Icing risk perception and identification method for ultra-high power transmission channel line

Abstract

The invention provides an icing risk perception and identification method for an ultra-high power transmission channel line, and belongs to the technical field of risk monitoring, and the method specifically comprises the steps: taking the line loss data of a reference date as the basis, determining the deviation condition of the line loss data of each power transmission power interval in different reference dates, and obtaining the line loss data of each power transmission power interval; screening specified dates in the reference dates based on the deviation condition, determining the change condition of the weather data in the specified dates, and determining change correlation factors of the line loss data and the weather data in different specified dates in combination with the change condition of the line loss data in each transmission power interval in the specified dates; when it is determined that the correlation date exists by using the change correlation factor, and when it is determined that the icing risk exists in the line loss abnormal area according to the matching result of the weather data of the different correlation dates and the current date, the icing risk is sensed on the basis of the weather data of the line loss abnormal area, and the line loss identification processing efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of risk monitoring, and in particular relates to a method for sensing and identifying icing risks of ultra-high-speed transmission channel lines. Background Art

[0002] Due to the complexity of the ultra-high voltage transmission line system and the diversity and variability of its external natural environment, it is susceptible to the multi-state, multi-dimensional and multi-level influences of macro and micro heterogeneous natural disasters and social disasters in different regions and their complex risk influencing factors. It is easy to cause sudden failure groups that have a major impact on the normal operation of the ultra-high voltage transmission line system - usually called "black swan type crisis event groups" and "gray rhino type crisis event groups". Ultra-high voltage transmission refers to the transmission of electric energy using a voltage level of 500 kV to 1000 kV, which is generally used for cross-regional and long-distance transmission. This makes it inevitable that the transmission channel lines will be covered with ice, seriously affecting the safety of the transmission lines.

[0003] Therefore, in order to identify the icing risk of transmission lines, in the invention patent application CN202410923288.3 "A method and device for dynamic prediction of icing risk of transmission lines based on scenario construction", a pre-trained BP neural network model is used for prediction according to the initial icing thickness to obtain the real-time predicted icing thickness, which can improve the accuracy of icing risk prediction in the power grid area. However, there are the following technical problems:

[0004] In the process of perceiving and identifying the icing risk of transmission lines, the existing technical solutions have neglected to further combine the line loss monitoring data to perceive the icing risk in different areas of the transmission lines. Generally, when icing occurs on transmission lines, it will inevitably lead to greater line losses of the transmission lines. At the same time, due to the long distance of ultra-high transmission channels, if the line loss data cannot be combined, the perception and identification efficiency of the icing risk areas will be low.

[0005] In response to the above technical problems, the present application specifically provides a method for sensing and identifying icing risks of ultra-high-speed transmission channel lines. Summary of the invention

[0006] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0007] According to one aspect of the present invention, a method for sensing and identifying icing risks of ultra-high-speed transmission channel lines is provided.

[0008] A method for sensing and identifying ice risk of ultra-high transmission channel lines, specifically comprising:

[0009] S1 divides the ultra-high power transmission channel into multiple line areas, and screens the line loss abnormal area in the line area based on the analysis result of the line loss data of the line area;

[0010] S2 determines a reference date based on weather data of the line abnormal area on different dates within a preset period, determines deviations of line loss data in each transmission power interval on different reference dates based on line loss data on the reference date, and screens a specified date in the reference date based on the deviations;

[0011] S3: determining the change of weather data on the specified date, and combining the change of line loss data of each transmission power interval on the specified date, determining the change correlation factor between line loss data and weather data on different specified dates;

[0012] S4 uses the variable correlation factor to determine that there is a correlation date, and when it is determined that there is an icing risk in the abnormal line loss area based on the matching results of the weather data of different correlation dates and the current date, the icing risk is perceived based on the weather data of the abnormal line loss area.

[0013] The beneficial effects of the present invention are:

[0014] Based on the analysis results of the line loss data of the line area, the line loss abnormal areas in the line area are screened, thereby fully considering the correlation between the icing situation and the line loss situation, and realizing the screening of the line loss abnormal areas in the line area, avoiding the technical problem of excessive complexity of the evaluation process caused by the evaluation of the icing risk of all transmission lines, and improving the processing efficiency of the evaluation and analysis of the icing risk.

[0015] According to the number of associated dates and the matching results of the weather data of the associated dates with the current date, it is determined whether there is an icing risk in the line loss abnormal area. This takes into account the number of associated dates with a higher probability of icing among the reference dates with weather temperatures similar to the current date, and also takes into account the matching results of the weather data of different associated dates with the current date. This enables accurate assessment of the icing risk in the line loss abnormal area from multiple dimensions, and lays the foundation for further differentiated assessment of icing risks.

[0016] A further technical solution is to divide the ultra-high transmission channel into multiple line areas, including:

[0017] Based on the monitoring equipment in the ultra-high power transmission channel line, the ultra-high power transmission channel lines equipped with independent monitoring equipment are divided into the same line area.

[0018] A further technical solution is that a method for screening the line loss abnormal area in the line area is:

[0019] The baseline line loss at different times is determined by taking the average value of line loss data of other line areas at different times on the current date;

[0020] Determining line loss deviation moments at different moments according to deviations between line loss data of the line area at different moments and a baseline line loss amount;

[0021] Whether the line area is a line loss abnormal area is determined based on the number of line loss deviation moments in the current date.

[0022] A further technical solution is that the line loss deviation moment is the moment when the deviation between the line loss data and the baseline line loss amount does not meet the requirement.

[0023] A further technical solution is that when the number of line loss deviation moments in the line area on the current date is greater than the number of preset deviation moments, the line area is determined to be a line loss abnormal area.

[0024] A further technical solution is to determine whether the line loss abnormal area has an ice risk, specifically including:

[0025] Obtaining the number of the associated dates, and determining the basic risk factor using the ratio of the number of the associated dates to the number of the specified dates;

[0026] According to the matching results of the weather data of different associated dates and the current date, the deviation rates of the weather data of different associated dates and the current date in different dimensions are determined, the deviation coefficients of different associated dates are determined according to the average values ​​of the deviation rates of the weather data in different dimensions, and the average value of the deviation coefficients is determined based on the average values ​​of the deviation coefficients of different associated dates;

[0027] The estimated risk coefficient of the abnormal line loss area is determined by the ratio of the basic risk coefficient to the average value of the deviation coefficient, and the estimated risk coefficient is used to determine whether there is an icing risk in the abnormal line loss area.

[0028] A further technical solution is to use the inferred risk coefficient to determine whether there is an icing risk in the line loss abnormal area, specifically including:

[0029] When the inferred risk coefficient is greater than a preset risk coefficient threshold, it is determined that there is an icing risk in the line loss abnormal area.

[0030] A further technical solution is to perceive the icing risk based on the weather data of the abnormal line loss area, specifically including:

[0031] Based on the weather data of the abnormal line loss area, determining the weather data of the abnormal line loss area at different time periods;

[0032] Weather data of different time periods are used as input, and the icing risk of the abnormal line loss area is obtained using a preset simulation model.

[0033] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0034] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0036] Figure 1 It is a flow chart of a method for sensing and identifying ice risk of ultra-high transmission channel lines;

[0037] Figure 2 is a flow chart of a method for screening an abnormal line loss area in a line area;

[0038] Figure 3 is a flow chart of a method for screening a specified date among reference dates;

[0039] Figure 4 The present invention is a flowchart of a method for determining a change correlation factor between line loss data and weather data on a specified date. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0041] Line loss abnormal area: a line area whose average deviation from line loss data in other line areas is greater than a preset deviation is regarded as a line abnormal area.

[0042] Determine whether there is an icing risk in the line loss abnormal area: determine the deviation weight coefficients for different associated dates based on the average value of the deviation rate of the weather temperature between different associated dates and the current date, determine the inferred risk coefficient based on the average value of the deviation weight coefficients for different associated dates, and regard the line abnormal area with an inferred risk coefficient greater than 0.6 as the line loss abnormal area with icing risk.

[0043] Perception of icing risk: The weather data of the line loss abnormal area is used as input, and the icing risk is determined using a preset prediction model, where the preset prediction model is constructed using the Bayesian analysis method.

[0044] Optionally, the preset model is constructed using a big data intelligent Bayesian method, specifically including:

[0045] The Bayesian analysis method provides a method for calculating the probability of a hypothesis, that is, combining the prior information about the unknown parameters with the sample information according to the Bayesian formula to obtain the posterior information, and then inferring the unknown parameters based on the posterior information. The prior information comes from previous statistical conclusions, experience or assumptions. The formula is as follows:

[0046]

[0047] In the formula, A1, ...An are mutually incompatible and are a complete event group in the sample space, P(Ai)>0, P(B)>0. The above formula is called the Bayesian formula, where P(Ai) represents the probability of Ai occurring, that is, the prior probability, which is a known probability. Therefore, the conditional probability of B occurring under the condition of Ai occurring can be calculated based on the sample information, that is, P(B|Ai). Then, according to the Bayesian formula, the probability of conditional Ai occurring under the condition of the occurrence of the result event B can be calculated P(Ai|B). This probability is the probability determined after the experiment, that is, the posterior probability.

[0048] This patented invention will combine the Bayesian method to infer and evaluate the icing risk of ultra-high voltage transmission lines. It will identify the risks in the early and mid-term, analyze the prior distribution probability of weather influencing factors, and then infer the probability of risk occurrence, providing important decision-making references for digital governance.

[0049] ① Bayesian network modeling. Construct a causal relationship diagram of disaster risk factors and use the Bayesian network to quantify the causal relationship. The joint probability distribution formula of the Bayesian network is:

[0050]

[0051] Where X i is the ith risk factor, Pa(X i ) as its parent node set.

[0052] Conditional probability calculation of ice risk. Based on historical data and expert knowledge, the conditional probability P(X i |Pa(X i )).

[0053] ②Risk level classification. Take meteorology as an example. The meteorological forecast data is connected to the model to dynamically adjust the risk prediction results. Assume that the meteorological data is W = {w 1 ,w 2 ,…,w k}, analyze future meteorological conditions through time series Combined with the Bayesian network model, the posterior probability of occurrence is calculated, that is, divided into five risk levels R(t):

[0054]

[0055] Where L i Indicates the weight of the risk level.

[0056] The risk level is divided into five levels (I to V), and the threshold {T 1 ,T 2 ,…,t 4}Achieve grading:

[0057]

[0058] ③ The prior probability is dynamically adjusted to the posterior probability

[0059] Through the dynamic adjustment of prior probability and posterior probability, the risk changes under different disaster scenarios are reflected. The posterior probability is calculated based on the Bayesian formula:

[0060]

[0061] P(H|E): The posterior probability of event H occurring after observing evidence E.

[0062] P(H): The prior probability of event H, usually set based on historical data or experience.

[0063] P(E|H): The probability of evidence E appearing under the condition that event H occurs.

[0064] P(E): The total probability of evidence E appearing.

[0065] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a method for sensing and identifying ice risk of ultra-high transmission channel lines is provided, which specifically includes:

[0066] S1 divides the ultra-high power transmission channel into multiple line areas, and screens the line loss abnormal area in the line area based on the analysis result of the line loss data of the line area;

[0067] Furthermore, the ultra-high transmission channel line is divided into multiple line areas, including:

[0068] Based on the monitoring equipment in the ultra-high power transmission channel line, the ultra-high power transmission channel lines equipped with independent monitoring equipment are divided into the same line area.

[0069] Specifically, Figure 2 As shown, the method for screening the line loss abnormal area in the line area is:

[0070] The baseline line loss at different times is determined by taking the average value of line loss data of other line areas at different times on the current date;

[0071] Determining line loss deviation moments at different moments according to deviations between line loss data of the line area at different moments and a baseline line loss amount;

[0072] Whether the line area is a line loss abnormal area is determined based on the number of line loss deviation moments in the current date.

[0073] Optionally, the line loss deviation moment is the moment when the deviation between the line loss data and the baseline line loss amount does not meet the requirement.

[0074] It should be noted that when the number of line loss deviation moments in the line area on the current date is greater than the number of preset deviation moments, the line area is determined to be a line loss abnormal area.

[0075] It should also be noted that the method for screening the line loss abnormal area in the line area is:

[0076] The baseline line loss at different times is determined by taking the average value of line loss data of other line areas at different times on the current date;

[0077] Determining line loss deviations at different times according to deviations between line loss data of the line area at different times and a baseline line loss;

[0078] Whether the line area is a line loss abnormal area is determined based on an average value of line loss deviations at different times on the current date.

[0079] Furthermore, when the average value of the line loss deviations at different times on the current date is not within a preset deviation range, the line area is determined to be an abnormal line loss area.

[0080] In another embodiment, the method for screening the line loss abnormal area in the line area is:

[0081] S11 determines preset line loss amounts at different moments according to the transmission powers corresponding to different moments, determines deviation amounts at different moments according to the deviation amounts between the line loss data and the preset line loss amounts, and determines the screening deviation moments in the moments according to the deviation amounts;

[0082] Optionally, the above step S11 includes the following contents:

[0083] S111 determines the preset line loss amount at different moments according to the transmission power corresponding to different moments, and determines the deviation amount at different moments according to the deviation amount between the line loss data and the preset line loss amount. When it is determined by the deviation amount that there is no screening deviation moment in the moment, it is determined that the line area does not belong to the line loss abnormal area. When it is determined by the deviation amount that there is a screening deviation moment in the moment, the process proceeds to step S112.

[0084] S112: obtaining the number of screening deviation moments; when the number of screening deviation moments is greater than the preset number of screening moments, determining that the line area belongs to the line loss abnormal area; when the number of screening deviation moments is not greater than the preset number of deviation moments, proceeding to step S113;

[0085] S113 determines the time periods whose number ratio is greater than the preset number ratio based on the number ratio of the screening deviation moments in different time periods. When the number of time periods whose number ratio is greater than the preset number ratio is greater than the preset number of time periods, it is determined that the line area belongs to the line loss abnormal area. When the number of time periods whose number ratio is greater than the preset number ratio is not greater than the preset number of time periods, proceed to step S12.

[0086] S12 determines the baseline line loss amount at different screening deviation moments by taking the average value of the line loss data of other line areas at different screening deviation moments on the current date, and determines the line loss deviation amount at different screening deviation moments according to the deviation amount between the line loss data of the line area at different screening deviation moments and the baseline line loss amount;

[0087] Optionally, the above step S12 includes the following contents:

[0088] S121 determines the baseline line loss amount at different screening deviation moments by taking the average value of the line loss data of other line areas at different screening deviation moments on the current date, and determines the line loss deviation amount at different screening deviation moments according to the deviation amount between the line loss data of the line area at different screening deviation moments and the baseline line loss amount;

[0089] S122: when there is no screening deviation moment when the line loss deviation does not meet the requirement, it is determined that the line area belongs to the line loss abnormal area; when there is a screening deviation moment when the line loss deviation does not meet the requirement, the process proceeds to step S113;

[0090] S123 obtains the number of screening deviation moments when the line loss deviation does not meet the requirements. When the number of screening deviation moments when the line loss deviation does not meet the requirements is greater than the preset number of moments, it is determined that the line area belongs to the line loss abnormal area. When the number of screening deviation moments when the line loss deviation does not meet the requirements is not greater than the preset number of moments, proceed to step S13.

[0091] S13 determines the deviation coefficients of different screening deviation moments through the line loss deviation amounts at different screening deviation moments and the average values ​​of the deviation amounts, and uses the deviation coefficients to determine the deviation moments in the screening deviation moments, and uses the number of deviation moments to determine whether the line area is an abnormal line loss area.

[0092] Optionally, the above step S13 includes the following contents:

[0093] S131 determines the deviation coefficients at different screening deviation moments by the line loss deviations at different screening deviation moments and the average values ​​of the deviations. When the average values ​​of the deviation coefficients at different screening deviation moments do not meet the requirements, the process proceeds to step S132. When the average values ​​of the deviation coefficients at different screening deviation moments meet the requirements, the process proceeds to step S133.

[0094] S132: when the number of the screened deviation moments is within the preset deviation moment number interval, it is determined that the line area belongs to the line loss abnormal area; when the number of the screened deviation moments is not within the preset deviation moment number interval, the process proceeds to step S133;

[0095] S133 uses the deviation coefficient to determine the deviation moment in the screening deviation moment, and uses the number of deviation moments to determine whether the line area is a line loss abnormal area.

[0096] Furthermore, the preset line loss amount is determined according to a preset corresponding relationship between the transmission power corresponding to the moment and the preset line loss amount.

[0097] Specifically, the deviation coefficient is determined according to the line loss deviation amount and the ratio of the average value of the deviation amount to the preset line loss amount at the corresponding moment.

[0098] S2 determines a reference date based on weather data of the line abnormal area on different dates within a preset period, determines deviations of line loss data in each transmission power interval on different reference dates based on line loss data on the reference date, and screens a specified date in the reference date based on the deviations;

[0099] Furthermore, the deviation of the line loss data of the transmission power interval is determined based on the deviation of the line loss data of the transmission power interval from a preset line loss threshold of the transmission power interval and the deviation from the line loss of other line areas at the corresponding moment.

[0100] Specifically, Figure 3 As shown, the method for filtering the specified date in the reference date is:

[0101] Determine the line loss deviation in each power transmission interval by using the line loss data in each power transmission interval on the reference date and the preset line loss amount in each power transmission interval, and use the line loss deviation to determine the average value of the line loss deviation in different time periods on the reference date, and use it as the deviation average value;

[0102] Whether the reference date is a designated date is determined by using the deviation average value.

[0103] Further, determining whether the reference date is a designated date by using the average value of the deviations specifically includes:

[0104] When the deviation average is greater than a preset deviation threshold, the reference date is determined to be a designated date.

[0105] In another embodiment, the method for screening the specified date in the reference date is:

[0106] Determine the line loss deviation amount in each power transmission power interval by using the line loss data in each power transmission power interval on the reference date and the preset line loss amount in each power transmission power interval, and determine the line loss deviation moment in the reference date by using the line loss deviation amount;

[0107] Whether the reference date is a designated date is determined by the number ratio of the line loss deviation moments.

[0108] Optionally, the method for screening the specified date in the reference date is:

[0109] S21: determining the line loss deviation in each power transmission interval based on the line loss data in each power transmission interval on the reference date and the preset line loss amount in each power transmission interval, and determining the line loss deviation moment in each power transmission interval using the line loss deviation, and determining the line loss deviation coefficient according to the proportion of the number of line loss deviation moments;

[0110] Optionally, the above step S21 includes the following contents:

[0111] S211 determines the line loss deviation in each power transmission interval by using the line loss data in each power transmission interval on the reference date and the preset line loss amount in each power transmission interval, and when it is determined by using the line loss deviation that there is no line loss deviation moment, it is determined that the reference date does not belong to the specified date, and when it is determined by using the line loss deviation that there is a line loss deviation moment, the process proceeds to step S212;

[0112] S212: when the number of line loss deviation moments is less than the preset moment number threshold, it is determined that the reference date does not belong to the specified date; when the number of line loss deviation moments is not less than the preset moment number threshold, the process proceeds to step S213;

[0113] S213 determines a line loss deviation coefficient according to the number ratio of line loss deviation moments. When the line loss deviation coefficient is less than a preset deviation coefficient threshold, the process proceeds to step S214. When the line loss deviation coefficient is not less than the preset deviation coefficient threshold, it is determined that the reference date belongs to a specified date.

[0114] S214 obtains the line loss deviation at different moments of line loss deviation. When the average value of the line loss deviation at different moments of line loss deviation is greater than the deviation setting value, the process proceeds to step S21. When the average value of the line loss deviation at different moments of line loss deviation is not greater than the deviation setting value, it is determined that the reference date does not belong to the specified date.

[0115] S22 determines whether the reference date is a designated date according to the line loss deviation coefficient.

[0116] Furthermore, the line loss deviation coefficient of the reference date has a value range between 0 and 1, wherein when the line loss deviation coefficient of the reference date is greater than a preset coefficient threshold, the reference date is determined to be a designated date.

[0117] It should also be noted that the matching coefficient of the reference date is determined according to the average value of the line loss deviation coefficient and the temperature variation coefficient.

[0118] S3: determining the change of weather data on the specified date, and combining the change of line loss data of each transmission power interval on the specified date, determining the change correlation factor between line loss data and weather data on different specified dates;

[0119] Specifically, Figure 4 As shown, the method for determining the change correlation factor between the line loss data and the weather data on the specified date is:

[0120] Determine the weather temperature between different adjacent time periods based on the change of weather data on a specified date, and use the weather temperature to determine the time period when the ice coverage increases and the time period when the ice coverage decreases;

[0121] Determine the variation of the line loss data in each transmission power interval according to the variation data of the line loss data in each transmission power interval between different adjacent time periods, and determine the variation of the line loss between different adjacent time periods by using the average value of the variation of the line loss data in each transmission power interval;

[0122] The line loss change period in the period of increased ice coverage is determined by the line loss change between the period of increased ice coverage and the adjacent period. The line loss change period in the period of reduced ice coverage is determined by the line loss change between the period of reduced ice coverage and the adjacent period. The change correlation factor between the line loss data and the weather data on the specified date is determined according to the proportion of the number of line loss change periods.

[0123] Furthermore, the value range of the change correlation factor is between 0 and 1, wherein when the change correlation factor of the specified date is greater than a preset correlation factor threshold, the specified date is determined to be a correlation date.

[0124] In another embodiment, the method for determining the change correlation factor between the line loss data and the weather data on the specified date is:

[0125] Determine the weather temperature between different adjacent time periods based on the change of weather data on a specified date, and use the weather temperature to determine the time period when the ice coverage increases and the time period when the ice coverage decreases;

[0126] Determine the variation of the line loss data in each transmission power interval according to the variation data of the line loss data in each transmission power interval between different adjacent time periods, and determine the variation of the line loss between different adjacent time periods by using the average value of the variation of the line loss data in each transmission power interval; when the variation of the line loss between different adjacent time periods is within the preset variation range, determine that the specified date does not belong to the associated date;

[0127] When there are adjacent periods where the line loss variation is not within the preset variation range:

[0128] When the line loss change between the period of increased ice coverage and the adjacent period is used to determine that there is no line loss change period in the period of increased ice coverage, and the line loss change between the period of reduced ice coverage and the adjacent period is used to determine that there is no line loss change period in the period of reduced ice coverage:

[0129] It is determined that the specified date does not belong to the associated date;

[0130] Obtaining the number ratio of the line loss change period in the ice-covering increase period and the number ratio of the line loss change period in the ice-covering decrease period, when the number ratio of the line loss change period in the ice-covering increase period and the number ratio of the line loss change period in the ice-covering decrease period are both less than the time period number ratio setting value, determining that the specified date does not belong to the associated date;

[0131] When any one of the proportion of the line loss change period in the ice-covering increase period and the proportion of the line loss change period in the ice-covering decrease period is not less than the set period proportion value:

[0132] The change correlation factor between the line loss data and the weather data on the specified date is determined according to the proportion of the number of the line loss change periods.

[0133] S4 uses the variable correlation factor to determine that there is a correlation date, and when it is determined that there is an icing risk in the abnormal line loss area based on the matching results of the weather data of different correlation dates and the current date, the icing risk is perceived based on the weather data of the abnormal line loss area.

[0134] Further, determining that the line loss abnormal area has an ice risk specifically includes:

[0135] Obtaining the number of the associated dates, and determining the basic risk factor using the ratio of the number of the associated dates to the number of the specified dates;

[0136] According to the matching results of the weather data of different associated dates and the current date, the deviation rates of the weather data of different associated dates and the current date in different dimensions are determined, the deviation coefficients of different associated dates are determined according to the average values ​​of the deviation rates of the weather data in different dimensions, and the average value of the deviation coefficients is determined based on the average values ​​of the deviation coefficients of different associated dates;

[0137] The estimated risk coefficient of the abnormal line loss area is determined by the ratio of the basic risk coefficient to the average value of the deviation coefficient, and the estimated risk coefficient is used to determine whether there is an icing risk in the abnormal line loss area.

[0138] Specifically, determining whether there is an icing risk in the line loss abnormal area by using the estimated risk coefficient specifically includes:

[0139] When the inferred risk coefficient is greater than a preset risk coefficient threshold, it is determined that there is an icing risk in the line loss abnormal area.

[0140] It can be understood that the perception of icing risk based on the weather data of the abnormal line loss area specifically includes:

[0141] Based on the weather data of the abnormal line loss area, determining the weather data of the abnormal line loss area at different time periods;

[0142] Weather data of different time periods are used as input, and the icing risk of the abnormal line loss area is obtained using a preset model.

[0143] The patent of this invention sets the "risk prediction accuracy index" to: the prediction accuracy reaches more than 80%.

[0144] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0145] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0146] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A method for sensing and identifying ice risk of ultra-high transmission channel lines, characterized in that: Specifically include: The ultra-high transmission channel is divided into a plurality of line areas, and the line loss abnormal area in the line area is screened based on the analysis result of the line loss data of the line area; Determine a reference date based on weather data of the line abnormality area on different dates within a preset period, determine the deviation of the line loss data in each transmission power interval on different reference dates based on the line loss data on the reference date, and screen the specified date in the reference date based on the deviation; Determine the change of weather data on a specified date, and determine the change correlation factor between line loss data and weather data on different specified dates in combination with the change of line loss data in each transmission power interval on the specified date; When the existence of an associated date is determined by using the variable associated factor, when it is determined that there is an icing risk in the abnormal line loss area based on the matching results of the weather data of different associated dates and the current date, the icing risk is perceived based on the weather data of the abnormal line loss area.

2. The method for sensing and identifying ice risk of ultra-high transmission channel lines according to claim 1, characterized in that: The ultra-high transmission channel is divided into multiple line areas, including: Based on the monitoring equipment in the ultra-high power transmission channel line, the ultra-high power transmission channel lines equipped with independent monitoring equipment are divided into the same line area.

3. The method for sensing and identifying ice risk of ultra-high transmission channel lines according to claim 1 is characterized in that: The method for screening the line loss abnormal area in the line area is: The baseline line loss at different times is determined by taking the average value of line loss data of other line areas at different times on the current date; Determining line loss deviation moments at different moments according to deviations between line loss data of the line area at different moments and a baseline line loss amount; Whether the line area is a line loss abnormal area is determined based on the number of line loss deviation moments in the current date.

4. The method for sensing and identifying ice risk of ultra-high transmission channel lines according to claim 3 is characterized in that: The line loss deviation moment is the moment when the deviation between the line loss data and the baseline line loss amount does not meet the requirement.

5. The method for sensing and identifying ice risk of ultra-high transmission channel lines according to claim 1, characterized in that: When the number of line loss deviation moments in the line area on the current date is greater than the preset number of deviation moments, the line area is determined to be a line loss abnormal area.

6. The method for sensing and identifying ice risk of ultra-high transmission channel lines according to claim 1, characterized in that: The deviation of the line loss data of the transmission power interval is determined based on the deviation of the line loss data of the transmission power interval from a preset line loss threshold of the transmission power interval and the deviation from the line loss of other line areas at the corresponding time.

7. The method for sensing and identifying ice risk of ultra-high transmission channel lines according to claim 1, characterized in that: The method for filtering the specified date in the reference date is: Determine the line loss deviation in each power transmission interval by using the line loss data in each power transmission interval on the reference date and the preset line loss amount in each power transmission interval, and use the line loss deviation to determine the average value of the line loss deviation in different time periods on the reference date, and use it as the deviation average value; Whether the reference date is a designated date is determined by using the deviation average value.

8. The method for sensing and identifying ice risk of ultra-high transmission channel lines according to claim 7, characterized in that: Determining whether the reference date is a specified date by using the average value of the deviations specifically includes: When the deviation average is greater than a preset deviation threshold, the reference date is determined to be a designated date.

9. The method for sensing and identifying ice risk of ultra-high transmission channel lines according to claim 1, characterized in that: Determine whether the line loss abnormal area has the risk of icing, including: Obtaining the number of the associated dates, and determining the basic risk factor using the ratio of the number of the associated dates to the number of the specified dates; According to the matching results of the weather data of different associated dates and the current date, the deviation rates of the weather data of different associated dates and the current date in different dimensions are determined, the deviation coefficients of different associated dates are determined according to the average values ​​of the deviation rates of the weather data in different dimensions, and the average value of the deviation coefficients is determined based on the average values ​​of the deviation coefficients of different associated dates; The estimated risk coefficient of the abnormal line loss area is determined by the ratio of the basic risk coefficient to the average value of the deviation coefficient, and the estimated risk coefficient is used to determine whether there is an icing risk in the abnormal line loss area.

10. The method for sensing and identifying ice risk of ultra-high power transmission channel lines according to claim 9, characterized in that: Determining whether there is an icing risk in the line loss abnormal area by using the estimated risk coefficient specifically includes: When the inferred risk coefficient is greater than a preset risk coefficient threshold, it is determined that there is an icing risk in the line loss abnormal area.

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