CN117674418A - Power transmission line state monitoring method, system, equipment and medium - Google Patents
Power transmission line state monitoring method, system, equipment and medium Download PDFInfo
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Abstract
The invention discloses a power transmission line state monitoring method, a system, equipment and a medium. And carrying out model training on the multidimensional vector, and constructing a target preset classification algorithm model and a state normal range. And carrying out transmission line abnormality detection on the real-time data based on the state normal range through a target preset classification algorithm model, and generating line abnormal data. When the abnormal line data are of a preset type, carrying out fuzzy recognition on the real-time line state based on the normal state range through a target preset classification algorithm model, and generating line state data. And constructing a power transmission line state monitoring report corresponding to the power transmission line based on the line state data and a preset measure library. The state of the power transmission line is classified and processed through the classification algorithm model, so that the detection precision and timeliness are improved, and the normal operation of the power transmission line is ensured.
Description
Technical Field
The invention relates to the technical field of power transmission lines, in particular to a power transmission line state monitoring method, a power transmission line state monitoring system, power transmission line state monitoring equipment and a power transmission line state monitoring medium.
Background
As an important component of the power system, the safe operation of the power transmission line has an important influence on the reliability and stability of the power grid. However, the transmission line is often affected by environmental factors and human factors, and faults with different degrees, such as overload, short circuit, poor contact and the like, may occur, and these faults may affect the normal operation of the transmission line, and even may bring about catastrophic effects on the power grid. Therefore, it is necessary to perform status monitoring and fault detection on the power transmission line.
At present, physical detection is adopted to monitor and detect faults of the power transmission line. The physical detection refers to monitoring and analyzing physical quantities such as voltage, current, power and the like of the power transmission line through a traditional electric power parameter measuring instrument so as to judge whether the state of the power transmission line is normal or not. However, physical detection requires a large number of sensors to be deployed, is costly to maintain, and is subject to significant environmental disturbances and errors.
Therefore, the physical detection method adopted by the existing power transmission line state monitoring method is difficult to accurately detect and analyze, is easily affected by environmental noise and errors, and results in low accuracy of monitoring results.
Disclosure of Invention
The invention provides a power transmission line state monitoring method, a system, equipment and a medium, which solve the technical problems that the physical detection method adopted by the existing power transmission line state monitoring method is difficult to accurately detect and analyze, is easily influenced by environmental noise and errors, and causes low accuracy of monitoring results.
The invention provides a power transmission line state monitoring method, which comprises the following steps:
acquiring historical data and real-time data of a power transmission line, performing data preprocessing on the historical data, and constructing a multidimensional vector;
model training is carried out on the multidimensional vector, and a target preset classification algorithm model and a state normal range are constructed;
carrying out transmission line abnormality detection on the real-time data based on the state normal range through the target preset classification algorithm model to generate line abnormality data;
when the abnormal line data are of a preset type, carrying out fuzzy recognition on the real-time line state on the real-time data based on the normal state range through the target preset classification algorithm model to generate line state data;
and constructing a power transmission line state monitoring report corresponding to the power transmission line according to the line state data and a preset measure library.
Optionally, the history data includes initial horizontal segment history data and initial vertical segment history data; the step of obtaining the historical data and the real-time data of the power transmission line, carrying out data preprocessing on the historical data and constructing a multidimensional vector comprises the following steps:
acquiring initial horizontal segment historical data, initial vertical segment historical data and real-time data corresponding to the power transmission line through a patrol device;
respectively carrying out data cleaning on the initial horizontal segment historical data and the initial vertical segment historical data to generate middle horizontal segment historical data and middle vertical segment historical data;
performing feature extraction on the middle horizontal segment historical data and the middle vertical segment historical data by adopting a time-frequency feature extraction method to generate target horizontal segment historical data and target vertical segment historical data;
and performing feature scaling and mean value normalization processing on the target horizontal segment historical data and the target vertical segment historical data to generate a multidimensional vector.
Optionally, the step of performing model training on the multidimensional vector to construct a target preset classification algorithm model and a normal state range includes:
training an initial preset classification algorithm model by adopting a machine learning algorithm and the multidimensional vector to generate a target preset classification algorithm model;
And carrying out working state division by combining the target preset classification algorithm model with preset expert experience and reference standard specifications, and generating a state normal range.
Optionally, the step of detecting the transmission line abnormality of the real-time data based on the state normal range by using the target preset classification algorithm model and generating line abnormality data includes:
removing noise in the real-time data, and carrying out normalization processing to generate first analysis data;
extracting the characteristics of the first analysis data to generate a plurality of first characteristic vectors;
respectively comparing the first characteristic vector with a normal vector in the state normal range by adopting a k nearest neighbor algorithm to generate comparison data;
when the comparison data is in an abnormal state, an abnormal signal is sent out and emergency measures are executed;
and constructing line abnormal data by adopting the abnormal signals and the emergency measures.
Optionally, when the line abnormal data is of a preset type, the step of generating line state data by performing power transmission line state fuzzy recognition on the real-time data based on the state normal range through the target preset classification algorithm model includes:
When the line abnormal data is of a preset type, removing noise in the real-time data, and carrying out normalization processing to generate second analysis data;
extracting the characteristics of the second analysis data to generate a plurality of second characteristic vectors;
performing fuzzy calculation on the second characteristic vector and the corresponding deviation value which do not belong to the normal state range, and generating a fuzzy output variable;
and deblurring the blurred output variable to generate line state data.
Optionally, the step of constructing a power transmission line state monitoring report corresponding to the power transmission line according to the line state data and a preset measure library includes:
matching the state identification data with a preset measure library to generate alarm data corresponding to the state identification data;
marking real-time data corresponding to the line state data as abnormal data;
selecting abnormal data which appear in the normal range of the state for a preset number of times, and generating correction data;
correcting an initial history database corresponding to the power transmission line by adopting the correction data to generate a target history database;
and constructing a power transmission line state monitoring report corresponding to the power transmission line by adopting the line state data, the alarm information, the correction data and the target history database.
The invention also provides a power transmission line state monitoring system, which comprises:
the multidimensional vector construction module is used for acquiring historical data and real-time data of the power transmission line, preprocessing the historical data and constructing multidimensional vectors;
the target preset classification algorithm model and state normal range construction module is used for carrying out model training on the multidimensional vector to construct a target preset classification algorithm model and a state normal range;
the circuit abnormal data generation module is used for carrying out transmission line abnormal detection on the real-time data based on the state normal range through the target preset classification algorithm model to generate circuit abnormal data;
the line state data generation module is used for carrying out power transmission line state fuzzy recognition on the real-time data based on the state normal range through the target preset classification algorithm model when the line abnormal data are of a preset type, so as to generate line state data;
and the power transmission line state monitoring report construction module is used for constructing a power transmission line state monitoring report corresponding to the power transmission line according to the line state data and a preset measure library.
Optionally, the history data includes initial horizontal segment history data and initial vertical segment history data; the multidimensional vector building module comprises:
The data acquisition module is used for acquiring initial horizontal segment historical data, initial vertical segment historical data and real-time data corresponding to the power transmission line through the inspection device;
the middle horizontal segment historical data and middle vertical segment historical data generation module is used for respectively carrying out data cleaning on the initial horizontal segment historical data and the initial vertical segment historical data to generate middle horizontal segment historical data and middle vertical segment historical data;
the target horizontal segment historical data and target vertical segment historical data generation module is used for respectively carrying out feature extraction on the middle horizontal segment historical data and the middle vertical segment historical data by adopting a time-frequency feature extraction method to generate target horizontal segment historical data and target vertical segment historical data;
and the multidimensional vector construction submodule is used for carrying out characteristic scaling and mean value normalization processing on the target horizontal segment historical data and the target vertical segment historical data to generate a multidimensional vector.
The invention also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the computer program when executed by the processor causes the processor to execute the steps for realizing the power transmission line state monitoring method according to any one of the above.
The invention also provides a computer readable storage medium having stored thereon a computer program which when executed implements a transmission line condition monitoring method as described in any of the above.
From the above technical scheme, the invention has the following advantages:
according to the method, the historical data and the real-time data of the power transmission line are obtained, the historical data are subjected to data preprocessing, and the multidimensional vector is constructed. And carrying out model training on the multidimensional vector, and constructing a target preset classification algorithm model and a state normal range. And carrying out transmission line abnormality detection on the real-time data based on the state normal range through a target preset classification algorithm model, and generating line abnormal data. When the abnormal line data are of a preset type, carrying out fuzzy recognition on the real-time line state based on the normal state range through a target preset classification algorithm model, and generating line state data. And constructing a power transmission line state monitoring report corresponding to the power transmission line based on the line state data and a preset measure library. The method solves the technical problems that the physical detection method adopted by the existing power transmission line state monitoring method is difficult to accurately detect and analyze, is easily influenced by environmental noise and errors, and causes low accuracy of monitoring results. And adopting a multidimensional vector to reflect the voltage change mode of the power transmission line, and constructing a target preset classification algorithm model by using a classification algorithm of a machine learning technology. The state of the power transmission line is classified and processed through the classification algorithm model, corresponding maintenance or repair measures are carried out based on a preset measure library, the detection precision and timeliness are improved, and the normal operation of the power transmission line is ensured.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained from these drawings without inventive faculty for a person skilled in the art.
Fig. 1 is a flowchart of steps of a method for monitoring a status of a power transmission line according to a first embodiment of the present invention;
fig. 2 is a flowchart of steps of a method for monitoring a status of a power transmission line according to a second embodiment of the present invention;
fig. 3 is a block diagram of a power transmission line status monitoring system according to a third embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a power transmission line state monitoring method, a system, equipment and a medium, which are used for solving the technical problems that a physical detection method adopted by the existing power transmission line state monitoring method is difficult to accurately detect and analyze, is easily influenced by environmental noise and errors, and causes low accuracy of a monitoring result.
In order to make the objects, features and advantages of the present invention more comprehensible, the technical solutions in the embodiments of the present invention are described in detail below with reference to the accompanying drawings, and it is apparent that the embodiments described below are only some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating steps of a power transmission line status monitoring method according to an embodiment of the present invention.
The first embodiment of the invention provides a power transmission line state monitoring method, which comprises the following steps:
step 101, acquiring historical data and real-time data of the power transmission line, and carrying out data preprocessing on the historical data to construct a multidimensional vector.
In an embodiment of the present invention, the history data includes initial horizontal segment history data and initial vertical segment history data. And acquiring initial horizontal segment historical data, initial vertical segment historical data and real-time data corresponding to the power transmission line through the inspection device. And respectively cleaning the initial horizontal segment historical data and the initial vertical segment historical data to generate middle horizontal segment historical data and middle vertical segment historical data. And respectively carrying out feature extraction on the middle horizontal segment historical data and the middle vertical segment historical data by adopting a time-frequency feature extraction method to generate target horizontal segment historical data and target vertical segment historical data. And performing feature scaling and mean value normalization processing on the target horizontal segment historical data and the target vertical segment historical data to generate a multidimensional vector.
And 102, carrying out model training on the multidimensional vector, and constructing a target preset classification algorithm model and a state normal range.
In the embodiment of the invention, a machine learning algorithm and a multidimensional vector are adopted to train an initial preset classification algorithm model, and a target preset classification algorithm model is generated. And carrying out working state division by combining a target preset classification algorithm model with preset expert experience and a reference standard specification, and generating a state normal range.
And 103, carrying out transmission line abnormality detection on the real-time data based on the state normal range through a target preset classification algorithm model, and generating line abnormal data.
In the embodiment of the invention, noise in the real-time data is removed, and normalization processing is performed to generate first analysis data. And extracting the characteristics of the first analysis data to generate a plurality of first characteristic vectors. And extracting the characteristics of the first analysis data to generate a plurality of first characteristic vectors. And when the comparison data is in an abnormal state, sending out an abnormal signal and executing emergency measures. And constructing line abnormal data by adopting abnormal signals and emergency measures.
And 104, when the abnormal line data are of a preset type, carrying out fuzzy recognition on the real-time line state based on the normal state range through a target preset classification algorithm model, and generating line state data.
The preset type refers to that a transmission circuit has abnormal line.
In the embodiment of the invention, when the abnormal line data is of a preset type, noise in the real-time data is removed, and normalization processing is performed to generate second analysis data. And extracting the characteristics of the second analysis data to generate a plurality of second characteristic vectors. And carrying out fuzzy calculation on the second characteristic vector and the corresponding deviation value which do not belong to the normal state range, and generating a fuzzy output variable. And (5) defuzzifying the fuzzified output variable to generate line state data.
And 105, constructing a power transmission line state monitoring report corresponding to the power transmission line according to the line state data and the preset measure library.
In the embodiment of the invention, the state identification data is matched with a preset measure library, and alarm data corresponding to the state identification data is generated. And marking the real-time data corresponding to the line state data as abnormal data. And selecting abnormal data which appear in the normal range of the state for a preset number of times, and generating correction data. And correcting the initial historical database corresponding to the power transmission line by adopting the correction data to generate a target historical database. And constructing a power transmission line state monitoring report corresponding to the power transmission line by adopting line state data, alarm information, correction data and a target historical database.
In the embodiment of the invention, the historical data and the real-time data of the power transmission line are obtained, and the historical data are subjected to data preprocessing to construct the multidimensional vector. And carrying out model training on the multidimensional vector, and constructing a target preset classification algorithm model and a state normal range. And carrying out transmission line abnormality detection on the real-time data based on the state normal range through a target preset classification algorithm model, and generating line abnormal data. When the abnormal line data are of a preset type, carrying out fuzzy recognition on the real-time line state based on the normal state range through a target preset classification algorithm model, and generating line state data. And constructing a power transmission line state monitoring report corresponding to the power transmission line based on the line state data and a preset measure library. The method solves the technical problems that the physical detection method adopted by the existing power transmission line state monitoring method is difficult to accurately detect and analyze, is easily influenced by environmental noise and errors, and causes low accuracy of monitoring results. And adopting a multidimensional vector to reflect the voltage change mode of the power transmission line, and constructing a target preset classification algorithm model by using a classification algorithm of a machine learning technology. The state of the power transmission line is classified and processed through the classification algorithm model, corresponding maintenance or repair measures are carried out based on a preset measure library, the detection precision and timeliness are improved, and the normal operation of the power transmission line is ensured.
Referring to fig. 2, fig. 2 is a flowchart illustrating steps of a power transmission line status monitoring method according to a second embodiment of the present invention.
The second embodiment of the invention provides another power transmission line state monitoring method, which comprises the following steps:
step 201, acquiring historical data and real-time data of the power transmission line, and performing data preprocessing on the historical data to construct a multidimensional vector.
Further, the history data includes initial horizontal segment history data and initial vertical segment history data. Step 201 may comprise the following sub-steps S11-S14:
s11, acquiring initial horizontal segment historical data, initial vertical segment historical data and real-time data corresponding to the power transmission line through a patrol device.
And S12, respectively carrying out data cleaning on the initial horizontal segment historical data and the initial vertical segment historical data to generate middle horizontal segment historical data and middle vertical segment historical data.
And S13, performing feature extraction on the middle horizontal segment historical data and the middle vertical segment historical data by adopting a time-frequency feature extraction method to generate target horizontal segment historical data and target vertical segment historical data.
And S14, performing feature scaling and mean value normalization processing on the target horizontal segment historical data and the target vertical segment historical data to generate a multidimensional vector.
In the embodiment of the invention, in order to improve the monitoring efficiency and accuracy, a patrol device is required to be arranged on a horizontal section and a distance section, namely a vertical section, of the power transmission line, and the patrol device is used for acquiring the historical data of the initial horizontal section, the historical data of the initial vertical section and the real-time data corresponding to the power transmission line. The inspection device can acquire data without manual intervention, so that the monitoring efficiency and accuracy are improved, and the inspection device has stable performance and high-precision measurement capability. The inspection device comprises a sensor, a sampling device, a processing device and a storage device, wherein the sensor, the sampling device, the processing device and the storage device are all electrically connected. The sensor of the inspection device can comprise a voltage sensor, a current sensor and the like, different aspects of the state information of the power transmission line can be acquired, and meanwhile, a plurality of sampling points can be arranged on the sampling device, so that the monitoring precision and accuracy are improved. Other algorithms, such as an SVM algorithm, may be used for the classification algorithm in the processing means. The storage device can store the monitoring result in the cloud server, is convenient to manage and analyze data, can also provide a data sharing function, and the method of the embodiment can be applied to monitoring of various power transmission lines, including alternating current power transmission lines and direct current power transmission lines, can also carry out multi-line parallel monitoring, and improves monitoring efficiency.
By adopting different parameter settings, such as measurement intervals, sampling frequency and the like, the monitoring and diagnosis of the state of the power transmission line can be realized, data such as voltage values, current values, voltage fluctuation, current fluctuation and the like are detected on a horizontal section and a distance section, namely a vertical section, of the power transmission line, the detected data are recorded in a historical database corresponding to the power transmission line, the historical database is used for comparing with a future measurement data set, namely real-time data, so as to judge whether the state of the power transmission line is abnormal or not, and the integrity and accuracy of the data are ensured in the data recording process so as to facilitate the data analysis of a subsequent classification algorithm. The history database is an important component for storing transmission line history data and real-time data. The historical database can be stored in various forms, including a relational database, a non-relational database, a data warehouse and the like, the data stored in the historical database can be used for training a classification algorithm model to improve the accuracy and reliability of the model, and the data stored in the historical database comprises various aspects, such as the historical record of various indexes of voltage, current, temperature and the like of a power transmission line, the record of equipment faults, the record of environmental factors such as weather changes and the like, and the data can be used for training the classification algorithm model to realize automatic monitoring and classification identification of the state of the power transmission line.
And respectively carrying out data cleaning on the initial horizontal segment historical data and the initial vertical segment historical data to generate middle horizontal segment historical data and middle vertical segment historical data, and respectively carrying out feature extraction on the middle horizontal segment historical data and the middle vertical segment historical data by adopting a time-frequency feature extraction method to generate target horizontal segment historical data and target vertical segment historical data. In the aspect of feature extraction, effective features are extracted by using methods such as wavelet transformation, time-frequency analysis and the like so as to enhance the interpretation capability of a classification algorithm on data, and the accuracy and the stability of the classification algorithm can be effectively improved by comprehensively using the techniques, so that the state of a power transmission line can be predicted better. Finally, performing feature scaling and mean value normalization processing on the target horizontal segment historical data and the target vertical segment historical data to generate multidimensional vectors, and ensuring that data in different dimensions have the same weight and scale, thereby avoiding overlarge influence of certain dimensions on classification results. The multidimensional vector can reflect the voltage change mode of the power transmission line, and the step can be combined with the actual situation to flexibly set parameters so as to improve the quality of data and the accuracy of a classification algorithm.
When historical data and real-time data are acquired, the data of various indexes such as voltage, current and temperature of the power transmission line and the data of environmental factors such as weather changes can be acquired through equipment such as a sensor and a monitoring device. The data can be stored in a historical database and used for training an initial preset classification algorithm model, and can also be input into the initial preset classification algorithm model in real time so as to realize real-time monitoring and classification recognition of the state of the power transmission line.
And 202, training an initial preset classification algorithm model by adopting a machine learning algorithm and multidimensional vectors to generate a target preset classification algorithm model.
The initial preset classification algorithm model is a model which is set based on actual requirements and is used for classifying the state of the power transmission line.
In the embodiment of the invention, the preprocessed historical data, namely multidimensional vector data, is used for training an initial preset classification algorithm model by adopting machine learning algorithms such as decision trees, support vector machines, neural networks and the like, so as to obtain a target preset classification algorithm model. The classification algorithm in the initial preset sub-algorithm model uses a machine learning technology to distinguish normal data modes from abnormal data modes, wherein the classification algorithm comprises a KNN algorithm, namely a k-nearest neighbor algorithm and a fuzzy algorithm, a multidimensional vector is used as input of the classification algorithm to train and debug the model, the accuracy and reliability of the model are improved by continuously optimizing parameters and characteristics of the model, and the trained target preset sub-algorithm model can be used for automatic monitoring and classification recognition of the state of a power transmission line.
Through training an initial preset sub-algorithm model, a more accurate classifier can be obtained and used for automatically monitoring and classifying and identifying the state of the power transmission line, and meanwhile, real-time data can be input into the classifying algorithm model to realize real-time monitoring and alarming, so that the system can effectively monitor the state of the power transmission line and timely take corresponding measures to ensure safe and stable operation of the power transmission line.
And 203, dividing the working state by combining a target preset classification algorithm model with preset expert experience and a reference standard specification, and generating a state normal range.
In the embodiment of the invention, sample training is also a method for determining the normal state range, and after a target preset classification algorithm model is obtained by training a plurality of samples through a classification algorithm, the normal state range can be determined through the target preset classification algorithm model. The working state division is performed by combining expert experience and reference standard specifications, and the working state division can be realized by expert knowledge of the working mode of the line and guidance of the industry specifications. The historical data can be used for establishing a statistical model, and the normal range of the state can be determined by analyzing the historical data, including data during normal operation and data during faults, so as to obtain a typical operation mode of the line.
And 204, carrying out transmission line abnormality detection on the real-time data based on the state normal range through a target preset classification algorithm model, and generating line abnormal data.
Further, step 204 may include the following substeps S21-S25:
s21, removing noise in the real-time data, and performing normalization processing to generate first analysis data.
S22, extracting the features of the first analysis data to generate a plurality of first feature vectors.
S23, respectively comparing the first feature vectors with normal vectors in a state normal range by adopting a k nearest neighbor algorithm, and generating comparison data.
S24, when the comparison data is in an abnormal state, an abnormal signal is sent out and emergency measures are executed.
S25, constructing line abnormal data by adopting abnormal signals and emergency measures.
In the embodiment of the invention, the sensor is used for collecting real-time data of the power transmission line, removing noise in the real-time data, and carrying out normalization processing to generate first analysis data. And extracting the characteristics of the first analysis data to generate a plurality of first characteristic vectors. Based on the KNN algorithm, namely the k nearest neighbor algorithm, the characteristic vector is compared with a normal vector in a normal state range, and whether the characteristic vector belongs to a normal state or not is judged. For example, detecting data of a certain node, and determining whether the characteristic vector between the X nodes and the certain node is abnormal by determining whether the characteristic vector is equal to the characteristic vector corresponding to the normal state in the sample space.
When the feature vector does not belong to the normal state, an abnormal signal is sent out, and emergency measures such as sounding an alarm, powering off and the like are taken. And constructing line abnormal data by adopting abnormal signals and emergency measures. When the feature vector belongs to the normal state, marking the real-time data as correct data, and updating the corresponding initial historical database to ensure that the data in the historical database is always the latest and accurate data.
And 205, when the abnormal line data are of a preset type, carrying out fuzzy recognition on the real-time line state based on the normal state range by using a target preset classification algorithm model, and generating line state data.
Further, step 205 may include the following substeps S31-S34:
and S31, removing noise in the real-time data when the line abnormal data are of a preset type, and carrying out normalization processing to generate second analysis data.
S32, performing feature extraction on the second analysis data to generate a plurality of second feature vectors.
S33, performing fuzzy calculation on the second characteristic vector and the corresponding deviation value which do not belong to the normal state range, and generating a fuzzy output variable.
S34, defuzzifying the fuzzified output variable to generate line state data.
In the embodiment of the invention, when the line abnormal data is of a preset type which is a set abnormal type, the noise in the real-time data is removed, and normalization processing is carried out to generate second analysis data. And then extracting the characteristics of the second analysis data to generate a plurality of second characteristic vectors. And then, carrying out fuzzy calculation on the second characteristic vector and the corresponding deviation value which do not belong to the normal state range, and obtaining a fuzzy output variable. Specifically, information such as voltage, current, load and the like of the power transmission line is collected, values of the information with normal ranges are compared, if the values are out of the normal ranges, abnormal marking is carried out, and then fuzzy calculation is carried out. For example, the second eigenvector includes a voltage I, a current U, and a load W, and the calculating method is that the fuzzy output variables f=ia+uv+wn and I, V, N are the bias values corresponding to the voltage, the current, and the load, respectively. If the second eigenvector is within the range, the line state data is 0, and if the second eigenvector is not within the range, the second eigenvector is input according to the absolute value of the maximum value or the minimum value of the second eigenvector and the range, so as to perform fuzzy calculation and obtain a fuzzy output variable. And (5) defuzzifying the fuzzified output variable to generate line state data.
And 206, constructing a power transmission line state monitoring report corresponding to the power transmission line according to the line state data and the preset measure library.
Further, step 206 may include the following substeps S41-S45:
s41, matching the state identification data with a preset measure library to generate alarm data corresponding to the state identification data.
S42, marking real-time data corresponding to the line state data as abnormal data.
S43, selecting abnormal data which appear in a normal state range for a preset number of times, and generating correction data.
S44, correcting the initial history database corresponding to the power transmission line by using the correction data to generate a target history database.
S45, constructing a power transmission line state monitoring report corresponding to the power transmission line by adopting line state data, alarm information, correction data and a target historical database.
The preset measure library is to set corresponding early warning grades based on the state type corresponding to the state identification data, and each early warning grade is provided with corresponding alarm data, wherein the alarm data comprises an alarm mode and alarm information. The alarm mode comprises the steps of sending out alarm sound through the sound alarm device, sending out flash lamp alarm through the illumination alarm device, sending out vibration alarm through the vibration alarm device and sending alarm information to related operators through a man-machine interface.
The alarm information comprises remote monitoring, on-site inspection and equipment maintenance, wherein the remote monitoring means that the state of the power transmission line is monitored in a network connection mode, and the alarm is given out and processed through manual intervention detection. The on-site inspection refers to that an inspector makes an inspection on the power transmission line in the field, and performs state judgment and processing through manual intervention detection. The equipment maintenance refers to the operation of manually maintaining, replacing and the like related equipment of the power transmission line so as to ensure the normal operation of the power transmission line, and the alarm information can be used independently or matched with each other according to specific conditions, thereby realizing the omnibearing monitoring and management of the state of the power transmission line.
In the embodiment of the invention, when the power transmission line state is abnormal, the system can automatically judge the abnormal type, namely, the power transmission line state fuzzy recognition is carried out on the real-time data based on the state normal range through the target preset classification algorithm model, so as to generate the line state data. And matching the state identification data with a preset measure library to generate alarm data corresponding to the state identification data. For example, when the system detects that the transmission line voltage is too high or too low, an audible alarm may be employed to sound an alarm so that on-site personnel take timely action. When the system detects that the transmission line vibrates too much, a vibration alarm can be sent out through a vibration alarm device, so that surrounding personnel can check whether the line has a problem or not in time. When the system detects that the power transmission line is overloaded, a flash lamp alarm can be sent out through the illumination alarm device so as to draw attention.
If the real-time collected data is abnormal, the system triggers an alarm, manual intervention is needed to detect, corresponding maintenance or repair measures are carried out, and the real-time data corresponding to the line state data is marked as abnormal data. In the step, an operator can analyze and evaluate the abnormal data, judge the severity and the influence range of the fault, and take corresponding measures to repair or repair so as to ensure the normal operation of the power transmission line. Recording abnormal data which appear in a normal range for a plurality of times, and generating correction data. And correcting the initial historical database corresponding to the power transmission line by adopting the correction data to generate a target historical database. In this step, the system will make statistics and analysis on the abnormal data that appears many times, find out its commonality and law, in order to improve the classification algorithm and raise its accuracy. And meanwhile, the corrected target historical database is used for comparing with future measurement data, timely finding out abnormal states of the power transmission line and taking corresponding measures. And finally, constructing a power transmission line state monitoring report corresponding to the power transmission line by adopting line state data, alarm information, correction data and a target historical database.
In the embodiment of the invention, the efficiency and the accuracy of monitoring the state of the power transmission line can be improved by installing the inspection device on the power transmission line, the workload of manual inspection is reduced by automatic detection, human errors are avoided, and the detection precision and the timeliness are improved.
The classification algorithm adopted by the embodiment of the invention comprises a KNN algorithm and a fuzzy algorithm, the classification algorithm processes the collected multidimensional vector data, so that the state of the power transmission line is classified and monitored, the classification algorithm records abnormal data appearing for a plurality of times, the historical database is corrected according to the abnormal data, the accuracy of the classification algorithm is improved, and the state of the power transmission line is predicted better. Other classification algorithms, such as an SVM algorithm, a decision tree algorithm, a neural network algorithm, etc., can be used in combination as required to process the data, and in order to better monitor the state of the transmission line, the classification algorithm can be used in combination with other technologies, such as a fault diagnosis technology, a data mining technology, a visualization technology, etc. The application of the techniques can more comprehensively analyze the state of the power transmission line, discover potential problems in time and improve the safety and stability of the power transmission line. The data is processed by using the classification algorithm, so that abnormal conditions of the power transmission line can be found and alarmed in time, stable operation of the power transmission line and safety of equipment can be guaranteed, and the fault rate and maintenance cost caused by the abnormal conditions can be reduced. Through continuous recording and correction of the historical database, the occurrence frequency and distribution condition of abnormal conditions can be recorded and analyzed, more refined management and maintenance of the power transmission line are facilitated, and the reliability and safety of the line are improved.
Referring to fig. 3, fig. 3 is a block diagram of a power transmission line status monitoring system according to a third embodiment of the present invention.
The third embodiment of the present invention provides a power transmission line state monitoring system, including:
the multidimensional vector construction module 301 is configured to obtain historical data and real-time data of the power transmission line, perform data preprocessing on the historical data, and construct a multidimensional vector.
The target preset classification algorithm model and state normal range construction module 302 is configured to perform model training on the multidimensional vector, and construct a target preset classification algorithm model and a state normal range.
The line abnormal data generating module 303 is configured to perform transmission line abnormal detection on the real-time data based on the state normal range through a target preset classification algorithm model, and generate line abnormal data.
The line state data generating module 304 is configured to perform power transmission line state fuzzy recognition on the real-time data based on the state normal range through the target preset classification algorithm model when the line abnormal data is of a preset type, and generate line state data.
The power transmission line state monitoring report construction module 305 is configured to construct a power transmission line state monitoring report corresponding to the power transmission line according to the line state data and the preset measure library.
Optionally, the history data includes initial horizontal segment history data and initial vertical segment history data. The multidimensional vector construction module 301 includes:
the data acquisition module is used for acquiring initial horizontal segment historical data, initial vertical segment historical data and real-time data corresponding to the power transmission line through the inspection device.
And the middle horizontal segment historical data and middle vertical segment historical data generation module is used for respectively cleaning the initial horizontal segment historical data and the initial vertical segment historical data to generate middle horizontal segment historical data and middle vertical segment historical data.
And the target horizontal segment historical data and target vertical segment historical data generation module is used for respectively carrying out feature extraction on the middle horizontal segment historical data and the middle vertical segment historical data by adopting a time-frequency feature extraction method to generate target horizontal segment historical data and target vertical segment historical data.
And the multidimensional vector construction submodule is used for carrying out characteristic scaling and mean value normalization on the target horizontal segment historical data and the target vertical segment historical data to generate a multidimensional vector.
Optionally, the target preset classification algorithm model and state normal range construction module 302 includes:
the target preset classification algorithm model generation module is used for training the initial preset classification algorithm model by adopting a machine learning algorithm and multidimensional vectors to generate a target preset classification algorithm model.
The state normal range generation module is used for dividing the working state by combining a target preset classification algorithm model with preset expert experience and a reference standard specification to generate a state normal range.
Optionally, the line anomaly data generation module 303 includes:
the first analysis data generation module is used for removing noise in the real-time data and carrying out normalization processing to generate first analysis data.
The first feature vector generation module is used for carrying out feature extraction on the first analysis data to generate a plurality of first feature vectors.
And the comparison data generation module is used for respectively comparing the first characteristic vectors with normal vectors in the state normal range by adopting a k-nearest neighbor algorithm to generate comparison data.
And the abnormal signal and emergency measure executing module is used for sending out an abnormal signal and executing an emergency measure when the comparison data is in an abnormal state.
And the line abnormal data generation sub-module is used for constructing line abnormal data by adopting an abnormal signal and the emergency measure.
Optionally, the line status data generation module 304 includes:
and the second analysis data generation module is used for removing noise in the real-time data and carrying out normalization processing to generate second analysis data when the line abnormal data is of a preset type.
And the second feature vector generation module is used for carrying out feature extraction on the second analysis data to generate a plurality of second feature vectors.
And the fuzzification output variable generation module is used for performing fuzzification calculation on the second characteristic vector and the corresponding deviation value which do not belong to the normal state range, so as to generate a fuzzification output variable.
And the line state data generation sub-module is used for defuzzifying the fuzzified output variable to generate line state data.
Optionally, the transmission line status monitoring report construction module 305 includes:
and the alarm data generation module is used for matching the state identification data with a preset measure library and generating alarm data corresponding to the state identification data.
And the abnormal data determining module is used for marking the real-time data corresponding to the line state data as abnormal data.
The correction data generation module is used for selecting abnormal data of preset times in the normal range of the current state and generating correction data.
The target historical database generation module is used for correcting the initial historical database corresponding to the power transmission line by adopting the correction data to generate a target historical database.
The power transmission line state monitoring report construction submodule is used for constructing a power transmission line state monitoring report corresponding to the power transmission line by adopting line state data, alarm information, correction data and a target historical database.
The embodiment of the invention also provides electronic equipment, which comprises: a memory and a processor, the memory storing a computer program; the computer program, when executed by a processor, causes the processor to perform the transmission line condition monitoring method of any of the embodiments described above.
The memory may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. The memory has memory space for program code to perform any of the method steps described above. For example, the memory space for the program code may include individual program code for implementing the various steps in the above method, respectively. The program code can be read from or written to one or more computer program products. These computer program products comprise a program code carrier such as a hard disk, a Compact Disc (CD), a memory card or a floppy disk. The program code may be compressed, for example, in a suitable form. The codes, when executed by a computing processing device, cause the computing processing device to perform the steps in the transmission line condition monitoring method described above.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the power transmission line state monitoring method according to any of the embodiments above.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for monitoring a power transmission line condition, comprising:
acquiring historical data and real-time data of a power transmission line, performing data preprocessing on the historical data, and constructing a multidimensional vector;
model training is carried out on the multidimensional vector, and a target preset classification algorithm model and a state normal range are constructed;
carrying out transmission line abnormality detection on the real-time data based on the state normal range through the target preset classification algorithm model to generate line abnormality data;
when the abnormal line data are of a preset type, carrying out fuzzy recognition on the real-time line state on the real-time data based on the normal state range through the target preset classification algorithm model to generate line state data;
And constructing a power transmission line state monitoring report corresponding to the power transmission line according to the line state data and a preset measure library.
2. The transmission line status monitoring method according to claim 1, wherein the history data includes initial horizontal segment history data and initial vertical segment history data; the step of obtaining the historical data and the real-time data of the power transmission line, carrying out data preprocessing on the historical data and constructing a multidimensional vector comprises the following steps:
acquiring initial horizontal segment historical data, initial vertical segment historical data and real-time data corresponding to the power transmission line through a patrol device;
respectively carrying out data cleaning on the initial horizontal segment historical data and the initial vertical segment historical data to generate middle horizontal segment historical data and middle vertical segment historical data;
performing feature extraction on the middle horizontal segment historical data and the middle vertical segment historical data by adopting a time-frequency feature extraction method to generate target horizontal segment historical data and target vertical segment historical data;
and performing feature scaling and mean value normalization processing on the target horizontal segment historical data and the target vertical segment historical data to generate a multidimensional vector.
3. The power transmission line state monitoring method according to claim 1, wherein the step of performing model training on the multidimensional vector to construct a target preset classification algorithm model and a state normal range comprises the following steps:
training an initial preset classification algorithm model by adopting a machine learning algorithm and the multidimensional vector to generate a target preset classification algorithm model;
and carrying out working state division by combining the target preset classification algorithm model with preset expert experience and reference standard specifications, and generating a state normal range.
4. The power transmission line state monitoring method according to claim 1, wherein the step of performing power transmission line abnormality detection on the real-time data based on the state normal range by the target preset classification algorithm model, and generating line abnormality data includes:
removing noise in the real-time data, and carrying out normalization processing to generate first analysis data;
extracting the characteristics of the first analysis data to generate a plurality of first characteristic vectors;
respectively comparing the first characteristic vector with a normal vector in the state normal range by adopting a k nearest neighbor algorithm to generate comparison data;
When the comparison data is in an abnormal state, an abnormal signal is sent out and emergency measures are executed;
and constructing line abnormal data by adopting the abnormal signals and the emergency measures.
5. The method for monitoring the status of a power transmission line according to claim 1, wherein when the abnormal data of the power transmission line is of a preset type, the step of performing fuzzy recognition of the status of the power transmission line on the real-time data based on the normal range of the status by the target preset classification algorithm model, and generating the status data of the power transmission line comprises the steps of:
when the line abnormal data is of a preset type, removing noise in the real-time data, and carrying out normalization processing to generate second analysis data;
extracting the characteristics of the second analysis data to generate a plurality of second characteristic vectors;
performing fuzzy calculation on the second characteristic vector and the corresponding deviation value which do not belong to the normal state range, and generating a fuzzy output variable;
and deblurring the blurred output variable to generate line state data.
6. The method for monitoring the status of a power transmission line according to claim 1, wherein the step of constructing a power transmission line status monitoring report corresponding to the power transmission line according to the line status data and a preset measure library comprises the following steps:
Matching the state identification data with a preset measure library to generate alarm data corresponding to the state identification data;
marking real-time data corresponding to the line state data as abnormal data;
selecting abnormal data which appear in the normal range of the state for a preset number of times, and generating correction data;
correcting an initial history database corresponding to the power transmission line by adopting the correction data to generate a target history database;
and constructing a power transmission line state monitoring report corresponding to the power transmission line by adopting the line state data, the alarm information, the correction data and the target history database.
7. A transmission line condition monitoring system, comprising:
the multidimensional vector construction module is used for acquiring historical data and real-time data of the power transmission line, preprocessing the historical data and constructing multidimensional vectors;
the target preset classification algorithm model and state normal range construction module is used for carrying out model training on the multidimensional vector to construct a target preset classification algorithm model and a state normal range;
the circuit abnormal data generation module is used for carrying out transmission line abnormal detection on the real-time data based on the state normal range through the target preset classification algorithm model to generate circuit abnormal data;
The line state data generation module is used for carrying out power transmission line state fuzzy recognition on the real-time data based on the state normal range through the target preset classification algorithm model when the line abnormal data are of a preset type, so as to generate line state data;
and the power transmission line state monitoring report construction module is used for constructing a power transmission line state monitoring report corresponding to the power transmission line according to the line state data and a preset measure library.
8. The transmission line status monitoring system according to claim 7, wherein the history data includes initial horizontal segment history data and initial vertical segment history data; the multidimensional vector building module comprises:
the data acquisition module is used for acquiring initial horizontal segment historical data, initial vertical segment historical data and real-time data corresponding to the power transmission line through the inspection device;
the middle horizontal segment historical data and middle vertical segment historical data generation module is used for respectively carrying out data cleaning on the initial horizontal segment historical data and the initial vertical segment historical data to generate middle horizontal segment historical data and middle vertical segment historical data;
the target horizontal segment historical data and target vertical segment historical data generation module is used for respectively carrying out feature extraction on the middle horizontal segment historical data and the middle vertical segment historical data by adopting a time-frequency feature extraction method to generate target horizontal segment historical data and target vertical segment historical data;
And the multidimensional vector construction submodule is used for carrying out characteristic scaling and mean value normalization processing on the target horizontal segment historical data and the target vertical segment historical data to generate a multidimensional vector.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the transmission line condition monitoring method according to any one of claims 1 to 6.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed, implements the transmission line status monitoring method according to any one of claims 1 to 6.
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