Method and system for diagnosing and positioning multiple hidden danger discharge of line

Abstract

The invention discloses a line multi-position hidden danger discharge diagnosis and positioning method and system. The method comprises the steps of obtaining first historical data, wherein the first historical data is historical discharge data of a multi-point hidden danger discharge phenomenon in the same power transmission line in a certain time period; performing first preprocessing on the first historical data to obtain second data; a first hidden danger early warning algorithm is preset, and whether hidden danger discharging exists or not is judged according to the second data in combination with the first hidden danger early warning algorithm; and carrying out hidden danger discharge positioning by combining a first positioning algorithm according to a judgment result. The method can effectively identify and position a plurality of hidden discharge phenomena in the power transmission line, so that measures can be taken in time for maintenance and repair, and line faults and shutdown caused by hidden discharge are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of diagnosing and locating discharge hazards at multiple locations on a line, and in particular to a method and system for diagnosing and locating discharge hazards at multiple locations on a line. Background Art

[0002] Transmission and distribution lines are prone to insulator degradation, contamination, floating foreign objects, etc., which can lead to weakened insulation strength between the high-voltage side conductors and the ground wires and towers at the ground potential, causing hidden discharges. If not cleared in time, it will develop into a fault and eventually cause the line to trip and stop operating, resulting in significant economic losses and safety risks.

[0003] In order to accurately and timely identify and eliminate discharges on the line, it is necessary to first identify the existence and type of hidden discharges on the line. Currently, most transmission and distribution lines of various voltage levels have installed distributed traveling wave fault locating devices, which collect high-frequency discharge traveling waves generated and propagated from the discharge point at both ends of the line, calculate their characteristics, and compare and determine whether they have abnormal characteristics, so as to identify the existence and type of discharge, and notify the line operation and maintenance personnel to carry out maintenance work.

[0004] At present, similar technologies mainly extract high-frequency discharge waveform fragments to solve the problem of hidden danger discharge identification and classification, calculate specified features such as amplitude, phase, power frequency accompanying periodicity, etc., use pattern classification algorithms such as decision trees, support vector machines, neural networks, etc., and finally output the existence or type label of hidden dangers. However, in reality, it is very likely that hidden dangers of transmission lines occur in multiple places at the same time. When the transmission line crosses mountains and there is insulator deterioration in one place, there are trees that are too high in another place, or even multiple places. The high-frequency discharge waveforms they generate propagate in the line, affecting the diagnosis and location of hidden dangers. Therefore, the existing technology extracts a single or several waveform fragments and calculates features. This method does not take into account the development and accumulation of abnormal hidden dangers in the line. It is impossible to comprehensively judge the hidden danger status of the line from the entire process and predict future development trends. Therefore, it is easy to produce false alarms and missed alarms. Summary of the invention

[0005] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In view of the above existing problems, the present invention is proposed.

[0007] Therefore, the present invention provides a method and system for diagnosing and locating discharge hazards at multiple locations in a line, which can solve the problems mentioned in the background technology.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] In a first aspect, the present invention provides a method for diagnosing and locating discharge hazards at multiple locations of a line, comprising:

[0010] Acquire first historical data, where the first historical data is historical discharge data of a multi-point hidden danger discharge phenomenon in the same transmission line within a certain time period;

[0011] Performing a first preprocessing on the first historical data to obtain second data;

[0012] A first hidden danger warning algorithm is preset, and whether there is a hidden danger discharge is determined according to the second data combined with the first hidden danger warning algorithm;

[0013] According to the judgment result, the hidden danger discharge is located in combination with the first positioning algorithm.

[0014] As a preferred solution of the method for diagnosing and locating discharge hazards at multiple locations of a line according to the present invention, the first hidden danger warning algorithm includes:

[0015] The first hidden danger warning algorithm is any algorithm that can directly or indirectly determine whether there is a hidden danger discharge or whether there is a hidden danger discharge based on the second data;

[0016] The first hidden danger warning algorithm at least includes a first threshold judgment;

[0017] The first threshold value is used to determine a data state in the second data that satisfies a potential discharge hazard.

[0018] As a preferred solution of the method for diagnosing and locating multiple potential danger discharges in a line according to the present invention, the first locating algorithm includes:

[0019] The first positioning algorithm is any algorithm that can locate the hidden danger discharge according to the judgment result or can directly or indirectly obtain the location of the hidden danger discharge;

[0020] The first positioning algorithm includes at least a first position marking operation;

[0021] The first position marking operation is used to determine the number of discharge sources and the position of the discharge point.

[0022] As a preferred solution of the method for diagnosing and locating multiple potential discharge hazards of a line according to the present invention, the first threshold judgment at least includes threshold judgments on the number of data satisfying the potential discharge hazard and the potential discharge hazard value.

[0023] As a preferred solution of the method for diagnosing and locating discharge hazards at multiple locations of a line described in the present invention, the first position marking operation at least includes marking the number of discharges, marking the discharge positioning result, and marking the discharge intensity.

[0024] As a preferred solution of the method for diagnosing and locating discharge hazards at multiple locations on a line described in the present invention, the first positioning algorithm further includes: extracting wave heads from data uploaded by the left and right pole tower devices.

[0025] As a preferred solution of the method for diagnosing and locating multiple hidden danger discharges in a line according to the present invention, the first preprocessing includes: the first preprocessing is used to screen hidden danger discharge data and non-hidden danger discharge data.

[0026] In a second aspect, the present invention provides a system for diagnosing and locating discharge hazards at multiple locations of a line, comprising:

[0027] A data acquisition module, used to acquire first historical data, wherein the first historical data is historical discharge data of a multi-point hidden danger discharge phenomenon in the same transmission line within a certain time period;

[0028] A data preprocessing module, used for performing a first preprocessing on the first historical data to obtain second data;

[0029] An early warning module is used to preset a first hidden danger early warning algorithm, and judge whether there is a hidden danger discharge according to the second data combined with the first hidden danger early warning algorithm;

[0030] The positioning module is used to locate the hidden danger discharge according to the judgment result in combination with the first positioning algorithm.

[0031] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method when executing the computer program.

[0032] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0033] Compared with the prior art, the invention has the following beneficial effects: the invention proposes a method and system for diagnosing and locating multiple hidden danger discharges in a line, obtains first historical data, and the first historical data is historical discharge data of multiple hidden danger discharges in the same transmission line within a certain period of time; performs first preprocessing on the first historical data to obtain second data; presets a first hidden danger warning algorithm, and judges whether there is hidden danger discharge according to the second data combined with the first hidden danger warning algorithm; and locates the hidden danger discharge according to the judgment result and in combination with the first positioning algorithm. It can effectively identify and locate multiple hidden danger discharge phenomena in the transmission line, so as to take timely measures for maintenance and repair, and avoid line failure and shutdown caused by hidden danger discharge. The method and system for diagnosing and locating multiple hidden danger discharges in the line of the present application improves the accuracy of hidden danger discharge identification and positioning accuracy by comprehensively analyzing historical discharge data and combining advanced early warning algorithms and positioning algorithms. In addition, the present application can also mark the number of discharges, positioning results and discharge intensity, providing more comprehensive and intuitive diagnostic information for line operation and maintenance personnel, which is helpful to formulate more effective maintenance strategies and reduce economic losses and safety risks caused by line failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing 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 labor. Among them:

[0035] Figure 1 A method flow chart of a method and system for diagnosing and locating multiple potential discharge hazards in a line provided by an embodiment of the present invention;

[0036] Figure 2 A schematic diagram of a method and system for diagnosing and locating discharge hazards at multiple locations on a line provided by an embodiment of the present invention, showing a situation in which discharge data is collected by equipment;

[0037] Figure 3 A schematic diagram of analysis object data of a method and system for diagnosing and locating multiple potential danger discharges in a line provided by an embodiment of the present invention;

[0038] Figure 4 A schematic diagram of a discharge traveling wave waveform of a method and system for diagnosing and locating discharge hazards at multiple locations in a line provided by an embodiment of the present invention;

[0039] Figure 5 A flowchart of an early warning algorithm execution of a method and system for diagnosing and locating multiple potential danger discharges in a line provided by an embodiment of the present invention;

[0040] Figure 6 An example of a hidden danger warning SMS message of a method and system for diagnosing and locating hidden danger discharges at multiple locations on a line provided by an embodiment of the present invention;

[0041] Figure 7 A flowchart of a method and system for diagnosing and locating multiple potential danger discharge points of a line provided by an embodiment of the present invention;

[0042] Figure 8 A schematic diagram of a traveling wave head distortion point of a method and system for diagnosing and locating multiple potential discharge hazards in a line provided by an embodiment of the present invention;

[0043] Fig. 9 A method and system for diagnosing and locating multiple potential discharge hazards in a line provided by an embodiment of the present invention and a distribution diagram of potential discharge hazards before and after clustering;

[0044] Fig.10 An internal structural diagram of a computer device of a method and system for diagnosing and locating discharge hazards at multiple locations in a line provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0046] Example 1

[0047] Reference Figure 1-Figure 10 , which is the first embodiment of the present invention, provides a method and system for diagnosing and locating discharge hazards at multiple locations of a line, including:

[0048] There are some problems in the existing related technologies. For example, the traditional hidden danger detection method relies on manual inspection, which is inefficient and easily affected by human factors, resulting in the discovery of hidden dangers not being timely and accurate. In addition, when there are multiple hidden dangers in the line, the existing diagnostic technology is often difficult to effectively identify and locate multiple hidden danger points at the same time, which limits the efficiency and accuracy of fault handling to a certain extent.

[0049] The present application provides a method that can effectively solve the above-mentioned problems. Next, a method for diagnosing and locating discharge hazards at multiple locations of the line will be described in detail in combination with multiple embodiments.

[0050] Figure 1A method flow chart of a method and system for diagnosing and locating multiple potential discharge hazards in a line is shown, including:

[0051] S101, obtaining first historical data, where the first historical data is historical discharge data of a multi-point hidden danger discharge phenomenon in the same transmission line within a certain time period;

[0052] In an optional embodiment, historical discharge data of multi-point hidden danger discharge phenomena in the same transmission line within a certain period of time can be automatically collected by data acquisition equipment and sent to the central processing unit through a wireless transmission module. The central processing unit uses advanced data analysis algorithms to process and analyze the collected data to identify the characteristics and patterns of discharge events. In this way, real-time monitoring and early warning of multi-point hidden danger discharge phenomena can be achieved, thereby improving the safety and reliability of the line.

[0053] In an optional embodiment, the historical discharge data may include multiple parameters, such as the magnitude of the discharge current, the discharge frequency, the discharge duration, and the specific time point when the discharge occurred. These parameters can provide more comprehensive information for analyzing the discharge event, and help to more accurately determine the severity of the discharge and the location of possible hidden dangers. In addition, by comparing historical data with real-time data, the accuracy of diagnosis can be further improved, and new hidden dangers that may appear in the transmission line can be discovered in a timely manner, so that corresponding maintenance measures can be taken.

[0054] In an optional embodiment, a certain time period is a manually set time period, which can be the past 24 hours, a week, a month, or any specific period. The selection of the time period depends on the frequency and importance of the discharge events that need to be monitored. For example, for lines where discharges occur frequently, a shorter time period may be required to ensure timely discovery and handling of problems. For lines with fewer discharge events, a longer time period may be sufficient. In this way, the monitoring strategy can be flexibly adjusted to adapt to the specific needs and conditions of different lines.

[0055] In the embodiment of the present application, historical discharge data (with line inspection results) of multi-point hidden danger discharge phenomena in the same transmission line within a certain period of time are selected from the on-site historical hidden danger discharge waveform library as test samples, as follows Figure 1 The hidden danger discharge waveform detected by the equipment shown is as follows: Figure 2 As shown;

[0056] In the embodiment of the present application, the test sample includes all the hidden danger discharge data uploaded by the pole tower equipment at both ends during the period. For example, if the left pole tower equipment collects m test samples, then the 2m test samples uploaded by the pole tower equipment at both ends are stored in a new waveform database in the form of discrete data sets, as follows Figure 3 shown.

[0057] It should be noted that obtaining the first historical data, which is the historical discharge data of the multi-point hidden danger discharge phenomenon in the same transmission line within a certain period of time, can provide a more accurate discharge pattern recognition benchmark. By analyzing these historical data, the discharge characteristics of a specific line under different conditions can be determined, thereby providing a reference for real-time monitoring. In addition, these data can also be used to train diagnostic and positioning algorithms to improve their recognition accuracy and response speed for current discharge events. Ultimately, this will help reduce false alarms and missed alarms and ensure the safe and stable operation of transmission lines.

[0058] S102, performing a first preprocessing on the first historical data to obtain second data;

[0059] In the embodiment of the present application, the first preprocessing includes: the first preprocessing is used to screen potential danger discharge data and non-potential danger discharge data.

[0060] In an optional embodiment, the first preprocessing can distinguish between potential danger discharge data and non-potential danger discharge data by setting a threshold. Specifically, a voltage or current threshold can be set, and when the data exceeds this threshold, it is considered as potential danger discharge data; otherwise, it is considered as non-potential danger discharge data. In this way, potential danger discharge data that needs further analysis can be effectively screened out from a large amount of data, thereby improving the efficiency of diagnosis and positioning.

[0061] In an optional embodiment, the preprocessing steps may be further refined according to the characteristics of the discharge data, such as the discharge waveform, frequency, etc., to adapt to different types of discharge phenomena.

[0062] In an optional embodiment, a layer of judgment conditions can be added in the early stage to filter out invalid, non-periodic waveform data, that is, non-hazardous discharge data, to further improve the accuracy of data processing. This layer of judgment conditions can be based on the periodic characteristics of the waveform data, for example, by analyzing the frequency distribution and repetitive patterns of the waveform data to identify periodic waveforms. Periodic waveforms are usually related to signals generated during normal operation of the equipment, while non-periodic waveforms may indicate potential hazards. By screening such judgment conditions, the misjudgment of non-periodic waveform data can be reduced, thereby reducing unnecessary analysis workload and improving the efficiency and accuracy of overall diagnosis and positioning.

[0063] In the embodiment of the present application, the extracted data is not necessarily the hidden danger discharge data, so a layer of judgment conditions is added in the early stage of the project to filter out invalid and non-periodic waveform data, that is, non-hidden danger discharge data. Since the purpose of the hidden danger warning algorithm is to determine whether a long hidden danger waveform (t≥40ms) has the hidden danger discharge characteristics, a hidden danger warning algorithm can be developed according to the morphological characteristics of the hidden danger discharge waveform in the time domain. Since it contains spike pulses with a sufficiently high amplitude and appears regularly at a periodic time of approximately 10ms, the extreme value interval method is used as the research and development theory of the warning algorithm, which can effectively identify non-periodic discharge data.

[0064] It should be noted that the first historical data is subjected to the first preprocessing to obtain the second data, which can effectively remove noise interference and improve the accuracy and reliability of the data. Through preprocessing, the accuracy of subsequent analysis can be ensured to avoid misjudgment caused by abnormal values ​​or noise in the original data. In addition, the preprocessed data is more suitable for applying various signal processing techniques, such as filtering, normalization, etc., thereby providing a clearer signal basis for the extraction of hidden danger discharge characteristics.

[0065] S103, presetting a first hidden danger warning algorithm, and judging whether there is a hidden danger of discharge according to the second data combined with the first hidden danger warning algorithm;

[0066] In an optional embodiment, the first hidden danger warning algorithm can be designed by different algorithms to adapt to different types of discharge waveforms. For example, an algorithm based on wavelet transform can be used to identify discharge characteristics by analyzing detailed information of the waveform at different scales. In addition, machine learning techniques, such as support vector machines (SVM) or neural networks, can be used to train models to identify discharge waveforms. These algorithms can learn the characteristics of discharge waveforms from a large amount of historical data, and accurately classify and warn new waveform data in practical applications. Through these methods, the accuracy and efficiency of hidden danger warnings can be further improved, providing strong technical support for the safe operation of power systems.

[0067] In an optional embodiment, the first hidden danger warning algorithm can also form a hybrid model by integrating the advantages of multiple algorithms to enhance the recognition ability of complex discharge patterns. For example, the wavelet transform can be combined with the neural network, and the advantages of the wavelet transform in time-frequency analysis can be used to extract the characteristics of the discharge waveform, and then the pattern recognition can be performed through the powerful nonlinear mapping ability of the neural network. In addition, a genetic algorithm can be introduced to optimize the weights and structure of the neural network to improve the generalization ability and prediction accuracy of the model. Through this hybrid algorithm, the uncertainty and noise of the data can be handled more effectively, thereby achieving more accurate hidden danger warnings in practical applications.

[0068] In an optional embodiment, the first hidden danger warning algorithm can also be defined by the extreme value interval method analysis step, the extreme value points in the discharge waveform data are identified and marked, and then the severity of the discharge is judged according to the interval time between these extreme value points. By setting a reasonable threshold, when the detected extreme value interval time is less than or equal to the threshold, the system will issue a warning signal to prompt the operator to conduct further inspection and processing. This method can effectively identify sudden discharge events that may be ignored by traditional methods, thereby further improving the sensitivity and reliability of the early warning system.

[0069] In the embodiment of the present application, the first hidden danger warning algorithm includes:

[0070] The first hidden danger warning algorithm is any algorithm that can directly or indirectly determine whether there is a hidden danger discharge or whether there is a hidden danger discharge based on the second data;

[0071] The first hidden danger warning algorithm at least includes a first threshold judgment;

[0072] The first threshold value is used to determine a data state in the second data that satisfies a potential discharge hazard.

[0073] In the embodiment of the present application, the extreme value interval method (also called the extreme point method or the extreme value interval search method) is a method commonly used in data analysis, signal processing or optimization problems, mainly used to find extreme points (i.e., maximum or minimum points) in a data sequence and the intervals between them. This method is particularly suitable for scenarios where periodic data needs to be analyzed. The basic analysis steps are as follows:

[0074] ① Data preprocessing: Ensure that the data is clean and orderly. Preprocessing steps such as denoising, smoothing or normalization may be required to improve the accuracy of extreme value detection.

[0075] ② Extreme point detection: traverse the data sequence and compare the size of each point with its adjacent points. If a point is larger (or smaller) than the previous and next points, it is considered to be a local maximum (or minimum). Pay attention to the boundary conditions. The first and last points of the sequence cannot usually be directly judged as extreme values.

[0076] ③ Calculate the extreme value interval: Once all extreme value points are identified, the distance (i.e. interval) between adjacent extreme value points can be calculated. For periodic data, these intervals may show certain regularity, which is helpful for further analysis.

[0077] ④Analysis and application: According to the characteristics of extreme value intervals, various analyses can be performed, such as determining the period of the signal, identifying outliers, and performing pattern classification. In signal processing, extreme value intervals can be used for frequency estimation or event detection.

[0078] It should be stated that the extreme value interval method is a powerful tool to extract valuable information from data, but it should be noted that its effectiveness depends largely on the characteristics of the data and the quality of preprocessing.

[0079] In the embodiment of the present application, the hidden danger warning determination based on the extreme value interval method is performed in the following specific steps:

[0080] ① Preprocessing stage: Calculate the standard deviation of each hidden danger discharge data, compare the standard deviation of the noise interference data, and determine the stability of the hidden danger discharge data. If it is higher than the standard deviation of the noise interference waveform, proceed to the next step;

[0081] ② Find the spike set for the entire hidden danger discharge data and form a set W1, as follows Figure 4 As shown, the set contains the time point corresponding to each spike.

[0082] ③ Group all spikes into groups according to the adjacent spikes with a time difference of less than 0.5ms, forming a set W2;

[0083] ④ For all spike groups in set W2, the time difference is solved according to the pairing principle, and sent to the judgment function module for difference calculation. If the interval is about 10ms, it is judged as a valid waveform and stored in the valid waveform data group according to the line name partition; otherwise, it is stored in the invalid waveform data group;

[0084] ⑤ When a valid discharge data is accumulated in the valid waveform data group and the threshold is greater than b, a hidden danger warning message is issued, where a and b values ​​are both manually set empirical values. The algorithm execution process framework is as follows Figure 5 As shown, the warning notification is sent via SMS, for example Figure 6 shown.

[0085] In the embodiment of the present application, the first threshold determination at least includes threshold determination of the quantity of data satisfying the potential danger discharge and the potential danger discharge value.

[0086] In an optional embodiment, the first threshold value may be to set effective discharge data and a corresponding threshold value, set a data volume judgment standard, and a corresponding data judgment standard.

[0087] It should be noted that the first hidden danger warning algorithm is preset, and judging whether there is a hidden danger discharge based on the second data combined with the first hidden danger warning algorithm can monitor the line status in real time and discover potential discharge problems in time, thereby avoiding power system interruption or damage caused by discharge failure. Through the preset hidden danger warning algorithm, the system can analyze the collected discharge data, and evaluate the health status of the line in combination with historical data and empirical thresholds. When the detected discharge data exceeds the preset threshold, the system will automatically trigger the early warning mechanism and send an alarm to the operation and maintenance personnel, prompting them to conduct further inspection and maintenance. This method not only improves the operational safety of the power system, but also reduces the maintenance cost and time generated by troubleshooting.

[0088] S104, based on the judgment result, the hidden danger discharge is located in combination with the first positioning algorithm.

[0089] In an optional embodiment, the first positioning algorithm can accurately predict the discharge location by analyzing the time series characteristics of the discharge data, combined with the physical layout of the line and historical fault records. The algorithm uses advanced signal processing techniques, such as wavelet transform or Fourier transform, to extract the characteristics of the discharge signal, and uses pattern recognition technology to match known discharge patterns to determine the specific location of the discharge event. In addition, the algorithm can also predict the development trend of the discharge event based on the changing trend of the discharge intensity and frequency, and provide decision support for operation and maintenance personnel. In this way, the workload of on-site inspections can be effectively reduced and the accuracy and efficiency of fault location can be improved.

[0090] In an optional embodiment, the first positioning algorithm can further optimize the prediction of the discharge position by integrating a machine learning model. The model learns a large number of discharge data samples during training and improves the accuracy of positioning by identifying complex patterns and associations in the data.

[0091] In an optional embodiment, the embodiment may also include a feedback mechanism that allows the system to continuously adjust and optimize algorithm parameters based on the on-site inspection results of the operation and maintenance personnel and the actual discharge situation to adapt to the discharge characteristics under different lines and environmental conditions. In this way, the first positioning algorithm can continue to evolve and provide more reliable protection for the safe and stable operation of the power system.

[0092] In an embodiment of the present application, the first positioning algorithm includes:

[0093] The first positioning algorithm is any algorithm that can locate the hidden danger discharge according to the judgment result or can directly or indirectly obtain the location of the hidden danger discharge;

[0094] The first positioning algorithm includes at least a first position marking operation;

[0095] The first position marking operation is used to determine the number of discharge sources and the position of the discharge point.

[0096] In the embodiment of the present application, the first position marking operation at least includes marking the number of discharges, marking the discharge positioning result, and marking the discharge intensity.

[0097] In the embodiment of the present application, the first positioning algorithm further includes: extracting wave heads from the data uploaded by the left and right pole tower devices.

[0098] Exemplarily, the historical discharge data of different hidden danger discharge locations of the same transmission line in a certain period of time are used as the source analysis object of the multi-point hidden danger location algorithm. First, the wave heads of all the selected discharge data are identified and calculated, and the wave head sets of the traveling waves are paired two by two according to the GPS time and the double-end tower equipment, so that each group of traveling waves comes from the equipment of different towers. The positioning results are then calculated by polling through the double-end positioning algorithm, and the positioning results are written into the abnormal discharge information table. Then, the clustering algorithm is used to screen out relatively concentrated positioning results from the massive positioning results, and their degree of aggregation is judged. When a specific obvious discharge point appears on the line, the line positioning calculation result will show obvious clustering characteristics. Therefore, according to the number of positioning results with a higher degree of aggregation, it can be judged how many hidden danger discharge points there are on the line, and the position corresponding to each hidden danger discharge point is the location with a higher degree of aggregation. The specific implementation process is as follows Figure 7 As shown;

[0099] Furthermore, the wave head extraction algorithm is called to extract all the hidden danger discharge data uploaded within this period of time after filtering, and all the traveling wave head distortion points are extracted. The traveling wave head sets are paired according to the GPS time and the double-end tower equipment, and then the interval is determined according to the GPS time difference. The double-end positioning calculation and analysis are performed on multiple groups of traveling waves within the interval, and the positioning results of all possible discharge points of the line within this period of time are collected, as follows Figure 8 The traveling wave head distortion moment is shown.

[0100] In summary, the present invention proposes a method for diagnosing and locating multiple hidden danger discharges in a line, obtaining first historical data, wherein the first historical data is historical discharge data of multiple hidden danger discharge phenomena in the same transmission line within a certain period of time; performing a first preprocessing on the first historical data to obtain second data; presetting a first hidden danger warning algorithm, judging whether there is a hidden danger discharge according to the second data combined with the first hidden danger warning algorithm; and locating the hidden danger discharge according to the judgment result in combination with the first positioning algorithm. It can effectively identify and locate multiple hidden danger discharge phenomena in the transmission line, so as to take timely measures for maintenance and repair, and avoid line failure and shutdown caused by hidden danger discharge. The method and system for diagnosing and locating multiple hidden danger discharges in the line of the present application improves the accuracy of hidden danger discharge identification and the accuracy of positioning by comprehensively analyzing historical discharge data and combining advanced early warning algorithms and positioning algorithms. In addition, the present application can also mark the number of discharges, positioning results and discharge intensity, providing more comprehensive and intuitive diagnostic information for line operation and maintenance personnel, which is helpful to formulate more effective maintenance strategies and reduce economic losses and safety risks caused by line failures.

[0101] Example 2

[0102] In a preferred embodiment, considering that there are multiple sets of discharge waveform matching pairs for multiple hidden dangers, multiple positioning calculations are performed on the premise of identifying these matching pairs, which will form statistical features under multiple samples. By calling the DBSCAN clustering algorithm, multiple positioning results and the discharge intensity of each time are clustered and analyzed to obtain several clusters. Fig. 9 As shown in the figure, after clustering, the number of clusters corresponds to the number of discharge sources, including 2 discharge sources; the center point of the cluster corresponds to the location result of each discharge source, but because the algorithm has located the same hidden danger discharge source multiple times, affected by interference and the attenuation dispersion of the discharge traveling wave itself, the actual positioning result is distributed near the actual occurrence location. Therefore, the position information contained in the data points belonging to the same cluster is averaged to overcome the positioning error caused by interference and attenuation dispersion, so as to obtain the accurate location of the hidden danger discharge source, and finally realize the positioning of multiple hidden danger discharge points. After calculation, the positions of the two discharge sources are at 2715 meters and 6206 meters respectively, corresponding to the actual towers near the 12# tower and the 23# tower.

[0103] Example 3

[0104] This embodiment also provides a system for diagnosing and locating discharge hazards at multiple locations of a line, including:

[0105] A data acquisition module, used to acquire first historical data, wherein the first historical data is historical discharge data of a multi-point hidden danger discharge phenomenon in the same transmission line within a certain time period;

[0106] A data preprocessing module, used for performing a first preprocessing on the first historical data to obtain second data;

[0107] An early warning module is used to preset a first hidden danger early warning algorithm, and judge whether there is a hidden danger discharge according to the second data combined with the first hidden danger early warning algorithm;

[0108] The positioning module is used to locate the hidden danger discharge according to the judgment result in combination with the first positioning algorithm.

[0109] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0110] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Fig.10 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for diagnosing and locating discharge hazards at multiple locations in a line is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse, etc.

[0111] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0112] Acquire first historical data, where the first historical data is historical discharge data of a multi-point hidden danger discharge phenomenon in the same transmission line within a certain time period;

[0113] Performing a first preprocessing on the first historical data to obtain second data;

[0114] A first hidden danger warning algorithm is preset, and whether there is a hidden danger discharge is determined according to the second data combined with the first hidden danger warning algorithm;

[0115] According to the judgment result, the hidden danger discharge is located in combination with the first positioning algorithm.

[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0117] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0118] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0121] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0122] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for diagnosing and locating discharge hazards at multiple locations in a line, characterized in that: include: Acquire first historical data, where the first historical data is historical discharge data of a multi-point hidden danger discharge phenomenon in the same transmission line within a certain time period; Performing a first preprocessing on the first historical data to obtain second data; A first hidden danger warning algorithm is preset, and whether there is a hidden danger discharge is determined according to the second data combined with the first hidden danger warning algorithm; According to the judgment result, the hidden danger discharge is located in combination with the first positioning algorithm.

2. The method for diagnosing and locating discharge hazards at multiple locations of a line as claimed in claim 1, characterized in that: The first hidden danger early warning algorithm comprises: The first hidden danger warning algorithm is any algorithm that can directly or indirectly determine whether there is a hidden danger discharge or whether there is a hidden danger discharge based on the second data; The first hidden danger warning algorithm at least includes a first threshold judgment; The first threshold value is used to determine a data state in the second data that satisfies a potential discharge hazard.

3. The method for diagnosing and locating discharge hazards at multiple locations of a line as claimed in claim 2, characterized in that: The first positioning algorithm comprises: The first positioning algorithm is any algorithm that can locate the hidden danger discharge according to the judgment result or can directly or indirectly obtain the location of the hidden danger discharge; The first positioning algorithm includes at least a first position marking operation; The first position marking operation is used to determine the number of discharge sources and the position of the discharge point.

4. The method for diagnosing and locating discharge hazards at multiple locations of a line as claimed in claim 3, characterized in that: The first threshold determination at least includes threshold determination of the quantity of data satisfying the potential discharge hazard and the potential discharge hazard value.

5. The method for diagnosing and locating discharge hazards at multiple locations of a line as claimed in claim 4, characterized in that: The first position marking operation at least includes marking the number of discharges, marking the discharge positioning result, and marking the discharge intensity.

6. The method for diagnosing and locating discharge hazards at multiple locations of a line as claimed in claim 5, characterized in that: The first positioning algorithm also includes: extracting wave heads from data uploaded by the left and right pole tower devices.

7. The method for diagnosing and locating discharge hazards at multiple locations of a line as claimed in claim 6, characterized in that: The first preprocessing includes: the first preprocessing is used to screen potential danger discharge data and non-potential danger discharge data.

8. A system for diagnosing and locating discharge hazards at multiple locations in a line, characterized in that: include: A data acquisition module, used to acquire first historical data, wherein the first historical data is historical discharge data of a multi-point hidden danger discharge phenomenon in the same transmission line within a certain time period; A data preprocessing module, used for performing a first preprocessing on the first historical data to obtain second data; An early warning module is used to preset a first hidden danger early warning algorithm, and judge whether there is a hidden danger discharge according to the second data combined with the first hidden danger early warning algorithm; The positioning module is used to locate the hidden danger discharge according to the judgment result in combination with the first positioning algorithm.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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