Power transmission line early warning method and device, computer device and storage medium

Abstract

The application relates to a power transmission line early warning method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a first forecast of a target power transmission line in a target period, and a first fault probability corresponding to each fault state of the target power transmission line under the first forecast; determining a candidate fault state corresponding to the target power transmission line based on the size relationship between the first fault probability and a first threshold value; acquiring a second forecast corresponding to a sub-period in the target period, calculating a second fault probability corresponding to the candidate fault state based on the second forecast; and obtaining early warning information of the target power transmission line in the sub-period based on each second fault probability. The method can improve the power transmission line early warning efficiency.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, computer equipment, storage medium, and computer program product for early warning of power transmission lines. Background Technology

[0002] Transmission line early warning refers to the act of sending an emergency signal to relevant departments before a natural disaster or other danger that needs to be guarded against occurs, reporting the dangerous situation, in order to prevent transmission line accidents from happening without knowledge or adequate preparation, thereby minimizing the losses caused by the accident.

[0003] In traditional techniques, early warning personnel rely on past patterns or observed potential precursors to issue early warnings for power transmission lines. This requires a long analysis time and results in low efficiency for early warning of power transmission lines. Summary of the Invention

[0004] Therefore, it is necessary to provide a transmission line early warning method, device, computer equipment, computer-readable storage medium, and computer program product that can improve the efficiency of transmission line early warning in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for early warning of power transmission lines. The method includes:

[0006] Obtain a first forecast of the target transmission line within a target time period, and a first fault probability corresponding to each fault state of the target transmission line under the first forecast;

[0007] Based on the relationship between the first fault probability and the first threshold, the candidate fault state corresponding to the target transmission line is determined;

[0008] Obtain the second forecast corresponding to the sub-time period within the target time period, and calculate the second fault probability corresponding to the candidate fault state based on the second forecast;

[0009] Based on each of the second fault probabilities, early warning information for the target transmission line in the sub-period is obtained.

[0010] In one embodiment, calculating the second fault probability corresponding to the candidate fault state based on the second forecast includes:

[0011] Obtain the target component category corresponding to the candidate fault state, and the target component identifier of each target component of the target component category in the target transmission line;

[0012] Obtain the parameter information of the target element corresponding to the target element identifier, and calculate the component failure probability corresponding to each target element based on the second prediction and parameter information;

[0013] Based on the fault probabilities of each of the aforementioned components, the second fault probability of the target transmission line corresponding to the candidate fault state is calculated.

[0014] In one embodiment, calculating the component failure probability corresponding to each of the target components based on the second forecast and parameter information includes:

[0015] If the second forecast includes a target weather forecast that causes the target component to fail, then the component failure probability corresponding to the target component is calculated based on the target weather forecast and the parameter information.

[0016] If the second forecast includes at least two types of target weather forecasts that could cause the target component to fail, then for each type of target weather forecast, the single failure probability of the target component under the target weather forecast is calculated, and based on the single failure probability corresponding to each target weather forecast, the component failure probability corresponding to the target component is obtained.

[0017] In one embodiment, obtaining the component failure probability corresponding to the target component based on the single failure probability corresponding to each of the target forecast weather includes:

[0018] Based on the single failure probability of the target component under each of the target weather forecasts, the single normal probability of the target component under each of the target weather forecasts is obtained; the sum of the single failure probability and the single normal probability of the target component under the same target weather forecast is 1.

[0019] The element normal probability of the same target element is obtained by fusing the single normal probabilities corresponding to each of the target forecast weather conditions.

[0020] Based on the normal component probability of the target component, the component failure probability corresponding to the target component is obtained.

[0021] In one embodiment, the transmission line early warning method further includes:

[0022] Obtain the identifiers of candidate transmission lines in the target area, as well as the node load, generator output, and branch transmission power of each node in the candidate transmission line corresponding to the identifier.

[0023] Based on the node load, generator output and branch transmission power of each node, the load loss of each node is calculated.

[0024] The total load loss of the candidate transmission line is obtained by accumulating the load losses corresponding to each node of the candidate transmission line.

[0025] Candidate transmission lines whose total load loss exceeds the loss threshold are identified as target transmission lines in the target area.

[0026] In one embodiment, the transmission line early warning method further includes:

[0027] Based on each of the candidate fault states, the set of fault states of the target transmission line under the second forecast corresponding to the current sub-time period is obtained;

[0028] Based on the second forecast corresponding to the current sub-period, calculate the reference fault probability corresponding to the non-candidate fault state;

[0029] The reference fault probability is compared with the first threshold, and non-candidate fault states whose reference fault probability is greater than the first threshold are determined as new candidate fault states.

[0030] The fault state set is updated based on the newly added candidate fault states to obtain an updated fault state set, which is used as the fault state set under the second forecast corresponding to the next sub-period.

[0031] In one embodiment, obtaining the early warning information for the target transmission line in the sub-period based on each of the second fault probabilities includes:

[0032] The second fault probabilities are sorted to obtain the fault probability sorting result;

[0033] Based on the fault probability ranking results, the early warning fault status of the target transmission line under the second forecast is determined;

[0034] Based on the aforementioned early warning fault status, early warning information for the target transmission line in the sub-period is obtained.

[0035] Secondly, this application also provides a transmission line early warning device. The device includes:

[0036] The acquisition module is used to acquire a first forecast of the target transmission line within a target time period, and a first fault probability corresponding to each fault state of the target transmission line under the first forecast.

[0037] The comparison module is used to determine the candidate fault state corresponding to the target transmission line based on the relationship between the first fault probability and the first threshold.

[0038] The calculation module is used to obtain the second forecast corresponding to the sub-time period within the target time period, and calculate the second fault probability corresponding to the candidate fault state based on the second forecast;

[0039] The early warning module is used to obtain early warning information for the target transmission line in the sub-period based on each of the second fault probabilities.

[0040] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0041] Obtain a first forecast of the target transmission line within a target time period, and a first fault probability corresponding to each fault state of the target transmission line under the first forecast;

[0042] Based on the relationship between the first fault probability and the first threshold, the candidate fault state corresponding to the target transmission line is determined;

[0043] Obtain the second forecast corresponding to the sub-time period within the target time period, and calculate the second fault probability corresponding to the candidate fault state based on the second forecast;

[0044] Based on each of the second fault probabilities, early warning information for the target transmission line in the sub-period is obtained.

[0045] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0046] Obtain a first forecast of the target transmission line within a target time period, and a first fault probability corresponding to each fault state of the target transmission line under the first forecast;

[0047] Based on the relationship between the first fault probability and the first threshold, the candidate fault state corresponding to the target transmission line is determined;

[0048] Obtain the second forecast corresponding to the sub-time period within the target time period, and calculate the second fault probability corresponding to the candidate fault state based on the second forecast;

[0049] Based on each of the second fault probabilities, early warning information for the target transmission line in the sub-period is obtained.

[0050] The aforementioned transmission line early warning method, device, computer equipment, storage medium, and computer program product acquire a first forecast of the target transmission line within a target time period, and a first fault probability corresponding to each fault state of the target transmission line under the first forecast. Based on the relationship between the first fault probability and a first threshold, candidate fault states corresponding to the target transmission line are determined. Then, a second forecast corresponding to a sub-time period within the target time period is acquired. Based on the second forecast, a second fault probability corresponding to the candidate fault state is calculated. Based on each second fault probability, early warning information of the target transmission line in the sub-time period is obtained. By identifying the first fault corresponding to each fault state of the target transmission line under the first forecast, candidate fault states of the target transmission line are determined. Then, based on the second forecast corresponding to the sub-time period, the second fault probability corresponding to the candidate fault states of the target transmission line is calculated. The early warning information of the target transmission line in the sub-time period is determined based on the second fault probability. By identifying candidate fault states from each fault state as the fault states that the target transmission line needs to pay attention to in the target time period, and by only calculating the second fault probability corresponding to the candidate fault states in each sub-time period, the early warning information corresponding to each sub-time period can be determined, which reduces the amount of calculation in each sub-time period, shortens the time to obtain the early warning information of the transmission line in each sub-time period, and improves the efficiency of transmission line early warning. Attached Figure Description

[0051] Figure 1 This is an application environment diagram of the transmission line early warning method in one embodiment;

[0052] Figure 2 This is a flowchart illustrating a transmission line early warning method in one embodiment;

[0053] Figure 3 This is a flowchart illustrating the second fault probability calculation step in one embodiment;

[0054] Figure 4 This is a flowchart illustrating the steps for determining the target transmission line in one embodiment;

[0055] Figure 5 This is a flowchart illustrating the steps for updating the candidate fault state set in one embodiment;

[0056] Figure 6 This is a flowchart illustrating a transmission line early warning method in one embodiment;

[0057] Figure 7 This is a structural block diagram of a power transmission line early warning device in one embodiment;

[0058] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0060] The transmission line early warning method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated on server 104 or placed on the cloud or other network servers. Both the terminal and the server can be used independently to execute the transmission line early warning method provided in this embodiment. The terminal and server can also be used collaboratively to execute the transmission line early warning method provided in this embodiment. For example, a computer device obtains a first forecast of the target transmission line within a target time period, and a first fault probability corresponding to each fault state of the target transmission line under the first forecast. Based on the relationship between the first fault probability and a first threshold, it determines the candidate fault state corresponding to the target transmission line. Then, it obtains a second forecast corresponding to a sub-time period within the target time period, calculates the second fault probability corresponding to the candidate fault state based on the second forecast, and obtains the early warning information of the target transmission line in the sub-time period based on each second fault probability. The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0061] In one embodiment, such as Figure 2 As shown, a transmission line early warning method is provided. This method can be applied to computer equipment, which can be a terminal or a server. The method can be executed independently by the terminal or server, or it can be implemented through interaction between the terminal and the server. This embodiment illustrates the application of this method to a computer device, including steps 202 to 208.

[0062] Step 202: Obtain the first forecast of the target transmission line within the target time period, and the first fault probability corresponding to each fault state of the target transmission line under the first forecast.

[0063] Transmission lines refer to high-voltage power lines that transmit electricity. Transmission lines consist of poles, conductors, insulator strings, and lead wires, and are erected above the ground. Based on the nature of the transmitted current, transmission lines are divided into AC transmission lines and DC transmission lines. A target time period refers to a specific time interval. It can be understood as a future time period, which is a longer period than a sub-time period. For example, the target time period might be 12 hours after 12:00, or from 12:00 to 24:00. The first forecast refers to the weather forecast for the target time period. A weather forecast is the result of analyzing and predicting the future weather of a specific region based on the basic theories and techniques of atmospheric science. The first forecast includes, but is not limited to, weather, air pressure, wind direction, humidity, precipitation, and air quality. Fault status refers to the type of fault in a transmission line. Fault status can be classified according to the components that cause the fault, such as lead wire faults, pole / tower faults, insulator string faults, etc. The first fault probability refers to the probability that the target transmission line will experience a fault status under the first forecast. The probability of failure is related to factors such as the first prediction, the location of the component installation, and the service life of the component.

[0064] For example, the computer device obtains a first forecast of the target transmission line within a target time period, and then calculates the first fault probability corresponding to each fault state of the target transmission line under the first forecast based on the first forecast.

[0065] In one embodiment, the computer device acquires a first forecast of the target transmission line within a target time period, and the fault component category corresponding to the target fault state; then, it acquires the fault component identifier of the fault component category in the target transmission line, and the fault component parameter corresponding to each fault component identifier; for each fault component identifier, based on the first forecast and the fault component parameter corresponding to the fault component identifier, it calculates the first component probability of the fault component corresponding to the fault component identifier; based on the first component probability corresponding to each fault component identifier, it obtains the first fault probability corresponding to the target fault state of the target transmission line under a first forecast.

[0066] Step 204: Based on the relationship between the first fault probability and the first threshold, determine the candidate fault state corresponding to the target transmission line.

[0067] Here, the first threshold refers to the threshold value of the failure probability. It can be understood as the minimum failure probability corresponding to determining the failure state as a candidate failure state.

[0068] For example, the computer device compares the first fault probability corresponding to each fault state with a first threshold. If the first fault probability corresponding to the fault state is greater than or equal to the first threshold, the fault state is determined as a candidate fault state.

[0069] In one embodiment, the computer device determines candidate fault states corresponding to the target transmission line based on the relationship between a first fault probability and a threshold interval. If the first fault probability corresponding to the fault state falls within the threshold interval, the fault state is determined as a candidate fault state.

[0070] In one embodiment, the computer device obtains the standard threshold corresponding to the transmission line, obtains the first threshold of the target transmission line under the first forecast based on the first forecast and the standard threshold, and determines the candidate fault state corresponding to the target transmission line based on the relationship between the first fault probability and the first threshold.

[0071] Step 206: Obtain the second forecast corresponding to the sub-time period within the target time period, and calculate the second fault probability corresponding to the candidate fault state based on the second forecast.

[0072] A sub-period refers to a time period within the target period. The target period can be divided into multiple sub-periods, each with a duration that may be equal or unequal. For example, if the target period is 12:00-24:00, the sub-periods could be 12:00-16:00, 16:00-20:00, and 20:00-24:00. The second forecast refers to the weather forecast for the sub-period. The second fault probability refers to the probability that a transmission line will experience a candidate fault state under the second forecast.

[0073] For example, the computer device obtains a second forecast corresponding to a sub-period within the target time period, and calculates a second fault probability of the target transmission line under the second forecast based on the second forecast.

[0074] Step 208: Based on each second fault probability, obtain the early warning information of the target transmission line in the sub-period.

[0075] Warning information refers to alert information. Warning information includes, but is not limited to, warning elements, warning levels, preventive measures, and the location of the target transmission line.

[0076] For example, the computer device compares the probabilities of each second fault, obtains the comparison results, and then obtains the early warning information of the target transmission line in the sub-period based on the comparison results.

[0077] In one embodiment, the computer device compares the probabilities of each second fault to obtain the highest second fault probability, determines the candidate fault state corresponding to the highest second fault probability as the target fault state, obtains the fault component category corresponding to the target fault state, and obtains the early warning information of the target transmission line in the sub-period based on the fault component category.

[0078] In the aforementioned transmission line early warning method, candidate fault states of the target transmission line are determined by identifying the first faults corresponding to each fault state under the first forecast. Then, based on the second forecast corresponding to the sub-time period, the second fault probability corresponding to the candidate fault states of the target transmission line is calculated. The early warning information of the target transmission line in the sub-time period is determined based on the second fault probability. By identifying candidate fault states from each fault state as the fault states that the target transmission line needs to pay attention to during the target time period, and by calculating the second fault probability corresponding to the candidate fault states in each sub-time period, the early warning information corresponding to each sub-time period can be determined. This reduces the amount of calculation for each sub-time period, shortens the time to obtain transmission line early warning information in each sub-time period, and improves the efficiency of transmission line early warning.

[0079] In one embodiment, such as Figure 3 As shown, the second fault probability corresponding to the candidate fault state calculated based on the second forecast includes:

[0080] Step 302: Obtain the target component category corresponding to the candidate fault state, and the target component identifier of each target component in the target component category of the target transmission line.

[0081] The target component category refers to the type of component that causes the candidate fault state to occur. For example, the target component category could be poles, conductors, insulator strings, and current-carrying wires, etc. The target component refers to each component of the target component category within the target transmission line. The target component identifier is a string representing the target component. The target component identifier can consist of letters, numbers, symbols, etc.

[0082] For example, the computer device obtains the target component category corresponding to the candidate fault state, and then obtains the target component identifier of each target component of the target component category in the target transmission line.

[0083] In one embodiment, the computer device obtains the target component category corresponding to the candidate fault state and the record table corresponding to the target transmission line. The record table includes information such as the component identifier, component category and component parameters corresponding to each component in the target transmission line. Then, it obtains all target component identifiers corresponding to the target component category from the record table corresponding to the target transmission line.

[0084] Step 304: Obtain the parameter information of the target element corresponding to the target element identifier, and calculate the component failure probability corresponding to each target element based on the second prediction and parameter information.

[0085] Here, parameter information refers to parameters related to the target component. Parameter information includes, but is not limited to, component installation location, component installation height, component service life, and component brand. Component failure probability refers to the probability that the target component will fail under the second forecast.

[0086] For example, the computer device obtains the parameter information of the target element corresponding to the target element identifier, and then calculates the component failure probability corresponding to the target element based on the second prediction and the parameter information corresponding to the target element.

[0087] Step 306: Based on the fault probabilities of each component, calculate the second fault probability corresponding to the candidate fault state of the target transmission line.

[0088] For example, the computer device calculates the component failure probability corresponding to each target component to obtain the second failure probability of the target transmission line under the second forecast of the candidate failure state.

[0089] In one embodiment, the computer device calculates the non-fault probability of the target element based on the component failure probability of the target element, then multiplies the non-fault probabilities of each target element to obtain the second non-fault probability corresponding to the non-fault state of the target transmission line under the second forecast, and finally obtains the second fault probability of the target transmission line occurring in the candidate fault state under the second forecast based on the second non-fault probability.

[0090] In one embodiment, the target transmission line includes n target elements, and the second fault probability P of the target transmission line occurring in a candidate fault state under the second prediction is... 输电线路 for:

[0091]

[0092] Where pi is the component failure probability of the i-th target component under the second forecast, and n is the number of target components in the target transmission line.

[0093] In this embodiment, the target element identifier and the corresponding parameter information in the target transmission line are determined by the target element category corresponding to the candidate fault state. Based on the second forecast and the parameter information, the component fault probability corresponding to the target element under the second forecast is calculated. Then, based on the component fault probability of the target element in the target transmission line, the second fault probability of the target transmission line occurring in the candidate fault state under the second forecast is calculated. The calculation of the second fault probability is based on real and accurate data, which improves the accuracy of the second fault probability calculation.

[0094] In one embodiment, calculating the component failure probability corresponding to each target component based on the second forecast and parameter information includes:

[0095] If the second forecast includes a target weather forecast that causes the target component to fail, then the component failure probability corresponding to the target component is calculated based on the target weather forecast and parameter information; if the second forecast includes at least two target weather forecasts that cause the target component to fail, then for each target weather forecast, the single failure probability of the target component under the target weather forecast is calculated, and the component failure probability corresponding to the target component is obtained based on the single failure probability corresponding to each target weather forecast.

[0096] The target forecast weather refers to weather conditions that may cause the target component to malfunction. The second forecast includes at least one target forecast weather condition. For example, the target forecast weather could be one of a typhoon, heavy rain, hail, or snow. The single failure probability refers to the probability that the target component will malfunction under a single target forecast weather condition.

[0097] For example, the computer device obtains the target forecast weather from the second forecast. If there is only one target forecast weather, it calculates the component failure probability of the target component under the second forecast based on the parameter information corresponding to the target component and the target forecast weather. If there are two or more target forecast weathers, it calculates the single failure probability of the target component under each forecast weather for each target forecast weather. Then, based on the single failure probability of the target component under each target forecast weather, it obtains the component failure probability corresponding to the target component.

[0098] In one embodiment, the computer device obtains a target forecast weather reference table, then obtains candidate forecast weather in the second forecast, and determines the target forecast weather in the second forecast based on the target forecast weather reference table and the candidate forecast weather.

[0099] In this embodiment, the component failure probability calculation method of the target component is determined based on the number of target forecast weather conditions, which improves the accuracy of component failure probability calculation.

[0100] In one embodiment, the component failure probability corresponding to each target component is obtained based on the single failure probability corresponding to the forecast weather for each target, including:

[0101] Based on the single failure probability of the target component under each target weather forecast, the single normal probability of the target component under each target weather forecast is obtained; the sum of the single failure probability and the single normal probability of the target component under the same target weather forecast is 1; the single normal probabilities of the same target component under each target weather forecast are fused to obtain the component normal probability of the target component; based on the component normal probability of the target component, the component failure probability of the target component is obtained.

[0102] Among them, the single normal probability refers to the probability that the target component will not malfunction under the target forecast weather.

[0103] For example, the computer device subtracts the single failure probability of the target component under the target forecast weather from 1 to obtain the single normal probability of the target component under the target forecast weather. Then, it multiplies the single normal probabilities of the same target component under various target forecast weather conditions to obtain the component normal probability of the target component. Finally, it subtracts the component normal probability of the target component from 1 to obtain the component failure probability of the target component.

[0104] In one embodiment, the second forecast includes two target weather forecasts, and the component failure probability P corresponding to the target component. 元件 for:

[0105] P 元件 =1-(1-P1)(1-P2) Formula (2)

[0106] Wherein, P1 is the single failure probability of the component under the first target weather forecast among the two target weather forecasts, and P2 is the single failure probability of the component under the second target weather forecast among the two target weather forecasts.

[0107] In this embodiment, the normal probability of the target element is obtained based on the single normal probability corresponding to the target element under the target forecast weather. Then, based on the normal probability of the target element, the failure probability of the target element is obtained, which improves the accuracy of the component failure probability calculation.

[0108] In one embodiment, such as Figure 4 As shown, the early warning methods for transmission lines also include:

[0109] Step 402: Obtain the candidate transmission line identifiers in the target area, as well as the node load, generator output, and branch transmission power of each node in the candidate transmission line corresponding to the candidate transmission line identifier.

[0110] The target area refers to the region to be warned. It can be understood as the area containing the target transmission line to be warned. Candidate transmission lines are the transmission lines included in the target area. The target area must include at least one candidate transmission line. The candidate transmission line identifier is a string representing the candidate transmission line. A node is the connection point of two or more conductors in a transmission line. It can be understood as the connection point between the main line and a branch line. Node load refers to the total electrical power supplied by a node to the electrical equipment of a power user at a given moment. Generator output refers to the electrical energy generated by a generator per unit time. Branch transmission power refers to the electrical power supplied by a branch line to the electrical equipment of a power user at a given moment.

[0111] For example, the computer device obtains the candidate transmission line identifiers in the target area, as well as the identifiers of each node in the candidate transmission line corresponding to the candidate transmission line identifiers, and then obtains the node load, generator output and branch transmission power corresponding to each node identifier.

[0112] Step 404: Based on the node load, generator output and branch transmission power of each node, calculate the load loss of each node.

[0113] Load loss refers to the loss caused by a fault in the transmission line, which prevents the node from providing power to the electrical equipment.

[0114] For example, for each node, the computer device calculates the node load, generator output, and branch transmission power corresponding to the node to obtain the load loss corresponding to the node.

[0115] In one embodiment, the computer device subtracts the generator output from the node load to obtain a first load loss, and then adds the transmission power of each branch to the first load loss to obtain the load loss corresponding to the node.

[0116] In one embodiment, for each node, the computer device adds up the transmission power of each branch corresponding to the node to obtain the branch load loss of the node, then subtracts the generator output corresponding to the node from the node load to obtain the main load loss of the node, and then adds the main load loss of the node to the branch load loss of the node to obtain the load loss of the node.

[0117] In one embodiment, the load loss p of node i i for:

[0118]

[0119] p Zij =B ij *(a ij1 -a ij2 ) Formula (4)

[0120] Where, p Ii Let p be the node load of node i. Gi For the generator output at node i, p Zij Let B be the branch transmission power of the j-th branch of node i, m be the number of branches connected to node i, and B be the branch transmission power of the j-th branch of node i. ij Let a be the admittance of the j-th branch of node i. ij1 Let a be the voltage phase angle at one end of the j-th branch of node i. ij2 Let be the voltage phase angle at the other end of the j-th branch of node i.

[0121] Step 406: Accumulate the load losses corresponding to each node of the candidate transmission line to obtain the total load loss of the candidate transmission line.

[0122] Total load loss refers to the total loss caused by the inability of transmission lines to provide power to electrical equipment.

[0123] For example, the computer device obtains the identifiers of each node included in the candidate transmission line, then obtains the load loss corresponding to each node identifier, and adds up the load losses to obtain the total load loss of the candidate transmission line.

[0124] In one embodiment, the total load loss L of the candidate transmission line is:

[0125]

[0126] Where, p i Let represent the load loss of the i-th node, and n represent the number of nodes in the candidate transmission line.

[0127] Step 408: Candidate transmission lines with total load loss greater than the loss threshold are identified as target transmission lines in the target area.

[0128] The loss threshold refers to the minimum load loss required to identify a candidate transmission line as the target transmission line.

[0129] For example, the computer device compares the load loss corresponding to the candidate transmission line with a loss threshold, and identifies the candidate transmission line whose load loss is greater than the loss threshold as the target transmission line.

[0130] In this embodiment, the load loss of each candidate transmission line is calculated, and the target candidate transmission line is determined based on the load loss of the candidate transmission lines. Only the target candidate transmission line needs to be determined once, instead of selecting the target transmission line in each sub-time period, which reduces the amount of calculation and improves the efficiency of obtaining early warning information.

[0131] In one embodiment, such as Figure 5 As shown, the early warning methods for transmission lines also include:

[0132] Step 502: Based on each candidate fault state, obtain the set of fault states of the target transmission line under the second forecast in the current sub-period.

[0133] The fault state set refers to the set that includes all candidate fault states.

[0134] For example, the computer device assembles the candidate fault states into a fault state set, which is used in the process of determining the warning information corresponding to the target transmission line in the current sub-period.

[0135] Step 504: Calculate the reference fault probability corresponding to the non-candidate fault state based on the second forecast corresponding to the current sub-period.

[0136] Non-candidate fault states refer to fault states that exist in all fault states but not in the set of fault states. They can be understood as fault states that are not candidate fault states. The reference fault probability refers to the probability that a non-candidate fault state will occur in the target transmission line under the second forecast corresponding to the current sub-time period.

[0137] For example, the computer device calculates the reference fault probability for each non-candidate fault state based on the second forecast corresponding to the current sub-period.

[0138] In one embodiment, the computer device obtains the target component identifier corresponding to the non-candidate fault state and the parameter information of the target component corresponding to the target component identifier. Then, based on the second prediction and the parameter information corresponding to the target component, it calculates the component fault probability corresponding to the target component and obtains the reference fault probability corresponding to the non-candidate fault state based on the component fault probability.

[0139] Step 506: Compare the reference fault probability with the first threshold, and determine the non-candidate fault states whose reference fault probability is greater than the first threshold as new candidate fault states.

[0140] For example, the computer device compares the reference fault probability corresponding to the non-candidate fault state with a first threshold, and determines the non-candidate fault state whose reference fault probability is greater than the first threshold as a new candidate fault state.

[0141] Step 508: Update the fault state set based on the newly added candidate fault states to obtain the updated fault state set. The updated fault state set is used as the fault state set under the second forecast corresponding to the next sub-period.

[0142] For example, the computer device adds new candidate fault states to the fault state set, resulting in an updated fault state set. The updated fault state set is then used as the fault state set for the second forecast corresponding to the next sub-period.

[0143] In this embodiment, new candidate fault states are determined based on the relationship between the reference fault probability corresponding to non-candidate fault states and a first threshold. These new candidate fault states are then added to the fault state set, resulting in an updated fault state set. This updated fault state set is used as the fault state set under the second forecast for the next sub-period. Updating the fault state set based on the second forecast for the current sub-period expands the range of candidate fault states, improves the accuracy of the warning information for the next sub-period, and updates the fault state set before its use in the next sub-period, eliminating the need for waiting and improving the efficiency of warning information generation for the next sub-period.

[0144] In one embodiment, the early warning information for the target transmission line in a sub-period, based on each second fault probability, includes:

[0145] The probabilities of each second fault are sorted to obtain the fault probability sorting result; based on the fault probability sorting result, the early warning fault state of the target transmission line under the second forecast is determined; based on the early warning fault state, the early warning information of the target transmission line in the sub-period is obtained.

[0146] For example, the computer device sorts the second fault probabilities corresponding to each candidate fault state to obtain the fault probability sorting result, then determines the early warning fault state of the target transmission line under the second forecast based on the fault probability sorting result, and finally obtains the early warning information of the target transmission line in the sub-period based on the early warning fault state.

[0147] In one embodiment, the computer device sorts the second fault probabilities corresponding to each candidate fault state to obtain a fault probability sorting result. Based on the fault probability sorting result, the device determines the highest second fault probability, takes the candidate fault state corresponding to the highest second fault probability as the warning fault state, and obtains the warning information of the target transmission line in the sub-period based on the warning fault state.

[0148] In one embodiment, the computer device sorts the second fault probabilities corresponding to each candidate fault state to obtain a fault probability sorting result. Based on the fault probability sorting result, starting from the largest second fault probability, a preset number of second fault probabilities are obtained sequentially. The candidate fault states corresponding to the obtained second fault probabilities are used as early warning fault states. Early warning information for the target transmission line in the sub-period is obtained based on the early warning fault states.

[0149] In this embodiment, the warning fault status is determined based on the fault probability ranking result, and the warning information of the target transmission line in the sub-time period is obtained based on the warning fault status. The generation process of the warning information has less computation and improves the generation efficiency of the warning information.

[0150] In an exemplary embodiment, a structural block diagram for generating early warning information for a target transmission line during a typhoon and heavy rain is shown below. Figure 6 As shown, it includes the following steps:

[0151] Identify the target transmission line in the target area. Obtain the identifiers of candidate transmission lines in the target area, as well as the identifiers of each node in the candidate transmission line corresponding to the identifiers of candidate transmission lines. Then, obtain the node load and generator output corresponding to each node identifier, as well as the admittance of each branch and the voltage phase angle at both ends of each branch. Calculate the branch transmission power of each branch according to formula (4). Calculate the node load, generator output and branch transmission power corresponding to each node according to formula (3) to obtain the load loss corresponding to each node. Add the load losses corresponding to each node according to formula (5) to obtain the total load loss of the candidate transmission line. Compare the load loss corresponding to the candidate transmission line with the loss threshold. The candidate transmission line with a load loss greater than the loss threshold is identified as the target transmission line.

[0152] Determine the candidate fault state set. Obtain the short-term forecast of the target area (short-term forecast refers to the description of weather parameters from 0 to 12 hours in the future). The short-term forecast includes two target weather forecasts: typhoon and rainstorm. Take typhoon as the first target weather forecast and rainstorm as the second target weather forecast. For each target weather forecast, calculate the single fault probability of the target element under the target weather forecast to obtain the typhoon fault probability and the rainstorm fault probability. Calculate the element fault probability of the target element according to formula (2). Then, obtain the first fault probability corresponding to various fault states of the transmission line under the near-term forecast according to formula (1). Compare the first fault probability corresponding to each fault state with the first threshold. If the first fault probability corresponding to the fault state is greater than or equal to the first threshold, the fault state is determined as a candidate fault state, and the candidate fault states are combined into a candidate fault state set.

[0153] Determine the early warning information for the target transmission line under short-term forecasts. Obtain the nowcast for the target area (nowcast refers to the description of weather parameters for the next 0 to 2 hours). Based on the nowcast, calculate the second fault probability of the target transmission line under the candidate fault state under the nowcast. Compare the various second fault probabilities, obtain the highest second fault probability, determine the candidate fault state corresponding to the highest second fault probability as the target fault state, obtain the fault component category corresponding to the target fault state, and obtain the early warning information for the target transmission line in the near-term based on the fault component category.

[0154] Update the candidate fault state set. Obtain the target component identifier corresponding to the non-candidate fault state, and the parameter information of the target component corresponding to the target component identifier. Then, based on the nowcast and the parameter information corresponding to the target component, calculate the component fault probability corresponding to the target component. Based on the component fault probability, obtain the reference fault probability corresponding to the non-candidate fault state of the target transmission line under the nowcast. Compare the reference fault probability corresponding to the non-candidate fault state with a first threshold. Non-candidate fault states with a reference fault probability greater than the first threshold are identified as new candidate fault states and added to the fault state set to obtain the updated fault state set.

[0155] Obtain the next nearest forecast for the target area, and repeat the above steps until the warning information for the last nearest period in the short-term forecast is obtained.

[0156] In this embodiment, candidate fault states of the target transmission line are determined by identifying the first faults corresponding to each fault state under the first forecast. Then, based on the second forecast corresponding to the sub-time period, the second fault probability corresponding to the candidate fault states of the target transmission line is calculated. The warning information of the target transmission line in the sub-time period is determined based on the second fault probability. Candidate fault states are identified from each fault state as the fault states that need to be monitored by the target transmission line in the target time period. Each sub-time period only needs to calculate the second fault probability corresponding to the candidate fault state to determine the warning information corresponding to each sub-time period, which reduces the amount of calculation for each sub-time period, shortens the time to obtain the transmission line warning information for each sub-time period, and improves the efficiency of transmission line warning.

[0157] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0158] Based on the same inventive concept, this application also provides a transmission line early warning device for implementing the aforementioned transmission line early warning method. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more embodiments of the transmission line early warning device provided below can be found in the limitations of the transmission line early warning method described above, and will not be repeated here.

[0159] In one embodiment, such as Figure 7 As shown, a transmission line early warning device is provided, comprising: an acquisition module 702, a comparison module 704, a calculation module 706, and an early warning module 708, wherein:

[0160] The acquisition module 702 is used to acquire the first forecast of the target transmission line within the target time period, and the first fault probability corresponding to each fault state of the target transmission line under the first forecast.

[0161] Comparison module 704 is used to determine the candidate fault state corresponding to the target transmission line based on the relationship between the first fault probability and the first threshold.

[0162] The calculation module 706 is used to obtain the second forecast corresponding to the sub-period within the target time period, and calculate the second fault probability corresponding to the candidate fault state based on the second forecast;

[0163] The early warning module 708 is used to obtain early warning information for the target transmission line in the sub-period based on each second fault probability.

[0164] In one embodiment, the calculation module 706 is further configured to: obtain the target component category corresponding to the candidate fault state, and the target component identifier of each target component of the target component category in the target transmission line; obtain the parameter information of the target component corresponding to the target component identifier, and calculate the component fault probability corresponding to each target component based on the second prediction and parameter information; and calculate the second fault probability of the target transmission line corresponding to the candidate fault state based on the fault probability of each component.

[0165] In one embodiment, the calculation module 706 is further configured to: if the second forecast includes a target forecast weather that causes the target component to fail, calculate the component failure probability corresponding to the target component based on the target forecast weather and parameter information; if the second forecast includes at least two target forecast weathers that cause the target component to fail, calculate the single failure probability of the target component under the target forecast weather for each target forecast weather, and obtain the component failure probability corresponding to the target component based on the single failure probability corresponding to each target forecast weather.

[0166] In one embodiment, the calculation module 706 is further configured to: obtain the single normal probability of the target element under each target forecast weather condition based on the single failure probability of the target element under each target forecast weather condition; the sum of the single failure probability and the single normal probability of the target element under the same target forecast weather condition is 1; fuse the single normal probabilities of the same target element under each target forecast weather condition to obtain the element normal probability of the target element; and obtain the element failure probability of the target element based on the element normal probability of the target element.

[0167] In one embodiment, the transmission line early warning device further includes a selection module, which is used to: acquire candidate transmission line identifiers in the target area, and the node load, generator output, and branch transmission power corresponding to each node in the candidate transmission line corresponding to the candidate transmission line identifier; calculate the load loss corresponding to each node based on the node load, generator output, and branch transmission power corresponding to each node; accumulate the load losses corresponding to each node included in the candidate transmission line to obtain the total load loss of the candidate transmission line; and determine the candidate transmission line with a total load loss greater than the loss threshold as the target transmission line in the target area.

[0168] In one embodiment, the transmission line early warning device further includes an update module, which is configured to: obtain a set of fault states of the target transmission line under the second forecast corresponding to the current sub-period based on each candidate fault state; calculate the reference fault probability corresponding to the non-candidate fault state based on the second forecast corresponding to the current sub-period; compare the reference fault probability with a first threshold, and determine the non-candidate fault states whose reference fault probability is greater than the first threshold as newly added candidate fault states; update the fault state set based on the newly added candidate fault states to obtain an updated fault state set, which is used as the set of fault states under the second forecast corresponding to the next sub-period.

[0169] In one embodiment, the early warning module 708 is further configured to: sort the various second fault probabilities to obtain a fault probability sorting result; determine the early warning fault state of the target transmission line under the second forecast based on the fault probability sorting result; and obtain the early warning information of the target transmission line in the sub-period based on the early warning fault state.

[0170] Each module in the aforementioned transmission line early warning device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0171] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a power transmission line early warning method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0172] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0173] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0174] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0175] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0176] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0177] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0178] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0179] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for early warning of power transmission lines, characterized in that, The method includes: Obtain a first forecast for the target transmission line within a target time period, and a first fault probability corresponding to each fault state of the target transmission line under the first forecast; the first forecast refers to the weather forecast for the target time period. Based on the relationship between the first fault probability and the first threshold, the candidate fault state corresponding to the target transmission line is determined; The process involves: obtaining a second forecast corresponding to a sub-period within the target time period; obtaining the target component category corresponding to the candidate fault state; and obtaining the target component identifier of each target component of the target component category in the target transmission line. The sub-period refers to a time period within the target time period. The process also involves obtaining parameter information of the target component corresponding to the target component identifier. If the second forecast includes a target weather forecast that causes the target component to fail, the process calculates the component failure probability corresponding to the target component based on the target weather forecast and the parameter information. If the second forecast includes at least two target weather forecasts that cause the target component to fail, the process calculates the single failure probability of the target component under each target weather forecast. Based on the single failure probabilities corresponding to each target weather forecast, the process obtains the component failure probability corresponding to the target component. Based on the component failure probabilities, the process calculates the second failure probability of the target transmission line corresponding to the candidate fault state. The probabilities of each second fault are compared to obtain the highest second fault probability. The candidate fault state corresponding to the highest second fault probability is determined as the target fault state. The fault component category corresponding to the target fault state is obtained. Based on the fault component category, the early warning information of the target transmission line in the sub-period is obtained. Based on each of the candidate fault states, a set of fault states for the target transmission line under the second forecast corresponding to the current sub-period is obtained; based on the second forecast corresponding to the current sub-period, a reference fault probability corresponding to a non-candidate fault state is calculated; the reference fault probability is compared with a first threshold, and non-candidate fault states with a reference fault probability greater than the first threshold are determined as new candidate fault states; the set of fault states is updated based on the new candidate fault states to obtain an updated set of fault states, which is used as the set of fault states under the second forecast corresponding to the next sub-period.

2. The method according to claim 1, characterized in that, The first forecast includes weather, air pressure, wind direction, humidity, precipitation, and air quality.

3. The method according to claim 1, characterized in that, The target time period is divided into multiple sub-time periods, each with a different duration.

4. The method according to claim 1, characterized in that, The process of obtaining the component failure probability corresponding to the target component based on the single failure probability corresponding to each of the target forecast weather includes: Based on the single failure probability of the target component under each of the target weather forecasts, the single normal probability of the target component under each of the target weather forecasts is obtained; the sum of the single failure probability and the single normal probability of the target component under the same target weather forecast is 1. The element normal probability of the same target element is obtained by fusing the single normal probabilities corresponding to each of the target forecast weather conditions. Based on the normal component probability of the target component, the component failure probability corresponding to the target component is obtained.

5. The method according to claim 1, characterized in that, The method further includes: Obtain the identifiers of candidate transmission lines in the target area, as well as the node load, generator output, and branch transmission power of each node in the candidate transmission line corresponding to the identifier. Based on the node load, generator output and branch transmission power of each node, the load loss of each node is calculated. The total load loss of the candidate transmission line is obtained by accumulating the load losses corresponding to each node of the candidate transmission line. Candidate transmission lines whose total load loss exceeds the loss threshold are identified as target transmission lines in the target area.

6. The method according to claim 1, characterized in that, The step of calculating the reference fault probability corresponding to the non-candidate fault state based on the second forecast corresponding to the current sub-time period includes: Obtain the target component identifier corresponding to the non-candidate fault state, and the parameter information of the target component corresponding to the target component identifier; Based on the second forecast and the parameter information corresponding to the target component, the component failure probability corresponding to the target component is calculated; Based on the component failure probability, the reference failure probability corresponding to the non-candidate failure state is obtained.

7. The method according to claim 1, characterized in that, The step of determining the candidate fault state corresponding to the target transmission line based on the relationship between the first fault probability and the first threshold includes: Obtain the standard threshold corresponding to the target transmission line; Based on the first forecast and the standard threshold, a first threshold for the target transmission line under the first forecast is obtained; Based on the relationship between the first fault probability and the first threshold, the candidate fault state corresponding to the target transmission line is determined.

8. A transmission line early warning device, characterized in that, The device includes: The acquisition module is used to acquire a first forecast of the target transmission line within a target time period, and a first fault probability corresponding to each fault state of the target transmission line under the first forecast; the first forecast refers to the weather forecast within the target time period. The comparison module is used to determine the candidate fault state corresponding to the target transmission line based on the relationship between the first fault probability and the first threshold. The calculation module is used to obtain a second forecast corresponding to a sub-period within the target time period, obtain the target component category corresponding to the candidate fault state, and the target component identifier of each target component of the target component category in the target transmission line; the sub-period refers to a time period within the target time period; obtain parameter information of the target component corresponding to the target component identifier; if the second forecast includes a target forecast weather that causes the target component to fail, calculate the component failure probability corresponding to the target component based on the target forecast weather and the parameter information; if the second forecast includes at least two target forecast weathers that cause the target component to fail, calculate the single failure probability of the target component under the target forecast weather for each target forecast weather, obtain the component failure probability corresponding to the target component based on the single failure probability corresponding to each target forecast weather; and calculate the second failure probability of the target transmission line corresponding to the candidate fault state based on the component failure probabilities. The early warning module is used to compare each of the second fault probabilities, obtain the maximum second fault probability, determine the candidate fault state corresponding to the maximum second fault probability as the target fault state, obtain the fault component category corresponding to the target fault state, and obtain the early warning information of the target transmission line in the sub-period based on the fault component category. The update module is configured to: obtain a set of fault states of the target transmission line under the second forecast corresponding to the current sub-period based on each of the candidate fault states; calculate the reference fault probability corresponding to the non-candidate fault states based on the second forecast corresponding to the current sub-period; compare the reference fault probability with the first threshold, and determine the non-candidate fault states whose reference fault probability is greater than the first threshold as new candidate fault states; update the set of fault states based on the new candidate fault states to obtain an updated set of fault states, which is used as the set of fault states under the second forecast corresponding to the next sub-period.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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