A Power Line Fault Identification and Capture System Based on Multi-Source Signal Triggering Mechanism

Abstract

This invention discloses an intelligent power line fault identification and image capture system based on a multi-source signal triggering mechanism, belonging to the field of power line fault identification technology. It includes a multi-source analysis module, a data acquisition terminal, and a platform terminal. The multi-source analysis module is used to analyze power line faults and determine multi-source monitoring items. The data acquisition terminal includes a monitoring module, a monitoring analysis module, an image capture control module, and a storage module. The monitoring module is used to monitor the power line in real time and obtain line monitoring information. The monitoring analysis module is used to analyze the received line monitoring information and obtain corresponding image capture analysis results. When the image capture analysis result indicates an image capture, an image capture command is generated and sent to the image capture control module. The image capture control module is used to control a high-speed camera to capture images upon receiving the image capture command, obtaining fault image captures. The storage module is used to store the fault image captures and send the stored images to the platform terminal.

Description

Technical Field

[0001] This invention belongs to the field of power line fault identification technology, specifically a power line fault identification and capture system based on a multi-source signal triggering mechanism. Background Technology

[0002] Traditional power line fault monitoring relies on manual inspections and periodic preventative tests, which is inefficient, makes it difficult to detect hidden faults in a timely manner, and cannot capture the moment of a fault. With technological advancements, although high-precision current and voltage sensors, fiber optic temperature sensors, intelligent algorithms, high-speed cameras, solid-state drives, and various advanced sensing, signal processing, and image capture and storage technologies have emerged based on single-signal monitoring devices, power line faults are complex, and single-signal monitoring is prone to misjudgment.

[0003] Based on this, the present invention provides an intelligent capture system for power line fault identification based on a multi-source signal triggering mechanism. Summary of the Invention

[0004] To address the problems of the above solutions, this invention provides an intelligent capture system for power line fault identification based on a multi-source signal triggering mechanism.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A power line fault identification and capture system based on a multi-source signal triggering mechanism includes a multi-source analysis module, a data acquisition terminal, and a platform terminal.

[0007] The multi-source analysis module is used to perform fault analysis on power lines and determine multi-source monitoring items.

[0008] Furthermore, the methods for determining multi-source monitoring items include:

[0009] Establish a fault information table, which is used to statistically analyze potential faults in power lines and the corresponding fault phenomena.

[0010] Based on the fault information table, a fault phenomenon monitoring simulation is performed to obtain the monitoring simulation results of the fault phenomenon, and multi-source monitoring items are determined based on the monitoring simulation results.

[0011] Furthermore, the methods for establishing the fault information table include:

[0012] Historical fault data of power lines is acquired, fault identification is performed on the historical fault data to obtain several historical line faults, and the deduplicated historical line faults are marked as potential faults.

[0013] The potential fault symptoms are set according to the historical fault data; and the potential faults and fault symptoms are updated according to the updates to the historical fault data.

[0014] Set up a fault information table based on potential faults and fault symptoms.

[0015] Furthermore, methods for setting potential fault phenomena based on historical fault data include:

[0016] Obtain normal state data of power lines, set normal phenomenon standards for each potential fault based on the normal state data, and establish a difference identification model based on the normal phenomenon standards. The expression of the difference identification model is:

[0017]

[0018] In the formula: U is the input data, and the output data is the cell difference value CY(U), where the cell difference value is 1 or 0;

[0019] Historical fault data is classified according to the potential faults to obtain fault classification data for each potential fault. Corresponding unit data is extracted from the fault classification data, and the unit data corresponding to the potential faults is deduplicated and integrated into the input data.

[0020] The input data is analyzed using a difference recognition model to obtain the unit difference values ​​of the unit data;

[0021] The fault phenomenon is set based on the unit data with a unit difference value of 1.

[0022] Furthermore, the methods for monitoring and simulating fault phenomena based on the fault information table include:

[0023] Identify the fault phenomena in the fault information table, obtain the various monitoring methods of the fault phenomena in real time, and mark them as the initial selection methods;

[0024] Obtain the user's monitoring requirements, evaluate the initial selection method for the fault phenomenon based on the monitoring requirements, evaluate whether the initial selection method meets the monitoring requirements, and mark the initial selection method that meets the monitoring requirements as the candidate method;

[0025] The candidate methods are filtered to obtain the multi-source monitoring methods for the fault phenomenon, and multi-source monitoring items are set according to the multi-source monitoring methods.

[0026] Furthermore, the methods for screening the candidate methods include:

[0027] Identify the potential faults monitored corresponding to the candidate methods and obtain the weight coefficients of the potential faults; estimate the cost of the candidate methods and mark them as candidate costs; analyze the candidate methods and obtain the compliance values ​​of the candidate methods for the potential faults.

[0028] Label potential faults as i, i = 1, 2, ..., n, where n is the number of potential faults; label the compliance value of the candidate method for potential faults as DB. i The weighting coefficient for potential faults is denoted as δ. i Mark the alternative cost of the alternative method as CB;

[0029] Calculate the filter value for the corresponding candidate methods based on the filter formula: Filter formula:

[0030]

[0031] In the formula: SQ is the screening value; b1 and b2 are both proportional coefficients, with a value range of 0. <b1≤1,0<b2≤1;

[0032] The candidate methods are sorted in descending order of the filter values ​​to obtain a recommended list of the fault phenomena. Users then determine the multi-source monitoring method for the fault phenomena based on the recommended list.

[0033] The acquisition terminal includes a monitoring module, a monitoring and analysis module, a snapshot control module, and a storage module;

[0034] The monitoring module is used to monitor the power line in real time according to the preset multi-source monitoring items and multi-source monitoring methods, obtain the line monitoring information of the power line, and send the line monitoring information to the monitoring analysis module.

[0035] The monitoring and analysis module is used to analyze the received line monitoring information and obtain corresponding snapshot analysis results, which include snapshots and non-snapshots.

[0036] When the capture analysis result indicates a capture, a capture command is generated and sent to the capture control module.

[0037] When the capture analysis result is no capture, no corresponding processing is performed.

[0038] Furthermore, methods for analyzing line monitoring information include:

[0039] Set monitoring and analysis requirements, and set simulated monitoring data according to the monitoring and analysis requirements; set target analysis methods based on the monitoring and analysis requirements and simulated monitoring data; set corresponding monitoring and analysis models according to the target analysis methods;

[0040] The monitoring information of the line is analyzed by a preset monitoring and analysis model to obtain the line analysis results.

[0041] Furthermore, methods for setting target analysis methods based on monitoring and analysis needs and simulated monitoring data include:

[0042] Obtain a monitoring and analysis method that meets the monitoring and analysis requirements according to the monitoring and analysis requirements and simulated monitoring data;

[0043] Identify the benchmark analysis method; compare the monitoring and analysis method with the benchmark analysis method to obtain the efficiency priority value and accuracy priority value of the monitoring and analysis method

[0044] Calculate the priority evaluation value of the monitoring and analysis method according to the priority evaluation formula. The priority evaluation formula is:

[0045] PY = b3×XL + b4×QL;

[0046] In the formula: PY is the priority evaluation value; b3 and b4 are both proportionality coefficients, and the value range is 0 < b3 ≤ 1, 0 < b4 ≤ 1; XL is the efficiency priority value; QL is the accuracy priority value;

[0047] Eliminate the monitoring and analysis methods with a priority evaluation value less than 0, sort the remaining monitoring and analysis methods in descending order of the priority evaluation value to obtain a method sequence; the user determines the target analysis method according to the method sequence.

[0048] The capture control module is used to control the high-speed camera to capture images when receiving a capture instruction, obtain a fault capture image, and send the fault capture image to the storage module for storage.

[0049] The storage module is used to store the fault capture images and send the stored fault capture images to the platform side.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] It abandons the disadvantages of traditional single-signal monitoring and innovatively integrates multi-source signals such as line current, voltage, temperature, arc light and sound in the surrounding environment. Taking short-circuit fault monitoring as an example, when a line short circuit occurs suddenly, not only will the current rise sharply instantaneously, but the voltage will also drop correspondingly. At the same time, strong arc light, rapid temperature rise will occur around the fault point, and there will also be special sounds of line discharge. With the multi-source signal acquisition module, these synchronous changes are keenly captured and the information is summarized to the monitoring and analysis module. By means of advanced machine learning algorithms such as built-in neural networks, learning and modeling are carried out on a large amount of historical data to accurately identify the combination of fault characteristics. This multi-pronged approach greatly reduces the misjudgment rate and significantly improves the accuracy rate, effectively ensuring the accuracy of fault determination and laying a solid foundation for subsequent repair decisions.

[0052] The response speed from fault detection to image capture is incredibly fast, with the entire process completed in milliseconds. The high-speed data acquisition card monitors the line status in real time with an ultra-high sampling rate. Once fluctuations matching fault characteristics appear in multiple source signals, the intelligent algorithm immediately performs rapid calculations and accurate judgments, triggering image capture. This module relies on a high-speed trigger circuit to wake up the high-speed camera in microseconds. Unlike traditional interval or loop shooting, the device is online 24 / 7, ready to capture the moment of a fault at any time. Whether it's a sudden tripping caused by lightning, a flashover that doesn't trip, or the instant a tree accidentally touches the power line, it can accurately freeze the moment, preserving the most original and crucial fault footage, saving precious time for repairs. Simultaneously, it provides conclusive evidence for determining responsibility in incidents such as lightning strikes where the power line doesn't trip, reducing unnecessary disputes.

[0053] By carefully deploying various types of sensors, a multi-dimensional "penetration" of power lines is achieved. Electrically, current and voltage sensors provide oversight; physical conditions are monitored by temperature and arc flash sensors; and acoustic characteristics are captured by sound sensors. Maintenance personnel can not only discern the surface symptoms of faults from captured images but also delve deeper into the root causes by combining temperature, voltage, and sound data, even providing early warnings. The wide-angle lenses of high-speed cameras excel in their capabilities, capturing a broad field of view that showcases both the macroscopic layout of the surrounding lines and the details of the fault point, providing comprehensive data support for fault diagnosis and emergency repairs. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a block diagram illustrating the principle of the present invention. Detailed Implementation

[0056] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0057] like Figure 1 As shown, a power line fault identification and capture system based on a multi-source signal triggering mechanism includes a multi-source analysis module, a data acquisition terminal, and a platform terminal.

[0058] The multi-source analysis module is used to perform fault analysis on power lines and determine multi-source monitoring items, such as current, voltage, temperature, arc light, sound, etc.; subsequently, the monitoring module in the acquisition terminal is set according to the multi-source monitoring items.

[0059] In one embodiment, multi-source monitoring items can be set up based on existing methods, such as aggregating various existing single signal monitoring items or relying on human experience.

[0060] In one embodiment, the method for determining multi-source monitoring items includes:

[0061] Historical fault data of power lines are analyzed to identify potential faults. By combining historical fault data with other existing information, the fault phenomena of each potential fault when it occurs are statistically analyzed, i.e., what different phenomena exist compared with the normal state, and these are marked as fault phenomena. A fault information table is set up based on potential faults and fault phenomena, and the fault information table is dynamically updated according to subsequent fault changes.

[0062] Based on the fault information table, conduct fault phenomenon monitoring simulation to understand whether the fault phenomenon meets the user's monitoring requirements, that is, whether the fault phenomenon can be monitored under the monitoring requirements, and obtain the monitoring simulation results of each fault phenomenon; determine multi-source monitoring items based on the monitoring simulation results of each fault phenomenon.

[0063] In one embodiment, the method for setting the fault phenomenon includes:

[0064] Acquire normal state data of power lines, set normal phenomenon standards for each potential fault based on the normal state data, and set training data based on the normal phenomenon standards and historical fault data. The training data consists of the normal phenomenon standards, fault data, and fault phenomena for the corresponding potential fault. Build a difference recognition model based on the training data. The expression of the difference recognition model is:

[0065]

[0066] In the formula: U is the input data, which consists of the unit data corresponding to the potential fault, i.e., the unit data set; the output data is the unit difference value CY(U), and the unit difference value is 1 or 0;

[0067] Historical fault data is classified according to potential faults to obtain fault classification data corresponding to each potential fault. Corresponding unit data is extracted from the fault classification data according to line phenomena, that is, the phenomenon data in the fault classification data is extracted as unit data. The unit data corresponding to potential faults is deduplicated and integrated into the input data.

[0068] The input data is analyzed using a difference recognition model to obtain the unit difference values ​​of the corresponding unit data;

[0069] The fault phenomenon is set based on the unit data with a unit difference value of 1.

[0070] In one embodiment, a method for simulating fault phenomenon monitoring based on a fault information table includes:

[0071] The system identifies fault phenomena in the fault information table in real time, determines various monitoring methods to monitor the fault phenomena based on existing monitoring technologies, and monitors corresponding current, voltage, temperature, arc light, and sound through Hall effect current sensors, capacitive voltage divider sensors, fiber optic temperature sensors, ultraviolet photosensitive sensors, and sound sensors; and marks the obtained monitoring methods as the initial selection methods.

[0072] The monitoring requirements obtained from users mainly focus on aspects such as monitoring efficiency and anti-interference capabilities. Since subsequent application monitoring will be conducted on power lines, failing to meet background monitoring requirements will hinder normal operation. Furthermore, to achieve timely analysis and image capture, there are also limitations on monitoring and data processing efficiency. To address highly transient faults such as lightning strikes causing tripping or failure to trip, or trees touching power lines, the process from the onset of a fault to line tripping can be completed in milliseconds or even microseconds, thus requiring high efficiency. Therefore, corresponding monitoring requirements are set based on the user's actual needs.

[0073] Each preliminary selection method is evaluated according to the monitoring requirements to determine whether it meets the monitoring requirements. Preliminary selection methods that meet the monitoring requirements are marked as candidate methods.

[0074] Screen the proposed methods, determine the multi-source monitoring methods for the corresponding fault phenomena, and set up multi-source monitoring items according to the multi-source monitoring methods.

[0075] In one embodiment, the selection method can be screened based on existing screening methods, such as screening based on parameters such as cost, accuracy, and efficiency; or it can be screened using an intelligent model.

[0076] In one embodiment, the method for filtering the options includes:

[0077] Identify the potential faults monitored by the candidate methods, that is, which potential faults(s) the data monitored by the candidate method will be used to identify. Because some fault phenomena are common to several potential faults, there may be situations where multiple potential faults are identified. Moreover, even different candidate methods corresponding to the same fault phenomenon may have different monitoring data due to differences in the candidate methods. If some candidate methods may monitor other aspects of data, they can also be applied to other potential faults. Determine whether it can be matched to the potential fault according to the user's monitoring requirements, generally from the corresponding potential fault candidates; set the weight coefficient for each potential fault, which can be set according to the importance of the potential fault, the fault loss, etc., generally according to the user's needs.

[0078] Estimate the cost of each candidate method and mark it as the candidate cost;

[0079] A standard evaluation model is established based on neural networks such as CNN or DNN. The model is trained manually using a training set, which includes input and output data. The input data consists of candidate methods, user monitoring requirements, potential faults, and weight coefficients. The output data represents the degree to which the candidate method meets the requirements relative to the potential fault, labeled as the compliance value. The calculation is primarily based on the degree to which each monitoring requirement is exceeded, with pre-defined coefficients for each requirement. The trained standard evaluation model is then analyzed to obtain the compliance value of the candidate method for the corresponding potential fault.

[0080] The potential faults are labeled as i, i = 1, 2, ..., n, where n is the number of potential faults;

[0081] Mark the target value for potential faults of the candidate methods as DB. i If a potential fault does not belong to the candidate method, its compliance value is 0; the weight coefficient of the potential fault is marked as δ. i Mark the alternative cost of the alternative method as CB;

[0082] Calculate the filter value for the corresponding candidate methods based on the filter formula: Filter formula:

[0083]

[0084] In the formula: SQ is the screening value; b1 and b2 are both proportional coefficients, with a value range of 0. <b1≤1,0<b2≤1;

[0085] The candidate methods are sorted in descending order of the filter values ​​to obtain a recommended list of corresponding fault phenomena. Users can then determine the multi-source monitoring method for the corresponding fault phenomenon based on the recommended list.

[0086] The acquisition terminal is used for subsequent installation in power lines, generally on transmission towers, and includes a monitoring module, a monitoring and analysis module, a snapshot control module, and a storage module;

[0087] The monitoring module is used to monitor the power lines in real time according to preset multi-source monitoring items and methods, obtain the line monitoring information of the power lines, and send the line monitoring information to the monitoring and analysis module.

[0088] For example, multi-source monitoring items include current, voltage, temperature, arc light, sound, etc.; the corresponding monitoring methods are to monitor them through high-precision Hall effect current sensors, capacitive voltage divider voltage sensors, fiber optic temperature sensors, ultraviolet photosensitive sensors, and sound sensors.

[0089] Current sensor: A high-precision Hall effect current sensor is adopted. Based on the Hall effect principle, when a current I flows through the power line, a magnetic field B is applied perpendicular to the current direction. The charge carriers are deflected under the action of the Lorentz force, and a Hall voltage VH is generated on both sides of the Hall element of the sensor. The relationship between the voltage and the current sensor is given by the formula:

[0090] V H =k H ·I·B;

[0091] Here, kH is the Hall coefficient, which is related to factors such as the material and size of the Hall element. By rationally designing the magnetic field strength and selecting a suitable Hall element, the line current can be accurately measured, with a measurement range from milliamperes to thousands of amperes. The output is a standard analog electrical signal (0-5V or 4-20mA), which can sensitively reflect subtle changes in current, providing crucial information for subsequent fault diagnosis. The sensor is installed on the phase and neutral lines of the power line near fault-prone locations such as branch points and joints, and is tightly fitted using a specially designed clamp to ensure accurate measurement.

[0092] Voltage sensor: It obtains the line voltage signal accurately by dividing the voltage through a series connection of high-voltage capacitor C1 and low-voltage capacitor C2 according to the following voltage division formula, and outputs a digital pulse signal. It is insulated and connected between the crossarm of the tower and the phase line to stably monitor the voltage.

[0093] in,

[0094] Fiber optic temperature sensors are tightly wound around wires, insulators, etc. Utilizing photothermal properties and the relationship between light transmission loss and temperature function L=f(T), they accurately sense temperature fluctuations, converting optical signals into electrical signals for output with an accuracy of ±0.5℃, providing real-time warnings of potential overheating.

[0095] Ultraviolet (UV) photosensor: This sensor uses a photosensitive element that is sensitive to ultraviolet light. When a power line fault generates an arc, the intensity of UV light in the arc increases sharply. Let the UV intensity be IUV. The electrical signal IUV output by the sensor is approximately proportional to the UV intensity, i.e.:

[0096] V UV =k UV ·I UV ;

[0097] Wherein, kUV is the proportionality coefficient, which is determined by the material and characteristics of the sensor. The sensor is installed near insulators, line joints, or other areas prone to arcing, and fixed to the auxiliary support of the tower. The angle is adjusted so that it faces the direction where arcing may occur, ensuring maximum capture of arcing signals.

[0098] Sound sensor: A piezoelectric sound sensor is selected. Based on the piezoelectric effect, when external sound causes the piezoelectric crystal inside the sensor to vibrate, it generates an electrical signal related to the sound intensity and frequency. Let the sound pressure be P and the generated charge be Q; the two satisfy the piezoelectric equation:

[0099] Q = d·P;

[0100] Where d is the piezoelectric constant, which depends on the properties of the piezoelectric material. This sensor features high sensitivity and a wide frequency response range, effectively capturing various sounds in the environment surrounding power lines. Installed on the crossarm of a power line pole or suspended a certain distance below the conductor, it employs vibration damping and wind protection measures to avoid false signals caused by pole vibration or wind. Its output electrical signal is initially amplified by a preamplifier circuit before being sent to subsequent processing stages.

[0101] Electric field sensor: Based on the principle of charge induction, lightning generates a strong electric field change in the surrounding space. When the electric field strength E changes, the sensing electrodes inside the sensor generate induced charges. By measuring the current or voltage generated by these induced charges, the change in electric field strength can be reflected. The relationship between its output signal and the electric field strength satisfies the formula:

[0102] V = k·E;

[0103] Where V is the voltage signal output by the sensor, and k is a proportionality constant related to the sensor. The sensor can monitor changes in the electric field before lightning strikes, enabling early warning. It features high sensitivity and high response speed, allowing for rapid and accurate detection of changes in electric field intensity. Simultaneously, it has good anti-interference capabilities and can operate stably in complex environments. Installed in open areas near power lines, it can be fixed to poles using brackets to ensure effective sensing of surrounding electric field changes.

[0104] Due to the varying signal strengths and types, signals first enter the preamplifier. Weak signals from sound sensors are amplified 500-1000 times, while signals from other sensors are amplified according to their own characteristics. A low-noise operational amplifier chip (OP07) is used to ensure signal purity and boost magnitude for subsequent fine processing.

[0105] Next, the filter bank begins operation. The current signal is filtered by a low-pass filter (transfer function H(s) = 1 / (1+sRC), cutoff frequency set to 10kHz) to remove high-frequency noise; the voltage signal is filtered by a band-pass filter (50Hz-5kHz band) to extract key frequency components; temperature, arc light, and sound signals are also filtered by adaptive filters to remove irrelevant interference, ensuring the signal quality of input to subsequent stages.

[0106] The pre-amplified and filtered signal quickly flows to the high-speed data acquisition card. The acquisition card samples at a high speed of 10MS / s, and acquires the signals from each sensor simultaneously through multiple channels. The analog signal is converted to digital according to the following quantization formula (12-16 bit sampling precision) and stored in a 1MB-10MB first-in-first-out (FIFO) high-speed buffer to ensure data integrity and real-time availability for immediate use by the algorithm.

[0107]

[0108] Where D represents the quantized digital value; Va is the analog input voltage; Vr is the reference voltage; and n is the number of bits. The quantized digital value D is determined by comparing Va and Vr and based on the quantization level 2^n. Simply put, this is the process of converting a continuous analog signal into a discrete digital signal. Afterwards, the data is normalized and effective features are extracted to complete data preprocessing; thus, line monitoring information is obtained.

[0109] The monitoring and analysis module is used to analyze the received line monitoring information and obtain corresponding snapshot analysis results, which include snapshots and non-snapshots.

[0110] When the capture analysis result indicates a capture, a capture command is generated and sent to the capture control module.

[0111] When the capture analysis result is no capture, no corresponding processing is performed.

[0112] The line monitoring information is analyzed to determine whether there is an abnormal fault. If there is an abnormal fault, the snapshot analysis result is to capture an image; otherwise, the snapshot analysis result is not to capture an image.

[0113] In one embodiment, existing technologies can be used to determine whether a power line has an abnormal fault, such as a built-in neural network algorithm. The input layer sets corresponding nodes based on the characteristics of collected line monitoring information (current surge rate, voltage drop amplitude, abnormal temperature rise, arc intensity, sound characteristics, etc.). These nodes are then weighted and calculated through hidden layers (1-3 layers, adjusting the number of points according to complexity). The output layer outputs a fault-corresponding numerical table to determine the fault. During training, massive amounts of historical fault and normal data are used as training data. Backpropagation is used to adjust weight thresholds to construct an accurate fault feature model. During operation, real-time feature values ​​are input, faults are determined in real time, and the model is periodically updated adaptively based on new data.

[0114] In one embodiment, as technology iterates and monitoring conditions become increasingly complex and changeable, there is a need to continuously achieve efficient monitoring and analysis. Therefore, in this embodiment, the method for analyzing line monitoring information includes:

[0115] Set monitoring and analysis requirements, which involves analyzing the line monitoring information to determine if there are any abnormal faults, and adding other requirements such as analysis efficiency; set simulated monitoring data according to the monitoring and analysis requirements, which are the preset line monitoring information and the corresponding snapshot analysis results;

[0116] The target analysis method is determined based on the monitoring and analysis requirements and simulated monitoring data; a corresponding monitoring and analysis model is set according to the target analysis method. The monitoring and analysis model is used to analyze the line monitoring and determine the capture analysis results; that is, the target analysis method and monitoring and analysis model are dynamically adjusted as technology changes and line monitoring information changes.

[0117] The monitoring information of the line is analyzed by a preset monitoring and analysis model to obtain the line analysis results.

[0118] In one embodiment, a method for determining the target analysis method based on monitoring and analysis needs and simulated monitoring data includes:

[0119] Based on the monitoring and analysis requirements and simulated monitoring data, determine the monitoring and analysis methods that meet those requirements;

[0120] Mark the target analysis method currently being applied as the baseline analysis method; compare the monitoring analysis method with the baseline analysis method to determine the efficiency priority value and accuracy priority value of the monitoring analysis method. The efficiency priority value is the efficiency that exceeds that of the baseline analysis method; the accuracy priority value is the accuracy that exceeds that of the baseline analysis method. If they are 10 and 9 respectively, then it is better than 10-9=1. If they are 9 and 10 respectively, then it is better than 9-10=-1.

[0121] The priority assessment value of the monitoring and analysis method is calculated based on the priority assessment formula, which is as follows:

[0122] PY = b3 × XL + b4 × QL;

[0123] Where: PY is the priority evaluation value; b3 and b4 are both proportionality coefficients, and the value range is 0 < b3 ≤ 1, 0 < b4 ≤ 1; XL is the efficiency priority value; QL is the precision priority value;

[0124] Eliminate the monitoring and analysis methods with a priority evaluation value less than 0, and sort the remaining monitoring and analysis methods in descending order of the priority evaluation value to obtain a method sequence;

[0125] The user determines the target analysis method according to the method sequence, that is, finally determined according to the user's needs.

[0126] The capture control module is used to control the high-speed camera to capture when receiving a capture instruction, obtain a fault capture image, and send the fault capture image to the storage module for storage.

[0127] Exemplarily, after receiving the capture instruction, a microsecond-level start response mechanism is used for active fault capture. Through a carefully designed high-speed trigger circuit (ensuring that the trigger instruction transmission delay td ≤ 1 microsecond, realized by a high-speed logic chip and optimized wiring), a high-speed camera with a frame rate of 100 frames per second, a 120-degree wide-angle lens and optical anti-shake is instantly awakened, and the fault scene is recorded within 8 microseconds.

[0128] When the camera starts, the autofocus system uses phase detection autofocus (PDF). According to the image phase difference Δφ and the focus distance function d = f(Δφ), it accurately focuses on the key area of the circuit; automatic exposure depends on the built-in light sensor to monitor the light intensity I p , using the center-weighted average photometry algorithm, intelligently adjusts the aperture, shutter speed, and sensitivity to ensure that the picture is clear and bright, and completely captures the fault moment details such as electric arcs and line jitters.

[0129] In one embodiment, according to the development of capture technology, dynamic capture analysis is performed to realize the dynamic update of capture devices and systems.

[0130] The storage module is used to store the fault capture images and send the stored fault capture images to the platform side.

[0131] Using high-capacity solid-state drives (SSDs), with sequential read / write speeds of up to 500MB / s and above, allows for the rapid reception and storage of this multi-source data. Following rigorous storage rules, a multi-level directory structure is meticulously built based on time, line number, and fault type. Each captured image is named in a standardized format such as {date-line-faulttype-seq}.jpg, where date represents the date, line refers to the line number, faulttype is the fault type, and seq is the image sequence number. This ensures that image data is archived in an orderly manner, greatly facilitating rapid and accurate retrieval by maintenance personnel.

[0132] In one embodiment, to ensure the continuous and efficient utilization of SSD storage space, the system has a built-in intelligent cleaning program that performs data cleaning tasks periodically. It operates based on preset dual cleaning conditions: first, it screens according to a time cycle, cleaning up outdated image data every 30 days; second, based on storage capacity threshold judgment, once the SSD's used space reaches 90%, the cleaning process is immediately initiated to accurately screen out expired or duplicate images, always reserving sufficient storage space for newly generated faulty images, ensuring stable and efficient operation of the storage process.

[0133] Upon completion of local storage, crucial fault images and related detailed information are simultaneously transmitted to the platform using cutting-edge wireless communication technologies such as 4G / 5G / APN. To ensure secure and reliable data transmission, the widely recognized HTTPS encryption protocol is used throughout the process, effectively resisting external interference and potential data theft risks, ensuring that critical data arrives at its destination undamaged.

[0134] Considering the limited bandwidth of wireless transmission and the large amount of data, intelligent compression processing is performed on the image at the transmitting end, prioritizing the JPEG compression algorithm. Thanks to its excellent compression performance, the original image data size can be reduced to S... o Significantly compressed to S c It can typically achieve compression ratios as high as 10:1 or even better, that is, it satisfies:

[0135]

[0136] This significantly reduces the network transmission burden and improves transmission efficiency, enabling data to be delivered to the platform as quickly as lightning. To further ensure the accuracy and integrity of the platform, the system meticulously deploys a CRC check mechanism. At the data sending end, a unique CRC checksum is generated for each batch of transmitted data based on a specific algorithm and sent along with the data. When the platform, as the receiving end, receives the data, it immediately recalculates the checksum using the same algorithm and rigorously compares it with the checksum attached to the sending end. If a discrepancy is found, the receiving end immediately sends a retransmission command to the sending end, requesting a retransmission of the batch of data. Through this rigorous verification process, the accuracy of fault data received by maintenance personnel is comprehensively guaranteed, ultimately achieving remote real-time monitoring and efficient management of power line faults, laying a solid foundation for line maintenance and the safe and stable operation of the power grid.

[0137] The platform is used by users and is generally used as an operations and maintenance center or data center.

[0138] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0139] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A power line fault identification and capture system based on a multi-source signal triggering mechanism, characterized in that, Includes a multi-source analysis module, a data acquisition terminal, and a platform terminal; The multi-source analysis module is used to perform fault analysis on power lines and determine multi-source monitoring items; The acquisition terminal includes a monitoring module, a monitoring and analysis module, a snapshot control module, and a storage module; The monitoring module is used to monitor the power line in real time according to the preset multi-source monitoring items and multi-source monitoring methods, obtain the line monitoring information of the power line, and send the line monitoring information to the monitoring analysis module. The monitoring and analysis module is used to analyze the received line monitoring information and obtain corresponding snapshot analysis results, which include snapshots and non-snapshots. When the capture analysis result indicates a capture, a capture command is generated and sent to the capture control module. When the capture analysis result is no capture, no corresponding processing is performed; The capture control module is used to control the high-speed camera to capture images when a capture command is received, obtain fault capture images, and send the fault capture images to the storage module for storage. The storage module is used to store fault capture images and send the stored fault capture images to the platform. Methods for determining multi-source monitoring items include: Establish a fault information table, which is used to statistically analyze potential faults in power lines and the corresponding fault phenomena. Based on the fault information table, a fault phenomenon monitoring simulation is performed to obtain the monitoring simulation results of the fault phenomenon, and multi-source monitoring items are determined based on the monitoring simulation results; Methods for monitoring and simulating fault phenomena based on fault information tables include: Identify the fault phenomena in the fault information table, obtain the various monitoring methods of the fault phenomena in real time, and mark them as the initial selection methods; Obtain the user's monitoring requirements, evaluate the initial selection method for the fault phenomenon based on the monitoring requirements, evaluate whether the initial selection method meets the monitoring requirements, and mark the initial selection method that meets the monitoring requirements as a candidate method; The candidate methods are filtered to obtain the multi-source monitoring methods for the fault phenomenon, and multi-source monitoring items are set according to the multi-source monitoring methods. Methods for analyzing line monitoring information include: Set monitoring and analysis requirements, and set simulated monitoring data according to the monitoring and analysis requirements; set target analysis methods based on the monitoring and analysis requirements and simulated monitoring data; set corresponding monitoring and analysis models according to the target analysis methods; The monitoring information of the line is analyzed by a preset monitoring and analysis model to obtain the line analysis results.

2. The intelligent capture system for power line fault identification based on a multi-source signal triggering mechanism according to claim 1, characterized in that, The methods for creating a fault information table include: Historical fault data of power lines is acquired, fault identification is performed on the historical fault data to obtain several historical line faults, and the deduplicated historical line faults are marked as potential faults. The potential fault symptoms are set according to the historical fault data; and the potential faults and fault symptoms are updated according to the updates to the historical fault data. Set up a fault information table based on potential faults and fault symptoms.

3. The intelligent capture system for power line fault identification based on a multi-source signal triggering mechanism according to claim 2, characterized in that, Methods for setting potential fault phenomena based on historical fault data include: Obtain the normal state data of the power line, set the normal phenomenon standard for each potential fault according to the normal state data, and establish a difference identification model based on the normal phenomenon standard. The expression of the difference identification model is: ； Where: U is the input data, the output data is the unit difference value CY(U), and the unit difference value is 1 or 0; Classify the historical fault data according to the potential faults, obtain the fault classification data for each potential fault, extract the corresponding unit data from the fault classification data, and integrate the unit data corresponding to the potential faults after removing duplicates as the input data; Analyze the input data through the difference identification model to obtain the unit difference value of the unit data; Set the fault phenomenon according to the unit data with the unit difference value of 1.

4. The intelligent capture system for power line fault identification based on a multi-source signal triggering mechanism according to claim 2, characterized in that, The method for screening candidate methods includes: Identify the potential faults monitored by the candidate method, and obtain the weight coefficient of the potential faults; estimate the cost of the candidate method and mark it as the candidate cost; analyze the candidate method to obtain the compliance value of the candidate method for the potential faults; Label potential faults as i, i = 1, 2, ..., n, where n is the number of potential faults; label the compliance value of the candidate method for potential faults as DB. i The weighting coefficient for potential faults is denoted as δ. i Mark the alternative cost of the alternative method as CB; Calculate the screening value of the corresponding candidate method according to the screening formula. The screening formula is: ； Where: SQ is the screening value; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; Sort the candidate methods in descending order according to the screening value to obtain the recommended list of the fault phenomena. The user determines the multi-source monitoring method of the fault phenomena according to the recommended list.

5. The intelligent capture system for power line fault identification based on a multi-source signal triggering mechanism according to claim 1, characterized in that, The method for setting the target analysis method based on the monitoring analysis requirements and simulated monitoring data includes: Obtain the monitoring analysis methods that meet the monitoring analysis requirements according to the monitoring analysis requirements and simulated monitoring data; Identify the reference analysis method; compare the monitoring analysis method with the reference analysis method to obtain the efficiency priority value and accuracy priority value of the monitoring analysis method; Calculate the priority evaluation value of the monitoring analysis method according to the priority evaluation formula. The priority evaluation formula is: PY = b3 × XL + b4 × QL; Where: PY is the priority evaluation value; b3 and b4 are both proportionality coefficients, and the value range is 0 < b3 ≤ 1, 0 < b4 ≤ 1; XL is the efficiency priority value; QL is the accuracy priority value; Eliminate the monitoring analysis methods with the priority evaluation value less than 0, sort the remaining monitoring analysis methods in descending order according to the priority evaluation value to obtain the method sequence; the user determines the target analysis method according to the method sequence.

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