CN117970032A - Method, system, equipment and medium for monitoring field intensity of power transmission line - Google Patents
Method, system, equipment and medium for monitoring field intensity of power transmission line Download PDFInfo
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Abstract
The invention discloses a method, a system, equipment and a medium for monitoring the field intensity of a power transmission line, which are characterized in that firstly, the electric field distribution rule of the power transmission line is obtained according to an analog charge method, a mathematical model of the power transmission line is established, and a simulation platform is built for simulation; meanwhile, field intensity data of the power transmission line are obtained according to a coupling capacitance voltage division principle; analyzing and processing the field intensity data of the power transmission line in real time based on a Spark distributed system architecture, and extracting characteristics related to the field intensity state of the power transmission line to obtain a characteristic vector; then, a fuzzy KNN algorithm is established by using a Scala language, and the feature vectors are classified based on the fuzzy KNN algorithm, so that a classification result is obtained; and finally judging and early warning the field intensity state of the power transmission line according to the classification result. The invention can improve the data analysis efficiency of the power transmission line and simultaneously meet the real-time analysis requirement of the power transmission line.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method, a system, an apparatus, and a medium for monitoring a field intensity of a power transmission line.
Background
At present, the electricity inspection operation in the process of external damage prevention or electric power overhaul of the operation under the transmission line is mainly finished by manpower, and a plurality of hidden dangers still exist in the process of manual operation. The power transmission line is widely dispersed and has multiple branches, is easily influenced by factors such as large boom line collision, foreign matter short circuit, smoke and fire short circuit, forest fire theft and the like, lacks real-time effective management all day, and is extremely easy to damage by external force and the like.
However, in the field intensity monitoring method of the power transmission line, the existing data analysis algorithm is low in efficiency when processing data with unobvious category boundaries, and cannot meet the requirement of real-time analysis of the power transmission line.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method, a system, equipment and a medium for monitoring the field intensity of a power transmission line, which are used for improving the data analysis efficiency of the power transmission line and meeting the real-time analysis requirement of the power transmission line.
In order to solve the technical problems, the invention provides a method for monitoring the field intensity of a power transmission line, which comprises the following steps:
acquiring an electric field distribution rule of a power transmission line according to an analog charge method, establishing a mathematical model of the power transmission line, and constructing a simulation platform for simulation;
acquiring field intensity data of the power transmission line according to a coupling capacitance voltage division principle;
Analyzing and processing field intensity data of the power transmission line in real time based on a Spark distributed system architecture, and extracting characteristics related to the field intensity state of the power transmission line to obtain a characteristic vector;
Establishing a fuzzy KNN algorithm by using a Scala language, and classifying the feature vectors based on the fuzzy KNN classification algorithm to obtain a classification result;
and judging and early warning the field intensity state of the power transmission line according to the classification result.
Further, the obtaining the field intensity data of the power transmission line according to the coupling capacitance voltage division principle includes:
Performing coupling capacitance voltage division operation on the space field intensity around the power transmission line to form a coupling voltage division sensor; the high-low voltage arm of the coupling partial pressure sensor is composed of coupling capacitances between the sensor and the ground and between the sensor and the transmission wires;
Detecting a change signal of the electric charge under the electric field of the power transmission line to obtain field intensity data of the power transmission line.
Preferably, the creating a fuzzy KNN algorithm using a Scala language, classifying the feature vector based on the fuzzy KNN classification algorithm, to obtain a classification result, includes:
Acquiring training samples of the power transmission line, and clustering the training samples to obtain a clustering center;
calculating Euclidean distance between the feature vector and the clustering center, and selecting k training samples with the minimum distance from the feature vector;
Assigning weights to the k training samples;
substituting the feature vector into a membership function, and classifying the feature vector according to the value of the membership function to obtain a classification result; wherein, the expression of the membership function is:
Wherein x i is the ith electrical quantity characteristic value, i is the serial number of the electrical quantity characteristic value, u c(xi) is the membership degree of x i, K is the neighbor number, K is the neighbor serial number, u c(wk) is the membership degree of w k, w k is the clustering center of the kth training sample in the neighbor, and p is the neighbor weight.
Preferably, the obtaining a training sample of the power transmission line, and clustering the training sample to obtain a cluster center includes:
taking the historical feature vector of the power transmission line as a training sample;
establishing a Spark distributed ISODATA algorithm by using a Scala language;
and clustering the training samples by taking the training samples as input of an ISODATA algorithm to obtain a clustering center.
The invention also provides a system for monitoring the field intensity of the power transmission line, which comprises the following components:
the simulation unit is used for acquiring an electric field distribution rule of the power transmission line according to a simulated charge method, establishing a mathematical model of the power transmission line, and constructing a simulation platform for simulation;
the data acquisition unit is used for acquiring field intensity data of the power transmission line according to a coupling capacitance voltage division principle;
the data processing unit is used for analyzing and processing the field intensity data of the power transmission line in real time based on the Spark distributed system architecture, extracting the characteristics related to the field intensity state of the power transmission line and obtaining the characteristic vector;
the classification unit is used for establishing a fuzzy KNN algorithm by using a Scala language, classifying the feature vectors based on the fuzzy KNN algorithm, and obtaining a classification result;
and the early warning unit is used for judging and early warning the field intensity state of the power transmission line according to the classification result.
Preferably, the data acquisition unit is specifically configured to:
Performing coupling capacitance voltage division operation on the space field intensity around the power transmission line to form a coupling voltage division sensor; the high-low voltage arm of the coupling partial pressure sensor is composed of coupling capacitances between the sensor and the ground and between the sensor and the transmission wires;
Detecting a change signal of the electric charge under the electric field of the power transmission line to obtain field intensity data of the power transmission line.
Preferably, the classifying unit is specifically configured to:
Acquiring training samples of the power transmission line, and clustering the training samples to obtain a clustering center;
calculating Euclidean distance between the feature vector and the clustering center, and selecting k training samples with the minimum distance from the feature vector;
Assigning weights to the k training samples;
substituting the feature vector into a membership function, and classifying the feature vector according to the value of the membership function to obtain a classification result; wherein, the expression of the membership function is:
Wherein x i is the ith electrical quantity characteristic value, i is the serial number of the electrical quantity characteristic value, u c(xi) is the membership degree of x i, K is the neighbor number, K is the neighbor serial number, u c(wk) is the membership degree of w k, w k is the clustering center of the kth training sample in the neighbor, and p is the neighbor weight.
Further, the system further comprises a clustering unit for:
taking the historical feature vector of the power transmission line as a training sample;
establishing a Spark distributed ISODATA algorithm by using a Scala language;
and clustering the training samples by taking the training samples as input of an ISODATA algorithm to obtain a clustering center.
The invention also provides a terminal device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the method for monitoring the field intensity of the electric transmission line according to any one of the above when executing the computer program.
The invention also provides a computer readable storage medium, which comprises a stored computer program, wherein the computer program is used for controlling equipment where the computer readable storage medium is located to execute the transmission line field intensity monitoring method according to any one of the above.
Compared with the prior art, the invention has the following beneficial effects:
The invention discloses a method, a system, equipment and a medium for monitoring the field intensity of a power transmission line, which are characterized in that firstly, the electric field distribution rule of the power transmission line is obtained according to an analog charge method, a mathematical model of the power transmission line is established, and a simulation platform is built for simulation; meanwhile, field intensity data of the power transmission line are obtained according to a coupling capacitance voltage division principle; analyzing and processing the field intensity data of the power transmission line in real time based on a Spark distributed system architecture, and extracting characteristics related to the field intensity state of the power transmission line to obtain a characteristic vector; then, a fuzzy KNN algorithm is established by using a Scala language, and the feature vectors are classified based on the fuzzy KNN algorithm, so that a classification result is obtained; and finally judging and early warning the field intensity state of the power transmission line according to the classification result. The fuzzy KNN algorithm adopted by the invention can cope with uncertainty and ambiguity in the field intensity data of the power transmission line, and improves the accuracy and robustness of data classification; the distributed system architecture real-time data acquisition and processing based on Spark are combined with electric field distribution rule modeling, so that abnormal conditions of the power transmission line can be found in time, corresponding processing and maintenance are performed, and comprehensive technical support can be provided for field intensity monitoring of the power transmission line.
Drawings
Fig. 1 is a flowchart of a preferred embodiment of a method for monitoring field strength of a power transmission line according to the present invention;
FIG. 2 is a block diagram of a preferred embodiment of a transmission line field strength monitoring system provided by the present invention;
fig. 3 is a block diagram of a preferred embodiment of a terminal device according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art without the inventive effort, are intended to be within the scope of the present invention, based on the embodiments herein.
Referring to fig. 1, a flowchart of a preferred embodiment of a method for monitoring field strength of a power transmission line according to the present invention is shown. The method comprises the following steps:
S1, acquiring an electric field distribution rule of a power transmission line according to an analog charge method, establishing a mathematical model of the power transmission line, and constructing a simulation platform for simulation;
it can be understood that in the embodiment of the invention, firstly, according to an analog charge method, a corresponding mathematical model of the power transmission line is established by researching and analyzing the electric field distribution rule of the power transmission line; then, based on the mathematical model, a set of simulation platform is built for simulation and experiment of electric field characteristics of the power transmission line; the simulation platform can simulate electric field distribution conditions under different working conditions, so that reliable tools and platform support are provided for further researching electric field characteristics of a power transmission line.
S2, acquiring field intensity data of the power transmission line according to a coupling capacitance voltage division principle;
further, the obtaining the field intensity data of the power transmission line according to the coupling capacitance voltage division principle includes:
Performing coupling capacitance voltage division operation on the space field intensity around the power transmission line to form a coupling voltage division sensor; the high-low voltage arm of the coupling partial pressure sensor is composed of coupling capacitances between the sensor and the ground and between the sensor and the transmission wires;
Detecting a change signal of the electric charge under the electric field of the power transmission line to obtain field intensity data of the power transmission line.
The high-low voltage arm of the sensor is composed of the sensor, the ground and the coupling capacitance between the sensor and the transmission wire respectively. When the electric testing device is close to or far away from the low-voltage capacitor, induced charges Q are generated on the induction end sensor, the magnitude of the induced charges changes along with the change of the electric field intensity generated by the electric potential of the central conductor at the position of the measuring end face, and field intensity voltage data of the power transmission line are acquired by detecting a change signal of the charges through the electric testing device. The field intensity data obtained by the coupling capacitance voltage division principle can monitor the electric field state around the power transmission line in real time, provide more accurate data, and is helpful for timely finding and processing the abnormal situation of electric field change. In addition, the sensors may send the collected data to a data processing center, and a message queuing system, such as Kafka, may be used to transmit the data in real-time to the Spark cluster.
S3, analyzing and processing field intensity data of the power transmission line in real time based on a Spark distributed system architecture, extracting characteristics related to the field intensity state of the power transmission line, and obtaining a characteristic vector;
It is worth to be noted that, in the embodiment of the present invention, a Spark-based distributed system architecture is adopted to implement real-time analysis and processing of transmission line field intensity data. By means of the architecture, a large amount of data can be processed efficiently, and real-time analysis of transmission line field intensity data can be achieved by means of distributed computing power provided by Spark. In the process, we can extract the characteristics related to the state of the transmission line, such as the trend of the electric field intensity, abnormal value, etc., and then combine these characteristics into a characteristic vector. Therefore, the characteristic information related to the state of the power transmission line can be quickly and accurately obtained, and a foundation is provided for subsequent data analysis and processing. By using a Spark-based distributed system architecture, we can realize efficient processing and feature extraction of large-scale transmission line field intensity data.
In a preferred embodiment, the SPARK STREAMING module may be used to receive the transmitted field strength data stream, then analyze and process the data in real time, and perform feature extraction on the analyzed data, for example, find out the features of maximum, minimum, average, fluctuation range, spectrum distribution, etc. which are representative for the line state; finally, the extracted features are combined into feature vectors which represent the state of the transmission line and can be used for subsequent data analysis and processing.
S4, establishing a fuzzy KNN algorithm by using a Scala language, and classifying the feature vectors based on the fuzzy KNN classification algorithm to obtain a classification result;
In the field intensity monitoring of the power transmission line, after the field intensity data are obtained, more importantly, the common faults of the power transmission line are further analyzed and processed through analysis of the field intensity data. In the prior art, a k-Nearest Neighbor (KNN) algorithm is generally used to classify faults, and the KNN algorithm is one of the classification methods with the best use effect as a classical pattern recognition method. The basic principle of classification is that when classifying a new sample, only k samples most similar to the new sample are found out from the training data set, and then the class of the unknown sample is judged according to the class of the k most similar samples. Therefore, the KNN method has the characteristics of intuitiveness, no need of priori statistical knowledge, no supervision and the like, and is an important non-parameter classification algorithm. Moreover, the KNN method does not rely on a method of discriminating category boundaries to ascertain the category to which the sample belongs. But rather the decision is made based primarily on a limited number of neighboring samples around. It follows that the KNN method is more suitable than the rest of the method for data sets in which the boundaries between the categories of the data sample sets cross each other, or overlap each other too much.
According to the characteristics of data such as current and voltage in the transmission line data. Therefore, the dissimilarity between the data samples is calculated by selecting the Euclidean distance in the KNN algorithm, and the mathematical expression of the principle is as follows:
Let the sample to be classified be (i=1, 2,3, … …, n) and the class be (c=1, 2,3, … …, n). The basic idea of the KNN algorithm is: first, according to the Euclidean distance value, the dissimilarity between the sample to be classified and each training sample is calculated. Then, k data samples having the smallest dissimilarity with the sample to be classified are selected as k nearest neighbors. Finally, the category is judged according to k nearest neighbors.
The algorithm initially builds a priority queue of capacity k, arranged from large to small in distance, to store the nearest neighbors of the test sample. And k samples are randomly selected from the training data to serve as initial nearest neighbor samples. And respectively calculating the distances from the sample to be classified to the k nearest neighbors, and storing the labels and the distances of the training samples into a priority queue. Then, the training data is traversed, the distance between the current training sample and the data to be classified is calculated, and the obtained distance L is compared with the maximum distance in the priority queue. If L > =, eliminate the training sample, traverse the next; and if L <, deleting the training sample with the largest distance in the current priority queue, and storing the current training sample in the priority queue. When the training data is completely traversed, the most number of k training sample class labels in the priority queue is calculated, and the most number is used as the class label of the sample to be classified. In order to achieve the high accuracy of the algorithm, different k values are set for training again, and finally the k value with the highest accuracy is taken.
However, although the accuracy is improved but the efficiency of the algorithm is sacrificed by the traditional KNN algorithm, aiming at the problems of the KNN algorithm in the analysis of the data of the power transmission line, according to the characteristics of the data of the power transmission line, the embodiment of the invention adopts fuzzy boundary lines for describing each factor and factor by using membership, performs membership function compound operation on the test data of the power transmission line to predict and analyze the fault type of the power transmission line, and improves the analysis efficiency of the data of the power transmission line while solving the problem of unobvious category boundary.
In the embodiment of the invention, the basic principle of the adopted fuzzy KNN algorithm is as follows: firstly, selecting k samples with the minimum distance with a sample to be classified according to Euclidean distance between a test sample x and a clustering center W obtained in a training stage; then, giving a large weight to the neighbor of the test sample; and finally judging the class of x according to the membership function of the test sample x. It concludes that the membership function of test sample x to category is:
in a preferred embodiment, the creating a fuzzy KNN algorithm using a Scala language, classifying the feature vector based on the fuzzy KNN classification algorithm, and obtaining a classification result includes:
Acquiring training samples of the power transmission line, and clustering the training samples to obtain a clustering center;
calculating Euclidean distance between the feature vector and the clustering center, and selecting k training samples with the minimum distance from the feature vector;
Assigning weights to the k training samples;
substituting the feature vector into a membership function, and classifying the feature vector according to the value of the membership function to obtain a classification result; wherein, the expression of the membership function is:
Wherein x i is the ith electrical quantity characteristic value, i is the serial number of the electrical quantity characteristic value, u c(xi) is the membership degree of x i, K is the neighbor number, K is the neighbor serial number, u c(wk) is the membership degree of w k, w k is the clustering center of the kth training sample in the neighbor, and p is the neighbor weight.
Further, the obtaining the training sample of the power transmission line, clustering the training sample to obtain a clustering center includes:
taking the historical feature vector of the power transmission line as a training sample;
establishing a Spark distributed ISODATA algorithm by using a Scala language;
and clustering the training samples by taking the training samples as input of an ISODATA algorithm to obtain a clustering center.
The feature vectors are representative features extracted from the transmission line field intensity data, and are used as training samples. The ISODATA (ITERATIVE SELF-organization DATA ANALYSIS technology) is a classical clustering algorithm, is suitable for processing large-scale data, can automatically determine the clustering quantity, performs clustering processing on the field intensity data of the power transmission line, and obtains a clustering center. The clustering centers are helpful for further analyzing the state of the power transmission line and can provide more information and support for intelligent prediction and fault diagnosis. The fuzzy KNN algorithm is built by adopting the Scala language, and meanwhile, the Spark distributed computing framework is combined, so that large-scale data can be efficiently processed, the clustering processing speed and the classification process are accelerated, and more powerful technical support is provided for rapid classification and analysis of the power transmission line state.
S5, judging and early warning the field intensity state of the power transmission line according to the classification result.
It will be appreciated that the category of the field strength state of the transmission line may be derived from a fuzzy KNN classification algorithm, for example, possible field strength state categories include normal, abnormal, potentially faulty, etc. According to different field intensity state types, corresponding early warning strategies and measures can be formulated in advance. For the power transmission line in a normal state, monitoring and maintenance can be continued; for the transmission line in an abnormal state or a potential fault state, preventive maintenance measures can be immediately taken, or corresponding early warning notification is provided. By means of the early warning information, related personnel can take action in time, severe faults or damages of the power transmission line are avoided, and reliable power supply of the power grid is guaranteed.
The embodiment of the invention also provides a system for monitoring the field intensity of the power transmission line, which is used for realizing the method for monitoring the field intensity of the power transmission line.
Referring to fig. 2, fig. 2 is a block diagram of a preferred embodiment of a transmission line field strength monitoring system provided by the present invention. The system comprises:
the simulation unit is used for acquiring an electric field distribution rule of the power transmission line according to a simulated charge method, establishing a mathematical model of the power transmission line, and constructing a simulation platform for simulation;
the data acquisition unit is used for acquiring field intensity data of the power transmission line according to a coupling capacitance voltage division principle;
the data processing unit is used for analyzing and processing the field intensity data of the power transmission line in real time based on the Spark distributed system architecture, extracting the characteristics related to the field intensity state of the power transmission line and obtaining the characteristic vector;
the classification unit is used for establishing a fuzzy KNN algorithm by using a Scala language, classifying the feature vectors based on the fuzzy KNN algorithm, and obtaining a classification result;
and the early warning unit is used for judging and early warning the field intensity state of the power transmission line according to the classification result.
In a preferred embodiment, the data acquisition unit is specifically configured to:
Performing coupling capacitance voltage division operation on the space field intensity around the power transmission line to form a coupling voltage division sensor; the high-low voltage arm of the coupling partial pressure sensor is composed of coupling capacitances between the sensor and the ground and between the sensor and the transmission wires;
Detecting a change signal of the electric charge under the electric field of the power transmission line to obtain field intensity data of the power transmission line.
In a preferred embodiment, the classifying unit is specifically configured to:
Acquiring training samples of the power transmission line, and clustering the training samples to obtain a clustering center;
calculating Euclidean distance between the feature vector and the clustering center, and selecting k training samples with the minimum distance from the feature vector;
Assigning weights to the k training samples;
substituting the feature vector into a membership function, and classifying the feature vector according to the value of the membership function to obtain a classification result; wherein, the expression of the membership function is:
Wherein x i is the ith electrical quantity characteristic value, i is the serial number of the electrical quantity characteristic value, u c(xi) is the membership degree of x i, K is the neighbor number, K is the neighbor serial number, u c(wk) is the membership degree of w k, w k is the clustering center of the kth training sample in the neighbor, and p is the neighbor weight.
Further, the system further comprises a clustering unit for:
taking the historical feature vector of the power transmission line as a training sample;
establishing a Spark distributed ISODATA algorithm by using a Scala language;
and clustering the training samples by taking the training samples as input of an ISODATA algorithm to obtain a clustering center.
It should be noted that, the power transmission line field intensity monitoring system provided by the embodiment of the present invention can implement all the processes of the power transmission line field intensity monitoring method described in any one of the embodiments, and the actions and implemented technical effects of each unit in the system are respectively the same as those of each process of the power transmission line field intensity monitoring method described in the embodiment, and are not described herein again.
The embodiment of the invention also provides a terminal device, as shown in fig. 3, which is a block diagram of a preferred embodiment of the terminal device. The terminal device comprises a processor 31, a memory 32 and a computer program stored in the memory 32 and configured to be executed by the processor 31, the processor 31 implementing the transmission line field strength monitoring method according to any of the embodiments above when executing the computer program.
In addition, the embodiment of the invention also provides a computer readable storage medium, which comprises a stored computer program, wherein when the computer program runs, equipment where the computer readable storage medium is located is controlled to execute the power transmission line field intensity monitoring method according to any embodiment.
The processor 31, when executing the computer program, implements the steps of the above-described embodiment of the method for monitoring the field strength of a transmission line, for example, all the steps of the method for monitoring the field strength of a transmission line shown in fig. 1. Or the processor 31 when executing the computer program implements the functions of the modules in the above-described embodiments of the transmission line field strength monitoring system, for example the functions of the units of the transmission line field strength monitoring system shown in fig. 2.
Preferably, the computer program may be divided into one or more modules/units, which are stored in the memory 32 and executed by the processor 31 to complete the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used for describing the execution of the computer program in the terminal device.
The Processor 31 may be a central processing unit (Central Processing Unit, CPU), other general purpose Processor, digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc., or the Processor 31 may be a microprocessor, or the Processor 31 may be any conventional Processor, the Processor 31 being a control center of the terminal device, and various interfaces and lines being used to connect the various parts of the terminal device.
The memory 32 mainly includes a program storage area, which may store an operating system, application programs required for at least one function, and the like, and a data storage area, which may store related data and the like. In addition, the memory 32 may be a high-speed random access memory, a nonvolatile memory such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), etc., or the memory 32 may be other volatile solid-state memory devices.
It should be noted that the above-mentioned terminal device may include, but is not limited to, a processor, a memory, and those skilled in the art will understand that the structural block diagram shown in fig. 3 is merely an example of the structure of the above-mentioned terminal device, and does not limit the structure of the above-mentioned terminal device, and the above-mentioned terminal device may include more or less components than those shown, or may combine some components, or different components.
In summary, the embodiment of the invention discloses a method, a system, equipment and a medium for monitoring the field intensity of a power transmission line, which are characterized in that firstly, the electric field distribution rule of the power transmission line is obtained according to an analog charge method, a mathematical model of the power transmission line is built, and a simulation platform is built for simulation; meanwhile, field intensity data of the power transmission line are obtained according to a coupling capacitance voltage division principle; analyzing and processing the field intensity data of the power transmission line in real time based on a Spark distributed system architecture, and extracting characteristics related to the field intensity state of the power transmission line to obtain a characteristic vector; then, a fuzzy KNN algorithm is established by using a Scala language, and the feature vectors are classified based on the fuzzy KNN algorithm, so that a classification result is obtained; and finally judging and early warning the field intensity state of the power transmission line according to the classification result. The fuzzy KNN algorithm adopted by the invention can cope with uncertainty and ambiguity in the field intensity data of the power transmission line, and improves the accuracy and robustness of data classification; the distributed system architecture real-time data acquisition and processing based on Spark are combined with electric field distribution rule modeling, so that abnormal conditions of the power transmission line can be found in time, corresponding processing and maintenance are performed, and comprehensive technical support can be provided for field intensity monitoring of the power transmission line.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (10)
1. A method for monitoring the field strength of a power transmission line, comprising:
acquiring an electric field distribution rule of a power transmission line according to an analog charge method, establishing a mathematical model of the power transmission line, and constructing a simulation platform for simulation;
acquiring field intensity data of the power transmission line according to a coupling capacitance voltage division principle;
Analyzing and processing field intensity data of the power transmission line in real time based on a Spark distributed system architecture, and extracting characteristics related to the field intensity state of the power transmission line to obtain a characteristic vector;
establishing a fuzzy KNN algorithm by using a Scala language, and classifying the feature vectors based on the fuzzy KNN algorithm to obtain a classification result;
and judging and early warning the field intensity state of the power transmission line according to the classification result.
2. The method for monitoring the field intensity of the power transmission line according to claim 1, wherein the step of obtaining the field intensity data of the power transmission line according to the principle of coupling capacitance voltage division comprises the steps of:
Performing coupling capacitance voltage division operation on the space field intensity around the power transmission line to form a coupling voltage division sensor; the high-low voltage arm of the coupling partial pressure sensor is composed of coupling capacitances between the sensor and the ground and between the sensor and the transmission wires;
Detecting a change signal of the electric charge under the electric field of the power transmission line to obtain field intensity data of the power transmission line.
3. The method for monitoring the field intensity of the power transmission line according to claim 1, wherein the step of using a Scala language to establish a fuzzy KNN algorithm, and classifying the feature vector based on the fuzzy KNN algorithm to obtain a classification result comprises the steps of:
Acquiring training samples of the power transmission line, and clustering the training samples to obtain a clustering center;
calculating Euclidean distance between the feature vector and the clustering center, and selecting k training samples with the minimum distance from the feature vector;
Assigning weights to the k training samples;
substituting the feature vector into a membership function, and classifying the feature vector according to the value of the membership function to obtain a classification result; wherein, the expression of the membership function is:
Wherein x i is the ith electrical quantity characteristic value, i is the serial number of the electrical quantity characteristic value, u c(xi) is the membership degree of x i, K is the neighbor number, K is the neighbor serial number, u c(wk) is the membership degree of w k, w k is the clustering center of the kth training sample in the neighbor, and p is the neighbor weight.
4. The method for monitoring the field intensity of the power transmission line according to claim 3, wherein the step of obtaining the training samples of the power transmission line, and performing clustering processing on the training samples to obtain a clustering center comprises the steps of:
taking the historical feature vector of the power transmission line as a training sample;
establishing a Spark distributed ISODATA algorithm by using a Scala language;
and clustering the training samples by taking the training samples as input of an ISODATA algorithm to obtain a clustering center.
5. A transmission line field strength monitoring system, comprising:
the simulation unit is used for acquiring an electric field distribution rule of the power transmission line according to a simulated charge method, establishing a mathematical model of the power transmission line, and constructing a simulation platform for simulation;
the data acquisition unit is used for acquiring field intensity data of the power transmission line according to a coupling capacitance voltage division principle;
the data processing unit is used for analyzing and processing the field intensity data of the power transmission line in real time based on the Spark distributed system architecture, extracting the characteristics related to the field intensity state of the power transmission line and obtaining the characteristic vector;
the classification unit is used for establishing a fuzzy KNN algorithm by using a Scala language, classifying the feature vectors based on the fuzzy KNN algorithm, and obtaining a classification result;
and the early warning unit is used for judging and early warning the field intensity state of the power transmission line according to the classification result.
6. The transmission line field strength monitoring system according to claim 5, wherein the data acquisition unit is specifically configured to:
Performing coupling capacitance voltage division operation on the space field intensity around the power transmission line to form a coupling voltage division sensor; the high-low voltage arm of the coupling partial pressure sensor is composed of coupling capacitances between the sensor and the ground and between the sensor and the transmission wires;
Detecting a change signal of the electric charge under the electric field of the power transmission line to obtain field intensity data of the power transmission line.
7. The transmission line field strength monitoring system according to claim 5, wherein the classification unit is specifically configured to:
Acquiring training samples of the power transmission line, and clustering the training samples to obtain a clustering center;
calculating Euclidean distance between the feature vector and the clustering center, and selecting k training samples with the minimum distance from the feature vector;
Assigning weights to the k training samples;
substituting the feature vector into a membership function, and classifying the feature vector according to the value of the membership function to obtain a classification result; wherein, the expression of the membership function is:
Wherein x i is the ith electrical quantity characteristic value, i is the serial number of the electrical quantity characteristic value, u c(xi) is the membership degree of x i, K is the neighbor number, K is the neighbor serial number, u c(wk) is the membership degree of w k, w k is the clustering center of the kth training sample in the neighbor, and p is the neighbor weight.
8. The transmission line field strength monitoring system of claim 7, further comprising a clustering unit configured to:
taking the historical feature vector of the power transmission line as a training sample;
establishing a Spark distributed ISODATA algorithm by using a Scala language;
and clustering the training samples by taking the training samples as input of an ISODATA algorithm to obtain a clustering center.
9. A terminal device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the transmission line field strength monitoring method according to any one of claims 1 to 4 when executing the computer program.
10. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored computer program, wherein the computer program, when run, controls a device in which the computer readable storage medium is located to perform the transmission line field strength monitoring method according to any one of claims 1 to 4.
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