Safety hazard integrated device for overhead line

Abstract

The application provides a power collection line safety hazard integrated device, relates to the field of power system monitoring and management, and comprises a data acquisition unit, a signal processing unit, a data analysis unit, a positioning early warning unit, a data learning module and a big data unit. The data acquisition unit comprises a double-end traveling wave sensor module for collecting traveling wave signals at both ends of the power line in real time. This design can quickly and accurately locate the fault tower, guide the line operation and maintenance personnel to quickly find the fault point, realize rapid and accurate fault location, solve the problem of low detection effect by relying on manual inspection or using simple instruments, and further realize real-time monitoring, analysis and positioning. The operation and maintenance personnel can be timely notified to go to the scene for investigation, the occurrence of line fault tripping can be avoided, and each collected fault traveling wave can be compared and analyzed with the big data expert library to help the operation and maintenance personnel to dispose the fault on the scene, continuously learn and improve, and adapt to the changing environmental conditions.

Description

Technical Field

[0001] This invention relates to the field of power system monitoring and management, and in particular to the installation of an integrated safety hazard device on power transmission lines. Background Technology

[0002] Collector lines are an important component of wind farms and photovoltaic power stations. Their function is to collect dispersed electricity and transmit it to the load through high-voltage transmission lines, efficiently transmitting the power from multiple wind turbines to substations for further grid connection.

[0003] In daily life, power collection lines are highly susceptible to tripping accidents caused by various natural factors such as lightning, pollution, plants and animals, wind and grass disturbances, and external damage. Each tripping fault not only impacts the wind farm's power network but also damages insulators, conductors, and other facilities, leaving potential safety hazards for system operation. If the fault cannot be quickly resolved, it will cause a large amount of power generated by the wind turbines to be unable to be transmitted, resulting in direct losses to the wind farm's power production.

[0004] Therefore, the present invention provides an integrated device for removing safety hazards from power lines. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide an integrated device for removing safety hazards from power lines.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an integrated safety hazard device for power lines, comprising a data acquisition unit, a signal processing unit, a data analysis unit, a positioning and early warning unit, a data learning module, and a big data unit;

[0007] The data acquisition unit includes a dual-end traveling wave sensor module for real-time acquisition of traveling wave signals at both ends of the power line, and an environmental monitoring module for real-time monitoring of the operating status of the power system.

[0008] The signal processing unit includes a wavelet transform module for preprocessing the received data and extracting key feature information, and a GPS synchronization clock module for ensuring the time accuracy of all operations and providing an accurate time reference for data analysis.

[0009] In a preferred embodiment, the environmental monitoring module includes a voltage sensor for measuring voltage values ​​in an electrical system, a current sensor for monitoring current flow in the electrical system, a temperature sensor for monitoring ambient temperature and equipment operating temperature, and a humidity sensor for monitoring ambient humidity levels.

[0010] In one preferred embodiment, the analysis module includes a data comparison submodule for comparing current data with historical data to identify trends and anomalies; an anomaly detection submodule for detecting anomalies in the data using algorithms; a data repair submodule for repairing and filling damaged or missing data; a predictive maintenance submodule for predicting potential future problems and performing maintenance in advance based on historical and current data; and a fault construction submodule for building fault models based on data analysis results to provide a basis for subsequent fault diagnosis and location.

[0011] In a preferred embodiment, the positioning and early warning unit includes a dual-end traveling wave positioning module for quickly and accurately determining the fault location using dual-end traveling wave positioning technology, an early warning decision module for making corresponding response measures based on the positioning results, and a notification module for issuing alarms to inform of the fault situation and countermeasures.

[0012] In one preferred embodiment, the data learning unit includes an optimization module for continuously adjusting and optimizing algorithm parameters based on new data, and a multi-source data fusion module for integrating data from different sensors.

[0013] In a preferred embodiment, the optimization module includes a time-frequency analysis submodule for performing time-domain and frequency-domain analysis on the data to extract more useful feature information; a feature selection submodule for selecting the most representative and discriminative features from numerous features to improve the accuracy and efficiency of the model; a model training submodule for training the data using machine learning algorithms to establish a prediction model; a model optimization submodule for optimizing the trained model; and an incremental learning submodule for dynamically updating and optimizing the model as new data is continuously added.

[0014] In one preferred embodiment, the large data unit includes a fault traveling wave database for storing fault-related traveling wave data to facilitate fault analysis and location.

[0015] In one preferred embodiment, the guidance module includes a pattern matching submodule for matching current data with known patterns to find the most similar pattern, a classification submodule for classifying data, a decision submodule for making decision suggestions based on data analysis results, and a scheme optimization submodule for optimizing and adjusting decision schemes.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0017] This invention, by installing an integrated safety hazard device on the power line, collects traveling wave signals and environmental monitoring data from both ends of the power line in real time through a data acquisition unit. This enables rapid detection of faults and anomalies in the power system. The signal processing unit and data analysis unit preprocess and analyze the received data to quickly identify potential problems. This design can quickly and accurately locate faulty towers, guiding line maintenance personnel to quickly find the fault point and achieve rapid and accurate fault location, thus solving the problem of low detection efficiency relying on manual inspections or simple instruments.

[0018] By performing multi-dimensional analysis on the collected data, including time and frequency domain analysis, feature selection, and pattern recognition, we can not only identify the current fault points but also predict potential future problems and perform maintenance in advance. This design enables real-time monitoring, analysis, and location, allowing maintenance personnel to be notified promptly to conduct on-site troubleshooting, thus preventing line fault tripping and effectively reducing the probability of faults, extending equipment lifespan, and reducing maintenance costs.

[0019] The data learning unit continuously adjusts and optimizes algorithm parameters based on new data, and integrates data from different sensors through multi-source data fusion technology to form a unified dataset for subsequent analysis. Furthermore, the big data unit stores historical fault data, facilitating long-term trend analysis and fault location. This design compares and analyzes each collected fault traveling wave with the company's fault traveling wave big data expert database, identifying the nature of different types of line faults, diagnosing their causes, and providing technical guidance for maintenance personnel to handle faults on-site. Through continuous learning and improvement, it adapts to constantly changing environmental conditions and maintains high performance. Attached Figure Description

[0020] Figure 1 A flowchart of the integrated device for adding safety hazards to power lines provided by the present invention;

[0021] Figure 2 A schematic diagram of the data acquisition unit for the integrated device for adding safety hazards to power lines provided by the present invention;

[0022] Figure 3 A schematic diagram of the signal processing unit for the integrated device for adding safety hazards to power lines provided by the present invention;

[0023] Figure 4 A schematic diagram of the data analysis unit for the integrated device for adding safety hazards to power lines provided by the present invention;

[0024] Figure 5 A diagram of the positioning and early warning unit for the integrated safety hazard device for power lines provided by this invention;

[0025] Figure 6 A schematic diagram of the data learning module for the integrated device for adding safety hazards to power lines provided by the present invention;

[0026] Figure 7 A schematic diagram of the big data unit for the integrated device for adding safety hazards to power lines provided by the present invention;

[0027] Figure 8 The operation flowchart of the integrated safety hazard device for power line installation provided by the present invention Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] like Figure 1 - Figure 7 As shown, this embodiment provides a technical solution: an integrated safety hazard device for power lines, including a data acquisition unit, a signal processing unit, a data analysis unit, a positioning and early warning unit, a data learning module, and a big data unit;

[0030] The data acquisition unit includes a dual-end traveling wave sensor module for real-time acquisition of traveling wave signals at both ends of the power line, and an environmental monitoring module for real-time monitoring of the operating status of the power system.

[0031] The dual-end traveling wave sensor module is built on the basis of Fourier transform. The dual-end traveling wave sensor module collects traveling wave signals at both ends of the power line in real time. These signals are used to detect faults and abnormal conditions in the power system. By monitoring the changes in the traveling wave signals, the fault point in the line can be quickly identified, thereby greatly shortening the fault location time.

[0032] The environmental monitoring module includes a voltage sensor for measuring voltage values ​​in an electrical system, a current sensor for monitoring current flow in an electrical system, a temperature sensor for monitoring ambient temperature and equipment operating temperature, and a humidity sensor for monitoring ambient humidity levels.

[0033] The environmental monitoring module includes voltage and current sensors that can comprehensively monitor the operating status of the power system and provide multi-dimensional environmental data to help maintenance personnel better understand the system's operating conditions. Among them, the temperature sensor can monitor the temperature changes of the equipment to prevent equipment damage caused by overheating; the humidity sensor can monitor the humidity level in the environment to prevent electrical faults caused by humidity.

[0034] like Figure 1 , Figure 3 and Figure 8 As shown, the signal processing unit includes a wavelet transform module for preprocessing the received data and extracting key feature information, and a GPS synchronization clock module for ensuring the time accuracy of all operations and providing an accurate time reference for data analysis.

[0035] The wavelet transform module preprocesses the received data and extracts key feature information, effectively extracting useful information from complex signals. The GPS synchronization clock module ensures the time accuracy of all operations, providing an accurate time reference for data analysis. High-precision time synchronization can also locate faults, ensuring that data from different locations are accurately recorded and analyzed at the same point in time, thereby improving the accuracy of fault detection.

[0036] like Figure 1 , Figure 4 and Figure 8 The data analysis unit shown includes a feature extraction module for extracting key information from the processed data, a pattern recognition module for analyzing this information to identify potential problems, and an analysis module for further in-depth analysis of the identified problems.

[0037] The feature extraction module is built on the linear discriminant analysis algorithm. It extracts key information from the processed data, which typically includes characteristic parameters such as signal amplitude, frequency, and phase. The pattern recognition module is built on the neural network algorithm. Through machine learning algorithms or statistical methods, this module can automatically identify abnormal patterns in the data. The analysis module further analyzes the identified problems in depth.

[0038] The analysis module includes a data comparison submodule for comparing current data with historical data to identify trends and anomalies; an anomaly detection submodule for detecting anomalies in the data using algorithms; a data repair submodule for repairing and filling damaged or missing data; a predictive maintenance submodule for predicting potential future problems and performing maintenance in advance based on historical and current data; and a fault construction submodule for building fault models based on data analysis results to provide a basis for subsequent fault diagnosis and location.

[0039] The data comparison submodule, built on the difference algorithm, is responsible for comparing current data with historical data to identify trends and anomalies. The anomaly detection submodule, built on the isolation forest and local anomaly factor algorithms, detects anomalies in the data to promptly identify potential faults. The data repair submodule, built on interpolation and fitting algorithms, repairs and fills in damaged or missing data. In practical applications, some data may be lost or damaged for various reasons. The data repair submodule repairs and fills in this data to ensure its integrity. The predictive maintenance submodule, built on time series forecasting and machine learning prediction models, predicts potential future problems based on historical and current data, allowing for proactive maintenance measures to prevent faults. The fault construction submodule, built on decision trees, constructs fault models based on data analysis results, providing a basis for subsequent fault diagnosis and location.

[0040] like Figure 1 , Figure 5 and Figure 8 The positioning and early warning unit shown includes a dual-end traveling wave positioning module for quickly and accurately determining the fault location using dual-end traveling wave positioning technology, an early warning decision module for making corresponding response measures based on the positioning results, and a notification module for issuing alarms and informing the fault situation and response measures.

[0041] The dual-end traveling wave positioning module is built on the least squares algorithm and uses dual-end traveling wave positioning technology to quickly and accurately determine the fault location. The early warning decision module makes corresponding response measures based on the positioning results. Once the fault location is determined, the module makes response measures according to the preset. The notification module is responsible for issuing an alarm, informing the fault situation and the response measures. The module can send alarm information to relevant personnel through SMS, email, voice and other means.

[0042] like Figure 1 , Figure 6 and Figure 8 The data learning unit shown includes an optimization module for continuously adjusting and optimizing algorithm parameters based on new data, and a multi-source data fusion module for integrating data from different sensors;

[0043] The optimization module continuously adjusts the algorithm parameters based on new data. The multi-source data fusion module, built upon Kalman filtering and particle filtering, integrates data from different sensors, fusing these data to form a unified dataset for subsequent analysis.

[0044] The optimization module includes a time-frequency analysis submodule for analyzing data in the time and frequency domains to extract more useful feature information; a feature selection submodule for selecting the most representative and discriminative features from numerous features to improve the accuracy and efficiency of the model; a model training submodule for training data using machine learning algorithms to build a predictive model; a model optimization submodule for optimizing the trained model; and an incremental learning submodule for dynamically updating and optimizing the model as new data is continuously added.

[0045] The time-frequency analysis submodule is built upon Fourier transform and short-time Fourier transform to analyze data in both the time and frequency domains, extracting more useful feature information. Time-domain analysis focuses on the signal's variation over time, while frequency-domain analysis focuses on the signal's frequency components and their distribution. Combining time-domain and frequency-domain analysis allows for a more comprehensive understanding of the signal's characteristics and the extraction of more useful information. The feature selection submodule is built upon PCA and LDA algorithms to select the most representative and discriminative features from numerous features, improving the model's accuracy. In practical applications, due to the large number of sensors and the potential for each sensor to generate a large amount of data, selecting the most representative and discriminative features is crucial. The model training submodule is built upon SVM and neural network algorithms, using machine learning algorithms to train and build a predictive model. The model optimization submodule is built upon gradient descent and genetic algorithms, optimizing the trained model to improve its performance and accuracy. The incremental learning submodule is built upon transfer learning algorithms, dynamically updating and optimizing the model as new data is continuously added. In practical applications, constantly changing environments and conditions may cause the original model to become unsuitable or its performance to degrade. This submodule can use incremental learning methods to dynamically update and optimize the model to adapt to new environments and conditions, thereby improving the model's stability.

[0046] like Figure 1 , Figure 7 and Figure 8 The big data unit shown includes a fault traveling wave database for storing fault-related traveling wave data to facilitate fault analysis and location.

[0047] The optimization module is built on the basis of gradient descent and genetic algorithm, and continuously adjusts the optimization algorithm parameters according to new data. The multi-source data fusion module is built on the basis of Kalman filtering and particle filtering, and integrates data from different sensors to form a unified dataset for subsequent analysis.

[0048] The guidance module includes a pattern matching submodule for matching current data with known patterns to find the most similar patterns, a classification submodule for classifying data, a decision-making submodule for making decision suggestions based on data analysis results, and a scheme optimization submodule for optimizing and adjusting decision schemes.

[0049] The fault traveling wave database stores traveling wave data related to faults, facilitating fault analysis and location. It uses a NoSQL database management system to store and manage the data. The pattern matching submodule matches current data with known patterns to find the most similar pattern, built on the nearest neighbor algorithm. The classification submodule classifies data and makes decision suggestions based on data analysis results, built on the decision tree and support vector machine algorithms. The decision submodule makes decision suggestions based on data analysis results. The scheme optimization submodule optimizes and adjusts decision schemes with the help of temperature and humidity sensors to improve their feasibility and effectiveness, built on the genetic algorithm and simulated annealing algorithm.

[0050] Working principle:

[0051] like Figure 1 - Figure 8 As shown:

[0052] In operation: First, the data acquisition unit acquires traveling wave signals and system operating status data at both ends of the power line in real time through a dual-end traveling wave sensor module and an environmental monitoring module. The dual-end traveling wave sensor module uses Fourier transform technology to capture traveling wave signals in the line and detect faults and anomalies in the power system. Simultaneously, the environmental monitoring module monitors the status of the power system and equipment through voltage, current, temperature, and humidity sensors, comprehensively collecting environmental and electrical system operating data. Then, the signal processing unit preprocesses the acquired data. The wavelet transform module extracts key feature information from complex signals, making the data more concise and meaningful, improving data quality. At the same time, the GPS synchronization clock module ensures that all data is synchronized. The collected data all have accurate timestamps, ensuring the time accuracy of data analysis. This provides a reliable time reference for subsequent fault location and pattern analysis. Next, the data analysis unit performs in-depth analysis on the processed data. The feature extraction module uses a linear discriminant analysis algorithm to extract key features such as signal amplitude, frequency, and phase. The pattern recognition module automatically identifies abnormal patterns in the data through a neural network algorithm, promptly detecting potential problems. The further analysis module performs detailed analysis on the identified anomalies, including comparing with historical data to find trends, applying anomaly detection algorithms to identify anomalies in the data, and repairing damaged or missing data through the data repair submodule to ensure data integrity. In addition, the predictive maintenance module is based on historical data... Based on current data, potential future faults are predicted, and maintenance measures are taken in advance to reduce the probability of fault occurrence. The fault construction submodule builds fault models based on data analysis, providing a basis for subsequent fault diagnosis and location. Simultaneously, the location and early warning unit utilizes dual-end traveling wave positioning technology and the least squares algorithm to accurately determine the fault location. The location result drives the early warning decision module to make response decisions. Once the fault location is determined, the early warning decision module immediately takes predetermined response measures. Finally, the notification module is responsible for issuing timely alarms, informing relevant personnel of the fault situation, and providing specific countermeasures to ensure that maintenance personnel can respond and handle the situation quickly. Secondly, the data learning unit continuously optimizes and adjusts system performance with the input of new data. The optimization module, based on new data… The system automatically adjusts algorithm parameters based on the data to improve the overall prediction accuracy. The multi-source data fusion module fuses data from different sensors using Kalman and particle filtering algorithms to form a unified dataset for subsequent analysis. The time-frequency analysis submodule performs comprehensive time-domain and frequency-domain analysis on the data. The feature selection submodule selects the most representative and discriminative features to further improve the model's accuracy and efficiency. Finally, the big data unit stores and manages fault traveling wave data, providing historical data support for convenient subsequent fault analysis and localization. The fault traveling wave database uses a NoSQL database management system for storage, ensuring efficient data management and retrieval. The pattern matching submodule finds the most similar fault patterns by comparing them with known patterns.The classification submodule provides input to quickly identify fault types. The decision-making submodule, combined with data analysis results, provides fault response plans. The plan optimization submodule continuously optimizes and adjusts these plans to ensure their feasibility.

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments for application in other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. An integrated safety hazard device for power line installation, characterized in that: It comprises a data acquisition unit, a signal processing unit, a data analysis unit, a positioning and early warning unit, a data learning unit, and a big data unit. The data acquisition unit comprises a double-ended traveling wave sensor module for real-time acquisition of traveling wave signals at both ends of the power line, and an environmental monitoring module for real-time monitoring of the operating state of the power system. The signal processing unit comprises a wavelet transform module for pre-processing received data and extracting key feature information, and a GPS synchronous clock module for ensuring the time accuracy of all operations and providing an accurate time reference for data analysis. The data analysis unit comprises a feature extraction module for extracting key information from processed data, a pattern recognition module for analyzing the information to identify potential problems, and an analysis module for further in-depth analysis of identified problems. The positioning and early warning unit comprises a double-ended traveling wave positioning module for quickly and accurately determining the fault location using double-ended traveling wave positioning technology, a warning decision module for taking appropriate response measures based on the positioning results, and a notification module for issuing an alarm to inform the fault situation and response measures. The data learning unit comprises an optimization module for continuously adjusting and optimizing algorithm parameters based on new data, and a multi-source data fusion module for integrating data from different sensors. The optimization module comprises a time-frequency analysis submodule for analyzing data in time and frequency domains and extracting more useful feature information, a feature selection submodule for selecting the most representative and discriminative features from numerous features to improve the accuracy and efficiency of the model, a model training submodule for training data using machine learning algorithms to establish a prediction model, a model optimization submodule for optimizing the trained model, and an incremental learning submodule for dynamically updating and optimizing the model as new data is continuously added. The big data unit comprises a fault traveling wave database for storing fault-related traveling wave data to facilitate fault analysis and positioning, and a guidance module. The use method of the power line safety hazard integrated device specifically comprises: First, the data acquisition unit acquires real-time traveling wave signals at both ends of the power line and system operating state data through the double-ended traveling wave sensor module and the environmental monitoring module. The double-ended traveling wave sensor module captures the traveling wave signals in the line using Fourier transform technology to detect faults and abnormal conditions in the power system. Meanwhile, the environmental monitoring module monitors the state of the power system and equipment through voltage sensors, current sensors, temperature sensors, and humidity sensors to comprehensively collect environmental and electrical system operating data. Then, the signal processing unit preprocesses the collected data, and the wavelet transform module extracts key feature information from complex signals. At the same time, the GPS synchronous clock module ensures that all collected data has accurate timestamps. Secondly, the data analysis unit conducts in-depth analysis on the processed data, the feature extraction module extracts the key features of the signal using linear discriminant analysis algorithm, and the pattern recognition module automatically identifies the abnormal patterns in the data through neural network algorithm, discovers potential problems in time, the further analysis module analyzes the identified abnormalities in detail, including comparing with historical data to find out the trend of change, applying anomaly detection algorithm to identify abnormal points in the data, and repairing damaged or missing data through the data repair submodule to ensure the integrity of the data, in addition, the prediction maintenance submodule predicts the possible future faults based on historical data and current data, takes maintenance measures in advance to reduce the probability of failure, the fault construction submodule constructs a fault model based on data analysis, provides basis for subsequent fault diagnosis and positioning, at the same time, the positioning and early warning unit uses the double-end traveling wave positioning technology to accurately determine the fault location through the least square method algorithm, the positioning result drives the early warning decision module to make response decision, once the fault location is determined, the early warning decision module immediately makes the predetermined response measures, and sends out the alarm in time, informs the relevant personnel of the fault situation, and provides specific countermeasures to ensure that the operation and maintenance personnel can respond quickly and handle it, the data learning unit continuously optimizes and adjusts the system performance under the input of new data, the optimization module automatically adjusts the algorithm parameters based on the new data to improve the prediction accuracy of the whole system, the multi-source data fusion module fuses the data from different sensors through Kalman filtering and particle filtering algorithm to form a unified data set for subsequent analysis, the time-frequency analysis submodule conducts comprehensive analysis on the data in time domain and frequency domain, and the feature selection submodule selects the most representative and discriminant features to further improve the accuracy and efficiency of the model; Finally, the big data unit stores and manages the fault traveling wave data to provide historical data support for subsequent fault analysis and positioning, the fault traveling wave database uses NoSQL database management system for storage to ensure efficient management and query of data, the pattern matching submodule compares with known patterns to find the most similar fault pattern to provide input for the classification submodule, thereby realizing rapid identification of fault type, the decision submodule provides fault response scheme combined with data analysis results, and the scheme optimization submodule continuously optimizes and adjusts the decision scheme to ensure its feasibility.

2. The power line integrated safety hazard installation of claim 1, wherein: The environment monitoring module includes a voltage sensor for measuring voltage values in the electrical system, a current sensor for monitoring current flow in the electrical system, a temperature sensor for monitoring environmental temperature and equipment operating temperature, and a humidity sensor for monitoring environmental humidity level.

3. The power line integrated safety hazard installation of claim 1, wherein: The analysis module includes a data comparison submodule for comparing current data with historical data to find trends and outliers, an anomaly detection submodule for detecting anomalies in the data through algorithms, a data repair submodule for repairing and filling missing or damaged data, a predictive maintenance submodule for predicting future problems based on historical and current data, and a failure construction submodule for building a failure model based on the data analysis results to provide a basis for subsequent fault diagnosis and location.

4. The power line integrated safety hazard installation of claim 1, wherein: The guidance module includes a pattern matching submodule for matching current data with known patterns to find the most similar pattern, a classification submodule for classifying data, a decision submodule for making decision recommendations based on data analysis results, and a solution optimization submodule for optimizing and adjusting decision solutions.

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