Overhead transmission line defect risk processing method and system

Abstract

The invention discloses an overhead transmission line defect risk processing method and system, and the method comprises the steps: obtaining a defect data set of a target overhead transmission line, and the defect data set comprises all defect influence factors of the target overhead transmission line; processing the defect data set based on the overhead transmission line risk defect influence factor classification table to obtain a risk assessment value of each defect influence factor; based on line meteorological environment data and a risk assessment value which are obtained in advance, obtaining a defect and fault prediction result by using a trained defect and fault prediction model; and based on the defect fault prediction result, sending a corresponding risk processing log to a matched control terminal, so as to carry out corresponding maintenance operation on the target overhead transmission line according to countermeasures. According to the invention, defect risk prediction of the power transmission line can be carried out through the risk assessment and prediction model, and corresponding prevention and maintenance measures are taken, so that the fault rate of the overhead power transmission line is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of overhead power transmission lines in the electric power industry, and in particular to a method and system for handling defect risks of overhead power transmission lines. Background Art

[0002] Overhead transmission lines, as an important part of power transmission, mainly use insulators to fix the transmission wires on the towers erected on the ground to transmit electric energy. In recent years, the design, construction, operation and maintenance technology of overhead transmission lines in the power industry have made great progress, and the defects and faults of transmission lines have been greatly reduced. However, the transmission lines are widely distributed and the environment they are in is extremely complex, so defects and faults still occur frequently. Transmission line shutdown failures usually occur after the defects develop to a certain extent. After a large amount of data analysis, it is found that there is a certain regularity in the defects of transmission lines. Different voltage levels, different environmental conditions, and different design standards will have a certain impact on the location and frequency of defects.

[0003] In existing technologies, there is a lack of systematic assessment of defect risks and support for emergency plans, which has led to an increase in the safety risks of the power system. Power companies may not be able to deal with sudden failures or accidents quickly and effectively, resulting in the expansion of the situation and increased losses.

[0004] It can be seen that how to conduct effective risk assessment on overhead transmission lines has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the invention

[0005] The present invention provides an overhead power transmission line defect risk assessment method and system to solve the problem of how to conduct effective risk assessment on overhead power transmission lines, so as to take targeted measures for prevention and improvement, thereby improving the safety of the entire power system.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a method for handling overhead transmission line defect risks, comprising:

[0007] Acquire a defect data set of a target overhead transmission line, wherein the defect data set includes various defect influencing factors of the target overhead transmission line;

[0008] The defect data set is processed based on a pre-constructed classification table of risk defect influencing factors of overhead transmission lines to obtain risk assessment values ​​of each of the defect influencing factors, wherein the classification table of risk defect influencing factors of overhead transmission lines reflects the influence of risk calculation values ​​of different line components on the risk assessment value of the selected object;

[0009] Based on the pre-acquired line meteorological environment data and the risk assessment value, a defect fault prediction result is obtained using a trained defect fault prediction model, wherein the defect fault prediction result includes a mechanical defect fault result and an electrical defect fault result;

[0010] Based on the defect fault prediction result, a corresponding risk processing log is sent to a matching control terminal, wherein the control terminal includes a transmission line mechanical terminal and a transmission line electrical terminal.

[0011] Furthermore, the defect data set is processed based on the pre-constructed classification table of risk defect influencing factors of overhead transmission lines to obtain the risk assessment value of each defect influencing factor, including:

[0012] Based on the defect data set, a risk indicator comparison matrix is ​​constructed using the analytic hierarchy process;

[0013] Based on the risk indicator comparison matrix, a target eigenvector corresponding to the maximum eigenvalue is obtained by solving;

[0014] The target feature vector is normalized to determine the risk assessment value of the defect influencing factor.

[0015] Furthermore, the defect and fault prediction results are obtained based on the pre-acquired line meteorological environment data and the risk assessment value and the trained defect and fault prediction model, including:

[0016] Integrate the risk assessment value with the line meteorological environment data, and extract features based on time series to obtain a multi-dimensional risk data feature set;

[0017] The multi-dimensional risk data feature set is input into the defect fault prediction model to obtain a first probability of a mechanical defect fault occurring in the target overhead transmission line and a second probability of an electrical defect fault occurring in the target overhead transmission line.

[0018] Furthermore, the defect and fault prediction results are obtained based on the pre-acquired line meteorological environment data and the risk assessment value and using a trained defect and fault prediction model, and further include:

[0019] Obtaining the membership degree of the first probability and the second probability respectively based on a fuzzy comprehensive evaluation method;

[0020] assigning different weights to the first probability and the second probability according to historical defect data of the target overhead transmission line;

[0021] Based on the membership degree and the weight values ​​corresponding to the first probability and the second probability, a comprehensive fault risk index of the target overhead transmission line is calculated, and the defect fault prediction result is obtained according to the comprehensive fault risk index.

[0022] Furthermore, after sending the corresponding risk processing log to the matching control terminal, the method further includes:

[0023] After receiving the risk processing log, the control terminal performs corresponding maintenance operations on the target overhead transmission line according to the countermeasures in the risk processing log, and provides real-time feedback to the power system control center;

[0024] The power system control center monitors the real-time operating status of the target overhead transmission line and adjusts the response measures according to the operating status.

[0025] Another embodiment of the present invention provides an overhead transmission line defect risk processing system, comprising:

[0026] A data acquisition module, used to acquire a defect data set of a target overhead transmission line, wherein the defect data set includes various defect influencing factors of the target overhead transmission line;

[0027] A risk assessment module, used to process the defect data set based on a pre-constructed classification table of risk defect influencing factors of overhead transmission lines to obtain risk assessment values ​​of each of the defect influencing factors, wherein the classification table of risk defect influencing factors of overhead transmission lines reflects the influence of risk calculation values ​​of different line components on the risk assessment value of the selected object;

[0028] A risk prediction module, used to obtain defect and fault prediction results based on the pre-acquired line meteorological environment data and the risk assessment value and a trained defect and fault prediction model, wherein the defect and fault prediction results include mechanical defect and fault results and electrical defect and fault results;

[0029] The risk processing module is used to send a corresponding risk processing log to a matching control terminal based on the defect fault prediction result, and the control terminal includes a transmission line mechanical terminal and a transmission line electrical terminal.

[0030] Furthermore, the risk assessment module is specifically used to:

[0031] Based on the defect data set, a risk indicator comparison matrix is ​​constructed using the analytic hierarchy process;

[0032] Based on the risk indicator comparison matrix, a target eigenvector corresponding to the maximum eigenvalue is obtained by solving;

[0033] The target feature vector is normalized to determine the risk assessment value of the defect influencing factor.

[0034] Furthermore, the risk prediction module is specifically used for:

[0035] The risk assessment value and the line meteorological environment data are respectively integrated, and feature extraction is performed based on the time series to obtain a multi-dimensional risk data feature set;

[0036] The multi-dimensional risk data feature set is input into the defect fault prediction model to obtain a first probability of a mechanical defect fault occurring in the target overhead transmission line and a second probability of an electrical defect fault occurring in the target overhead transmission line.

[0037] Furthermore, the risk prediction module is further used to: obtain the membership degree of the first probability and the second probability respectively based on a fuzzy comprehensive evaluation method;

[0038] assigning different weights to the first probability and the second probability according to historical defect data of the target overhead transmission line;

[0039] Based on the membership degree and the weight values ​​corresponding to the first probability and the second probability, a comprehensive fault risk index of the target overhead transmission line is calculated, and the defect fault prediction result is obtained according to the comprehensive fault risk index.

[0040] Furthermore, the system further includes a risk measure implementation module, and the risk measure implementation module is specifically used to:

[0041] After receiving the risk handling log, the control terminal performs corresponding maintenance operations on the target overhead transmission line according to the countermeasures in the risk handling log, and provides real-time feedback to the power system control center; the power system control center monitors the real-time operating status of the target overhead transmission line and adjusts the countermeasures according to the operating status.

[0042] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0043] (1) The present invention can establish a classification table of factors affecting risk defects of overhead transmission lines to obtain primary and secondary indicators of risk defects, use a hierarchical analysis method to determine the risk value of transmission line defects, and realize defect fault prediction of overhead transmission lines by constructing a fault prediction model, so that potential fault factors can be discovered in advance according to the prediction results, and corresponding preventive measures can be taken to reduce the failure rate of overhead transmission lines.

[0044] (2) The defect risk value determined by the present invention is clear and comprehensive, and can achieve comprehensive coverage of the overall risk status of the transmission line. Through risk assessment, it can identify the potential safety hazards and weak links that may exist in the operation of overhead transmission lines, and provide effective support for the formulation of measures to improve the reliability of transmission lines, so as to take targeted measures for prevention and improvement, so as to improve the safety of the entire power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic flow chart of a method for handling overhead transmission line defect risks in one embodiment of the present invention;

[0046] Figure 2 is a schematic diagram of a process for determining a risk assessment value in one embodiment of the present invention;

[0047] Figure 3 It is a structural block diagram of an overhead transmission line defect risk management system in one embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0050] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0051] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0052] An embodiment of the present invention provides a method. For details, see Figure 1 , Figure 1 The figure shows a schematic flow chart of a method for handling overhead transmission line defect risks in one embodiment of the present invention, comprising the following steps:

[0053] S1. Acquire a defect data set of a target overhead transmission line, wherein the defect data set includes various defect influencing factors of the target overhead transmission line.

[0054] In some implementations, the defect dataset of the target overhead transmission line may be obtained in advance in the following flexible ways:

[0055] 1. If the assessment needs focus on a specific transmission line, then this specific transmission line will be used as the target overhead transmission line, and data collection should be carried out around this line, including all defect-related information such as the historical defect records, current operating status, inspection reports, and maintenance records of the line.

[0056] 2. When the assessment scope is expanded to multiple transmission lines within a specific area (such as a city, county, grid division, etc.), data collection is required for all relevant lines in the area as target overhead transmission lines. This usually involves more extensive database queries, regional inspection report summaries, and possible geographic information system (GIS)-assisted analysis to determine the overall defect status of lines in the area.

[0057] 3. It is also a common method to divide the assessment scope according to voltage level. Transmission lines of different voltage levels may differ in structure, operating conditions and defect types. Therefore, according to the assessment needs, we can focus on collecting defect data of a transmission line of a certain voltage level (such as high voltage, ultra-high voltage, ultra-high voltage, etc.) as the target overhead transmission line in order to conduct targeted risk assessment.

[0058] S2. Process the defect data set based on a pre-constructed classification table of risk defect influencing factors of overhead transmission lines to obtain risk assessment values ​​of each of the defect influencing factors, wherein the classification table of risk defect influencing factors of overhead transmission lines reflects the influence of the risk calculation values ​​of different line components on the risk assessment value of the selected object.

[0059] In this embodiment, seven factors, namely, hardware, insulator, pole tower and ancillary facilities, ground wire, grounding device, foundation and channel environment, are selected as the risk defect influencing factors of overhead transmission lines. Then, the above seven factors are further divided to construct a classification table of risk defect influencing factors of overhead transmission lines, as shown in Table 1:

[0060] Table 1. Classification of transmission line defect indicators

[0061]

[0062]

[0063] It can be seen from the content of Table 1 that this embodiment takes the risk defect influencing factors of overhead transmission lines as the primary indicators and classifies them to obtain the corresponding secondary indicators. Therefore, according to the above table, the line components that have an impact on the overhead transmission lines can be clearly identified, thereby clearly reflecting the impact of the risk calculation values ​​of different line components on the risk assessment value of the selected object.

[0064] In this embodiment, the risk assessment value of each defect influencing factor is calculated by using the analytic hierarchy process. The analytic hierarchy process can decompose the problem into different components according to the nature of the problem and the overall goal to be achieved, and cluster and combine the factors at different levels according to the mutual influence and affiliation between the factors to form a multi-level analysis structure model. By solving the eigenvector of the judgment matrix, the priority weight of each element at each level to an element at the previous level is obtained, and finally the final weight of each alternative plan to the overall goal is recursively merged by the weighted sum method. The one with the largest final weight is the optimal plan.

[0065] Specifically, in this embodiment, based on the defect data set, a risk indicator comparison matrix is ​​constructed using the hierarchical analysis method. Based on the risk indicator comparison matrix, the target eigenvector corresponding to the maximum eigenvalue is obtained by solving the target eigenvector, and the target eigenvector is normalized to determine the risk assessment value of the defect influencing factor. Figure 2 , Figure 2 The figure shows a schematic diagram of a process for determining a risk assessment value in one embodiment of the present invention.

[0066] First, the risk value of the secondary indicators needs to be determined according to Table 1 to obtain the evaluation results to construct a risk indicator comparison matrix. The risk value calculation is performed by taking the hardware category in Table 1 as an example. The first step is to determine the probability proportion of the hardware category in the primary indicators, as shown in Table 2 below:

[0067] Table 3. Proportion of primary indicators of transmission line defects

[0068]

[0069]

[0070] It can be seen that the probability of hardware in the primary indicators accounts for 37.3%.

[0071] The proportion probability of the secondary indicators corresponding to the hardware defects is calculated, and the risk value of each secondary indicator is scored. The proportion of the secondary indicators of 8 types of hardware defects and the risk value of each type are obtained. Specifically, it can be seen in Table 3:

[0072] Table 4. Proportion of secondary indicators of hardware defects and risk value assessment table

[0073]

[0074] It can be understood that the corresponding values ​​are substituted into the following formula for calculation:

[0075]

[0076] Thus, the risk assessment value of the factors affecting hardware defects is obtained:

[0077]

[0078] Furthermore, the remaining first-level indicator risk assessment values ​​are calculated, and the risk indicator comparison matrix is ​​constructed using the hierarchical analysis method. The comparison matrix A obtained is as follows:

[0079]

[0080] Solve the comparison matrix A to obtain the eigenvalue λ and the eigenvector α, and obtain the maximum eigenvalue λ based on the eigenvalue λ max And its corresponding target feature vector w is normalized to obtain the normalized vector w′:

[0081] w′=[0.28 0.30 0.20 0.10 0.06 0.05 0.01] T

[0082] That is, the risk weights of the seven first-level indicators of transmission line defects, namely, hardware fittings, insulators, poles and ancillary facilities, ground wires, grounding devices, foundations, and channel environment, are 0.28, 0.30, 0.20, 0.10, 0.06, 0.05, and 0.01, respectively.

[0083] S3. Based on the pre-acquired line meteorological environment data and the risk assessment value, a defect fault prediction result is obtained using a trained defect fault prediction model, wherein the defect fault prediction result includes a mechanical defect fault result and an electrical defect fault result.

[0084] First, the risk assessment value is integrated with the line meteorological environment data, and feature extraction is performed based on time series to obtain a multi-dimensional risk data feature set.

[0085] Specifically, in this embodiment, the risk assessment values ​​of all defect influencing factors calculated by the risk defect influencing factor classification table are integrated and corresponded to the corresponding line components. The pre-acquired line meteorological environment data, such as wind speed, ice thickness, temperature, humidity, lightning activity, etc., are sorted so that these data can correspond to the risk assessment values ​​in time and space.

[0086] In some implementations, the risk assessment value of each defect influencing factor and the corresponding line meteorological environment data are used as features to construct a feature vector, which can fully reflect the current status and potential risks of the overhead transmission line.

[0087] It is understandable that in some implementations, feature selection or dimensionality reduction techniques can be used to reduce the number of features and improve the efficiency and accuracy of the model. Alternatively, feature derivation can be performed based on the basic features of the line meteorological environment data, such as the daily change rate, weekly change rate, cumulative value, etc. of the meteorological parameters, so as to more comprehensively reflect the changing trend of the meteorological environment and its impact on line risks.

[0088] Furthermore, the acquired feature vectors are normalized. If there are missing values ​​in the data, appropriate methods (such as interpolation, filling default values, or deleting samples containing missing values) are used to handle these missing values. Then, all selected feature vectors are combined into a multi-dimensional risk data feature set.

[0089] Furthermore, the multi-dimensional risk data feature set is input into the defect fault prediction model to obtain a first probability of a mechanical defect fault occurring in the target overhead transmission line and a second probability of an electrical defect fault occurring in the target overhead transmission line.

[0090] In some embodiments, a suitable machine learning or deep learning model is selected for model training. For the multi-classification problem of the embodiment of the present invention: mechanical defect fault and electrical defect fault, a multi-class classifier can be used, such as logistic regression, support vector machine, random forest, gradient boosting tree or neural network. The risk assessment value and meteorological environment data of the transmission line to be predicted are input into the trained model to perform risk prediction. The defect fault prediction model outputs the probability of the target overhead transmission line having a mechanical defect fault, i.e., the first probability, and the probability of the electrical defect fault, i.e., the second probability.

[0091] It can be understood that after obtaining the first probability of mechanical defect failure and the second probability of electrical defect failure of the target overhead transmission line, in order to further comprehensively evaluate the failure risk of the overhead transmission line and obtain a more instructive defect failure prediction result, weighted average method, fuzzy comprehensive evaluation method, machine learning model fusion, hierarchical analysis method, Bayesian network and neural network can be used to process the probability data.

[0092] In this embodiment, preferably, a fuzzy comprehensive evaluation method is used to process the probability data. In the fuzzy comprehensive evaluation method, the probabilities of mechanical defect failures and electrical defect failures are regarded as elements in a fuzzy set, and the defect fault prediction results of the overhead transmission line are comprehensively evaluated through fuzzy relationship matrices and fuzzy operations.

[0093] Specifically, the membership degrees of the first probability and the second probability are respectively obtained based on the fuzzy comprehensive evaluation method, and the probabilities of mechanical defect failure and electrical defect failure are regarded as elements in a fuzzy set, each of which has a membership degree.

[0094] In order to obtain a scientific and comprehensive defect failure risk index, preferably, a weighted average method (or any other suitable aggregation method) can be used to combine the probabilities of mechanical defect failures and electrical defect failures. In this embodiment, the weighted average method is used to allocate weights. According to the historical defect data of the target overhead transmission line, different weights are allocated to the first probability and the second probability. These weights can be determined based on the historical fault data of the transmission line.

[0095] Furthermore, based on the degree of membership and the weight values ​​corresponding to the first probability and the second probability, a comprehensive fault risk index of the target overhead transmission line is calculated, and the calculation formula is as follows:

[0096] R=w1*p1+w2*p2

[0097] Among them, w1 and w2 are weight values ​​corresponding to the first probability and the second probability respectively, and p1 and p2 are the membership degrees of the first probability and the second probability respectively.

[0098] The comprehensive fault risk index of the target overhead transmission line is calculated according to the above formula, thereby obtaining the defect and fault prediction result of the target overhead transmission line. It can be understood that in this embodiment, the defect and fault prediction result is a prediction of the probability or risk level of a specific defect or fault occurring in the overhead transmission line in the future, and is divided into different risk levels (such as low, medium, and high).

[0099] According to the prediction results of the defect fault prediction model, the defect fault risk of the target overhead transmission line can be scientifically analyzed, and effective fault response and maintenance measures can be determined.

[0100] S4. Based on the defect fault prediction result, a corresponding risk processing log is sent to a matching control terminal, wherein the control terminal includes a transmission line mechanical terminal and a transmission line electrical terminal.

[0101] In some embodiments, different countermeasures are formulated for different types of defect faults based on the defect fault prediction results. For example, for mechanical defect faults, it may be necessary to strengthen inspections, replace aging components, or strengthen line structures; for electrical defect faults, it may be necessary to adjust insulator configurations, add lightning protection measures, or improve pollution conditions.

[0102] Transmission line mechanical terminals usually refer to equipment or systems directly related to the physical structure of the transmission line, such as towers, conductors, insulator strings, hardware, etc. For transmission line mechanical terminals, send corresponding risk handling logs, including: sending inspection instructions to inspection personnel or drones near the mechanical terminals, focusing on checking the components that may have defects mentioned in the prediction. For predicted high-risk areas or components, provide more specific maintenance suggestions, such as replacing aging insulators, reinforcing loose hardware, or adjusting conductor tension. If the prediction results show that there is an immediate risk of serious failure, an emergency response plan should be formulated, including evacuating surrounding personnel, cutting off power, and preparing repair teams.

[0103] The electrical terminals of transmission lines are more involved in the monitoring and control of electrical parameters, such as substations, switch stations, protection devices, etc. For such terminals, the risk handling logs sent include but are not limited to the following: Adjust electrical parameters to mitigate potential risks, such as adjusting voltage and current limit values, and optimizing protection settings. Send remote monitoring instructions to electrical terminals to strengthen real-time monitoring of key electrical parameters mentioned in the forecast so as to detect abnormal conditions in a timely manner. When high risks are predicted, preset automated control strategies can be automatically triggered, such as automatic reclosing, rapid isolation of fault areas, etc., to reduce the impact of faults on the power grid.

[0104] In some implementations, the risk processing log is sent to a matching control terminal via optical fiber communication, wireless communication, or a dedicated software platform.

[0105] The embodiment of the present invention can ensure that operation and maintenance personnel or automation systems can timely understand potential risks and take effective measures to deal with them by sending risk logs containing defect and fault prediction results in real time, thereby ensuring the safe and stable operation of the transmission line.

[0106] It is understandable that after receiving the risk processing log, the control terminal performs corresponding maintenance operations on the target overhead transmission line according to the countermeasures in the risk processing log, and feeds back to the power system control center in real time to monitor the real-time operating status of the target overhead transmission line and adjust the countermeasures according to the operating status. Specifically:

[0107] The control terminal will monitor the status of the target overhead transmission line in real time, including electrical parameters, physical status, environmental conditions, etc., and will provide real-time feedback on the monitored status information, operation results, and any abnormal conditions to the power system control center. After the risk treatment is completed, the power system control center will evaluate the treatment effect to verify the effectiveness and accuracy of the response measures. Based on the evaluation results and on-site feedback, the risk treatment process, prediction model, response measures, etc. will be continuously optimized and improved to improve the response capability and operating efficiency of the entire system.

[0108] In summary, the embodiment of the present invention constructs a classification table of risk defect influencing factors of overhead transmission line based on the fault factors that cause defect risks of overhead transmission line to conduct risk assessment of defect influencing factors. The risk assessment process of transmission line defects integrates objective data and hierarchical analysis algorithm, which can more accurately reflect the risk status of different parts of the transmission line; and combines the meteorological environment data affecting the transmission line with the risk assessment value to input the defect fault prediction model to predict defect faults, and generates effective response measures according to the prediction results, so as to take targeted measures for prevention and improvement, thereby improving the safety of the entire power system.

[0109] An embodiment of the present invention provides a system for handling overhead transmission line defect risks. For details, see Figure 3 , Figure 3 The structure block diagram of the overhead transmission line defect risk processing system in one embodiment of the present invention is shown, which specifically includes:

[0110] The data acquisition module M1 is used to acquire a defect data set of a target overhead transmission line, wherein the defect data set includes various defect influencing factors of the target overhead transmission line.

[0111] The risk assessment module M2 is used to process the defect data set based on a pre-constructed classification table of risk defect influencing factors of overhead transmission lines to obtain risk assessment values ​​of each of the defect influencing factors, wherein the classification table of risk defect influencing factors of overhead transmission lines reflects the influence of the risk calculation values ​​of different line components on the risk assessment value of the selected object.

[0112] The risk prediction module M3 is used to obtain defect and fault prediction results based on the pre-acquired line meteorological environment data and the risk assessment value and a trained defect and fault prediction model, wherein the defect and fault prediction results include mechanical defect and fault results and electrical defect and fault results.

[0113] The risk processing module M4 is used to send a corresponding risk processing log to a matching control terminal based on the defect fault prediction result, and the control terminal includes a transmission line mechanical terminal and a transmission line electrical terminal.

[0114] In this embodiment, the defect data set of the target overhead transmission line is obtained by the data acquisition module M1. As an example, the following methods can be flexibly selected:

[0115] 1. If the assessment needs focus on a specific transmission line, then this specific transmission line will be used as the target overhead transmission line, and data collection should be carried out around this line, including all defect-related information such as the historical defect records, current operating status, inspection reports, and maintenance records of the line.

[0116] 2. When the assessment scope is expanded to multiple transmission lines within a specific area (such as a city, county, grid division, etc.), data collection is required for all relevant lines in the area as target overhead transmission lines. This usually involves more extensive database queries, regional inspection report summaries, and possible geographic information system (GIS)-assisted analysis to determine the overall defect status of lines in the area.

[0117] 3. It is also a common method to divide the assessment scope according to voltage level. Transmission lines of different voltage levels may differ in structure, operating conditions and defect types. Therefore, according to the assessment needs, we can focus on collecting defect data of a transmission line of a certain voltage level (such as high voltage, ultra-high voltage, ultra-high voltage, etc.) as the target overhead transmission line in order to conduct targeted risk assessment.

[0118] In this embodiment, through the risk assessment module M2, a risk indicator comparison matrix is ​​constructed based on the defect data set using the hierarchical analysis method; based on the risk indicator comparison matrix, the target eigenvector corresponding to the maximum eigenvalue is solved; the target eigenvector is normalized to determine the risk assessment value of the defect influencing factor.

[0119] As an example, seven factors, namely, fittings, insulators, poles and ancillary facilities, ground wires, grounding devices, foundations and channel environments, are selected as influencing factors of overhead transmission line risk defects. Then, the above seven factors are further divided to construct a classification table of influencing factors of overhead transmission line risk defects, as shown in Table 1:

[0120] Table 1. Classification of transmission line defect indicators

[0121]

[0122] It can be seen from the content of Table 1 that this embodiment takes the risk defect influencing factors of overhead transmission lines as the primary indicators and classifies them to obtain the corresponding secondary indicators. Therefore, according to the above table, the line components that have an impact on the overhead transmission lines can be clearly identified, thereby clearly reflecting the impact of the risk calculation values ​​of different line components on the risk assessment value of the selected object.

[0123] In this embodiment, the risk assessment value of each defect influencing factor is calculated by using the analytic hierarchy process. The analytic hierarchy process can decompose the problem into different components according to the nature of the problem and the overall goal to be achieved, and cluster and combine the factors at different levels according to the mutual influence and affiliation between the factors to form a multi-level analysis structure model. By solving the eigenvector of the judgment matrix, the priority weight of each element at each level to an element at the previous level is obtained, and finally the final weight of each alternative plan to the overall goal is recursively merged by the weighted sum method. The one with the largest final weight is the optimal plan.

[0124] Specifically, in this embodiment, based on the defect data set, a risk indicator comparison matrix is ​​constructed using the hierarchical analysis method, and based on the risk indicator comparison matrix, a target eigenvector corresponding to the maximum eigenvalue is solved and obtained, and the target eigenvector is normalized to determine the risk assessment value of the defect influencing factor.

[0125] First, the risk value of the secondary indicators needs to be determined according to Table 1 to obtain the evaluation results to construct a risk indicator comparison matrix. The risk value calculation is performed by taking the hardware category in Table 1 as an example. The first step is to determine the probability proportion of the hardware category in the primary indicators, as shown in Table 2 below:

[0126] Table 3. Proportion of primary indicators of transmission line defects

[0127]

[0128] It can be seen that the probability of hardware in the primary indicators accounts for 37.3%.

[0129] The proportion probability of the secondary indicators corresponding to the hardware defects is calculated, and the risk value of each secondary indicator is scored. The proportion of the secondary indicators of 8 types of hardware defects and the risk value of each type are obtained. Specifically, it can be seen in Table 3:

[0130] Table 4. Proportion of secondary indicators of hardware defects and risk value assessment table

[0131]

[0132] It can be understood that the corresponding values ​​are substituted into the following formula for calculation:

[0133]

[0134] Thus, the risk assessment value of the factors affecting hardware defects is obtained:

[0135]

[0136] Furthermore, the remaining first-level indicator risk assessment values ​​are calculated, and the risk indicator comparison matrix is ​​constructed using the hierarchical analysis method. The comparison matrix A obtained is as follows:

[0137]

[0138] Solve the comparison matrix A to obtain the eigenvalue λ and the eigenvector α, and obtain the maximum eigenvalue λ based on the eigenvalue λ max And its corresponding target feature vector w is normalized to obtain the normalized vector w′:

[0139] w′=[0.28 0.30 0.20 0.10 0.06 0.05 0.01] T

[0140] That is, the risk weights of the seven first-level indicators of transmission line defects, namely, hardware fittings, insulators, poles and ancillary facilities, ground wires, grounding devices, foundations, and channel environment, are 0.28, 0.30, 0.20, 0.10, 0.06, 0.05, and 0.01, respectively.

[0141] Furthermore, this example integrates the risk assessment value with the line meteorological environment data through the risk prediction module M3, and extracts features based on the time series to obtain a multi-dimensional risk data feature set.

[0142] Specifically, in this embodiment, the risk assessment values ​​of all defect influencing factors calculated by the risk defect influencing factor classification table are integrated and corresponded to the corresponding line components. The pre-acquired line meteorological environment data, such as wind speed, ice thickness, temperature, humidity, lightning activity, etc., are sorted so that these data can correspond to the risk assessment values ​​in time and space.

[0143] In some implementations, the risk assessment value of each defect influencing factor and the corresponding line meteorological environment data are used as features to construct a feature vector, which can fully reflect the current status and potential risks of the overhead transmission line.

[0144] It is understandable that in some implementations, feature selection or dimensionality reduction techniques can be used to reduce the number of features and improve the efficiency and accuracy of the model. Alternatively, feature derivation can be performed based on the basic features of the line meteorological environment data, such as the daily change rate, weekly change rate, cumulative value, etc. of the meteorological parameters, so as to more comprehensively reflect the changing trend of the meteorological environment and its impact on line risks.

[0145] Furthermore, the acquired feature vectors are normalized. If there are missing values ​​in the data, appropriate methods (such as interpolation, filling default values, or deleting samples containing missing values) are used to handle these missing values. Then, all selected feature vectors are combined into a multi-dimensional risk data feature set.

[0146] In this embodiment, the multi-dimensional risk data feature set is input into the defect fault prediction model through the risk prediction module M3 to obtain a first probability of a mechanical defect fault occurring in the target overhead transmission line and a second probability of an electrical defect fault occurring in the target overhead transmission line.

[0147] In some embodiments, a suitable machine learning or deep learning model is selected for model training. For the multi-classification problem of the embodiment of the present invention: mechanical defect fault and electrical defect fault, a multi-class classifier can be used, such as logistic regression, support vector machine, random forest, gradient boosting tree or neural network. The risk assessment value and meteorological environment data of the transmission line to be predicted are input into the trained model to perform risk prediction. The defect fault prediction model outputs the probability of the target overhead transmission line having a mechanical defect fault, i.e., the first probability, and the probability of the electrical defect fault, i.e., the second probability.

[0148] It can be understood that after obtaining the first probability of mechanical defect failure and the second probability of electrical defect failure of the target overhead transmission line, in order to further comprehensively evaluate the failure risk of the overhead transmission line and obtain a more instructive defect failure prediction result, weighted average method, fuzzy comprehensive evaluation method, machine learning model fusion, hierarchical analysis method, Bayesian network and neural network can be used to process the probability data.

[0149] In this embodiment, preferably, a fuzzy comprehensive evaluation method is used to process the probability data. In the fuzzy comprehensive evaluation method, the probabilities of mechanical defect failures and electrical defect failures are regarded as elements in a fuzzy set, and the defect fault prediction results of the overhead transmission line are comprehensively evaluated through fuzzy relationship matrices and fuzzy operations.

[0150] Specifically, this embodiment obtains the membership of the first probability and the second probability respectively based on the fuzzy comprehensive evaluation method through the risk prediction module M3. The probability of mechanical defect failure and electrical defect failure is regarded as an element in a fuzzy set, and each element has a membership.

[0151] In order to obtain a scientific and comprehensive defect failure risk index, preferably, a weighted average method (or any other suitable aggregation method) can be used to combine the probabilities of mechanical defect failures and electrical defect failures. In this embodiment, the weighted average method is used to allocate weights. According to the historical defect data of the target overhead transmission line, different weights are allocated to the first probability and the second probability. These weights can be determined based on the historical fault data of the transmission line.

[0152] As an example, through the risk prediction module M3, based on the membership degree and the weight values ​​corresponding to the first probability and the second probability, the comprehensive fault risk index of the target overhead transmission line is calculated, and the calculation formula is as follows:

[0153] R=w1*p1+w2*p2

[0154] Among them, w1 and w2 are weight values ​​corresponding to the first probability and the second probability respectively, and p1 and p2 are the membership degrees of the first probability and the second probability respectively.

[0155] The comprehensive fault risk index of the target overhead transmission line is calculated according to the above formula, thereby obtaining the defect and fault prediction result of the target overhead transmission line. It can be understood that in this embodiment, the defect and fault prediction result is a prediction of the probability or risk level of a specific defect or fault occurring in the overhead transmission line in the future, and is divided into different risk levels (such as low, medium, and high).

[0156] According to the prediction results of the defect fault prediction model, the defect fault risk of the target overhead transmission line can be scientifically analyzed, and effective fault response and maintenance measures can be determined.

[0157] Furthermore, this embodiment formulates different response measures for different types of defect faults based on the defect fault prediction results through the risk processing module M4. For example, for mechanical defect faults, it may be necessary to strengthen inspections, replace aging components, or strengthen line structures; for electrical defect faults, it may be necessary to adjust insulator configurations, add lightning protection measures, or improve contamination conditions.

[0158] Transmission line mechanical terminals usually refer to equipment or systems directly related to the physical structure of the transmission line, such as towers, conductors, insulator strings, hardware, etc. For transmission line mechanical terminals, send corresponding risk handling logs, including: sending inspection instructions to inspection personnel or drones near the mechanical terminals, focusing on checking the components that may have defects mentioned in the prediction. For predicted high-risk areas or components, provide more specific maintenance suggestions, such as replacing aging insulators, reinforcing loose hardware, or adjusting conductor tension. If the prediction results show that there is an immediate risk of serious failure, an emergency response plan should be formulated, including evacuating surrounding personnel, cutting off power, and preparing repair teams.

[0159] The electrical terminals of transmission lines are more involved in the monitoring and control of electrical parameters, such as substations, switch stations, protection devices, etc. For such terminals, the risk handling logs sent include but are not limited to the following: Adjust electrical parameters to mitigate potential risks, such as adjusting voltage and current limit values, and optimizing protection settings. Send remote monitoring instructions to electrical terminals to strengthen real-time monitoring of key electrical parameters mentioned in the forecast so as to detect abnormal conditions in a timely manner. When high risks are predicted, preset automated control strategies can be automatically triggered, such as automatic reclosing, rapid isolation of fault areas, etc., to reduce the impact of faults on the power grid.

[0160] In some implementations, the risk processing log is sent to a matching control terminal via optical fiber communication, wireless communication, or a dedicated software platform.

[0161] The embodiment of the present invention can ensure that operation and maintenance personnel or automation systems can timely understand potential risks and take effective measures to deal with them by sending risk logs containing defect and fault prediction results in real time, thereby ensuring the safe and stable operation of the transmission line.

[0162] Furthermore, the system also includes a risk measure implementation module M5, which is specifically used for: after receiving the risk processing log, the control terminal performs corresponding maintenance operations on the target overhead transmission line according to the countermeasures in the risk processing log, and feeds back to the power system control center in real time. The power system control center monitors the real-time operating status of the target overhead transmission line and adjusts the countermeasures according to the operating status.

[0163] As an example, the control terminal will monitor the status of the target overhead transmission line in real time, including electrical parameters, physical status, environmental conditions, etc., and will feed back the monitored status information, operation results, and any abnormal conditions to the power system control center in real time. After the risk treatment is completed, the power system control center will evaluate the treatment effect to verify the effectiveness and accuracy of the response measures. Based on the evaluation results and on-site feedback, the risk treatment process, prediction model, response measures, etc. will be continuously optimized and improved to improve the response capability and operation efficiency of the entire system.

[0164] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for handling overhead transmission line defect risks, characterized in that: include: Acquire a defect data set of a target overhead transmission line, wherein the defect data set includes various defect influencing factors of the target overhead transmission line; The defect data set is processed based on a pre-constructed classification table of risk defect influencing factors of overhead transmission lines to obtain risk assessment values ​​of each of the defect influencing factors, wherein the classification table of risk defect influencing factors of overhead transmission lines reflects the influence of risk calculation values ​​of different line components on the risk assessment value of the selected object; Based on the pre-acquired line meteorological environment data and the risk assessment value, a defect fault prediction result is obtained using a trained defect fault prediction model, wherein the defect fault prediction result includes a mechanical defect fault result and an electrical defect fault result; Based on the defect fault prediction result, a corresponding risk processing log is sent to a matching control terminal, wherein the control terminal includes a transmission line mechanical terminal and a transmission line electrical terminal.

2. The overhead transmission line defect risk treatment method according to claim 1, characterized in that: The defect data set is processed based on the pre-constructed classification table of risk defect influencing factors of overhead transmission lines to obtain the risk assessment value of each defect influencing factor, including: Based on the defect data set, a risk indicator comparison matrix is ​​constructed using the analytic hierarchy process; Based on the risk indicator comparison matrix, a target eigenvector corresponding to the maximum eigenvalue is obtained by solving; The target feature vector is normalized to determine the risk assessment value of the defect influencing factor.

3. The overhead transmission line defect risk treatment method according to claim 1, characterized in that: The defect and fault prediction results are obtained based on the pre-acquired line meteorological environment data and the risk assessment value and the trained defect and fault prediction model, including: Integrate the risk assessment value with the line meteorological environment data, and extract features based on time series to obtain a multi-dimensional risk data feature set; The multi-dimensional risk data feature set is input into the defect fault prediction model to obtain a first probability of a mechanical defect fault occurring in the target overhead transmission line and a second probability of an electrical defect fault occurring in the target overhead transmission line.

4. The overhead transmission line defect risk treatment method according to claim 3, characterized in that: The method of obtaining a defect and fault prediction result based on the pre-acquired line meteorological environment data and the risk assessment value and using a trained defect and fault prediction model also includes: Obtaining the membership degree of the first probability and the second probability respectively based on a fuzzy comprehensive evaluation method; assigning different weights to the first probability and the second probability according to historical defect data of the target overhead transmission line; Based on the membership degree and the weight values ​​corresponding to the first probability and the second probability, a comprehensive fault risk index of the target overhead transmission line is calculated, and the defect fault prediction result is obtained according to the comprehensive fault risk index.

5. The overhead transmission line defect risk treatment method according to claim 1, characterized in that: After sending the corresponding risk processing log to the matching control terminal, the method further includes: After receiving the risk processing log, the control terminal performs corresponding maintenance operations on the target overhead transmission line according to the countermeasures in the risk processing log, and provides real-time feedback to the power system control center; The power system control center monitors the real-time operating status of the target overhead transmission line and adjusts the response measures according to the operating status.

6. An overhead transmission line defect risk management system, characterized in that: include: A data acquisition module, used to acquire a defect data set of a target overhead transmission line, wherein the defect data set includes various defect influencing factors of the target overhead transmission line; A risk assessment module, used to process the defect data set based on a pre-constructed classification table of risk defect influencing factors of overhead transmission lines to obtain risk assessment values ​​of each of the defect influencing factors, wherein the classification table of risk defect influencing factors of overhead transmission lines reflects the influence of risk calculation values ​​of different line components on the risk assessment value of the selected object; A risk prediction module, used to obtain defect and fault prediction results based on the pre-acquired line meteorological environment data and the risk assessment value and a trained defect and fault prediction model, wherein the defect and fault prediction results include mechanical defect and fault results and electrical defect and fault results; The risk processing module is used to send a corresponding risk processing log to a matching control terminal based on the defect fault prediction result, and the control terminal includes a transmission line mechanical terminal and a transmission line electrical terminal.

7. The overhead transmission line defect risk management system according to claim 6, characterized in that: The risk assessment module is specifically used for: Based on the defect data set, a risk indicator comparison matrix is ​​constructed using the analytic hierarchy process; Based on the risk indicator comparison matrix, a target eigenvector corresponding to the maximum eigenvalue is obtained by solving; The target feature vector is normalized to determine the risk assessment value of the defect influencing factor.

8. The overhead power transmission line defect risk management system according to claim 6, characterized in that: The risk prediction module is specifically used for: The risk assessment value and the line meteorological environment data are respectively integrated, and feature extraction is performed based on the time series to obtain a multi-dimensional risk data feature set; The multi-dimensional risk data feature set is input into the defect fault prediction model to obtain a first probability of a mechanical defect fault occurring in the target overhead transmission line and a second probability of an electrical defect fault occurring in the target overhead transmission line.

9. The overhead transmission line defect risk management system according to claim 6, characterized in that: The risk prediction module is also used to: obtain the membership degree of the first probability and the second probability respectively based on a fuzzy comprehensive evaluation method; assigning different weights to the first probability and the second probability according to historical defect data of the target overhead transmission line; Based on the membership degree and the weight values ​​corresponding to the first probability and the second probability, a comprehensive fault risk index of the target overhead transmission line is calculated, and the defect fault prediction result is obtained according to the comprehensive fault risk index.

10. The overhead power transmission line defect risk management system according to claim 6, characterized in that: The system further includes a risk measure implementation module, which is specifically used to: After receiving the risk handling log, the control terminal performs corresponding maintenance operations on the target overhead transmission line according to the countermeasures in the risk handling log, and provides real-time feedback to the power system control center; the power system control center monitors the real-time operating status of the target overhead transmission line and adjusts the countermeasures according to the operating status.

Images