is assumed that the normal value of the current of the transmission line is A amperes, and the normal value of the electric field is B volts / meter. In 5 consecutive cycles, the system observes the following changes in the current value: the root mean square value of the current in the first cycle: A×0.85, the root mean square value of the current in the second cycle: A×0.75, the root mean square value of the current in the third cycle: A×0.60, the root mean square value of the current in the fourth cycle: A×0.50, and the root mean square value of the current in the fifth cycle: A×0.40. And the following changes in the electric field value are observed: the root mean square value of the electric field in the first cycle: B×0.90, the root mean square value of the electric field in the second cycle: B×0.80, the root mean square value of the electric field in the third cycle: B×0.70, the root mean square value of the electric field in the fourth cycle: B×0.60, and the root mean square value of the electric field in the fifth cycle: B×0.50. Since the current value and the electric field value meet the conditions of S102, it can be preliminarily determined that there is a fault in the line, but further confirmation is needed. Therefore, the current calculation model is called to generate a current similarity vector based on the current root mean square value of these 5 cycles; at the same time, the electric field calculation model is called to generate an electric field similarity vector based on the electric field root mean square value of these 5 cycles.

[0051] S103, calculating the similarity confidence according to the matching degree between the current similarity vector and the electric field similarity vector, and if the similarity confidence is greater than or equal to a preset similarity threshold, it is determined that there is a fault in the transmission line at present.

[0052] Among them, the similarity confidence indicates the degree of similarity between the temporal variation of one signal and another signal. In other words, it can help determine whether certain features of a signal are reproduced in another signal in some form. Similarity confidence is used to detect delays, phase differences or common characteristics between signals. For example, if a line fault causes abnormalities in both the electric field and the current, the similarity confidence will increase significantly, thereby further determining the line fault. The preset similarity threshold is a set value for determining faults. Optionally, the quantitative similarity confidence between the current similarity vector and the electric field similarity vector can be calculated using cosine similarity, Euclidean distance or other correlation metrics.

[0053] When the similarity confidence is greater than or equal to this threshold, the system can consider that the correlation between the electric field and current signals is high enough and the fault detection result is reliable, thus determining that there is a fault in the line.

[0054] The above-mentioned transmission line fault monitoring method collects current and electric field data in real time, calculates their root mean square values ​​respectively, and calls the calculation model to generate the similarity vector of current and electric field when the root mean square value continues to drop to be greater than or equal to the preset threshold. Then, by matching the degree of the two similarity vectors, the similarity confidence is calculated. If the confidence is greater than or equal to the preset threshold, it is determined that there is a fault in the line. This method can detect faults more accurately, reduce false alarms, and improve the accuracy and efficiency of fault identification by comprehensively analyzing the changes in current and electric field signals.

[0055] In a specific embodiment, the above method further performs the following steps:

[0056] A1. Collect the acceleration value of the transmission line in real time, and calculate multiple third root mean square values ​​of the current value within a preset number of cycles.

[0057] The acceleration value refers to the line vibration or motion data collected in real time by the acceleration sensor installed on the transmission line. These data can reflect the changes of the line affected by the external environment, such as wind force, vibration, etc. The preset number of cycles here is the same as that in S101, and the root mean square value calculation method is also the same, so it will not be repeated.

[0058] A2. Call the acceleration calculation model to calculate the acceleration similarity vector according to multiple third root mean square values.

[0059] Among them, the acceleration similarity vector reflects the temporal variation trend of the acceleration signal.

[0060] A3. If the first distance between the acceleration similarity vector and the current similarity vector, and the second distance between the acceleration similarity vector and the electric field similarity vector are both smaller than a preset distance threshold, a preset floating confidence is added based on the calculated similarity confidence.

[0061] The first distance and the second distance refer to the difference between the acceleration similarity vector and the current similarity vector and the electric field similarity vector, which are usually calculated by a certain mathematical distance (such as Euclidean distance) to indicate the similarity between the acceleration data and the current and electric field data. If both the first distance and the second distance are less than the preset distance threshold, it indicates that the possibility of a fault in the system is further increased. At this time, the preset floating confidence is added on the basis of the calculated similarity confidence. In this way, by introducing acceleration data and conducting a comprehensive analysis with current and electric field signals, the line status can be more accurately reflected, especially the fault response caused by environmental factors such as wind power is more sensitive.

[0062] In a specific embodiment, the above method further performs the following steps:

[0063] B1. Segment the current value and the electric field value to obtain multiple electrical segments.

[0064] The electrical segments refer to the independent data segments obtained by segmenting the current value and the electric field value according to time or other conditions. We can segment the current value and the electric field value according to fixed time slices (for example, every 30 seconds as a segment) and calculate each electrical segment separately. We can also automatically segment the electrical segments according to the characteristics of the current and electric field signals, such as the frequency or amplitude changes of the signal fluctuations.

[0065] B2. Calculate the total harmonic distortion and the root mean square value of each electrical segment, and remove the electrical segments whose total harmonic distortion is greater than or equal to a preset first critical value and / or whose root mean square value is greater than or equal to a preset second critical value from among the multiple electrical segments.

[0066] The total harmonic distortion (THD) and root mean square (RMS) of each electrical segment are calculated separately. For segments with THD greater than a preset first critical value and / or RMS greater than a second critical value, they are removed, and the remaining segments are used as valid data for further fault analysis. This helps to avoid abnormal fluctuations in segments interfering with fault judgment and improve the stability of the system and the accuracy of fault identification.

[0067] In a specific embodiment, the above method further performs the following steps:

[0068] C1. Obtain sample electric field data and sample current data as training sets;

[0069] Among them, sample electric field data and sample current data are data sets used to train the model. They are usually electric field and current values ​​obtained through historical monitoring or experiments. Each data is annotated with a sample label, indicating whether each group of samples is in a normal state or a fault state.

[0070] C2. Extract the electric field rise rate and electric field drop rate from the training set as electric field features, and extract the current rise rate, current drop rate and surge current as current features.

[0071] Among them, the electric field rise rate and electric field fall rate respectively represent the rate at which the electric field strength increases or decreases within a certain period of time, and are used to capture the dynamic characteristics of electric field changes. The current rise rate and current fall rate respectively represent the rate at which the current strength increases or decreases within a certain period of time, and can reflect the changing trend of the current signal. Surge current refers to the phenomenon that the current suddenly increases in a short period of time, which usually occurs when the system is abnormal or the equipment is started.

[0072] C3. Input the electric field feature into a first classifier based on a decision tree, and input the current feature into a second classifier based on a decision tree to obtain an output prediction label.

[0073] The decision tree is a classification algorithm based on a tree structure, which gradually divides the data through a series of "yes / no" questions. Each node of the decision tree represents the judgment condition of a feature, and finally outputs the classification result. In this method, the first classifier is used to process the electric field feature, and the second classifier is used to process the current feature. The decision tree can identify the normal or fault state from these features.

[0074] The extracted electric field signal features (such as the rate of rise, rate of fall, etc.) are input into the first classifier, and the classifier determines the state (normal or faulty) corresponding to these features according to the rules learned during the training process. Similarly, the current signal features (such as surge current, etc.) are input into the second classifier, and the classifier predicts whether the line is in a normal state or a faulty state according to the different values ​​of the current features.

[0075] The predicted label is the judgment result given by the classifier. Through the calculation of the decision tree, the first classifier gives the state label corresponding to the electric field feature, and the second classifier gives the state label corresponding to the current feature. These labels are usually "normal" or "faulty".

[0076] By using decision trees to independently classify electric field and current features, the classification accuracy can be improved and the fault identification model can be made more interpretable. In this way, the system can quickly and accurately determine the status of the line, reducing the risk of failure to be discovered in time.

[0077] C4. Adjust the first classifier and the second classifier according to the difference between the sample label and the predicted label until the first classifier and the second classifier converge, and use the first classifier as the electric field calculation model and the second classifier as the current calculation model.

[0078] Here, the difference refers to the difference between the sample label and the predicted label. If the prediction result of the classifier is inconsistent with the sample label, it means that the model is not accurate enough and needs further adjustment. By gradually adjusting the first classifier and the second classifier by difference, the system can reduce the error of the classifier prediction and ensure that the model classifies the input features more and more accurately. When the difference between the classifier's prediction result and the actual sample label is minimized, and further adjustment no longer significantly improves the accuracy of the model, the model is considered to have "converged". At this point, the classifier has learned how to identify faults or normal states from the feature data, using the first classifier as an electric field calculation model and the second classifier as a current calculation model.

[0079] By continuously adjusting the classifier to match the actual sample labels, the accuracy of fault identification can be greatly improved. When the classifier converges, the system can detect faults in the transmission line in real time and stably, thereby reducing the false alarm rate and missed alarm rate, and improving the reliability and intelligence level of the system. This model training and optimization process can ensure that fault detection of electric field and current data is more accurate and timely.

[0080] In one embodiment, Figure 2 As shown, a fault monitoring device for a transmission line is proposed, the device comprising:

[0081] The RMS value calculation module 201 is used to collect the current value and the electric field value of the transmission line in real time, and calculate a plurality of first RMS values ​​of the current value and a plurality of second RMS values ​​of the electric field value within a preset number of cycles;

[0082] A similarity vector calculation module 202 is used to call the current calculation model to calculate the current similarity vector according to the multiple first root mean square values, and call the electric field calculation model to calculate the electric field similarity vector according to the multiple second root mean square values ​​if the multiple first root mean square values ​​continue to decrease and the maximum decrease amplitude is greater than or equal to the preset first decrease amplitude, and the multiple second root mean square values ​​continue to decrease and the maximum decrease amplitude is greater than or equal to the preset second decrease amplitude;

[0083] The fault judgment module 203 is used to calculate the similarity confidence according to the matching degree between the current similarity vector and the electric field similarity vector. If the similarity confidence is greater than or equal to a preset similarity threshold, it is determined that there is a fault in the transmission line.

[0084] In a specific embodiment, the fault monitoring device also includes: a confidence adjustment module, which is used to collect the acceleration value of the transmission line in real time, and calculate multiple third root mean square values ​​of the current value within a preset number of cycles; call the acceleration calculation model to calculate the acceleration similarity vector according to the multiple third root mean square values; if the first distance between the acceleration similarity vector and the current similarity vector, and the second distance between the acceleration similarity vector and the electric field similarity vector are both less than a preset distance threshold, then add a preset floating confidence based on the calculated similarity confidence.

[0085] In a specific embodiment, the fault monitoring device also includes: a filtering module, which is used to segment the current value and the electric field value to obtain multiple electrical segments; calculate the total harmonic distortion and the root mean square value of each electrical segment, and among the multiple electrical segments, eliminate the electrical segments whose total harmonic distortion is greater than or equal to a preset first critical value, and / or whose root mean square value is greater than or equal to a preset second critical value.

[0086] In a specific embodiment, the fault monitoring device also includes: a model training module, which is used to obtain sample electric field data and sample current data as a training set; wherein each data is annotated by a sample label; extracting the electric field rise rate and the electric field drop rate from the training set as electric field features, and extracting the current rise rate, the current drop rate and the surge current as current features; inputting the electric field features into a first classifier based on a decision tree, and inputting the current features into a second classifier based on a decision tree to obtain output prediction labels; adjusting the first classifier and the second classifier according to the difference between the sample label and the prediction label until the first classifier and the second classifier converge, and using the first classifier as an electric field calculation model and the second classifier as a current calculation model.

[0087] Figure 3 FIG. 2 shows an internal structure diagram of a terminal device in an embodiment. Figure 3 As shown, the terminal device includes a processor, a memory and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the terminal device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor may implement a method for monitoring faults in a power transmission line. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor may implement a method for monitoring faults in a power transmission line. Those skilled in the art will appreciate that Figure 3 The structure shown in the figure is only a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the terminal device to which the scheme of the present application is applied. The specific terminal device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0088] A computer-readable storage medium stores a computer program, which implements the following steps when executed by a processor: real-time acquisition of current values ​​and electric field values ​​of a transmission line, and calculation of multiple first root mean square values ​​of the current value and multiple second root mean square values ​​of the electric field value within a preset number of cycles; if multiple first root mean square values ​​continue to decrease and the maximum decrease amplitude is greater than or equal to the preset first decrease amplitude, and multiple second root mean square values ​​continue to decrease and the maximum decrease amplitude is greater than or equal to the preset second decrease amplitude, then calling a current calculation model to calculate a current similarity vector based on the multiple first root mean square values, and calling an electric field calculation model to calculate an electric field similarity vector based on the multiple second root mean square values; calculating a similarity confidence based on the degree of matching between the current similarity vector and the electric field similarity vector, and if the similarity confidence is greater than or equal to the preset similarity threshold, determining that a fault exists in the transmission line at present.

[0089] A terminal device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: collecting the current value and the electric field value of the transmission line in real time, calculating multiple first root mean square values ​​of the current value and multiple second root mean square values ​​of the electric field value within a preset number of cycles; if the multiple first root mean square values ​​continue to decrease and the maximum decrease amplitude is greater than or equal to the preset first decrease amplitude, and the multiple second root mean square values ​​continue to decrease and the maximum decrease amplitude is greater than or equal to the preset second decrease amplitude, then calling a current calculation model to calculate a current similarity vector based on the multiple first root mean square values, and calling an electric field calculation model to calculate an electric field similarity vector based on the multiple second root mean square values; calculating a similarity confidence based on the matching degree between the current similarity vector and the electric field similarity vector, and if the similarity confidence is greater than or equal to the preset similarity threshold, then determining that the transmission line is currently faulty.

[0090] It should be noted that the above-mentioned transmission line fault monitoring method, device, equipment and computer-readable storage medium belong to a general inventive concept, and the contents of the transmission line fault monitoring method, device, equipment and computer-readable storage medium embodiments are applicable to each other.

[0091] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0092] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0093] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for monitoring faults in a power transmission line, characterized in that: The method comprises: Collecting the current value and electric field value of the transmission line in real time, calculating a plurality of first root mean square values ​​of the current value and a plurality of second root mean square values ​​of the electric field value within a preset number of cycles; If the multiple first root mean square values ​​continue to decrease and the maximum decrease amplitude is greater than or equal to the preset first decrease amplitude, and the multiple second root mean square values ​​continue to decrease and the maximum decrease amplitude is greater than or equal to the preset second decrease amplitude, then the current calculation model is called to calculate the current similarity vector according to the multiple first root mean square values, and the electric field calculation model is called to calculate the electric field similarity vector according to the multiple second root mean square values; A similarity confidence is calculated according to the matching degree between the current similarity vector and the electric field similarity vector. If the similarity confidence is greater than or equal to a preset similarity threshold, it is determined that a fault currently exists in the power transmission line.

2. The method according to claim 1, characterized in that The method further comprises: Collect the acceleration value of the transmission line in real time, and calculate multiple third root mean square values ​​of the current value within a preset number of cycles; Calling the acceleration calculation model to calculate the acceleration similarity vector according to the plurality of third root mean square values; If a first distance between the acceleration similarity vector and the current similarity vector, and a second distance between the acceleration similarity vector and the electric field similarity vector are both smaller than a preset distance threshold, a preset floating confidence is added based on the calculated similarity confidence.

3. The method according to claim 1, characterized in that The method further comprises: Segmenting the current value and the electric field value to obtain a plurality of electrical segments; The total harmonic distortion and the root mean square value of each electrical segment are calculated, and among the plurality of electrical segments, electrical segments having a total harmonic distortion greater than or equal to a preset first critical value and / or a root mean square value greater than or equal to a preset second critical value are eliminated.

4. The method according to claim 1, characterized in that The method further comprises: Obtain sample electric field data and sample current data as a training set; wherein each data is annotated with a sample label; Extracting the electric field rise rate and the electric field drop rate as electric field features from the training set, and extracting the current rise rate, the current drop rate and the surge current as current features; Inputting the electric field feature into a first classifier based on a decision tree, and inputting the current feature into a second classifier based on a decision tree, to obtain an output prediction label; The first classifier and the second classifier are adjusted according to the difference between the sample label and the predicted label until the first classifier and the second classifier converge, and the first classifier is used as an electric field calculation model and the second classifier is used as a current calculation model.

5. A fault monitoring device, characterized in that: The fault monitoring device comprises: A root mean square value calculation module, used for collecting the current value and the electric field value of the transmission line in real time, and calculating a plurality of first root mean square values ​​of the current value and a plurality of second root mean square values ​​of the electric field value within a preset number of cycles; A similarity vector calculation module, configured to call a current calculation model to calculate a current similarity vector according to the multiple first root mean square values, and call an electric field calculation model to calculate an electric field similarity vector according to the multiple second root mean square values, if the multiple first root mean square values ​​continue to decrease and the maximum decrease amplitude is greater than or equal to the preset first decrease amplitude, and the multiple second root mean square values ​​continue to decrease and the maximum decrease amplitude is greater than or equal to the preset second decrease amplitude; The fault judgment module is used to calculate the similarity confidence according to the matching degree between the current similarity vector and the electric field similarity vector, and if the similarity confidence is greater than or equal to a preset similarity threshold, it is determined that there is a fault in the transmission line.

6. The fault monitoring device according to claim 5, characterized in that: The fault monitoring device also includes: The confidence adjustment module is used to collect the acceleration value of the transmission line in real time, calculate multiple third root mean square values ​​of the current value within a preset number of cycles; call the acceleration calculation model to calculate the acceleration similarity vector according to the multiple third root mean square values; if the first distance between the acceleration similarity vector and the current similarity vector, and the second distance between the acceleration similarity vector and the electric field similarity vector are both less than a preset distance threshold, then add a preset floating confidence on the basis of the calculated similarity confidence.

7. The fault monitoring device according to claim 5, characterized in that: The fault monitoring device also includes: The filtering module is used to segment the current value and the electric field value to obtain multiple electrical segments; calculate the total harmonic distortion and the root mean square value of each electrical segment, and eliminate the electrical segments whose total harmonic distortion is greater than or equal to a preset first critical value and / or whose root mean square value is greater than or equal to a preset second critical value from the multiple electrical segments.

8. The fault monitoring device according to claim 5, characterized in that: The fault monitoring device also includes: A model training module is used to obtain sample electric field data and sample current data as a training set; wherein each data is annotated by a sample label; extracting the electric field rise rate and the electric field drop rate from the training set as electric field features, and extracting the current rise rate, the current drop rate and the surge current as current features; inputting the electric field features into a first classifier based on a decision tree, and inputting the current features into a second classifier based on a decision tree to obtain output prediction labels; adjusting the first classifier and the second classifier according to the difference between the sample label and the prediction label until the first classifier and the second classifier converge, using the first classifier as an electric field calculation model, and using the second classifier as a current calculation model.

9. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 4.

10. A terminal device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 4.

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