CN117277592B - Protection switching method for monitoring high-voltage circuit signals - Google Patents
Protection switching method for monitoring high-voltage circuit signals Download PDFInfo
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
- H02J13/00036—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for the elements or equipment being or involving switches, relays or circuit breakers
- H02J13/0004—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for the elements or equipment being or involving switches, relays or circuit breakers involved in a protection system
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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Abstract
The application provides a protection switching method for monitoring high-voltage circuit signals, which relates to the technical field of abnormal protection, and comprises the following steps: firstly, establishing a basic characteristic set of a power transmission line, then carrying out line segmentation and generating segment identification, then establishing a signal risk data set, regenerating an abnormal signal identification network, then configuring monitoring nodes to generate real-time monitoring data, inputting the abnormal identification network to obtain an abnormal monitoring result, carrying out auxiliary identification by combining an unmanned aerial vehicle, and finally judging that a protection mode is activated to a compensation result of the abnormal identification result. The method mainly solves the problems that the prior art is high in cost, the circuit cannot be monitored in real time, protection measures can be implemented in real time, and monitoring risk is high and monitoring accuracy is low. And carrying out abnormal result recognition on the monitoring data through an abnormal recognition network, carrying out auxiliary recognition through the unmanned aerial vehicle, and finally judging whether to start the protection mode. The monitoring accuracy of the high-voltage circuit is improved, and the damage of elements is avoided.
Description
Technical Field
The invention relates to the technical field of abnormality protection, in particular to a protection switching method for monitoring high-voltage circuit signals.
Background
The abnormal protection of the high-voltage circuit can prevent the high-voltage line from faults and ensure the stable operation of the power system. The high-voltage line is an important part in the power system, if the high-voltage line fails, the normal operation of the power system can be affected, and the safety problems of equipment damage, fire disaster, explosion and the like can be caused, so that the life and property safety of people is threatened. The high-voltage line is an important channel for industrial and civil electricity, and if the high-voltage line fails, the power failure and the like can be caused, so that the production and the life of people are seriously influenced. Therefore, it is necessary to perform abnormality protection for the high-voltage circuit.
In the prior art, a high-voltage line is monitored in an artificial inspection mode, and abnormal problems of a circuit signal are found and repaired.
The prior art also has the problems of higher cost, incapability of monitoring a circuit in real time and low monitoring accuracy due to high monitoring risk by implementing protection measures in real time.
Disclosure of Invention
The method mainly solves the problems that the prior art is high in cost, the circuit cannot be monitored in real time, protection measures can be implemented in real time, and monitoring risk is high and monitoring accuracy is low.
In view of the foregoing, an embodiment of the present application provides a protection switching method for monitoring a high-voltage circuit signal, and in a first aspect, the embodiment of the present application provides a protection switching method for monitoring a high-voltage circuit signal, where the method includes: establishing a basic feature set of the high-voltage transmission line, wherein the basic feature set is obtained by connecting a high-voltage transmission line management system through communication, and comprises a factory quality inspection feature set and a calibration transmission feature set of the transmission line; based on the basic feature set, carrying out line segmentation of the high-voltage transmission line, and generating a segmentation identifier of a line segmentation result; according to the segment identification as a construction feature, a signal risk data set is established, and the signal risk data set is obtained by big data matching by taking the segment identification as the construction feature; generating an abnormal signal identification network by using the signal risk data set, wherein the abnormal signal identification network comprises N segmented node sub-networks, and the N segmented node sub-networks are in one-to-one correspondence with the line segmentation result; configuring signal monitoring nodes on a line segmentation result to generate real-time monitoring data, inputting the real-time monitoring data into an abnormal signal recognition network, and performing abnormal recognition through N corresponding segmentation node sub-networks to generate an abnormal recognition result; configuring an image monitoring unmanned aerial vehicle in a preset period, executing periodic image acquisition, and establishing auxiliary abnormal recognition factors based on a periodic image acquisition result and a basic feature set; and compensating the abnormal recognition result by the auxiliary abnormal recognition factor, judging whether to activate a high-voltage protection mode based on the compensation result, and executing protection switching of the control unit when the judgment result is that the high-voltage protection mode is activated.
In a second aspect, an embodiment of the present application provides a protection switching system for monitoring a high-voltage circuit signal, the system including: the basic feature set establishment module is used for establishing a basic feature set of the high-voltage transmission line, the basic feature set is obtained through a communication connection high-voltage transmission line management system, and the basic feature set comprises a delivery quality detection feature set and a calibration transmission feature set of the transmission line. The segment identification generation module is used for carrying out line segmentation of the high-voltage transmission line based on the basic feature set and generating segment identifications of line segmentation results. The signal risk data set establishing module is used for establishing a signal risk data set according to the segment identification as a construction feature, and the signal risk data set is obtained through big data matching by taking the segment identification as the construction feature. The abnormal recognition network generation module is used for generating an abnormal signal recognition network according to the signal risk data set, wherein the abnormal signal recognition network comprises N segmented node sub-networks, and the N segmented node sub-networks are in one-to-one correspondence with the line segmentation result; the real-time monitoring data generation module is used for configuring signal monitoring nodes at the line segmentation result to generate real-time monitoring data, inputting the real-time monitoring data into an abnormal signal recognition network, carrying out abnormal recognition through N corresponding segmentation node sub-networks and generating an abnormal recognition result; the auxiliary abnormal recognition factor establishing module is used for configuring the image monitoring unmanned aerial vehicle in a preset period, executing periodic image acquisition and establishing an auxiliary abnormal recognition factor based on a periodic image acquisition result and a basic feature set; and the protection mode execution module is used for compensating the abnormal recognition result through the auxiliary abnormal recognition factor, judging whether the high-voltage protection mode is activated or not based on the compensation result, and executing the protection switching of the control unit when the judgment result is that the high-voltage protection mode is activated.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
the application provides a protection switching method for monitoring high-voltage circuit signals, which relates to the technical field of abnormal protection, and comprises the following steps: firstly, establishing a basic characteristic set of a power transmission line, then carrying out line segmentation and generating segment identification, then establishing a signal risk data set, regenerating an abnormal signal identification network, then configuring monitoring nodes to generate real-time monitoring data, inputting the abnormal identification network to obtain an abnormal monitoring result, carrying out auxiliary identification by combining an unmanned aerial vehicle, and finally judging that a protection mode is activated to a compensation result of the abnormal identification result.
The method mainly solves the problems that the prior art is high in cost, the circuit cannot be monitored in real time, protection measures can be implemented in real time, and monitoring risk is high and monitoring accuracy is low. And carrying out abnormal result recognition on the monitoring data through an abnormal recognition network, carrying out auxiliary recognition through the unmanned aerial vehicle, and finally judging whether to start the protection mode. The monitoring accuracy of the high-voltage circuit is improved, and the damage of elements is avoided.
The foregoing description is merely an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
For a clearer description of the present disclosure or of the prior art, the drawings used in the description of the embodiments or of the prior art will be briefly described, it being obvious that the drawings in the description below are only exemplary and that other drawings may be obtained, without inventive effort, by a person skilled in the art, from the provided drawings.
Fig. 1 is a schematic flow chart of a protection switching method for monitoring a high-voltage circuit signal according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for updating auxiliary abnormal recognition factors in a protection switching method for monitoring high-voltage circuit signals according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a method for establishing auxiliary abnormal recognition factors in a protection switching method for monitoring high-voltage circuit signals according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a protection switching system for monitoring a high-voltage circuit signal according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a basic feature set establishing module 10, a segment identification generating module 20, a signal risk data set establishing module 30, an anomaly identification network generating module 40, a real-time monitoring data generating module 50, an auxiliary anomaly identification factor establishing module 60 and a protection mode executing module 70.
Detailed Description
The method mainly solves the problems that the prior art is high in cost, the circuit cannot be monitored in real time, protection measures can be implemented in real time, and monitoring risk is high and monitoring accuracy is low. And carrying out abnormal result recognition on the monitoring data through an abnormal recognition network, carrying out auxiliary recognition through the unmanned aerial vehicle, and finally judging whether to start the protection mode. The monitoring accuracy of the high-voltage circuit is improved, and the damage of elements is avoided.
For a better understanding of the foregoing technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments of the present invention:
example 1
A method for protection switching for high voltage circuit signal monitoring as shown in fig. 1, the method comprising:
establishing a basic feature set of the high-voltage transmission line, wherein the basic feature set is obtained by connecting a high-voltage transmission line management system through communication, and comprises a factory quality inspection feature set and a calibration transmission feature set of the transmission line;
specifically, a basic characteristic set of the high-voltage transmission line is established, relevant characteristic information of the high-voltage transmission line is collected and integrated into one characteristic set, and the characteristic set is obtained through a communication connection high-voltage transmission line management system for subsequent analysis and management. The method comprises the following steps of: the part of the characteristic set mainly comprises relevant data of quality inspection of the power transmission line before delivery, such as delivery date, manufacturer, product quality grade, specification, parameters and the like. Calibrating a power transmission characteristic set: the characteristic set mainly comprises calibration data, such as voltage level, current capacity, transmission power and the like, of the transmission line before the transmission line is put into use. These basic feature sets may be obtained through a communication connection to a high voltage transmission line management system. A data basis may be provided for a variety of advanced analyses and optimizations.
Based on the basic feature set, carrying out line segmentation of the high-voltage transmission line, and generating a segmentation identifier of a line segmentation result;
specifically, after obtaining the basic feature sets of the high-voltage transmission line, the line may be segmented based on the feature sets, and a segment identifier of a line segmentation result may be generated. The goal of line segmentation is typically to better manage and maintain the transmission line. The segmented lines may be grouped into different segments, each of which may be defined according to some particular characteristics or conditions. Determining a segmentation basis: the basis for the segmentation needs to be determined first, for example, according to the length of the line, the geographical location, the voltage level, the load situation, etc. Collecting line characteristics: and according to the selected segmentation basis, relevant line characteristic data are required to be acquired. For example, if the line length is based on segmentation, the length of each line segment needs to be acquired. Segmentation processing: the line may be divided into different segments according to the collected characteristic data. For example, the line may be divided into short, medium, and long three segments according to length, or into low, medium, and high three segments according to voltage class. Generating a segment identification: after the segmentation is completed, a unique identifier may be generated for each segment. This identifier may be a number, letter, or combination character for identifying and referencing each segment in subsequent administration and maintenance. These segment identifications may play an important role in the management and maintenance of high voltage transmission lines. For example, when the operations such as inspection, maintenance and fault processing are performed, specific line sections can be quickly positioned through the section identifiers, so that the working efficiency and the accuracy are improved.
According to the segment identification as a construction feature, a signal risk data set is established, and the signal risk data set is obtained by big data matching by taking the segment identification as the construction feature;
in particular, using these segment identities as building features, a signal risk dataset is built by big data matching. The signal risk data set is constructed with the aim of collecting signal risk data related to the high-voltage transmission line, and processing and analyzing the data. These risks may include line faults, abnormal operating conditions, safety hazards, etc. Determining a signal risk type: first, the type of signal risk that needs to be focused and studied needs to be determined. These risk types may include line faults, equipment anomalies, environmental factors, and the like. Collecting data: relevant data is collected based on the determined signal risk type. For example, real-time operation data, historical fault data, environmental monitoring data, etc. of the transmission line may be collected. Segment identification matching: the collected data is matched with the previously obtained segment identity. I.e. classifying and grouping the data into corresponding line segments according to the segment identification. Data cleaning and processing: and cleaning and processing the collected data, removing invalid and erroneous data, filling the missing value, processing the abnormal data and the like, so that the data is more accurate and usable. Constructing a feature vector: the segment identification is used as a construction feature, and the processed data is converted into a feature vector. Each segment identity corresponds to a feature vector, the dimensions of which correspond to the determined signal risk type. Big data matching: and matching the constructed feature vector with other related data through a big data matching algorithm. Clustering algorithms, association rule mining algorithms, time series analysis algorithms, etc. may be employed. Thereby obtaining a risk dataset.
Generating an abnormal signal identification network by using the signal risk data set, wherein the abnormal signal identification network comprises N segmented node sub-networks, and the N segmented node sub-networks are in one-to-one correspondence with the line segmentation result;
specifically, the abnormal signal recognition network is a deep learning model for automatically recognizing and predicting abnormal signals in a high-voltage transmission line. Selecting a network architecture: an appropriate network architecture is selected to construct the anomaly signal recognition network. Common deep learning models such as Convolutional Neural Network (CNN), cyclic neural network (RNN), long-term short-term memory network (LSTM) and the like can be selected. Data preprocessing: and preprocessing the signal risk dataset, including data standardization, normalization, missing value filling and the like, so that the data meets the input requirements of the model. Segmented node subnetwork: and generating N segmented node sub-networks according to the line segmentation result. Each segmented node sub-network corresponds to a line segment for identifying abnormal signals in the line segment. Feature vector input: the feature vectors of the signal risk dataset are taken as input, one for each sample. Training a model: the anomaly signal identification network is trained using a large number of data samples. The method can be used for training by using a supervised learning or unsupervised learning method to obtain an abnormal recognition network with accurate recognition.
Configuring signal monitoring nodes on a line segmentation result to generate real-time monitoring data, inputting the real-time monitoring data into an abnormal signal recognition network, and performing abnormal recognition through N corresponding segmentation node sub-networks to generate an abnormal recognition result;
specifically, a signal monitoring node is configured on the segmentation result of the high-voltage transmission line to generate real-time monitoring data. The monitoring data can reflect the running state and signal condition of the line and can be input into the abnormal signal identification network in real time. Configuration signal monitoring node: an appropriate signal monitoring node is configured on each segment of the high voltage transmission line. These monitoring nodes may include sensors, measuring devices and other data acquisition means for acquiring real-time operational data and signal conditions of the line. Real-time monitoring data input: and inputting real-time data acquired by the signal monitoring node into an abnormal signal identification network. The data may be time series data, image data, audio data or other types of transmission line related data. Segment node subnetwork input: and inputting the real-time monitoring data into the N corresponding segmented node sub-networks. Each segmented node subnetwork will receive data associated with the segment and perform anomaly identification and detection. Abnormality identification: the segmented node subnetwork uses a deep learning model to conduct anomaly identification through analysis of the input data. This may include Convolutional Neural Networks (CNNs), recurrent Neural Networks (RNNs), long-short-term memory networks (LSTMs), and the like. Abnormality recognition result: each segmented node subnetwork will generate a corresponding anomaly identification result. These results may include the type of anomaly, severity of the anomaly, possible causes, etc. Through the steps and the method, the real-time monitoring data of the high-voltage transmission line can be input into the abnormal signal identification network, and the abnormal identification is carried out through the corresponding segmented node sub-network, so that an accurate abnormal identification result is generated, potential problems can be found and processed in time, and the operation efficiency and the safety of the transmission line are improved.
Configuring an image monitoring unmanned aerial vehicle in a preset period, executing periodic image acquisition, and establishing auxiliary abnormal recognition factors based on a periodic image acquisition result and a basic feature set;
specifically, the image monitoring drone is configured to perform periodic image acquisition and establish an auxiliary anomaly identification factor based on the periodic image acquisition result and the base feature set. The image monitoring unmanned aerial vehicle is configured in a preset period: and selecting proper brands and models of unmanned aerial vehicles, and presetting proper period configuration image monitoring unmanned aerial vehicles according to the actual conditions of high-voltage transmission lines. Unmanned aerial vehicle operator training: unmanned aerial vehicle operators are trained to ensure that they are able to operate the unmanned aerial vehicle proficiently and to learn about performance characteristics and usage notes of the unmanned aerial vehicle. And (3) periodic image acquisition: the unmanned aerial vehicle is dispatched to perform image acquisition on the high-voltage transmission line according to a preset period, such as daily, weekly or monthly. Image processing and analysis: the acquired images are processed and analyzed to extract characteristic information related to the lines, such as the temperature of the line surface, vegetation conditions around the lines, color changes of the lines, and the like. Auxiliary anomaly identification factor establishment: and comparing and analyzing the extracted characteristic information with the basic characteristic set, and establishing auxiliary abnormal recognition factors. The image monitoring unmanned aerial vehicle is configured to acquire periodic images, and auxiliary abnormal recognition factors are established, so that the accuracy and the efficiency of abnormal recognition of the high-voltage transmission line can be further improved.
And compensating the abnormal recognition result by the auxiliary abnormal recognition factor, judging whether to activate a high-voltage protection mode based on the compensation result, and executing protection switching of the control unit when the judgment result is that the high-voltage protection mode is activated.
Specifically, the abnormality recognition result is compensated by the auxiliary abnormality recognition factor, so that the accuracy of abnormality recognition and the protection performance of the high-voltage transmission line can be further improved. Compensating an abnormal recognition result: and compensating an abnormal recognition result output by the abnormal signal recognition network by using the auxiliary abnormal recognition factor. The method can be realized by taking the auxiliary abnormal recognition factor as one of input features, combining the auxiliary abnormal recognition factor with the original feature vector, and inputting the combined auxiliary abnormal recognition factor into an abnormal signal recognition network for training and prediction. Judging whether to activate a high-voltage protection mode: based on the compensated abnormality recognition result, it can be judged whether the high voltage protection mode is activated. This may be done according to preset judgment conditions, for example, when the compensated abnormality recognition result exceeds a certain threshold or a specific mode is satisfied, it is considered that the high voltage protection mode needs to be activated. Performing a protection switch of the control unit: when the determination result is that the high voltage protection mode is activated, a protection switching operation of the control unit may be performed. This can be achieved by controlling switching devices, circuit breakers, protection devices etc. of the high voltage transmission line, for example by rapidly switching off the power supply when a line fault is detected, avoiding an expansion of the accident. Real-time monitoring, abnormality identification and protection control of the high-voltage transmission line can be realized, and the running efficiency and the safety of the transmission line are further improved.
Further, as shown in fig. 3, the method of the present application further includes:
recording working data of the high-voltage transmission line, wherein the working data comprise working parameters and working time;
load operation analysis results of each line segmentation result are carried out based on the calibration power transmission characteristic set according to the working parameters and the working time length;
generating a first auxiliary anomaly identification factor based on the load operation analysis result;
performing feature recognition on the high-voltage transmission line according to the periodic image acquisition result, and generating a second auxiliary abnormal recognition factor according to the feature recognition result;
establishing the auxiliary abnormality recognition factor through the first auxiliary abnormality recognition factor and the second auxiliary abnormality recognition factor.
Specifically, the work data is recorded: the working data comprises working parameters and working time. The working parameters may include parameters related to the running state of the transmission line, such as voltage, current, power, etc., and the working duration records the total working time of each section of the line. Load operation analysis is carried out based on the working parameters and the working time length: the load operation analysis is mainly to analyze the operation state of each segmented circuit according to the working parameters and the working time. This step includes counting the energy consumption of each segmented line, calculating the load rate of the line, etc. Generating a first auxiliary anomaly identification factor: the first auxiliary anomaly identification factor may be generated from the results of the load operation analysis. These factors may include indicators of the energy consumption level, load rate, etc. of the segments, which may reflect the operational status of the line. Feature recognition is carried out through a periodic image acquisition result: periodically acquiring images of the high-voltage transmission line, and identifying the characteristics of the line through image processing and analysis technology. Including color changes of the line, environmental changes around the line, etc. Generating a second auxiliary anomaly identification factor: based on the result of the feature recognition, a second auxiliary anomaly recognition factor may be generated. The method comprises the steps of providing indexes such as line color change degree, environment change degree and the like, wherein the indexes can reflect the state of a line. Establishing auxiliary anomaly identification factors: and combining the first auxiliary abnormal recognition factor and the second auxiliary abnormal recognition factor to establish a complete auxiliary abnormal recognition factor. The factors can reflect the state of the high-voltage transmission line more comprehensively, and the accuracy and the efficiency of abnormality identification are improved.
Further, as shown in fig. 2, the method of the present application further includes:
calling an auxiliary acquisition image of the image monitoring unmanned aerial vehicle, carrying out high-voltage transmission line and surrounding environment adaptation identification according to the auxiliary acquisition image, and establishing an environment hazard factor;
collecting regional natural environment data of a high-voltage transmission line, and establishing a natural environment data set, wherein the natural environment data set comprises a temperature data set, an illumination data set, a wind power data set and a rainfall data set;
performing natural environment influence analysis through the natural environment data set to generate natural environment influence factors;
updating the auxiliary anomaly identification factor by the environmental hazard factor and the natural environment influence factor.
Specifically, the auxiliary acquisition image call: and acquiring an image and video data of the high-voltage transmission line and the surrounding environment by using the unmanned aerial vehicle to acquire auxiliary images. And (3) environment adaptation identification: and carrying out adaptive recognition on the high-voltage transmission line and the surrounding environment on the image acquired in an auxiliary way through an image analysis technology and a machine learning algorithm. Including identifying characteristics of the color, shape, size, etc. of the line and environmental characteristics surrounding the line. Establishing an environmental hazard factor: from the result of the adaptation identification, an environmental hazard factor may be established. Including the characteristics of color difference, distance, space structure and the like of the line and the surrounding environment, which can cause potential harm to the safe and stable operation of the high-voltage transmission line. And (3) collecting regional natural environment data: and arranging sensors or other data acquisition devices around the high-voltage transmission line to acquire natural environment data of the region. Including temperature, light, wind speed, rainfall, etc. Establishing a natural environment data set: and (3) sorting the collected natural environment data into data sets, wherein the data sets comprise a temperature data set, an illumination data set, a wind power data set and a rainfall data set. Analysis of natural environment influence: and (3) researching the influence of the natural environment data set on the performance and stability of the high-voltage transmission line by analyzing the natural environment data set. For example, severe weather such as high temperature, strong wind, heavy rain, etc. may potentially affect safe and stable operation of the transmission line. Generating a natural environment influence factor: through analysis of the natural environment influence, a natural environment influence factor can be generated. The method comprises indexes such as the change rate and the extremum of the natural environment parameters, and the indexes can reflect the influence degree of the natural environment on the high-voltage transmission line. Updating the auxiliary anomaly identification factors: and combining the environmental hazard factors and the natural environment influence factors, and updating the auxiliary abnormal recognition factors. The factors can reflect the state and potential problems of the high-voltage transmission line more comprehensively, and the accuracy and efficiency of anomaly identification are improved.
Further, the method of the present application further comprises:
an aging evaluation network is established, wherein the aging evaluation network is established by taking the basic feature set as basic data;
synchronizing the natural environment data set to the aging evaluation network, and executing aging additional influence evaluation of the natural environment;
and completing natural environment influence analysis based on the additional influence evaluation result, and generating the natural environment influence factor.
Specifically, an aging evaluation network is established: the aging evaluation network may be built using various deep learning models, such as Convolutional Neural Network (CNN), recurrent Neural Network (RNN), or long-term memory network (LSTM), etc. The network uses the basic feature set as input to train and generate a model that can evaluate line aging. Synchronizing the natural environment data sets: and synchronizing the natural environment data set to an aging evaluation network, and providing data support in the aspect of natural environment for aging evaluation. Through data transmission, data sharing and other modes. Performing an aging additional influence evaluation of the natural environment: after synchronizing the natural environment data sets, an additional impact assessment of aging of the natural environment may be performed. This step involves analyzing the effect of natural environmental factors (e.g., temperature, light, wind, rain, etc.) on the aging of the high voltage transmission line and calculating the extent to which such effect affects the performance of the line. And (3) completing natural environment influence analysis based on the additional influence evaluation result: according to the result of the additional influence evaluation, the natural environment influence analysis can be further completed. The method comprises the steps of counting and analyzing the rule of influence of natural environment factors on the aging of the high-voltage transmission line and the influence degree of the influence on the performance of the line. Generating a natural environment influence factor: based on the results of the natural environment influence analysis, a natural environment influence factor may be generated. The factors can reflect the influence degree of natural environment factors on the aging of the high-voltage transmission line, can evaluate the influence of the natural environment factors on the aging of the high-voltage transmission line more comprehensively, and improve the pertinence and the effectiveness of line maintenance and management.
Further, the method of the present application further comprises:
obtaining line layout data of the high-voltage transmission line;
generating an initial running track of the image monitoring unmanned aerial vehicle through the line layout data, and configuring a trigger of a distance trigger sensor to adjust the tolerant distance, wherein the distance trigger sensor is arranged on the monitoring unmanned aerial vehicle and used for monitoring the distance between the image monitoring unmanned aerial vehicle and a high-voltage transmission line;
controlling an image to monitor the operation of the unmanned aerial vehicle according to the initial operation track, and receiving a distance signal of the distance triggering sensor in real time;
if the distance signal is within the trigger adjustment tolerance distance, generating a track adjustment instruction according to the distance signal;
and optimizing the initial running track through the track adjustment instruction so as to complete periodic image acquisition.
Specifically, line layout data of a high-voltage transmission line is obtained: such data may include information on the length, width, height, course, location and height of the support tower, etc. of the line. Generating an initial running track of the image monitoring unmanned aerial vehicle based on the line layout data: according to the line layout data, the flight track and shooting angle of the unmanned aerial vehicle can be planned, and the unmanned aerial vehicle can be ensured to acquire images of the high-voltage transmission line according to the requirements. Configuring a trigger adjustment tolerant distance of a distance trigger sensor: the setting of the tolerant distance is considered according to the specific conditions of the line, the safety performance of the unmanned aerial vehicle and other factors, and the aim is to ensure that the unmanned aerial vehicle safely and accurately collects images. Controlling the image to monitor the operation of the unmanned aerial vehicle: according to the initial running track, the running of the unmanned aerial vehicle can be controlled through a remote control system or a preset flight program. Receiving a distance signal of a distance triggering sensor in real time: the distance trigger sensor arranged on the unmanned aerial vehicle can monitor the distance between the unmanned aerial vehicle and the high-voltage transmission line in real time and send a distance signal back to the control system or the data processing center. Judging whether the distance signal is within the trigger adjustment tolerance distance: if the distance signal is within the trigger adjustment tolerance distance, the unmanned aerial vehicle is too close to the high-voltage transmission line, and the risk of collision exists. Generating a track adjustment instruction: according to the distance signal and the trigger adjustment tolerance distance, whether the unmanned aerial vehicle needs track adjustment or not can be judged so as to avoid collision. The control system or data processing center may generate track adjustment instructions if adjustments are needed. Optimizing an initial running track: by executing the track adjustment instruction, the flight track of the unmanned aerial vehicle can be optimized, and collision with the high-voltage transmission line is avoided. And (3) finishing periodic image acquisition: after track optimization, the unmanned aerial vehicle can continue to perform image acquisition work of the high-voltage transmission line according to the new track, and periodic image acquisition tasks are completed. The unmanned aerial vehicle can be used for carrying out efficient image acquisition and monitoring on the high-voltage transmission line, and meanwhile, the safe and stable operation of the unmanned aerial vehicle is guaranteed.
Further, the method of the present application further comprises:
if the distance signal is not within the trigger adjustment tolerance distance, generating an abnormal recording instruction;
recording the initial position according to the abnormal recording instruction, and executing continuous recording evaluation;
stopping recording when the distance signal of any node meets the trigger adjustment tolerance distance, and generating an abnormal space according to the starting position and the stopping position;
and carrying out abnormality early warning management on the abnormal space.
Specifically, an abnormal recording instruction is generated: when the distance signal is not within the trigger adjustment tolerance distance, the control system or the data processing center generates an abnormal recording instruction. Including specific cases where the distance is out of range, such as information about the distance, time, location, etc. Recording the starting position: according to the abnormal recording instruction, the unmanned aerial vehicle can record the current starting position. This location may be a geographical coordinate or a relative location with respect to a high voltage transmission line or other fixed point. Performing continuous recording evaluation: after recording the starting position, the drone may continue to perform a series of successive recording and evaluation operations. Including continuous monitoring of distance signals, assessment of anomaly level, collection and transmission of relevant data, etc. Stopping recording: when the distance signal reaches the trigger adjustment tolerance distance, the unmanned aerial vehicle stops recording. Including stopping the collection, storage, and transmission of data. Generating an abnormal space: based on the start position and the stop position, the drone may generate an anomaly space. This space may be a geometric shape, such as a rectangle, a circle, or an irregular shape, for describing the extent of the abnormal region. Performing abnormality early warning management: for this abnormal space, various kinds of early warning management operations can be performed. This may include analyzing data within the area, predicting possible outcomes, signaling early warning signals, initiating an emergency response program, etc. The early warning signal can comprise various forms such as sound, light, images or data and the like and is used for reminding a manager or an automatic control system to take corresponding countermeasures. The unmanned aerial vehicle can timely and accurately monitor and early warn the abnormal condition of the high-voltage transmission line, and the safety and stability of the line are improved.
Further, the method of the present application further comprises:
establishing line association of each segmented line based on a line segmentation result, wherein the line association is adjacent association, and the adjacent association is provided with an association influence factor;
and executing the result update of the compensation result through the line association, and executing the judgment of whether to activate the high-voltage protection mode or not based on the updated compensation result.
Specifically, the line segmentation results establish line associations for each segmented line: the high-voltage transmission line is segmented according to a certain criterion or need, and an association relation is established for each pair of adjacent line segments. The association relationship is used for subsequent power transmission control, fault detection, protection mechanism activation and the like. The line association is a neighboring association: "adjacent association" herein may refer to each line segment being associated only with the line segment immediately adjacent thereto. Such a setting may reduce processing complexity and more facilitate management of power transmission. Is provided with an associated influencing factor: modeling and setting the degree of interaction between adjacent line segments. This impact factor may be based on a variety of factors, such as the length of the line segment, current load, historical failure rate, and the like. Performing a result update of the compensation result by line association: errors or losses in the power transmission are compensated for using the line correlation information and some algorithmic model, and the associated data or model is updated after each compensation. Executing whether to activate the high voltage protection mode based on the updated compensation result: it is decided whether the high voltage protection mode needs to be activated or not according to the compensation result (possibly updated). If the compensated result does not yet reach the preset standard or threshold, it may be necessary to activate a high voltage protection mode to prevent possible damage to the electrical equipment or interruption of the power transmission.
Example two
Based on the same inventive concept as the protection switching method for monitoring a high-voltage circuit signal in the foregoing embodiment, as shown in fig. 4, the present application provides a protection switching system for monitoring a high-voltage circuit signal, the system comprising:
the basic feature set establishing module 10 is used for establishing a basic feature set of the high-voltage transmission line, the basic feature set is obtained through communication connection with a high-voltage transmission line management system, and the basic feature set comprises a delivery quality detection feature set and a calibration transmission feature set of the transmission line;
the segment identification generation module 20 is used for carrying out line segmentation of the high-voltage transmission line based on the basic feature set, and generating a segment identification of a line segmentation result;
the signal risk data set establishing module 30 is used for establishing a signal risk data set according to the segment identification as a construction feature, wherein the signal risk data set is obtained by big data matching by taking the segment identification as the construction feature;
an anomaly identification network generation module 40, where the anomaly identification network generation module 40 is configured to generate an anomaly signal identification network according to the signal risk dataset, and the anomaly signal identification network includes N segment node sub-networks, where the N segment node sub-networks are in one-to-one correspondence with the line segment results;
The real-time monitoring data generation module 50 is used for configuring signal monitoring nodes at the line segmentation result to generate real-time monitoring data, inputting the real-time monitoring data into an abnormal signal recognition network, performing abnormal recognition through N corresponding segmentation node sub-networks, and generating an abnormal recognition result;
an auxiliary anomaly identification factor establishing module 60, wherein the auxiliary anomaly identification factor establishing module 60 is used for configuring the image monitoring unmanned aerial vehicle in a preset period, executing periodic image acquisition and establishing an auxiliary anomaly identification factor based on a periodic image acquisition result and a basic feature set;
and a protection mode execution module 70, where the protection mode execution module 70 is configured to compensate the abnormality recognition result by the auxiliary abnormality recognition factor, determine whether to activate a high voltage protection mode based on the compensation result, and execute protection switching of the control unit when the determination result is that the high voltage protection mode is activated.
Further, the system further comprises:
the working data recording module is used for recording working data of the high-voltage transmission line, wherein the working data comprise working parameters and working time;
the result analysis module is used for carrying out load operation analysis results of each line segmentation result based on the calibration power transmission characteristic set according to the working parameters and the working time length;
The first auxiliary abnormal recognition factor generation module is used for generating a first auxiliary abnormal recognition factor based on the load operation analysis result;
the second auxiliary abnormal recognition factor generation module is used for carrying out characteristic recognition on the high-voltage transmission line according to the periodic image acquisition result and generating a second auxiliary abnormal recognition factor according to the characteristic recognition result;
and the auxiliary abnormal recognition factor establishing module is used for establishing the auxiliary abnormal recognition factors through the first auxiliary abnormal recognition factors and the second auxiliary abnormal recognition factors.
Further, the system further comprises:
the environment hazard factor establishing module is used for calling an auxiliary acquisition image of the image monitoring unmanned aerial vehicle, carrying out the adaptation identification of the high-voltage transmission line and the surrounding environment according to the auxiliary acquisition image, and establishing an environment hazard factor;
the natural data set establishing module is used for collecting regional natural environment data of the high-voltage transmission line and establishing a natural environment data set, wherein the natural environment data set comprises a temperature data set, an illumination data set, a wind power data set and a rainfall data set;
the natural environment influence factor generation module is used for carrying out natural environment influence analysis through the natural environment data set to generate natural environment influence factors;
And the abnormality recognition factor updating module is used for updating the auxiliary abnormality recognition factor through the environment hazard factor and the natural environment influence factor.
Further, the system further comprises:
the aging evaluation network establishment module is used for establishing an aging evaluation network, and the aging evaluation network is established by taking the basic feature set as basic data;
the aging additional influence evaluation execution module is used for synchronizing the natural environment data set to the aging evaluation network and executing the aging additional influence evaluation of the natural environment;
and the natural environment influence factor generation module is used for completing natural environment influence analysis based on the additional influence evaluation result and generating the natural environment influence factor.
Further, the system further comprises:
the layout data acquisition module is used for acquiring the line layout data of the high-voltage transmission line;
the system comprises a tolerant distance adjusting module, a distance triggering sensor and a high-voltage power transmission line, wherein the tolerant distance adjusting module is used for generating an initial running track of the image monitoring unmanned aerial vehicle through the line layout data and configuring the triggering of the distance triggering sensor to adjust the tolerant distance, and the distance triggering sensor is arranged on the monitoring unmanned aerial vehicle and used for monitoring the distance between the image monitoring unmanned aerial vehicle and the high-voltage power transmission line;
The distance signal receiving module is used for controlling the image to monitor the operation of the unmanned aerial vehicle according to the initial operation track and receiving the distance signal of the distance trigger sensor in real time;
the track adjustment instruction generation module is used for generating a track adjustment instruction according to the distance signal if the distance signal is within the trigger adjustment tolerance distance;
and the image acquisition module is used for optimizing the initial running track through the track adjustment instruction so as to complete periodic image acquisition.
Further, the system further comprises:
the abnormal recording instruction generation module is used for generating an abnormal recording instruction if the distance signal is not within the trigger adjustment tolerance distance;
the continuous recording evaluation execution module is used for recording the starting position according to the abnormal recording instruction and executing continuous recording evaluation;
the abnormal space generation module is used for stopping recording when the distance signal of any node meets the trigger adjustment tolerance distance, and generating an abnormal space according to the starting position and the stopping position;
and the abnormality early warning management module is used for carrying out abnormality early warning management on the abnormal space.
Further, the system further comprises:
the line association module is used for establishing line association of each segmented line based on a line segmentation result, wherein the line association is adjacent association, and the adjacent association is provided with an association influence factor;
And the protection mode activation module is used for executing the result update of the compensation result through the line association and executing the judgment of whether to activate the high-voltage protection mode or not based on the updated compensation result.
The foregoing detailed description of a protection switching method for monitoring a high-voltage circuit signal will be clear to those skilled in the art, and the protection switching system for monitoring a high-voltage circuit signal in this embodiment is described more simply because it corresponds to the embodiment disclosure device, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. A method for protection switching of high voltage circuit signal monitoring, the method comprising:
Establishing a basic feature set of the high-voltage transmission line, wherein the basic feature set is obtained by connecting a high-voltage transmission line management system through communication, and comprises a factory quality inspection feature set and a calibration transmission feature set of the transmission line;
based on the basic feature set, carrying out line segmentation of the high-voltage transmission line, and generating a segmentation identifier of a line segmentation result;
according to the segment identification as a construction feature, a signal risk data set is established, and the signal risk data set is obtained by big data matching by taking the segment identification as the construction feature;
generating an abnormal signal identification network by using the signal risk data set, wherein the abnormal signal identification network comprises N segmented node sub-networks, and the N segmented node sub-networks are in one-to-one correspondence with the line segmentation result;
configuring signal monitoring nodes on a line segmentation result to generate real-time monitoring data, inputting the real-time monitoring data into an abnormal signal recognition network, and performing abnormal recognition through N corresponding segmentation node sub-networks to generate an abnormal recognition result;
configuring an image monitoring unmanned aerial vehicle in a preset period, executing periodic image acquisition, and establishing auxiliary abnormal recognition factors based on a periodic image acquisition result and a basic feature set;
Compensating the abnormal recognition result by the auxiliary abnormal recognition factor, judging whether to activate a high-voltage protection mode based on the compensation result, and executing protection switching of the control unit when the judgment result is that the high-voltage protection mode is activated;
the method further comprises the steps of:
recording working data of the high-voltage transmission line, wherein the working data comprise working parameters and working time;
load operation analysis results of each line segmentation result are carried out based on the calibration power transmission characteristic set according to the working parameters and the working time length;
generating a first auxiliary anomaly identification factor based on the load operation analysis result;
performing feature recognition on the high-voltage transmission line according to the periodic image acquisition result, and generating a second auxiliary abnormal recognition factor according to the feature recognition result;
establishing the auxiliary abnormality recognition factor through the first auxiliary abnormality recognition factor and the second auxiliary abnormality recognition factor.
2. The method of claim 1, wherein the method further comprises:
calling an auxiliary acquisition image of the image monitoring unmanned aerial vehicle, carrying out high-voltage transmission line and surrounding environment adaptation identification according to the auxiliary acquisition image, and establishing an environment hazard factor;
Collecting regional natural environment data of a high-voltage transmission line, and establishing a natural environment data set, wherein the natural environment data set comprises a temperature data set, an illumination data set, a wind power data set and a rainfall data set;
performing natural environment influence analysis through the natural environment data set to generate natural environment influence factors;
updating the auxiliary anomaly identification factor by the environmental hazard factor and the natural environment influence factor.
3. The method of claim 2, wherein the method further comprises:
an aging evaluation network is established, wherein the aging evaluation network is established by taking the basic feature set as basic data;
synchronizing the natural environment data set to the aging evaluation network, and executing aging additional influence evaluation of the natural environment;
and completing natural environment influence analysis based on the additional influence evaluation result, and generating the natural environment influence factor.
4. The method of claim 1, wherein the method further comprises:
obtaining line layout data of the high-voltage transmission line;
generating an initial running track of the image monitoring unmanned aerial vehicle through the line layout data, and configuring a trigger of a distance trigger sensor to adjust the tolerant distance, wherein the distance trigger sensor is arranged on the monitoring unmanned aerial vehicle and used for monitoring the distance between the image monitoring unmanned aerial vehicle and a high-voltage transmission line;
Controlling an image to monitor the operation of the unmanned aerial vehicle according to the initial operation track, and receiving a distance signal of the distance triggering sensor in real time;
if the distance signal is within the trigger adjustment tolerance distance, generating a track adjustment instruction according to the distance signal;
and optimizing the initial running track through the track adjustment instruction so as to complete periodic image acquisition.
5. The method of claim 4, wherein the method further comprises:
if the distance signal is not within the trigger adjustment tolerance distance, generating an abnormal recording instruction;
recording the initial position according to the abnormal recording instruction, and executing continuous recording evaluation;
stopping recording when the distance signal of any node meets the trigger adjustment tolerance distance, and generating an abnormal space according to the starting position and the stopping position;
and carrying out abnormality early warning management on the abnormal space.
6. The method of claim 1, wherein the method further comprises:
establishing line association of each segmented line based on a line segmentation result, wherein the line association is adjacent association, and the adjacent association is provided with an association influence factor;
and executing the result update of the compensation result through the line association, and executing the judgment of whether to activate the high-voltage protection mode or not based on the updated compensation result.
7. A protection switching system for high voltage circuit signal monitoring, the system comprising:
a basic feature set establishing module for establishing a basic feature set of the high-voltage transmission line, the basic feature set is obtained through a communication connection high-voltage transmission line management system and comprises a transmission line delivery quality detection feature set and a calibration transmission feature set;
the segment identification generation module is used for carrying out line segmentation of the high-voltage transmission line based on the basic feature set and generating segment identifications of line segmentation results;
the signal risk data set establishing module is used for establishing a signal risk data set according to the segment identification as a construction characteristic, and the signal risk data set is obtained by big data matching by taking the segment identification as the construction characteristic;
the abnormal recognition network generation module is used for generating an abnormal signal recognition network according to the signal risk data set, wherein the abnormal signal recognition network comprises N segmented node sub-networks, and the N segmented node sub-networks are in one-to-one correspondence with the line segmentation result;
The real-time monitoring data generation module is used for configuring signal monitoring nodes at the line segmentation result to generate real-time monitoring data, inputting the real-time monitoring data into an abnormal signal recognition network, carrying out abnormal recognition through N corresponding segmentation node sub-networks and generating an abnormal recognition result;
the auxiliary abnormal recognition factor establishing module is used for configuring the image monitoring unmanned aerial vehicle in a preset period, executing periodic image acquisition and establishing an auxiliary abnormal recognition factor based on a periodic image acquisition result and a basic feature set;
the protection mode execution module is used for compensating the abnormal recognition result through the auxiliary abnormal recognition factor, judging whether to activate a high-voltage protection mode or not based on the compensation result, and executing protection switching of the control unit when the judgment result is that the high-voltage protection mode is activated;
the system further comprises:
the working data recording module is used for recording working data of the high-voltage transmission line, wherein the working data comprise working parameters and working time;
the result analysis module is used for carrying out load operation analysis results of each line segmentation result based on the calibration power transmission characteristic set according to the working parameters and the working time length;
The first auxiliary abnormal recognition factor generation module is used for generating a first auxiliary abnormal recognition factor based on the load operation analysis result;
the second auxiliary abnormal recognition factor generation module is used for carrying out characteristic recognition on the high-voltage transmission line according to the periodic image acquisition result and generating a second auxiliary abnormal recognition factor according to the characteristic recognition result;
and the auxiliary abnormal recognition factor establishing module is used for establishing the auxiliary abnormal recognition factors through the first auxiliary abnormal recognition factors and the second auxiliary abnormal recognition factors.
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