CN111126846A - Method for evaluating differentiation state of overhead transmission line - Google Patents
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Abstract
The invention discloses a method for evaluating the differentiated state of an overhead transmission line, which comprises the following steps: determining the state evaluation input quantity of the overhead transmission line to be evaluated according to the expert evaluation information, the line body information and the operation index quantity acquired by the online monitoring device; determining the section type of the overhead transmission line according to the longitude and latitude of the overhead transmission line; and calling a line state evaluation model corresponding to the determined section type to realize the differential state evaluation of the overhead transmission line. The method for evaluating the differentiated state of the overhead transmission line can better realize the differentiated evaluation of the working sections of the ground wires, realize the priority treatment of the defects of important areas such as crossed lines, disaster-prone areas and the like, improve the reliability of a power system as much as possible, avoid the defect that the traditional method only evaluates according to line operation data, and is beneficial to the differentiated operation and maintenance of the transmission line.
Description
Technical Field
The invention relates to the technical field of operation safety of power systems, in particular to a method for evaluating the differentiated state of an overhead transmission line.
Background
With the rapid growth of electric power construction in China, the scale of an electric power network is increasing day by day, and higher requirements are provided for the safe and stable operation, monitoring and protection of overhead transmission lines. The transmission capacity, the operation safety and the reliability level of the overhead transmission line are required to be ensured from the operation of the overhead transmission line, various states appearing in the operation process of the line influence the normal operation of the whole line to a certain extent, and even influence and threat of different degrees are brought to the safe operation of the whole power system. Therefore, how to improve the operation efficiency of the overhead transmission line, reduce the operation and maintenance cost, and ensure the long-term normal and stable operation of the line becomes a key problem which needs to be solved urgently in power construction and development. In order to ensure safe and reliable electric energy transmission, the method has important practical significance for evaluating the running state of the power transmission line.
At present, the traditional operation state evaluation method has the following defects:
firstly, the running state of the power transmission equipment is subjectively judged by operation and maintenance personnel, and the evaluation subjectivity is high;
secondly, the overhead transmission line is wide in area and complex in operation environment, so that data acquisition is difficult;
thirdly, the intelligent analysis of the state evaluation is insufficient;
and fourthly, the current power transmission line state evaluation guide rule does not consider the influence of external environmental factors and the operation years of the line, and the differentiated state evaluation of the line cannot be realized.
In view of the above, it is necessary to improve the reliability of the state evaluation of the overhead transmission line as soon as possible, which is also a significant strategic problem in relation to the national economic health development and the steady and prosperity of the society.
Disclosure of Invention
The invention provides a method for evaluating the differentiated state of an overhead transmission line, which aims to overcome the defects of the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a method for evaluating the differentiated state of an overhead transmission line comprises the following steps:
determining the state evaluation input quantity of the overhead transmission line to be evaluated according to the expert evaluation information, the line body information and the operation index quantity acquired by the online monitoring device;
determining the section type of the overhead transmission line according to the longitude and latitude of the overhead transmission line;
and calling a line state evaluation model corresponding to the determined section type to realize the differential state evaluation of the overhead transmission line.
Further, in the method for evaluating the differentiated state of the overhead transmission line, the online monitoring device is a microclimate device, a wire temperature measuring device, a sag deviation monitoring device or an unmanned aerial vehicle carrying a laser radar.
Further, in the method for evaluating the differentiated state of the overhead transmission line, the expert evaluation information is the rating evaluation of the corrosion, damage and flashover burning degree of the ground wire by an expert during manual line patrol.
Further, in the method for evaluating the differentiated state of the overhead transmission line, the line body information is input quantity obtained from a line ledger, and comprises line codes, line longitude and latitude, conductor splitting number, voltage level and commissioning time.
Further, in the method for evaluating the differentiated state of the overhead transmission line, the operation index amount comprises the number of broken strands of the wire, the sag deviation of the wire, the inter-phase sag deviation of the wire, the ground distance of the wire, the temperature of the wire, meteorological information and the clearance distance of the wire to trees.
Further, in the method for evaluating the differentiated state of the overhead transmission line, the section types include a defect multi-occurrence section and a normal section;
the defect-prone zones include lightning-prone zones, pollution-flashover-prone zones, and wind-prone zones.
Further, in the method for evaluating the differentiated state of the overhead transmission line, the method further includes:
and determining the position information of the defects which have occurred historically from historical defect data, maintenance data and positioning system records stored in the line ledger, positioning the position information on the GIS map according to the longitude and latitude as coordinates, and further realizing the definition of the defect-prone section through a k-means clustering algorithm.
Further, in the method for evaluating the differentiated state of the overhead transmission line, the method further includes:
extracting N training samples from historical sample data stored in a line account, wherein the N training samples are defined as (x)i,Ti)i∈[1,N](ii) a Wherein x isiAs a matrix of input quantities of samples, TiDetermining a line running state for a transmission line operation and maintenance expert according to the input quantity matrix of the sample;
in a k-ELM network model, taking an input quantity matrix of N training samples as input and a corresponding line running state as output, and obtaining a related weight of the network by solving a least square solution of a classification result error to obtain a line state evaluation model; wherein, k-ELM network model is expressed as:
wherein, ai＝(a1i,a2i,...,ani)TInput weight vector for the ith hidden layer node, βi＝(βj1,βj2,...,βjm)TIs the output weight vector of the ith hidden layer node, and G is the excitation functionThe number bi is the bias of the ith hidden layer node, n is the number of input layers, K is the number of hidden layers, and m is the number of output layers.
Further, in the method for evaluating the differentiated state of the overhead transmission line, the method further includes:
extracting sample data different from the training sample from the line ledger, and establishing a corresponding test sample;
inputting the input quantity matrix of the test sample into the line state evaluation model to obtain line running state output corresponding to the test sample;
determining the classification accuracy of the line state evaluation model by comparing the output line running state corresponding to the test sample with the line running state of the test sample;
and further optimizing the line state evaluation model according to the determined accuracy result.
The method for evaluating the differentiated state of the overhead transmission line provided by the embodiment of the invention can better realize the differentiated evaluation of the working sections of the ground wires, realize the priority treatment of the defects of important areas such as cross-spanning lines, disaster-prone areas and the like, improve the reliability of a power system as much as possible, avoid the defect that the traditional method only evaluates according to line operation data, and is beneficial to the differentiated operation and maintenance of the transmission line.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a method for evaluating a differentiated state of an overhead transmission line according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for evaluating the differentiated state of the overhead transmission line according to an embodiment of the present invention.
FIG. 3 is a schematic flow chart of the method for dividing the defect-prone zone by using the k-means clustering algorithm according to the embodiment of the present invention;
fig. 4 is a schematic network structure diagram of the k-ELM algorithm provided by the embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Referring to fig. 1 to 2, a flow chart of a method for evaluating a differentiated state of an overhead power transmission line according to an embodiment of the present invention is shown. The method specifically comprises the following steps:
s101, determining the state evaluation input quantity of the overhead transmission line to be evaluated according to expert evaluation information, line body information and an operation index quantity acquired through an online monitoring device;
the online monitoring device is a microclimate device, a lead temperature measuring device, a sag deviation monitoring device or an unmanned aerial vehicle carrying a laser radar.
According to the method, the state evaluation input quantity of the overhead transmission line to be evaluated is determined according to the expert evaluation information, the line body information and the operation index quantity acquired by the online monitoring device, so that the interference of medium-redundancy state quantity is overcome as far as possible. The remote monitoring of the running state of the overhead transmission line is realized through the online monitoring device, and the difficulty in data acquisition caused by terrain reasons is overcome. The influence of external environmental factors and the operation age on the operation state of the overhead transmission line is introduced, and the comprehensiveness of the evaluation result is improved. A set of complete state evaluation system is established, and the subjectivity of state evaluation can be reduced.
And the expert evaluation information is the scoring evaluation of the corrosion, damage and flashover burning degree of the lead and ground wires by the expert during manual line patrol.
The line body information is input quantity obtained from a line standing book and comprises line codes, line longitude and latitude, conductor splitting number, voltage level and commissioning time.
The operation index quantity comprises the number of broken strands of the wire, the sag deviation of the wire, the inter-phase sag deviation of the wire, the ground distance of the wire, the temperature of the wire, meteorological information and the clearance distance of the wire to the tree.
The above parameters together constitute a state evaluation input amount.
The traditional mode carries out state evaluation according to manual line patrol results, the manual line patrol period is generally one patrol every month or one patrol every quarter, the updating frequency of evaluation results is low, and the real-time reference value is not possessed. The invention adopts the on-line monitoring device to realize the real-time acquisition of the line running information, and the state evaluation of the line can be realized after the data measured by the on-line monitoring device is transmitted to the back-end server, thereby basically realizing the quasi-real-time evaluation of the line state.
S102, determining the section type of the overhead transmission line through the longitude and latitude of the overhead transmission line;
wherein the segment types include a defective multi-shot segment and a normal segment;
the defect-prone zones include lightning-prone zones, pollution-flashover-prone zones, and wind-prone zones.
Preferably, the method further comprises:
and determining the position information of the defects which have occurred historically from historical defect data, maintenance data and positioning system records stored in the line ledger, positioning the position information on the GIS map according to the longitude and latitude as coordinates, and further realizing the definition of the defect-prone section through a k-means clustering algorithm.
The invention provides a webservice-based data acquisition service, which achieves an interface protocol with a power system production management system database to realize line ledger data access of an overhead line, wherein the accessed data comprises information such as line voltage grade, longitude and latitude, conductor splitting number, windproof design standard and the like.
In the steps, the most important technical link is to realize the definition of the defect-prone zone through a k-means clustering algorithm. The k-means clustering algorithm is an unsupervised learning machine learning algorithm, and the core idea is to perform clustering operation on n known points in space by taking k points as a clustering centroid, and classify the objects closest to the n known points. And successively updating the central value of each cluster through repeated iterative calculation until the optimal clustering result is obtained.
FIG. 3 is a flow chart of clustering performed by a k-means clustering algorithm, and the classification method of the defect-prone zone is described by taking determination of the lightning damage-prone zone of the power transmission line as an example.
S201, acquiring longitude and latitude of the historical lightning damage defect by calling a historical lightning damage defect record, and marking the position of the historical lightning damage defect by taking the longitude and latitude as a coordinate;
s202, determining the number k of classification categories and determining the initial centroid of the classification categories;
s203, formula
Determining the distance between the sample and the initial centroid, and distributing the sample to the nearest centroid to generate k clusters;
s204, recalculating the mean value of all samples in each class, and taking the mean value as a new clustering center of mass;
s205, by convergence criterion formula
Judging whether the calculation result is converged, outputting the result when the calculation result is converged, otherwise returning to the step S203 to perform iterative calculation until the result is converged.
Wherein m isiIs of class CiClustered centroid of, ZqIs of class CiThe sample of (1).
When the classification definition of the defect multi-occurrence sections is carried out by adopting a clustering algorithm, the classification of the defect samples can be realized only by defining the number of clustering centers. It is worth noting that when clustering is achieved, the number of clustering centers needs to be adjusted continuously, and the optimal clustering number is determined by comparing contour coefficients when clustering is completed.
And (3) introducing an outline coefficient S to determine the optimal clustering number, wherein the outline coefficient is a parameter for judging the clustering effect, and the larger the overall outline coefficient is when the clustering effect is better.
Passing through type
Establishing a sample point contour coefficient of an arbitrary sample point i:
where p (i) is the minimum of the average distances of point i to all points in the class not to which it belongs, and p (i) is the average distance of point i to other points in the class to which it belongs.
Coefficient of total profile
And averaging the contour coefficients of all the sample points.
The purpose of the invention is to realize the differential state evaluation of the overhead lines in different sections. When the state is evaluated, the longitude and the latitude of the overhead line are firstly input, whether the overhead line is in the section with the multiple defects or not is determined, and if the overhead line is in the section with the multiple defects or in the corresponding severe weather condition, the state score of the corresponding reduction line is obtained through the algorithm.
The invention considers the influence of the service life of the overhead ground wire on the running state of the line. And fitting the Weibull distribution model by using the historical fault records to obtain a fitting curve of the fault rate and the operating life of the overhead ground wire. The distribution function relation of the overhead ground wires with different service lives and the fault rate is as follows
As shown.
The invention provides a distribution function lambda of the equipment failure rate and the equipment state score specified by the transmission line state evaluation guide ruleC·HIAnd obtaining a fitting curve of the equipment score and the fault rate. Parallel connection of different service life overhead ground wires and the distribution function relationship of the fault rate to obtain the function relationship between the state score of the ground wires and the operation age, which is expressed by the formula
And (4) showing. Defining the age variable weight coefficient of the overhead ground wire as k1＝HI(teq) In the formula of/HI (1), HI (t)eq) The fitting curve values of the overhead ground wires with different service lives and the fault rates of the insulators within the actual operation years are shown, and HI (1) represents the fitting curve values of the overhead ground wires with different service lives and the fault rates of the insulators within 1 year of the operation years. The influence of different equipment working ages on the equipment state grading value is embodied by the age variable coefficient.
S103, calling a line state evaluation model corresponding to the determined section type, and realizing the differential state evaluation of the overhead transmission line.
It should be noted that the line state evaluation model called in this step is trained by the k-ELM algorithm through the historical state evaluation record.
Specifically, fig. 4 shows a network structure diagram of the k-ELM algorithm provided by the embodiment of the present invention, the present invention establishes a line state evaluation model through the k-ELM algorithm, and the algorithm is a novel forward hidden layer network algorithm based on Moore-Penrose generalized inverse matrix theory. Compared with the traditional neural network model, the weight is calculated by randomly selecting the weight under the condition of minimizing the training error without repeatedly carrying out iterative calculation of a gradient descent method, so that the method has stronger generalization capability and higher calculation speed.
The specific steps for realizing the establishment of the state evaluation model through the k-ELM algorithm are as follows:
(1) and (5) establishing a training sample. Extracting N training samples from historical sample data stored in a database (line standing book), and defining the N training samples as (x)i,Ti)i∈[1,N]Wherein x isiThe method comprises the steps that an input quantity matrix of a sample is obtained, and data of the input quantity matrix comprise line operation information acquired by an online monitoring device, body data of a line and an environment area where the line is located; t isiAnd determining the line running state for the transmission line operation and maintenance expert according to the input quantity matrix of the sample.
(2) And establishing a k-ELM network model. And determining the number of input layers of the k-ELM network and the number of hidden layers and output layers according to the number of the input quantities in the sample input quantity matrix. For a K-ELM model with n input layers, K hidden layers and m output layers, the network model can be expressed as:
wherein, ai＝(a1i,a2i,...,ani)TInput weight vector for the ith hidden layer node, βi＝(βj1,βj2,...,βjm)TIs the output weight vector of the ith hidden layer node, G is the excitation function, and bi is the bias of the ith hidden layer node.
(3) Training of the k-ELM network is performed by historical samples. And (3) in the k-ELM network model in the step (2), taking the input quantity matrix of the N training samples obtained in the step (1) as input, taking the corresponding line running state as output, and obtaining a related weight of the network by solving a least square solution of a classification result error to obtain a primary line state evaluation model.
(4) And testing and verifying the model. And (4) extracting sample data different from the training sample from the database aiming at the line state evaluation model obtained by training in the step (3), and establishing a corresponding test sample. And (3) inputting the input quantity matrix of the test sample into the model obtained in the step (3) to obtain a state output corresponding to the input quantity matrix, determining the classification accuracy of the model by comparing the state output of the model with the line running state of the test sample, and further optimizing the line state evaluation model according to the test result.
(5) And putting the state evaluation model into practical application, and periodically checking and correcting the network.
When the k-ELM network model training is carried out, the training samples are preprocessed, the expert experience of the transmission line operation and maintenance experts is fully integrated when the operation states of the training samples are determined, the operation states are differentiated and graded according to the importance degree of the lines, the areas where the lines are located and other external factors, the defect that the traditional method only evaluates according to the line operation data is overcome, and the differential operation and maintenance of the transmission line is facilitated.
The evaluation results of the method of the present invention and the guideline are compared by specific examples.
Sample 2 is a 110kV conductor in plain with a 15 year operational life. The broken strand damage is found in a certain line patrol, and the number of the broken strands is 2. According to the guide rule, the deduction standard is that the wire 19 is broken by 2 strands, and the deduction 32 is in the attention state. The sample is finally evaluated to be in an abnormal state through the evaluation model of the invention aiming at the common working area.
Sample 3 is a 110kV conductor in a high lightning strike area with an operating life of 12 years. It is found that foreign matter suspension occurs in a certain line round. According to the guiding rule, the deduction standard is that the ground wire is hung by foreign matters, the safety distance is influenced, and the deduction 12 is in an attention state. The method is used for finally evaluating the sample to be in the attention state aiming at an evaluation model of a lightning stroke high-incidence area, but the comprehensive score is 74.2, and the standard that the foreign matter suspension of the ground wire in the guide rule is required to be immediately processed is achieved.
The method for evaluating the differentiated state of the overhead transmission line provided by the embodiment of the invention can better realize the differentiated evaluation of the working sections of the ground wires, realize the priority treatment of the defects of important areas such as cross-spanning lines, disaster-prone areas and the like, improve the reliability of a power system as much as possible, avoid the defect that the traditional method only evaluates according to line operation data, and is beneficial to the differentiated operation and maintenance of the transmission line.
The foregoing description of the embodiments has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure. Individual elements or features of a particular embodiment are generally not limited to that particular embodiment, but, where applicable, are interchangeable and can be used in a selected embodiment, even if not specifically shown or described. The same elements or features may also vary in many respects. Such variations are not to be regarded as a departure from the disclosure, and all such modifications are intended to be included within the scope of the disclosure.
Example embodiments are provided so that this disclosure will be thorough and will fully convey the scope to those skilled in the art. Numerous details are set forth, such as examples of specific parts, devices, and methods, in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent to those skilled in the art that specific details need not be employed, that example embodiments may be embodied in many different forms and that neither should be construed to limit the scope of the disclosure. In certain example embodiments, well-known processes, well-known device structures, and well-known technologies are not described in detail.
The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises" and "comprising" are intended to be inclusive and therefore specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order discussed and illustrated, unless explicitly indicated as an order of performance. It should also be understood that additional or alternative steps may be employed.
When an element or layer is referred to as being "on" … … "," engaged with "… …", "connected to" or "coupled to" another element or layer, it can be directly on, engaged with, connected to or coupled to the other element or layer, or intervening elements or layers may also be present. In contrast, when an element or layer is referred to as being "directly on … …," "directly engaged with … …," "directly connected to" or "directly coupled to" another element or layer, there may be no intervening elements or layers present. Other words used to describe the relationship of elements should be interpreted in a similar manner (e.g., "between … …" and "directly between … …", "adjacent" and "directly adjacent", etc.). As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. Although the terms first, second, third, etc. may be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms may be only used to distinguish one element, component, region or section from another element, component, region or section. Unless clearly indicated by the context, use of terms such as the terms "first," "second," and other numerical values herein does not imply a sequence or order. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the example embodiments.
Spatially relative terms, such as "inner," "outer," "below," "… …," "lower," "above," "upper," and the like, may be used herein for ease of description to describe a relationship between one element or feature and one or more other elements or features as illustrated in the figures. Spatially relative terms may be intended to encompass different orientations of the device in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "below" or "beneath" other elements or features would then be oriented "above" the other elements or features. Thus, the example term "below … …" can encompass both an orientation of facing upward and downward. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted.
Claims (9)
1. A method for evaluating the differentiated state of an overhead transmission line is characterized by comprising the following steps:
determining the state evaluation input quantity of the overhead transmission line to be evaluated according to the expert evaluation information, the line body information and the operation index quantity acquired by the online monitoring device;
determining the section type of the overhead transmission line according to the longitude and latitude of the overhead transmission line;
and calling a line state evaluation model corresponding to the determined section type to realize the differential state evaluation of the overhead transmission line.
2. The overhead transmission line differentiation state evaluation method according to claim 1, wherein the online monitoring device is a microclimate device, a wire temperature measuring device, a sag deviation monitoring device, or an unmanned aerial vehicle carrying a laser radar.
3. The overhead transmission line differentiation state evaluation method according to claim 1, wherein the expert evaluation information is a rating evaluation of corrosion, damage and flashover burn degree of a ground wire by an expert during manual line patrol.
4. The overhead transmission line differentiation state evaluation method according to claim 1, wherein the line ontology information is input quantities obtained from a line ledger, including line codes, line longitude and latitude, number of conductor splits, voltage class, and commissioning time.
5. The overhead transmission line differentiation state evaluation method according to claim 1, wherein the operation index amount includes a conductor strand breakage number, a conductor sag deviation, a conductor inter-phase sag deviation, a conductor-to-ground distance, a conductor temperature, meteorological information, and a conductor-to-tree clearance distance.
6. The overhead transmission line differentiation state evaluation method according to claim 1, wherein the section types include a defect multi-occurrence section and a normal section;
the defect-prone zones include lightning-prone zones, pollution-flashover-prone zones, and wind-prone zones.
7. The method for evaluating the differentiated state of the overhead transmission line according to claim 6, further comprising:
and determining the position information of the defects which have occurred historically from historical defect data, maintenance data and positioning system records stored in the line ledger, positioning the position information on the GIS map according to the longitude and latitude as coordinates, and further realizing the definition of the defect-prone section through a k-means clustering algorithm.
8. The method for evaluating the differentiated state of the overhead transmission line according to claim 1, further comprising:
extracting N training samples from historical sample data stored in a line account, wherein the N training samples are defined as (x)i,Ti)i∈[1,N](ii) a Wherein x isiAs a matrix of input quantities of samples, TiDetermining a line running state for a transmission line operation and maintenance expert according to the input quantity matrix of the sample;
in a k-ELM network model, taking an input quantity matrix of N training samples as input and a corresponding line running state as output, and obtaining a related weight of the network by solving a least square solution of a classification result error to obtain a line state evaluation model; wherein, k-ELM network model is expressed as:
wherein, ai＝(a1i,a2i,...,ani)TInput weight vector for the ith hidden layer node, βi＝(βj1,βj2,...,βjm)TThe output weight vector of the ith hidden layer node is G, an excitation function is G, bi is the bias of the ith hidden layer node, n is the number of input layers, K is the number of hidden layers, and m is the number of output layers.
9. The method for evaluating the differentiated state of the overhead transmission line according to claim 8, further comprising:
extracting sample data different from the training sample from the line ledger, and establishing a corresponding test sample;
inputting the input quantity matrix of the test sample into the line state evaluation model to obtain line running state output corresponding to the test sample;
determining the classification accuracy of the line state evaluation model by comparing the output line running state corresponding to the test sample with the line running state of the test sample;
and further optimizing the line state evaluation model according to the determined accuracy result.
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