Power transmission line non-outage detection method and system

Abstract

The invention discloses a power transmission line non-outage detection method and system, and the method comprises the following steps: S1, collecting the electrical parameters and environment parameters of a power transmission line in real time through a data collection module, and enabling the parameters to comprise current, voltage, temperature, humidity, wind speed and the like; s2, transmitting the acquired data to a central processing unit through a data transmission module; s3, pre-processing the received data through a central processing unit, wherein the pre-processing comprises filtering, denoising, normalization and the like; s4, adopting a fault diagnosis module to perform fault identification and positioning by using a machine learning algorithm or an expert system based on the preprocessed data; according to the power transmission line non-outage detection method and system provided by the invention, real-time monitoring and fault diagnosis can be carried out on the line under the condition of normal operation of the power transmission line, and the system has the characteristics of improving the reliability and safety of a power system, reducing the maintenance cost, improving the management level and the like, and has important practical application value.

Description

Technical Field

[0001] The invention belongs to the technical field of power system maintenance, and in particular relates to a method and system for non-stop power transmission line detection. Background Art

[0002] Transmission lines are an important part of the power system, responsible for transmitting the electricity generated by power stations to substations and users. The stable operation of transmission lines is crucial to ensuring power supply. However, due to environmental factors, equipment aging and other reasons, transmission lines may have various faults, such as insulation damage, wire breakage, etc. These faults may cause power outages and affect the normal operation of the power system.

[0003] Traditional transmission line detection methods usually require power outages, which not only affects the continuity of power supply, but also increases detection costs and time. In addition, the power outage detection method cannot monitor the line status in real time, and it is difficult to detect potential faults in time. In order to improve the reliability and safety of the power system, it is necessary to develop a method and system that can monitor the transmission line in real time without power outages. This method and system can detect and locate faults in time, reduce power outage time and maintenance costs, and improve the operating efficiency of the power system. Summary of the invention

[0004] The object of the present invention is to provide a method and system for detecting power transmission lines without power outages, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for detecting power transmission lines without power outages, comprising the following steps:

[0006] S1. The data acquisition module collects electrical parameters and environmental parameters of the transmission line in real time, wherein the electrical parameters include current and voltage, and the environmental parameters include temperature, humidity and wind speed;

[0007] S2. The collected data is transmitted to the central processing unit through the data transmission module;

[0008] S3. Preprocessing the received data through the central processing unit, including filtering, denoising, normalization, etc.;

[0009] S4. Use the fault diagnosis module to identify and locate faults based on preprocessed data using machine learning algorithms or expert systems;

[0010] S5. When a fault is detected, relevant personnel are notified through the alarm and notification module, and a fault report is generated;

[0011] S6. Store the collected data and fault records in the data storage module for analysis and audit;

[0012] S7. Real-time data, fault information and historical records are displayed through the user interface module for operators to monitor and manage.

[0013] Preferably, the S1 also includes:

[0014] S101. Establish a drone inspection system, using drones equipped with high-precision cameras and sensors to fly along the transmission lines to collect line images and defect data in real time;

[0015] S102. Set up a ground monitoring center to receive the data sent back by the drone in real time, and automatically identify the type and degree of line defects through professional software analysis and processing;

[0016] S103. Use high-frequency radio communication technology to complete data transmission between the UAV and the ground monitoring center.

[0017] Preferably, the method further comprises the following steps:

[0018] S8. Real-time monitoring of environmental conditions around the transmission line to provide more contextual information for fault diagnosis;

[0019] S9. Based on historical data and machine learning algorithms, predict future failure risks and take preventive measures in advance;

[0020] S10. Encrypt the transmitted and stored data to prevent the data from being stolen or tampered with;

[0021] S11. Perform system self-checks regularly to identify and fix potential problems and ensure stable operation of the system.

[0022] Preferably, the filtering in S3 uses a low-pass filter to remove high-frequency noise and a high-pass filter to remove low-frequency noise, including the following formula:

[0023] y[n]=b0x[n]+b1x[n-1]+...+b p x[np]-a1y[n-1]-...-a q y[nq];

[0024] The median filter is used to remove impulse noise, including the following formula:

[0025] y[n]=median(x[nk],x[n-k+1],…,x[n+k])

[0026] Where y[n] is the value of the output signal at time n; x[n] is the value of the input signal at time n; b i is the forward coefficient of the filter (i=0,1,...,p); a iis the feedback coefficient of the filter (i=1,2,...,q); p is the order of the forward part of the filter; q is the order of the feedback part of the filter; k is half the window size.

[0027] Preferably, the denoising in S3 adopts wavelet denoising, including the following formula:

[0028]

[0029] Where y[n] is the value of the denoised signal at time n; w k is the wavelet coefficient; ψ is the wavelet function; s is the scale parameter that controls the width of the wavelet function; k is the discrete time index.

[0030] A non-stop power transmission line detection system, characterized by comprising a data acquisition module, a data transmission module, a central processing unit, a fault diagnosis module, an alarm and notification module, a data storage module and a user interface module;

[0031] The data acquisition module is installed at a key position of the power transmission line and is used to collect electrical parameters and environmental parameters of the line in real time;

[0032] The data transmission module transmits the collected data to the central processing unit wirelessly or wiredly;

[0033] The central processing unit is used to receive and process data from the data acquisition module, and perform data analysis and fault diagnosis;

[0034] The fault diagnosis module identifies and locates the fault type and location based on the data analysis results;

[0035] The alarm and notification module is used to notify relevant personnel through SMS, email, sound and light alarm, etc. when a fault is detected;

[0036] The data storage module is used to store historical data and fault records for analysis and auditing;

[0037] The user interface module is used to provide a graphical interface to display real-time data, fault information and historical records for operators to monitor and manage.

[0038] Preferably, the data acquisition module includes a drone inspection system, a ground monitoring center and a data transmission unit. The drone inspection system is used to use a drone equipped with high-precision cameras and sensors to fly along the transmission line to collect line images and defect data in real time, and is equipped with a variety of monitoring equipment such as infrared sensors and ultraviolet sensors to identify different types of defects; the ground monitoring center is used to receive data sent back by the drone in real time, and through professional software analysis and processing, automatically identify the type and degree of line defects; the data transmission unit is used to use high-frequency radio communication technology to complete data transmission between the drone and the ground monitoring center.

[0039] Preferably, it also includes: an environmental monitoring module, a prediction and analysis module, a data encryption and security module, and a self-checking and self-repairing module;

[0040] The environmental monitoring module is used to monitor the environmental conditions around the transmission line in real time to provide more context information for fault diagnosis.

[0041] The predictive analysis module predicts future failure risks based on historical data and machine learning algorithms;

[0042] The data encryption and security module is used to encrypt the transmitted and stored data to prevent the data from being stolen or tampered with, and ensure the security of data transmission and storage;

[0043] The self-check and self-repair module is used to automatically detect the health status of the system itself, and to find and repair potential problems to ensure the stable operation of the system.

[0044] Preferably, the prediction analysis module predicts future failure risks based on historical data and a machine learning algorithm, and the machine learning algorithm includes a random forest model, and the formula is as follows:

[0045]

[0046] Where f(x) is the prediction result of the random forest model for the input x; B is the number of trees in the random forest; T b (x) is the prediction result of the bth decision tree for input x.

[0047] Preferably, the prediction analysis module uses an autoregressive model to predict future failure risks, including the following formula:

[0048]

[0049] Among them, X t is the value of the time series at time t; c is a constant term; φ i is the autoregressive coefficient; p is the order of the model; X t-i is the value of the time series at time ti; εt It's white noise.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] (1) The method and system of the present invention can monitor the status of the power transmission line in real time, detect and locate faults in time, reduce power outage time and maintenance costs, avoid safety hazards caused by power outages through non-stop detection, reduce the workload of manual inspections, improve maintenance efficiency, and facilitate operator monitoring and management through a graphical interface, thereby improving management level;

[0052] (2) The method and system of the present invention can also provide more contextual information for fault diagnosis by real-time monitoring of environmental conditions. Based on historical data and machine learning algorithms, it can predict future fault risks and take preventive measures in advance. In addition, through data encryption and security measures, it can ensure the security of data transmission and storage. At the same time, it can automatically detect the health status of the system itself and perform self-repair when possible, thereby improving the stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a structural block diagram of the present invention;

[0054] Figure 2 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] For example, see Figure 1-2 The present invention provides a technical solution: a method for detecting power transmission lines without power outages, comprising the following steps:

[0057] S1. The data acquisition module collects electrical parameters and environmental parameters of the transmission line in real time, wherein the electrical parameters include current and voltage, and the environmental parameters include temperature, humidity and wind speed;

[0058] S2. The collected data is transmitted to the central processing unit through the data transmission module;

[0059] S3. Preprocessing the received data through the central processing unit, including filtering, denoising, normalization, etc.;

[0060] S4. Use the fault diagnosis module to identify and locate faults based on preprocessed data using machine learning algorithms or expert systems;

[0061] S5. When a fault is detected, relevant personnel are notified through the alarm and notification module, and a fault report is generated;

[0062] S6. Store the collected data and fault records in the data storage module for analysis and audit;

[0063] S7. Real-time data, fault information and historical records are displayed through the user interface module for operators to monitor and manage.

[0064] In this technical solution, S1 also includes:

[0065] S101. Establish a drone inspection system, using drones equipped with high-precision cameras and sensors to fly along the transmission lines to collect line images and defect data in real time;

[0066] S102. Set up a ground monitoring center to receive the data sent back by the drone in real time, and automatically identify the type and degree of line defects through professional software analysis and processing;

[0067] S103. Use high-frequency radio communication technology to complete data transmission between the UAV and the ground monitoring center;

[0068] The specific process of the above steps includes: first, select a suitable UAV platform, such as a multi-rotor UAV, and equip it with necessary equipment such as high-definition cameras, infrared sensors, ultraviolet sensors, etc.; then, set the flight path of the UAV to ensure that it can cover all key parts of the transmission line; during the flight, the UAV uses the onboard sensors to collect images and operation data of the transmission line in real time, and the collected data is transmitted to the ground monitoring center in real time through high-frequency radio communication technology; after receiving the data, the ground monitoring center uses professional software to process and analyze the images and operation data, automatically identifies the type and degree of defects in the line, and generates a report; based on the analysis report, maintenance personnel can carry out targeted maintenance on the transmission line to ensure the safe and stable operation of the line. Emergency defects should be given priority to avoid potential safety risks.

[0069] This technical solution also includes the following steps:

[0070] S8. Real-time monitoring of environmental conditions around the transmission line to provide more contextual information for fault diagnosis;

[0071] S9. Based on historical data and machine learning algorithms, predict future failure risks and take preventive measures in advance;

[0072] S10. Encrypt the transmitted and stored data to prevent the data from being stolen or tampered with;

[0073] S11. Perform system self-checks regularly to identify and fix potential problems and ensure stable operation of the system.

[0074] In this technical solution, the filtering in S3 uses a low-pass filter to remove high-frequency noise and a high-pass filter to remove low-frequency noise, including the following formula:

[0075] y[n]=b0x[n]+b1x[n-1]+...+b p x[np]-a1y[n-1]-...-a q y[nq];

[0076] The median filter is used to remove impulse noise, including the following formula:

[0077] y[n]=median(x[nk],x[n-k+1],…,x[n+k])

[0078] Where y[n] is the value of the output signal at time n; x[n] is the value of the input signal at time n; b i is the forward coefficient of the filter (i=0,1,...,p); a i is the feedback coefficient of the filter (i=1,2,...,q); p is the order of the forward part of the filter; q is the order of the feedback part of the filter; k is half the window size.

[0079] In this technical solution, the denoising in S3 adopts wavelet denoising, including the following formula:

[0080]

[0081] Where y[n] is the value of the denoised signal at time n; w k is the wavelet coefficient; ψ is the wavelet function; s is the scale parameter that controls the width of the wavelet function; k is the discrete time index.

[0082] A non-stop power transmission line detection system, characterized by comprising a data acquisition module, a data transmission module, a central processing unit, a fault diagnosis module, an alarm and notification module, a data storage module and a user interface module;

[0083] The data acquisition module is installed at a key position of the power transmission line and is used to collect electrical parameters and environmental parameters of the line in real time;

[0084] The data transmission module transmits the collected data to the central processing unit wirelessly or wiredly;

[0085] The central processing unit is used to receive and process data from the data acquisition module, and perform data analysis and fault diagnosis;

[0086] The fault diagnosis module identifies and locates the fault type and location based on the data analysis results;

[0087] The alarm and notification module is used to notify relevant personnel through SMS, email, sound and light alarm, etc. when a fault is detected;

[0088] The data storage module is used to store historical data and fault records for analysis and auditing;

[0089] The user interface module is used to provide a graphical interface to display real-time data, fault information and historical records for operators to monitor and manage.

[0090] In this technical solution, the data acquisition module includes a drone inspection system, a ground monitoring center and a data transmission unit. The drone inspection system is used to use a drone equipped with high-precision cameras and sensors to fly along the transmission line to collect line images and defect data in real time. It is equipped with infrared sensors, ultraviolet sensors and other monitoring equipment to identify different types of defects; the ground monitoring center is used to receive the data sent back by the drone in real time, and through professional software analysis and processing, automatically identify the type and degree of line defects; the data transmission unit is used to use high-frequency radio communication technology to complete data transmission between the drone and the ground monitoring center.

[0091] This technical solution also includes: an environmental monitoring module, a prediction and analysis module, a data encryption and security module, and a self-checking and self-repairing module;

[0092] The environmental monitoring module is used to monitor the environmental conditions around the transmission line in real time to provide more context information for fault diagnosis.

[0093] The predictive analysis module predicts future failure risks based on historical data and machine learning algorithms;

[0094] The data encryption and security module is used to encrypt the transmitted and stored data to prevent the data from being stolen or tampered with, and ensure the security of data transmission and storage;

[0095] The self-check and self-repair module is used to automatically detect the health status of the system itself, and to find and repair potential problems to ensure the stable operation of the system.

[0096] In this technical solution, the prediction analysis module predicts future failure risks based on historical data and machine learning algorithms. The machine learning algorithm includes a random forest model, and the formula is as follows:

[0097]

[0098] Where f(x) is the prediction result of the random forest model for the input x; B is the number of trees in the random forest; T b (x) is the prediction result of the bth decision tree for input x.

[0099] In this technical solution, the prediction analysis module uses an autoregressive model to predict future failure risks, including the following formula:

[0100]

[0101] Among them, X t is the value of the time series at time t; c is the constant term; i is the autoregressive coefficient; p is the order of the model; X t-i is the value of the time series at time ti; ε t It's white noise.

[0102] Example 2 is improved on the basis of Example 1, please refer to Figure 1-2 Specifically: the data acquisition module is equipped with a current sensor, a voltage sensor, a temperature sensor, a humidity sensor, a wind speed sensor, etc. for data acquisition. The data acquisition module adopts wireless communication, such as 4G / 5G, LoRa, NB-IoT or wired communication, such as optical fiber, RS485, and adopts standard communication protocols, such as Modbus, TCP / IP to ensure the reliability and security of data transmission.

[0103] In this embodiment, preferably, the central processing unit adopts a high-performance server or industrial computer with powerful computing and storage capabilities, adopts a real-time operating system, such as Linux RTOS, supports multi-tasking and high concurrency, and uses statistical analysis, time series analysis, spectrum analysis and other methods to analyze the data during data analysis. It adopts high-reliability storage media, such as SSD, disk array, and regularly backs up data.

[0104] In this technical solution, the fault diagnosis module uses machine learning algorithms, such as support vector machines, random forests, neural networks or expert systems to identify faults, combines geographic information system GIS and sensor location information to accurately locate the fault location, and classifies the faults into different types, such as insulation faults, wire breakage, connector overheating, etc. Through data analysis, the root cause of the fault is found and a detailed fault report is provided.

[0105] The difference between the third embodiment and the second embodiment is that, please refer to Figure 1-2, specifically: the prediction analysis module is used to analyze historical data, extract failure modes and trends, establish prediction models, such as time series models, regression models, etc., evaluate the failure risk in the future, provide early warning information, and propose corresponding preventive measures based on the prediction results;

[0106] In this embodiment, preferably, the data encryption and security module uses encryption algorithms such as AES and RSA to encrypt the transmitted and stored data, uses two-factor authentication, biometrics, etc. to ensure the legitimacy of the user's identity, sets fine-grained access control policies, limits the access rights of different users, and deploys an intrusion detection system to promptly detect and prevent malicious attacks;

[0107] In this embodiment, preferably, the self-check and self-repair module is used to perform system self-checks regularly, detect the status of hardware and software, and automatically execute preset self-repair logic when a problem is detected, such as restarting services, updating software, etc. When self-repair is not possible, the alarm notification module is used to notify relevant personnel and record the self-check and self-repair process to facilitate subsequent analysis and improvement.

[0108] The present invention provides a method and system for non-stop power transmission line detection, which can perform real-time monitoring and fault diagnosis on the line when the transmission line is operating normally. The system has the characteristics of improving the reliability and safety of the power system, reducing maintenance costs, and improving management level. The newly added modules further enhance the function and flexibility of the system, and provide more comprehensive monitoring, prediction and control capabilities. The system has important practical application value.

[0109] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting power transmission lines without power outages, characterized in that: The following steps are involved: S1. The data acquisition module collects electrical parameters and environmental parameters of the transmission line in real time, wherein the electrical parameters include current and voltage, and the environmental parameters include temperature, humidity and wind speed; S2. The collected data is transmitted to the central processing unit through the data transmission module; S3. Preprocessing the received data through the central processing unit, including filtering, denoising, normalization, etc.; S4. Use the fault diagnosis module to identify and locate faults based on preprocessed data using machine learning algorithms or expert systems; S5. When a fault is detected, relevant personnel are notified through the alarm and notification module, and a fault report is generated; S6. Store the collected data and fault records in the data storage module for analysis and audit; S7. Real-time data, fault information and historical records are displayed through the user interface module for operators to monitor and manage.

2. The method for detecting power transmission lines without power outage according to claim 1, characterized in that: The S1 also includes: S101. Establish a drone inspection system, using drones equipped with high-precision cameras and sensors to fly along the transmission lines to collect line images and defect data in real time; S102. Set up a ground monitoring center to receive the data sent back by the drone in real time, and automatically identify the type and degree of line defects through professional software analysis and processing; S103. Use high-frequency radio communication technology to complete data transmission between the UAV and the ground monitoring center.

3. The method for detecting power transmission lines without power outage according to claim 1, characterized in that: The following steps are also included: S8. Real-time monitoring of environmental conditions around the transmission line to provide more contextual information for fault diagnosis; S9. Based on historical data and machine learning algorithms, predict future failure risks and take preventive measures in advance; S10. Encrypt the transmitted and stored data to prevent the data from being stolen or tampered with; S11. Perform system self-checks regularly to identify and fix potential problems and ensure stable operation of the system.

4. The method for detecting power transmission lines without power outage according to claim 1, characterized in that: The filtering in S3 uses a low-pass filter to remove high-frequency noise and a high-pass filter to remove low-frequency noise, including the following formula: y[n]=b0x[n]+b1x[n-1]+...+b p x[np]-a1y[n-1]-...-a q y[nq]: The median filter is used to remove impulse noise, including the following formula: y[n]=median(x[nk],x[n-k+1],…,x[n+k]) Where y[n] is the value of the output signal at time n; x[n] is the value of the input signal at time n; b i is the forward coefficient of the filter (i=0,1,...,p); a i is the feedback coefficient of the filter (i=1,2,...,q); p is the order of the forward part of the filter; q is the order of the feedback part of the filter; k is half the window size.

5. The method for detecting power transmission lines without power outage according to claim 1, characterized in that: The denoising in S3 adopts wavelet denoising, including the following formula: Where y[n] is the value of the denoised signal at time n; w k is the wavelet coefficient; ψ is the wavelet function; s is the scale parameter that controls the width of the wavelet function; k is the discrete time index.

6. A non-stop power transmission line detection system, characterized in that: It includes a data acquisition module, a data transmission module, a central processing unit, a fault diagnosis module, an alarm and notification module, a data storage module and a user interface module; The data acquisition module is installed at a key position of the power transmission line and is used to collect electrical parameters and environmental parameters of the line in real time; The data transmission module transmits the collected data to the central processing unit wirelessly or wiredly; The central processing unit is used to receive and process data from the data acquisition module, and perform data analysis and fault diagnosis; The fault diagnosis module identifies and locates the fault type and location based on the data analysis results; The alarm and notification module is used to notify relevant personnel through SMS, email, sound and light alarm, etc. when a fault is detected; The data storage module is used to store historical data and fault records for analysis and auditing; The user interface module is used to provide a graphical interface to display real-time data, fault information and historical records for operators to monitor and manage.

7. The power transmission line non-stop detection system according to claim 6, characterized in that: The data acquisition module includes a drone inspection system, a ground monitoring center and a data transmission unit. The drone inspection system is used to use a drone equipped with high-precision cameras and sensors to fly along the transmission line to collect line images and defect data in real time. It is equipped with various monitoring equipment such as infrared sensors and ultraviolet sensors to identify different types of defects; the ground monitoring center is used to receive data sent back by the drone in real time, and automatically identify the type and degree of line defects through professional software analysis and processing; the data transmission unit is used to use high-frequency radio communication technology to complete data transmission between the drone and the ground monitoring center.

8. The power transmission line non-stop detection system according to claim 6, characterized in that: Also includes: Environmental monitoring module, predictive analysis module, data encryption and security module, and self-checking and self-repair module; The environmental monitoring module is used to monitor the environmental conditions around the transmission line in real time to provide more context information for fault diagnosis; The predictive analysis module predicts future failure risks based on historical data and machine learning algorithms; The data encryption and security module is used to encrypt the transmitted and stored data to prevent the data from being stolen or tampered with, and ensure the security of data transmission and storage; The self-check and self-repair module is used to automatically detect the health status of the system itself, and to find and repair potential problems to ensure the stable operation of the system.

9. The power transmission line non-stop detection system according to claim 8, characterized in that: The prediction analysis module predicts future failure risks based on historical data and machine learning algorithms. The machine learning algorithm includes a random forest model, and the formula is as follows: Where f(x) is the prediction result of the random forest model for the input x; B is the number of trees in the random forest; T b (x) is the prediction result of the bth decision tree for input x.

10. The power transmission line non-stop detection system according to claim 8, characterized in that: The prediction analysis module uses an autoregressive model to predict future failure risks, including the following formula: Among them, X t is the value of the time series at time t; c is a constant term; φ i is the autoregressive coefficient; p is the order of the model; X t-i is the value of the time series at time ti; ε t It's white noise.

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