Intelligent monitoring and analyzing method for carbon emission of overhead transmission line

Abstract

The invention discloses an overhead power transmission line carbon emission intelligent monitoring and analysis method, which comprises the steps of constructing an overhead power transmission line carbon emission data acquisition cloud platform, and performing real-time acquisition on carbon emission data in the operation process of an overhead power transmission line in different specified time periods; adopting a depth isolation forest algorithm to detect abnormal state sampling data and discarding the abnormal state sampling data; constructing a carbon emission prediction model, and outputting predicted sequence data of each time point in the future through the monitoring data in the key node detection list; constructing a key node processing model, feeding back the processed data based on an unsafe key node list, and updating the processed data to the three-dimensional digital twin carbon emission monitoring model of the overhead transmission line; according to the invention, real-time monitoring and early warning of overhead transmission line carbon emission can be realized, informatization and scientific management of overhead transmission line carbon emission monitoring are realized, the updating frequency of a digital twin carbon emission monitoring model is reduced, and the carbon emission of the transmission line is reduced, so that the overhead transmission line is ensured to operate more stably and energy-saving.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence monitoring and early warning, and more specifically, to an intelligent monitoring and analysis method for carbon emissions of overhead transmission lines. Background Art

[0002] With the increasing global attention to environmental protection and sustainable development, carbon emissions monitoring has become particularly important in various industries. As a key component of power transmission, the monitoring and analysis of carbon emissions from overhead transmission lines are of great significance for evaluating and managing the carbon footprint of the power system. By accurately monitoring and analyzing the carbon emissions of overhead transmission lines, a scientific basis can be provided for energy conservation and emission reduction in the power system, thus effectively addressing climate change.

[0003] However, due to the complexity of carbon emissions monitoring of overhead transmission lines, problems such as untimely transfer of upstream and downstream professional information and difficult data management often occur in traditional two-dimensional designs. With the rise of digital technology, the use of three-dimensional digital twin technology can effectively achieve design collaboration among different specialties and effectively solve the integration and update of massive data. Therefore, the use of three-dimensional digital twin technology to realize the digital processing of carbon emissions monitoring of overhead transmission lines has gradually become the industry mainstream. However, there are still a large number of problems: First, there is currently a lack of means to predict the data for carbon emissions monitoring of overhead transmission lines, and abnormal carbon emission points cannot be accurately monitored in a timely manner through quantitative means, resulting in high carbon emissions; Second, due to factors such as a large number of monitoring nodes, different monitoring periods, and different carbon emission safety thresholds for each node, each monitoring node has its own maintenance period, lacking unified planning and processing, resulting in cumbersome updates of the three-dimensional digital overhead transmission line carbon emission twin technology platform, redundant data, and a lack of intelligent processing means. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent monitoring and analysis method for carbon emissions of overhead transmission lines to solve the above problems existing in the prior art.

[0005] Specifically, this application is as follows:

[0006] An intelligent monitoring and analysis method for carbon emissions of overhead transmission lines, comprising the following steps:

[0007] S1. Construct a carbon emission data collection cloud platform for overhead transmission lines, and based on different time periods under regulations, collect and summarize in real time the carbon emission data generated by resistance loss, corona loss, and insulator leakage loss respectively during the operation of the overhead transmission lines.

[0008] S2. Create several key node detection lists, sequentially monitor the carbon emission data generated by resistor loss, corona loss, and insulator leakage loss respectively, record each corresponding monitoring data into the key node detection lists, and record the time period of each monitoring key node.

[0009] S3. Use the deep isolation forest algorithm to detect the abnormal status of the carbon emission data in all key node detection lists, and perform discard preprocessing on the abnormal sampling data.

[0010] S4. Build a carbon emission prediction model, output the predicted sequence data for each future time point through the monitoring data in the key node detection lists, and mark the time period where the safety threshold is located based on the safety threshold of each monitoring data.

[0011] S5. Build a key node processing model, compare the sequence data predicted by the key node detection lists with the safety threshold, determine whether the key nodes are safe, output a uniformly maintained list of unsafe key nodes based on the marked time period, and feedback the processed data based on the list of unsafe key nodes, and update it to the three-dimensional digital twin carbon emission monitoring model of the overhead transmission line.

[0012] Further, the carbon emission data generated by resistor loss in S1 includes:

[0013] Install current transformers and voltage transformers at the key nodes of the transmission line, monitor the current and voltage information in the line in real time, calculate the power loss of the line by calculating the product of the current and voltage, and then calculate the resistor loss.

[0014] The carbon emission data generated by corona loss includes:

[0015] Install corona monitoring sensors and corona spectroscopy sensors at the key nodes of the transmission line, monitor the corona discharge situation around the conductor in real time, and obtain the data of corona loss.

[0016] The carbon emission data generated by insulator leakage loss includes: Install leakage current sensors on the insulators at the key nodes of the transmission line, monitor the leakage current on the surface of the insulators in real time, and thus obtain the data of insulator leakage loss.

[0017] Further, the key nodes in S3 include: the two ends nodes of the transmission line, the middle nodes of the transmission line, the terrain change nodes of the transmission line, the tower nodes, the insulator nodes, and the conductor nodes.

[0018] Further, the key nodes in S3 also include:

[0019] Frequently fault-occurring nodes: Based on the historical operation data of the transmission line, analyze the frequency and location of faults through a big data clustering analysis algorithm, and select the frequently fault-occurring area as the key node;

[0020] Nodes with large losses: Based on the historical operation data of the transmission line, identify the nodes with large losses in the transmission line through a big data clustering analysis algorithm, including the areas where the resistance loss, corona loss, and insulator leakage loss meet the set thresholds, and select this area as the key node.

[0021] Furthermore, S3 also includes:

[0022] When constructing several binary trees for each key node detection list, the criterion for stopping the construction of the binary tree of this key node is: whether the growth length of the current binary tree is within the termination interval determined in advance according to the maximum growth length. If so, stop the construction; if not, continue the construction. The growth length represents the ratio of the number of training samples selected from the real-time monitoring data of this key node on one side of the binary tree node to the number of training samples on the other side during the expansion of the current binary tree node.

[0023] Furthermore, the growth length X t represents the number of training samples selected from the real-time data of this key node detection list on the left side of the binary tree node during the expansion of the current binary tree node, and Y t represents the number of training samples selected from the real-time data of this key node detection list on the right side of the binary tree node during the expansion of the current binary tree node, then the termination interval is [0.5, 1.2].

[0024] Furthermore, the basis for the deep isolation forest algorithm in S3 to judge whether the carbon emission data in the key node detection list is abnormal data is:

[0025] Whether the anomaly score G(m,n) of the key node detection list m satisfies G(m,n) ∈ [0.9, 1]. If so, this key node is an anomaly point, and the sampling data of this key node is discarded; where, L(n) represents the average path length of n binary trees of the key node detection list point m constructed; g(m) represents the maximum path length of the current measurement point m, and D(h(m)) represents the expectation of g(m).

[0026] Furthermore, the construction of the carbon emission prediction model includes:

[0027] According to the historical data in the overhead transmission line carbon emission data acquisition cloud platform, obtain the monitoring time period of any monitoring key node and the monitoring value of each time period;

[0028] Constructing a grey improved prediction model based on the monitoring values includes:

[0029] Generate a new accumulated second original sequence data after performing first-order accumulation on the first original sequence data;

[0030] Calculate the grey derivative corresponding to the second original sequence data;

[0031] Establish a grey differential equation and obtain the grey improved prediction model based on the grey differential equation;

[0032] The specific implementation steps of the grey improved prediction model include:

[0033] The first original sequence data is generated in chronological order according to the monitoring values of each time period in the historical data, denoted as S0 = {Y0(1), Y0(2), Y0(3), ……, Y0(n)}, where S0 represents the first original sequence data, and Y0(1), Y0(2), Y0(3), …, Y0(n) represent the monitoring values of 1, 2, 3, ……, n time periods respectively;

[0034] Perform first-order accumulation generation processing on the basis of the first original sequence data S0:

[0035]

[0036] where k = 1, 2, ……, n; Y1(k) represents the element of the first original sequence data corresponding to the second original sequence data;

[0037] On the basis of the second original sequence data, obtain the grey derivative d(k) of Y1(k), and establish a GM(1,1) grey differential equation according to the derivative d(k):

[0038] d(k) + a(Y1(k) + (1 - b)Y1(k - 1)) = c,

[0039] where a is the development grey degree; c is the endogenous control grey degree;

[0040] Solve a and c in the GM(1,1) grey differential equation, let be the vector to be estimated, establish a first-order linear differential equation for fitting using the discrete first original sequence data, and calculate and solve a and c by the least square method;

[0041] Calculate the output of the grey improved prediction model for the (n + 1)-th period as:

[0042]

[0043] where Y0(n + 1) is the output of the grey improved prediction model for the (n + 1)-th period, and subsequent predicted monitoring values are based on the output of the grey improved prediction model;

[0044] Monitoring any key node forms predicted sequence data. Obtain the safety threshold for each key node monitoring, and mark the corresponding time period in the predicted sequence data where the safety threshold is located.

[0045] Furthermore, the construction of the key node processing model described in S5 includes:

[0046] S51: Construct a time axis and input the formed predicted sequence data into the time axis;

[0047] S52: Obtain all the monitored and marked time periods, and perform safety threshold comparison calculations on all key nodes within the monitored and marked time periods;

[0048] After the safety threshold comparison calculations for all key nodes are completed, take the maintenance time of the key nodes determined to be non-safe as the unified maintenance nodes. At the same time, sort the unified maintenance key nodes according to the priority level of the maintenance time, and the ones with higher priority are processed first. Based on the feedback data after processing the unified maintenance key nodes, update it to the 3D digital twin carbon emission monitoring model of the overhead transmission line;

[0049] The process of performing safety threshold comparison calculations on key nodes in S52 is as follows:

[0050] S521: The method for obtaining the safety threshold function is: Obtain the historical information of carbon emission data corresponding to the risks of key nodes, and select the safe data and risky data in the historical information of carbon emission data according to the physical characteristics of the carbon emission data; Assign values to the data according to the size of the selected data. If the data is continuous, assign continuous numerical values; If the data is discontinuous, assign categorical numerical values; Obtain the safety threshold function for continuous data and discontinuous data respectively through the Newton interpolation algorithm and the random forest algorithm;

[0051] S522: For continuous data, determine the safety threshold by calculating the intersection of the safe data and the risky data of the safety threshold function; For discontinuous data, determine the safety threshold by calculating the safety threshold function, that is, the input and output of the random forest neural network model; The determination of the safety threshold by the input and output of the random forest neural network model means: According to the data of the corresponding values in adjacent time periods input into the random forest neural network model multiple times, judge the type of the data through the output, and obtain the safety threshold by combining the data of the corresponding values in adjacent time periods input and the output;

[0052] S523: Compare the predicted sequence data of the key node with the safety threshold to determine whether the key node is a safe node.

[0053] Further, the update to the 3D digital twin carbon emission monitoring model of the overhead transmission line described in S53 includes:

[0054] Simulate and analyze the overhead transmission line structure model through QGIS open-source software to unify the overhead transmission line structure template;

[0055] Perform parametric modeling, develop a parametric family library for overhead transmission lines, and based on the parametric overhead transmission line family libraries of each specialty, initialize data according to all the key nodes actually deployed, and generate a 3D digital twin carbon emission monitoring model of the overhead transmission line through parametric means;

[0056] If there is feedback data of the non-safe key node list after processing, update it to the 3D digital twin carbon emission monitoring model of the overhead transmission line.

[0057] Compared with the prior art, the embodiments of the present invention achieve the following beneficial effects:

[0058] The embodiments of the present invention provide a carbon emission data acquisition cloud platform for overhead transmission lines, which can collect and summarize in real time the carbon emission data generated by resistance loss, corona loss, and insulator leakage loss during the operation of the overhead transmission line based on different time periods specified; create several key node detection lists, monitor each carbon emission data generated by resistance loss, corona loss, and insulator leakage loss and record it in the key node detection list, and record the time period of each monitored key node; use the deep isolation forest algorithm to detect the abnormal state of the carbon emission data in all key node detection lists, and discard and preprocess the abnormal sampling data; construct a carbon emission prediction model, output the predicted sequence data for each future time point through the monitoring data in the key node detection list, and mark the time period where the safety threshold is located based on each monitored safety threshold; construct a key node processing model, compare the predicted sequence data of the key node detection list with the safety threshold to determine whether the key node is safe, output a unified maintained non-safe key node list based on the marked time period, and update the processed data based on the non-safe key node list to the 3D digital twin carbon emission monitoring model of the overhead transmission line; the present invention can realize real-time monitoring and early warning of the carbon emissions of the overhead transmission line, realize informatized and scientific management of the carbon emission monitoring of the overhead transmission line, reduce the update frequency of the digital twin carbon emission monitoring model, reduce the carbon emissions, and thus ensure that the overhead transmission line operates more stably and energy-efficiently. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flowchart of a method for intelligent monitoring and analysis of carbon emissions from overhead transmission lines provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] The present invention will be described in detail below with reference to the accompanying drawings.

[0061] Embodiment 1

[0062] An embodiment of the present invention provides an intelligent monitoring and analysis method for carbon emissions of overhead transmission lines, as Figure 1 follows:

[0063] S1. Construct a carbon emission data collection cloud platform for overhead transmission lines, and collect and summarize in real time the carbon emission data generated by resistance loss, corona loss, and insulator leakage loss respectively during the operation of the overhead transmission line based on different time periods under regulations;

[0064] S2. Create several key node detection lists, monitor the carbon emission data generated by resistance loss, corona loss, and insulator leakage loss in sequence, record each corresponding monitoring data in the key node detection list, and record the time period of each monitoring key node;

[0065] S3. Use the deep isolation forest algorithm to detect the abnormal status of the carbon emission data in all key node detection lists, and perform discard preprocessing on the abnormal sampling data;

[0066] S4. Construct a carbon emission prediction model, output the predicted sequence data for each future time point through the monitoring data in the key node detection list, and mark the time period where the safety threshold is located based on the safety threshold of each monitoring data;

[0067] S5. Construct a key node processing model, compare the sequence data predicted by the key node detection list with the safety threshold, judge whether the key node is safe, output a uniformly maintained list of unsafe key nodes based on the marked time period, and feedback the processed data based on the list of unsafe key nodes to update the three-dimensional digital twin carbon emission monitoring model of the overhead transmission line.

[0068] Specifically, an overhead transmission line carbon emission data collection cloud platform is constructed. Based on different time periods specified, the carbon emission data generated by resistance loss, corona loss, and insulator leakage loss during the operation of the overhead transmission line is collected and summarized in real time. A number of key node detection lists are created, and each monitoring record of the carbon emission data generated by resistance loss, corona loss, and insulator leakage loss is recorded in the key node detection list, and the time period of each monitoring key node is recorded. The deep isolation forest algorithm is used to detect the abnormal state of the carbon emission data in all key node detection lists, and the abnormal sampling data is discarded for preprocessing. A carbon emission prediction model is constructed, and the predicted sequence data for each future time point is output through the monitoring data in the key node detection list. Based on each monitored safety threshold, the time period where the safety threshold is located is marked. A key node processing model is constructed, and the predicted sequence data in the key node detection list is compared with the safety threshold to determine whether the key node is safe. Based on the marked time period, a list of non-safe key nodes maintained uniformly is output, and the processed data is fed back based on the list of non-safe key nodes and updated to the three-dimensional digital twin carbon emission monitoring model of the overhead transmission line. This embodiment can realize real-time monitoring and early warning of the carbon emissions of the overhead transmission line, realize information-based and scientific management of the carbon emission monitoring of the overhead transmission line, reduce the update frequency of the digital twin carbon emission monitoring model, reduce the carbon emissions, and thus ensure that the overhead transmission line operates more stably and energy-efficiently.

[0069] In the above embodiment, specifically, the carbon emissions of the overhead transmission line mainly include the carbon emissions during the construction and operation stages. This embodiment mainly studies the monitoring of carbon emissions during the operation stage. By collecting carbon emissions at several key points, intensive monitoring of all points of the entire overhead transmission line is reduced. The key points are selected through a clustering algorithm, and the clustering algorithm includes one or more combinations of, but is not limited to, the k-means clustering algorithm, the K-prototype clustering algorithm, the DBSCAN algorithm, and the OPTICS algorithm. In this embodiment, the k-means clustering algorithm is used to select key nodes, and the load of the digital twin carbon emission monitoring model is reduced through the filtering of key node selection.

[0070] Specifically, through the K-prototype clustering algorithm, the selection of key points is classified respectively, and the data is divided into two categories: main classification data and other classification data; the main classification data is retained, and the main classification is used as the key points, and the other classification data is removed; after the data is classified through the clustering algorithm, the classification with the highest score and the densest distribution after classification is used as the main classification data, and the remaining other classifications are used as other classification data;

[0071] The K-prototype clustering algorithm for the sample set D = {y1, y2,... yn} The method for classification is as follows: By continuously updating the central object, obtain the cluster division C = {C1, C2,... C k}, where C1, C2, C k all represent a single cluster, and each cluster includes multiple individual samples in the same cluster; the clustering rule is to minimize the mean square error, and the mean square error formula is: where y represents the sample data assigned to cluster C i , δ represents the Hamming distance, and w i is the mean vector of cluster C i , and the smaller the value of P d , the higher the similarity of the samples y within the cluster.

[0072] In the above embodiment, specifically, the carbon emission data generated by the resistance loss in S1 includes:

[0073] Install current transformers and voltage transformers at the key nodes of the transmission line to monitor the current and voltage information in the line in real time. By calculating the product of the current and voltage, obtain the power loss of the line, and then calculate the resistance loss;

[0074] The carbon emission data generated by the corona loss includes:

[0075] Install corona monitoring sensors and corona spectroscopy sensors at the key nodes of the transmission line to monitor the corona discharge situation around the conductor in real time and obtain the data of the corona loss;

[0076] The carbon emission data generated by the insulator leakage loss includes: Install leakage current sensors on the insulators at the key nodes of the transmission line to monitor the leakage current on the surface of the insulators in real time, so as to obtain the data of the insulator leakage loss.

[0077] Specifically, the carbon emission at the key node is calculated according to the following formula:

[0078]

[0079] where C Ri represents the key carbon emission quantity, corresponding to the key corona loss quantity, the key resistance loss quantity, and the key insulator leakage loss quantity respectively, and C Yi represents the additional carbon emission influence coefficient, corresponding to the additional corona loss influence coefficient, the additional resistance loss influence coefficient, and the additional insulator leakage loss influence coefficient respectively;

[0080] The carbon emission generated by the corona loss is calculated according to the following formula:

[0081]

[0082] C Y1= 0.02C R ,

[0083] Where: n is the number of loops; I is the phase current; N is the number of conductors in the phase conductor split; R is the AC resistance of the conductor; τ is the number of hours of maximum load loss; L is the line length, F d is the power carbon emission factor;

[0084] The carbon emissions generated by resistance loss are calculated according to the following formula:

[0085]

[0086] C Y2 = 0.03C R2 ,

[0087] Where: ρ represents the resistivity of the conductor material, and A represents the cross-sectional area of the conductor;

[0088] The carbon emissions generated by insulator leakage loss are calculated according to the following formula:

[0089] C R3 = D 2 CτLF d * 0.003,

[0090] C Y3 = 0.04C R3 ,

[0091] Where: D represents the insulator leakage current, and C represents the insulator leakage resistance.

[0092] Corona loss is a function of the conductor geometry, conductor electric field strength, voltage, and meteorological conditions in the line area. Corona loss does not change with the change of transmission capacity, and its magnitude is mainly related to the voltage level, conductor structure, and weather conditions;

[0093] The measured value of each node is collected three times, and the average of the three times is taken as the measured value.

[0094] In the above embodiment, specifically, the key nodes in S3 include: the nodes at both ends of the transmission line, the intermediate nodes of the transmission line, the nodes where the terrain of the transmission line changes, the tower nodes, the insulator nodes, and the conductor nodes.

[0095] In the above embodiment, specifically, the key nodes in S3 also include:

[0096] Frequently failed nodes: According to the historical operation data of the transmission line, the frequency and location of faults are analyzed through the big data clustering analysis algorithm, and the frequently failed areas are selected as key nodes;

[0097] Nodes with large losses: According to the historical operation data of the transmission line, through the big data clustering analysis algorithm, identify the nodes with large losses in the transmission line, including the areas where the resistance loss, corona loss, and insulator leakage loss meet the set threshold, and select this area as the key node.

[0098] In the above embodiment, specifically, S3 further includes:

[0099] When constructing several binary trees for each key node detection list, the criterion for stopping the construction of the binary tree of the key node is: whether the growth length of the current binary tree is within the termination interval determined in advance according to the maximum growth length. If so, stop the construction; if not, continue the construction. The growth length represents the ratio of the number of training samples selected from the real-time monitoring data of the key node on one side of the binary tree node to the number of training samples on the other side during the expansion of the current binary tree node.

[0100] In the above embodiment, specifically, the growth length X t represents the number of training samples selected from the real-time data of the key node detection list on the left side of the binary tree node during the expansion of the current binary tree node, and Y t represents the number of training samples selected from the real-time data of the key node detection list on the right side of the binary tree node during the expansion of the current binary tree node, then the termination interval is [0.5, 1.2].

[0101] In the above embodiment, specifically, the basis for the depth isolation forest algorithm in S3 to judge whether the carbon emission data in the key node detection list is abnormal data is:

[0102] Whether the anomaly score G(m,n) of the key node detection list m satisfies G(m,n) ∈ [0.9, 1]. If so, this key node is an anomaly point, and the sampling data of this key node is discarded; where L(n) represents the average path length of n binary trees of the key node detection list point m constructed; g(m) represents the maximum path length of the current measurement point m, and D(h(m)) represents the expectation of g(m).

[0103] Specifically, in the collected data, if the data collected at this key point is determined to be abnormal data, this abnormal data will be discarded. If the number of consecutive anomalies at this key point exceeds the preset threshold range, this key point is determined to be a key monitoring key point.

[0104] In the above embodiment, specifically, the construction of the carbon emission prediction model includes:

[0105] According to the historical data in the carbon emission data collection cloud platform of the overhead transmission line, obtain the monitoring time period of any monitoring key node and the monitoring value of each time period.

[0106] Constructing a grey improved prediction model based on the monitoring values includes:

[0107] Performing first-order accumulation on the first original sequence data to generate a new accumulated second original sequence data;

[0108] Calculating the grey derivative corresponding to the second original sequence data;

[0109] Establishing a grey differential equation and obtaining the grey improved prediction model based on the grey differential equation;

[0110] The specific implementation steps of the grey improved prediction model include:

[0111] The first original sequence data is generated in chronological order according to the monitoring values of each time period in the historical data, denoted as S0 = {Y0(1), Y0(2), Y0(3), ……, Y0(n)}, where S0 represents the first original sequence data, and Y0(1), Y0(2), Y0(3), …, Y0(n) represent the monitoring values of 1, 2, 3, ……, n time periods respectively;

[0112] Performing first-order accumulation generation processing on the basis of the first original sequence data S0:

[0113]

[0114] where k = 1, 2, ……, n; Y1(k) represents the element of the first original sequence data corresponding to the second original sequence data;

[0115] On the basis of the second original sequence data, obtaining the grey derivative d(k) of Y1(k), and establishing a GM(1,1) grey differential equation according to the derivative d(k):

[0116] d(k) + a(Y1(k) + (1 - b)Y1(k - 1)) = c,

[0117] where a is the development grey degree; c is the endogenous control grey degree;

[0118] Solving for a and c in the GM(1,1) grey differential equation, let be the vector to be estimated, establishing a first-order linear differential equation for fitting using the discrete first original sequence data, and calculating and solving for a and c by the least squares method;

[0119] Calculating the output of the grey improved prediction model for the (n + 1)-th period as:

[0120]

[0121] Among them, Y0(n + 1) is the output of the gray improved prediction model in the (n + 1)-th cycle, and subsequent predicted monitoring values are based on the output of the gray improved prediction model.

[0122] For the monitoring of any key node, predicted sequence data is formed. The safety threshold for each key node monitoring is obtained, and the corresponding time period in the predicted sequence data where the safety threshold is located is marked.

[0123] Specifically, the data detected for each key node are respectively resistance loss carbon emissions, corona loss carbon emissions, and insulator leakage carbon emissions, corresponding to three safety thresholds: the safety threshold for resistance loss carbon emissions, the safety threshold for corona loss carbon emissions, and the safety threshold for insulator leakage carbon emissions.

[0124] Furthermore, the construction of the key node processing model described in S5 includes:

[0125] S51: Construct a timeline and input the formed predicted sequence data into the timeline.

[0126] S52: Obtain all the marked time periods of the monitoring, and perform safety threshold comparison calculations for all key nodes within the marked time periods of the monitoring.

[0127] After the safety threshold comparison calculations for all key nodes are completed, the maintenance time of the key nodes determined to be non-safe is used as the unified maintenance node. At the same time, the unified maintenance key nodes are sorted according to the priority level of the maintenance time, and those with a higher ranking are processed first. Based on the feedback data after processing the unified maintenance key nodes, it is updated to the three-dimensional digital twin carbon emission monitoring model of the overhead transmission line.

[0128] The process of performing safety threshold comparison calculations for key nodes in S52 is as follows:

[0129] S521: The method for obtaining the safety threshold function is as follows: Obtain the historical information of the carbon emission data corresponding to the risk of the key node, and select the safe data and the data with risks in the historical information of the carbon emission data according to the physical characteristics of the carbon emission data; assign values to the data according to the size of the selected data. If the data is continuous, assign continuous numerical values; if the data is discontinuous, assign categorical numerical values; obtain the safety threshold function for continuous data and discontinuous data respectively through the Newton interpolation algorithm and the random forest algorithm.

[0130] S522: If it is continuous data, determine the safety threshold by calculating the intersection of the safe data and the risky data of the safety threshold function; if it is discontinuous data, then determine the safety threshold by calculating the safety threshold function, that is, the input and output of the random forest neural network model; determining the safety threshold by the input and output of the random forest neural network model means: according to the data of the corresponding values in adjacent time periods input multiple times in the random forest neural network model, judge the type of data through the output, and obtain the safety threshold by combining the data of the corresponding values in adjacent time periods input and the output;

[0131] S523: Compare the predicted sequence data of the key node with the safety threshold to determine whether the key node is a safe node.

[0132] In the above embodiment, specifically, the updating to the three-dimensional digital twin carbon emission monitoring model of the overhead transmission line in S53 includes:

[0133] Carry out simulation analysis on the overhead transmission line structure model through QGIS open-source software to unify the overhead transmission line structure template;

[0134] Perform parametric modeling, develop an overhead transmission line parametric family library, and based on the parametric overhead transmission line family libraries of each specialty, initialize the data according to all the key nodes actually deployed, and generate a three-dimensional digital twin carbon emission monitoring model of the overhead transmission line in a parametric manner;

[0135] If there is a list of non-safe key nodes that feedback the processed data, update it to the three-dimensional digital twin carbon emission monitoring model of the overhead transmission line.

[0136] Specifically, in the monitoring and display of the three-dimensional digital twin carbon emission monitoring model of the overhead transmission line, mark the key node with red for the non-safe carbon emission state, mark the key node with green for the safe carbon emission state, and if the key node turns into a safe state after safety maintenance measures in the abnormal state, mark the key node with green.

[0137] The algorithms and displays provided here are not inherently related to any specific computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings based here. Based on the above description, the structure required to construct such a system is obvious. In addition, the present invention is not directed to any specific programming language. It should be understood that the content of the present invention described here can be implemented using various programming languages, and the description of the specific language above is to disclose the best implementation mode of the present invention.

[0138] In the specification provided herein, numerous specific details are set forth. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail so as not to obscure an understanding of this description.

[0139] Similarly, it should be understood that in order to streamline this disclosure and assist in understanding one or more of the various inventive aspects, in the foregoing description of the exemplary embodiments of the invention, the various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, the disclosed method should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, the inventive aspects lie in less than all the features of the single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the invention.

[0140] Those skilled in the art will appreciate that the modules in the devices in the embodiments can be adaptively changed and disposed in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all the features disclosed in this specification (including the accompanying claims, abstract and drawings) and all the processes or units of any method or device so disclosed. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract and drawings) can be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0141] In addition, those skilled in the art will be able to understand that although some of the embodiments herein include certain features included in other embodiments but not others, the combination of the features of different embodiments means that it is within the scope of the invention and forms different embodiments. For example, in the following claims, any one of the claimed embodiments can be used in any combination.

[0142] Each component embodiment of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the device according to the embodiments of the present invention. The present invention can also be implemented as a device or device program (e.g., a computer program and a computer program product) for performing part or all of the methods described herein. Such a program for implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, or provided on a carrier signal, or in any other form.

Claims

1. An intelligent monitoring and analysis method for carbon emissions of overhead transmission lines, characterized in that: The following steps are involved: S1. Construct a cloud platform for collecting carbon emission data of overhead transmission lines. Based on different time periods specified under the regulations, real-time collection and summary of carbon emission data generated by resistance loss, corona loss and insulator leakage loss during the operation of overhead transmission lines are carried out; S2. Create several key node detection lists, monitor the carbon emission data generated by resistance loss, corona loss and insulator leakage loss respectively in turn, record each corresponding monitoring data into the key node detection list, and record the time period of each monitoring key node; S3, using the deep isolation forest algorithm to detect the abnormal state of carbon emission data in all key node detection lists, and discarding the abnormal sampling data for preprocessing; S4. Construct a carbon emission prediction model, output the predicted sequence data of each future time point through the monitoring data in the key node detection list, and mark the time period of the safety threshold based on the safety threshold of each monitoring data; S5. Construct a key node processing model, compare the sequence data predicted by the key node detection list with the safety threshold, determine whether the key nodes are safe, output a uniformly maintained list of unsafe key nodes based on the marked time period, and feed back the processed data based on the unsafe key node list to update the three-dimensional digital twin carbon emission monitoring model of overhead transmission lines.

2. According to claim 1, a method for intelligent monitoring and analysis of carbon emissions of overhead transmission lines is characterized in that: The carbon emissions generated by resistance losses in S1 include: Install current transformers and voltage transformers at key nodes of the transmission line to monitor the current and voltage information in the line in real time. By calculating the product of current and voltage, the power loss of the line is obtained, and then the resistance loss is calculated; Carbon emissions from corona losses include: Install corona monitoring sensors and corona spectrum sensors at key nodes of transmission lines to monitor the corona discharge around the conductors in real time and obtain data on corona loss; The carbon emission data generated by insulator leakage loss includes: installing leakage current sensors on insulators at key nodes of transmission lines to monitor the leakage current on the surface of insulators in real time, thereby obtaining data on insulator leakage loss.

3. The method for intelligent monitoring and analysis of carbon emissions from overhead power transmission lines according to claim 1, characterized in that: The key nodes in S3 include: nodes at both ends of the transmission line, nodes in the middle of the transmission line, nodes where terrain changes occur in the transmission line, tower nodes, insulator nodes, and conductor nodes.

4. The method for intelligent monitoring and analysis of carbon emissions from overhead power transmission lines according to claim 1, characterized in that: The key nodes in S3 also include: Frequent fault nodes: Based on the historical operation data of the transmission line, the frequency and location of faults are analyzed through the big data clustering analysis algorithm, and the frequent fault areas are selected as key nodes; Nodes with large losses: Based on the historical operation data of the transmission line, the big data clustering analysis algorithm is used to identify nodes with large losses in the transmission line, including areas where the resistance loss, corona loss and insulator leakage loss meet the set thresholds, and such areas are selected as key nodes.

5. The method for intelligent monitoring and analysis of carbon emissions from overhead power transmission lines according to claim 1, characterized in that: S3 also includes: When constructing several binary trees for each key node detection list, the criterion for stopping the construction of the binary tree of the key node is: whether the growth length of the current binary tree is within the termination interval determined in advance based on the maximum growth length. If so, stop the construction; if not, continue the construction. The growth length represents the ratio of the number of training samples selected from the real-time monitoring data of the key node in the current binary tree node expansion on one side of the binary tree node to the number on the other side of the binary tree node.

6. The method for intelligent monitoring and analysis of carbon emissions from overhead power transmission lines according to claim 5, characterized in that: The growth length X t Indicates the number of training samples on the left side of the binary tree node selected from the real-time data of the key node detection list during the current binary tree node expansion, Y t When it represents the number of training samples selected from the real-time data of the key node detection list in the current binary tree node expansion on the right side of the binary tree node, the termination interval is [0.5, 1.2].

7. The method for intelligent monitoring and analysis of carbon emissions from overhead power transmission lines according to claim 1, characterized in that: The basis for the deep isolation forest algorithm in S3 to determine whether the carbon emission data in the key node detection list is abnormal data is: The abnormal score G(m,n) of the key node detection list m satisfies G(m,n)∈[0.9,1]. If so, the key node is an abnormal point and the sampling data of the key node is discarded; L(n) represents the average path length of the n binary trees of the constructed key node detection list point m; g(m) represents the maximum path length of the current measurement point m, and D(h(m)) represents the expectation of g(m).

8. The method for intelligent monitoring and analysis of carbon emissions from overhead power transmission lines according to claim 1, characterized in that: The construction of the carbon emission prediction model comprises: According to the historical data in the overhead transmission line carbon emission data collection cloud platform, the monitoring time period of any key monitoring node and the monitoring value of each time period are obtained; The construction of grey improved prediction model based on monitoring values ​​includes: Generating second original sequence data after first-order accumulation based on the first original sequence data; Calculating and obtaining the grey derivative corresponding to the second original sequence data; Establishing a grey differential equation, and obtaining the grey improved prediction model based on the grey differential equation; The specific implementation steps of the grey improved prediction model include: The first original sequence data is generated in chronological order based on the monitoring values ​​of each time period in the historical data, and is recorded as S0={Y0(1), Y0(2), Y0(3), ..., Y0(n)}, where S0 represents the first original sequence data, and Y0(1), Y0(2), Y0(3), ..., Y0(n) represent the monitoring values ​​of 1, 2, 3, ..., n time periods respectively; A first-order accumulation generation process is performed on the basis of the first original sequence data S0: Wherein, k=1, 2, ..., n; Y1(k) represents the element of the first original sequence data corresponding to the second original sequence data; On the basis of the second original sequence data, the grey derivative d(k) of Y1(k) is obtained, and the GM(1,1) grey differential equation is established according to the derivative d(k): d(k)+a(Y1(k)+(1-b)Y1(k-1))=c, Among them, a is the development grayscale; c is the endogenous control grayscale; Solve a and c in the GM(1,1) gray differential equation, let is the vector to be estimated, and a and c are obtained by fitting the first-order linear differential equation using the discrete first original sequence data; The output of the gray improved prediction model for the n+1th period is calculated as: Among them, Y0(n+1) is the output of the gray improved prediction model in the n+1th period, and the subsequent prediction monitoring value is output based on the gray improved prediction model; For any key node monitoring, predicted sequence data is generated, the safety threshold of each key node monitoring is obtained, and the corresponding time period in the predicted sequence data where the safety threshold is located is marked.

9. The method for intelligent monitoring and analysis of carbon emissions from overhead power transmission lines according to claim 1, characterized in that: The construction of the key node processing model in S5 includes: S51, constructing a time axis, and entering the generated predicted sequence data into the time axis; S52, obtaining all the time periods marked by monitoring, and performing safety threshold comparison and calculation on all the key nodes within the time periods marked by monitoring; S53. After the safety threshold comparison and calculation of all key nodes are completed, the maintenance time of the nodes determined as non-safety critical nodes is used as the unified maintenance node. At the same time, the unified maintenance key nodes are sorted according to the maintenance time priority level, and the ones with the highest ranking are processed first. Based on the processed data fed back by the unified maintenance key nodes, the three-dimensional digital twin carbon emission monitoring model of the overhead transmission line is updated; The process of comparing and calculating the safety threshold of the key nodes in S52 is as follows: S521: The method for obtaining the safety threshold function is as follows: obtaining historical information of carbon emission data corresponding to risks at key nodes, selecting safe data and risky data from the historical information of carbon emission data according to the physical characteristics of the carbon emission data; assigning values ​​to the data according to the size of the selected data, if the data is continuous, assigning values ​​to continuous values; if the data is discontinuous, assigning values ​​to classified values; obtaining the safety threshold function for continuous data and discontinuous data by Newton interpolation algorithm and random forest algorithm respectively; S522: If the data is continuous, the safety threshold is determined by calculating the intersection of the safety data and the risky data of the safety threshold function; if the data is discontinuous, the safety threshold is determined by calculating the safety threshold function, that is, the input and output of the random forest neural network model; the input and output of the random forest neural network model determine the safety threshold, which means: according to the data of the corresponding values ​​of the adjacent time periods inputted into the random forest neural network model for multiple times, the type of the data is judged by the output, and the safety threshold is obtained by combining the data of the corresponding values ​​of the adjacent time periods inputted and the output; S523: Compare the predicted sequence data of the key node with the safety threshold to determine whether the key node is a safety node.

10. The method for intelligent monitoring and analysis of carbon emissions from overhead power transmission lines according to claim 9, characterized in that: The update to the three-dimensional digital twin carbon emission monitoring model of overhead transmission lines described in S53 includes: Use QGIS open source software to simulate and analyze the overhead transmission line structure model and unify the overhead transmission line structure template; Conduct parametric modeling and develop a parametric family library of overhead transmission lines. Based on the parametric family library of overhead transmission lines of various professions, initialize the data of all key nodes actually deployed, and generate a three-dimensional digital twin carbon emission monitoring model of overhead transmission lines in a parametric way; If there is a list of unsafe key nodes, the processed data will be fed back and updated to the three-dimensional digital twin carbon emission monitoring model of the overhead transmission line.

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