Power transmission and distribution line fault remote transmission indicator on-line monitoring method, system and equipment

Abstract

The invention relates to a power transmission and distribution line fault remote transmission indicator on-line monitoring method, system and device, and belongs to the technical field of power transmission and distribution line operation monitoring. Comprising the following steps of: acquiring multi-dimensional signals triggered by dynamic thresholds: acquiring line voltage, current, main harmonic frequency of transient signals and attenuation factor signals in real time through a fault indicator; direct electric connection signal transmission: the analog electric signal output by the fault indicator is directly accessed to an online monitoring device, and a digital signal is obtained through analog-to-digital conversion; multi-mode remote data uploading: the online monitoring device uploads data through a multi-mode communication module, and dynamically switches communication modes according to signal strength and data volume; a trigger threshold value can be dynamically adjusted, multi-dimensional signals are collected in real time, data are uploaded through the multi-mode communication module, accurate positioning and type identification of faults are achieved, and therefore the accuracy and timeliness of fault monitoring are improved.

Description

Technical Field

[0001] This invention relates to an online monitoring method, system, and equipment for remote fault indicators of power transmission and distribution lines, belonging to the field of power transmission and distribution line operation monitoring technology. Background Technology

[0002] Remote fault indicators for power transmission and distribution lines are core equipment for ensuring the stable operation of 10kV-35kV medium and low voltage power grids. Their online monitoring capabilities directly determine the fault response speed and operation and maintenance efficiency. Traditional fault monitoring methods often rely on manual inspections or fixed threshold triggering mechanisms. These methods suffer from slow response speeds, low accuracy, and susceptibility to environmental interference. Especially under complex and variable line loads and weather conditions, traditional fixed threshold methods often fail to accurately identify faults, leading to frequent false alarms or missed alarms.

[0003] Existing technologies generally use fixed voltage / current thresholds to trigger fault indications, which cannot dynamically adapt to changes in line load rate and the impact of extreme weather. For example, the online monitoring system for power line faults disclosed in patent authorization number CN207440228U uses a fixed voltage change rate threshold (e.g., 80V / ms) for the fault indicator, which does not consider the current fluctuation characteristics during peak line load, resulting in a high false trigger rate. Under extreme weather conditions such as rain and snow, the failure rate of fault detection increases. Such fixed threshold designs cannot balance the contradiction between "preventing false alarms" and "preventing failures," which seriously restricts the reliability of monitoring.

[0004] Meanwhile, the existing communication modes are limited: relying on GSM / GPRS or a single LoRa communication mode, resulting in weak signal coverage (such as in mountainous or remote areas), and failing to meet the requirements of low power consumption and high bandwidth. The system's battery life is also insufficient: lacking intelligent power management, solar power efficiency is low, battery life is short, and maintenance costs are high. Summary of the Invention

[0005] The purpose of this invention is to provide an online monitoring method, system, and device for remote transmission indicators of power transmission and distribution lines. This device can dynamically adjust the trigger threshold, collect multi-dimensional signals in real time, and upload data through a multi-mode communication module to achieve accurate fault location and type identification, thereby improving the accuracy and timeliness of fault monitoring.

[0006] The online monitoring method for remote fault indicators of power transmission and distribution lines according to the present invention includes the following steps: S1: Multi-dimensional signal acquisition triggered by dynamic threshold: Real-time acquisition of line voltage, current, main harmonic frequency and attenuation factor signals of transient signals through fault indicator; Dynamically adjust the trigger threshold based on line load rate, and drive fault indication when the voltage change rate, current pulse characteristic value or the main harmonic frequency and attenuation factor of transient signal reach the dynamic threshold. S2: Direct electrical connection signal transmission: The analog electrical signal output by the fault indicator is directly connected to the online monitoring device, and the digital signal is obtained through analog-to-digital conversion; S3: Multi-mode remote data upload: The online monitoring device uploads data through a multi-mode communication module and dynamically switches the communication mode according to the signal strength and data volume; S4: Distributed traveling wave localization and fault identification: The power transmission status perception platform uses a distributed traveling wave localization algorithm to calculate the fault location, identifies the fault type through a CNN-LSTM fusion model, and generates dynamic priority inspection tasks.

[0007] Preferably, the voltage change rate in step S1 is calculated using the following formula:

[0008] in, This is the voltage sample value at the current moment. This is the voltage value at the previous sampling time. The sampling interval is defined; a fault indication is triggered when k is greater than the set threshold. Preferably, the characteristic value of the current signal pulse is calculated using the following formula:

[0009] in, The peak value of the current pulse. The pulse duration is specified; a fault indication is triggered when S exceeds a set threshold.

[0010] Preferably, the dominant harmonic frequency and attenuation factor of the transient signal in step S1 are calculated using the Proni algorithm, and the formula is as follows:

[0011] in, For amplitude, As the attenuation factor, Main harmonic frequency, The phase is determined by matching the changing trends of the main harmonic frequency and attenuation factor with a preset model to achieve fault type determination.

[0012] Preferably, the adjustment formula for the dynamic threshold in step S1 is:

[0013] in, The baseline threshold is λ, where λ is the load adjustment coefficient. For the real-time load rate of the line, =0.2 is the weather adjustment factor. These are weather-related factors.

[0014] Preferably, the multi-mode communication in step S3 includes LoRa mode, NB-IoT mode and 4G / 5G mode; LoRa mode: signal strength RSSI ≥ -80dBm and data volume ≤ 100KB; NB-IoT mode: -100dBm ≤ RSSI < -80dBm and data volume ≤ 500KB; 4G / 5G mode: RSSI < -100dBm or data volume > 500KB.

[0015] Preferably, the formula for the distributed traveling wave positioning algorithm in step S4 is:

[0016] Where v is the traveling wave propagation speed. / The time it takes for the fault signal to reach the adjacent monitoring node. This represents the node spacing.

[0017] Preferably, the CNN-LSTM fusion model in step S4 includes: A CNN feature extraction module, consisting of 3 convolutional layers and 2 pooling layers, is used to extract local features of spatial dimension from the input signal; An LSTM time series processing module contains two LSTM hidden layers, used to learn the dependencies of the local features on the time series. An output module, comprising a fully connected layer and a Softmax activation function, is used to output the probability distribution of fault types, which include at least phase-to-phase short circuit, low-resistance grounding, and high-resistance grounding.

[0018] Preferably, the power management module integrates an intelligent energy management algorithm and is connected to a solar panel and a battery. The algorithm's logic is as follows: real-time monitoring of solar power supply; when the power is greater than or equal to the power consumed by the device during operation, solar power is supplied to charge the battery; when the solar power supply is insufficient, automatic switching to battery power supply is initiated. The transient signal sampling frequency of the dynamic threshold fault indicator module is 500kHz to ensure complete capture of transient signals.

[0019] The computer device of the present invention includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements an online monitoring method for a remote transmission indicator of power transmission and distribution line faults.

[0020] Compared with existing technologies, the online monitoring method, system, and equipment for remote transmission indicators of power transmission and distribution lines of the present invention have the following advantages: 1. Improved dynamic adaptability By adopting a dynamic threshold adjustment mechanism that links load rate and weather, it breaks away from the limitations of traditional fixed thresholds. It can flexibly optimize triggering conditions according to the real-time operating status of the line, effectively avoiding false alarms and missed alarms under extreme loads or severe weather, and significantly enhancing the reliability and environmental adaptability of fault monitoring.

[0021] 2. Full-scenario communication coverage By integrating multi-mode communication technology, the transmission mode can be dynamically switched according to different signal strengths and data volume requirements. This not only meets the needs of low-power long-distance transmission in open areas, but also solves the problem of data backhaul in weak signal scenarios such as mountainous and remote areas, ensuring that fault information is uploaded stably without dead zones and breaking the coverage bottleneck of a single communication mode.

[0022] 3. Precise fault decision support By combining distributed traveling wave localization with a CNN-LSTM fusion model, the system can accurately locate faults and intelligently identify fault types, providing maintenance personnel with intuitive and reliable decision-making support and replacing the traditional fuzzy processing method that relies on experience-based judgment.

[0023] 4. Long-term stable operation guarantee By optimizing the collaborative working mode of solar energy and batteries, and dynamically adjusting power consumption strategies based on environmental energy supply, the system significantly extends its operating range under harsh outdoor conditions, reduces the frequency of manual maintenance, and lowers the manpower and material investment required for long-term operation and maintenance. This provides core technical support for intelligent operation and maintenance of the power grid, and has significant practical application value and industry promotion potential. Attached Figure Description

[0024] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0025] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0026] Example 1: like Figure 1 As shown in the figure, this embodiment discloses an online monitoring method for a remote fault indicator of a power transmission and distribution line, including the following steps: S1: Multi-dimensional signal acquisition triggered by dynamic threshold: Real-time acquisition of line voltage, current, main harmonic frequency and attenuation factor signals of transient signals through fault indicator; Dynamically adjust the trigger threshold based on line load rate, and drive fault indication when the voltage change rate, current pulse characteristic value or the main harmonic frequency and attenuation factor of transient signal reach the dynamic threshold. The voltage change rate in step S1 is calculated using the following formula:

[0027] in, This is the voltage sample value at the current moment. This is the voltage value at the previous sampling time. The sampling interval is defined as k; a fault indication is triggered when k is greater than the set threshold; Δt = 10ms is the sampling interval. Preferably, the characteristic value of the current signal pulse is calculated using the following formula:

[0028] in, The peak value of the current pulse. The pulse duration is specified; a fault indication is triggered when S exceeds a set threshold. A fault indication is triggered when k ≥ 80V / ms or S ≥ 30A·ms.

[0029] The dominant harmonic frequency and attenuation factor of the transient signal in step S1 are calculated using the Proni algorithm, and the formula is as follows:

[0030] in, For amplitude, As the attenuation factor, Main harmonic frequency, The phase is used; the fault type is determined by matching the changing trend of the main harmonic frequency and the attenuation factor with a preset model.

[0031] Sampling unit parameters: Voltage sampling uses AD7606 chip (16-bit, sampling rate 1kHz), current sampling uses ACS712 chip (16-bit, sampling rate 1kHz), and transient signal sampling uses AD9288 chip (14-bit, sampling rate 500kHz). Dynamic threshold calculation:

[0032] in, The baseline threshold is λ, where λ is the load adjustment coefficient. For the real-time load rate of the line, =0.2 is the weather adjustment factor. These are weather influencing factors. In this embodiment, rainy day = 1.2, snowy day = 1.3, foggy day = 1.1, and sunny day = 1.0.

[0033] Voltage change rate reference threshold The load adjustment factor λ = 0.5, and the weather adjustment factor μ = 0.2; if the line load rate (Peak load), rainy weather, calculate the dynamic threshold: when the voltage change rate k ≥ When this occurs, a fault indication is triggered; Transient signal feature extraction: The Proni algorithm is used to extract the main harmonic frequency fi and the attenuation factor. Match the preset model.

[0034] S2: Direct electrical connection signal transmission: The fault indicator is directly electrically connected to the online monitoring device via a 485 bus. The analog signal is converted into a digital signal by a 16-bit analog-to-digital converter (sampling rate 1kHz), and the data transmission delay is ≤10ms.

[0035] S3: Multi-mode remote data upload: The online monitoring device uploads data through a multi-mode communication module and dynamically switches the communication mode according to the signal strength and data volume; Communication mode switching logic: LoRa mode: RSSI≥ 80dBm and data size ≤100KB (e.g., uploading fault data in mountainous areas); NB-IoT mode: -100dBm≤RSSI<-80dBm and data volume≤500KB (such as uploading daily monitoring data in urban areas); 4G / 5G mode: RSSI < -100dBm or data volume > 500KB (e.g., high-volume data upload in remote areas).

[0036] S4: Distributed traveling wave localization and fault identification: The power transmission status perception platform uses a distributed traveling wave localization algorithm to calculate the fault location, identifies the fault type through a CNN-LSTM fusion model, and generates dynamic priority inspection tasks.

[0037] Distributed traveling wave positioning:

[0038] Where v is the traveling wave propagation speed. / The time it takes for the fault signal to reach the adjacent monitoring node. This represents the node spacing.

[0039] For example, the distance d between adjacent nodes i and j ij =2km, fault signal arrival time t i =10μs, t j =15μs, traveling wave velocity =2.99×10 8 m / s, then the fault location is:

[0040] The fault is located between nodes i and j, 1747.5 meters from node i.

[0041] CNN-LSTM model training: The CNN layer consists of 3 convolutional layers and 2 max pooling layers to extract spatial features of the signal; The LSTM layer includes two hidden layers to extract time series features of the signal; Output layer: Fully connected layer and Softmax activation function, output fault types, including: phase-to-phase short circuit, low-resistance grounding and high-resistance grounding.

[0042] The training sample size is 150,000 (including 6 types of faults), the loss function is cross-entropy loss (70%) + mean squared error loss (30%), and the initial learning rate is 0.001.

[0043] Example 2: The online monitoring system for remote transmission indicators of power transmission and distribution line faults according to the present invention includes: Dynamic threshold fault indicator module: configured with voltage sampling unit, current sampling unit, transient signal sampling unit, dynamic threshold adjustment unit and direct electrical connection output interface; Multimode online monitoring device: directly electrically connected to the fault indicator module, configured with a 16-bit analog-to-digital converter, a multimode communication module, a distributed traveling wave acquisition unit, and a power management module; The power transmission status awareness platform includes a distributed traveling wave positioning module, a CNN-LSTM fault identification module, a dynamic priority decision-making module, and a visualization module.

[0044] The power management module adopts an energy management algorithm: when the solar power supply is greater than or equal to the device's power consumption, the solar power supply charges the battery; when the solar power supply is insufficient, the battery is replenished.

[0045] The transient signal sampling frequency of the dynamic threshold fault indicator module is 500kHz to ensure complete capture of transient signals.

[0046] Transient signal sampling unit: sampling rate of 500kHz to ensure complete capture of transient signals; Dynamic threshold adjustment unit: adopts STM32F407 microcontroller, with built-in load rate calculation model and weather factor mapping table: calculates load rate and weather factors in real time and outputs dynamic threshold.

[0047] Intelligent power management module: 20W solar panel power, 10Ah battery capacity; Energy management algorithm: When the solar power is ≥5W (power consumed by the device), the solar energy supplies power and charges the device. When the solar power is less than 5W, the battery provides supplemental power. Distributed traveling wave acquisition unit: Uses AD9481 chip (12-bit, sampling rate 1MHz) to capture traveling wave signals.

[0048] Power Transmission Status Awareness Platform Visualization module: Generates heatmaps of fault locations (updated every minute), Gantt charts of inspection tasks, and statistical reports on fault types; Data storage: It adopts a MySQL+Redis architecture and supports data storage and querying of 100,000 records per day.

[0049] Example 3: Based on Embodiment 1, the computer device of the present invention includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the online monitoring method of the remote transmission indicator for power transmission and distribution line faults described in Embodiment 1.

[0050] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for on-line monitoring of a power transmission line fault remote indicator, characterized in that, Comprising the following steps: S1: Dynamic threshold triggered multi-dimensional signal acquisition: Real-time acquisition of line voltage, current, transient signal main harmonic frequency and attenuation factor signal by fault indicator; Dynamic adjustment of trigger threshold based on line load rate, when the voltage rate of change, current pulse characteristic value or transient signal main harmonic frequency and attenuation factor arrival time meets the dynamic threshold, drive fault indication; S2: Direct electrical connection signal transmission: The analog electrical signal output by the fault indicator is directly connected to the online monitoring device, and the digital signal is obtained through analog-to-digital conversion; S3: Multi-mode remote data upload: The online monitoring device uploads data through a multi-mode communication module, and dynamically switches communication modes according to signal strength and data volume; S4: Distributed traveling wave positioning and fault identification: The power transmission state perception platform uses a distributed traveling wave positioning algorithm to calculate the fault location, identifies the fault type through a CNN-LSTM fusion model, and generates a dynamic priority patrol task.

2. The method of on-line monitoring of long distance transmission line fault indicator according to claim 1, characterized in that, The calculation of the voltage rate of change in step S1 uses the formula: wherein, is a voltage sample value at a current time instant, is a voltage value at a previous sample time instant, is a sampling interval; a fault indication is triggered when k is greater than a set threshold value; The calculation of the current signal pulse characteristic value uses the formula: wherein, is the current pulse peak value, is the pulse duration; a fault indication is triggered when S is greater than a set threshold.

3. The method of on-line monitoring of long distance transmission line fault indicator according to claim 1, characterized in that, The main harmonic frequency and attenuation factor of the transient signal in step S1 are calculated using the Prony algorithm, with the formula: wherein, is an amplitude, is a decay factor, is a main harmonic frequency, is a phase; by matching a preset model with a variation trend of the main harmonic frequency and the decay factor, fault type determination is realized.

4. The method of on-line monitoring of long distance transmission line fault indicator according to claim 1, characterized in that, The adjustment formula of the dynamic threshold in step S1 is: wherein, is a reference threshold value, λ is a load adjustment coefficient, is a line real-time load rate, = 0.2 is a weather adjustment coefficient, is a weather influence factor.

5. The method of on-line monitoring of long distance transmission line fault indicator according to claim 1, characterized in that, The multi-mode communication in step S3 includes LoRa mode, NB-IoT mode, and 4G / 5G mode; LoRa mode: signal strength RSSI≥-80dBm and data volume≤100KB; NB-IoT mode: -100dBm≤RSSI<-80dBm and data volume≤500KB; 4G / 5G mode: RSSI<-100dBm or data volume>500KB.

6. The method of on-line monitoring of long distance transmission line fault indicator according to claim 1, characterized in that, The formula of the distributed traveling wave positioning algorithm in step S4 is: where v is the wave propagation speed, t is the time of arrival of the fault signal at the adjacent monitoring node, is the node spacing.​ 7. The method of on-line monitoring of long distance transmission line fault indicator according to claim 1, characterized in that, The CNN-LSTM fusion model in step S4 includes: A CNN feature extraction module containing 3 convolution layers and 2 pooling layers, used to extract spatial dimension local features from input signals; An LSTM time series processing module containing 2 layers of LSTM hidden layers, used to learn the dependence of local features on time series; An output module containing a fully connected layer and a Softmax activation function, used to output the probability distribution of fault types, including at least phase-to-phase short circuit, low resistance grounding, and high resistance grounding.

8. An online monitoring system of power transmission and distribution line fault remote indicator based on the online monitoring method of power transmission and distribution line fault remote indicator according to any one of claims 1-7, characterized in that, The system includes: Dynamic threshold fault indicator module: Configured with voltage sampling unit, current sampling unit, transient signal sampling unit, dynamic threshold adjustment unit, and direct electrical connection output interface; Multi-mode online monitoring device: Directly connected with the fault indicator module, configured with 16-bit analog-to-digital conversion unit, multi-mode communication module, distributed traveling wave acquisition unit, and power management module; Power transmission state perception platform: Including distributed traveling wave positioning module, CNN-LSTM fault identification module, dynamic priority decision module, and visualization module.

9. The power line fault remote indicator on-line monitoring system according to claim 8, characterized in that, The power management module integrates intelligent energy management algorithm, and is connected with solar cell panel and storage battery; the logic of the algorithm is: real-time monitoring of solar power supply power, when the power is greater than or equal to device running consumption power, solar power supply and charging of the storage battery; when the solar power supply power is insufficient, automatically switching to storage battery power supply, the transient signal sampling frequency of the dynamic threshold fault indicator module is 500 kHz, ensuring complete capture of transient signals.

10. A computer device, comprising: The device comprises a memory and a processor, and the memory stores a computer program capable of running on the processor, and the processor implements the online monitoring method of the power transmission and distribution line fault remote indicator according to any one of claims 1 to 7 when running the computer program.

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