A method for sensing and identifying ice risk in ultra-high-speed transmission lines

Abstract

The present invention provides a method for perceiving and identifying icing risks of ultra-high-speed transmission channel lines, which belongs to the technical field of risk monitoring. The method specifically comprises: based on the line loss data of a reference date, determining the deviation of the line loss data in each transmission power interval on different reference dates, screening the specified dates in the reference date based on the deviation, determining the change of the weather data on the specified date, and combining the change of the line loss data in each transmission power interval on the specified date to determine the change correlation factor between the line loss data and the weather data on different specified dates; when determining the existence of an associated date using the change correlation factor, according to the matching results of the weather data of different associated dates with the current date, when determining that there is an icing risk in an abnormal line loss area, perceiving the icing risk based on the weather data in the abnormal line loss area, thereby improving the efficiency of line loss identification processing.

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 prone to sudden failure group events that have a major impact on the normal operation of the ultra-high voltage transmission line system - usually called "black swan type crisis event group" and "gray rhino type crisis event group". Ultra-high voltage transmission refers to the use of 500 kV to 1000 kV voltage levels to transmit electric energy, 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, the invention patent application CN202410923288.3, ​​"A method and device for dynamic prediction of icing risk of transmission lines based on scenario construction", uses a pre-trained BP neural network model to predict the initial ice thickness, obtaining a real-time predicted ice thickness. This can improve the accuracy of icing risk prediction for 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, existing technical solutions have neglected to further combine line loss monitoring data to perceive the icing risk in different areas of the transmission lines. Generally, when icing occurs on a transmission line, it will inevitably lead to greater line losses. At the same time, due to the long distance of ultra-high transmission channels, if line loss data cannot be combined, the perception and identification efficiency of 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 line is provided.

[0008] A method for sensing and identifying ice cover risks on ultra-high-voltage transmission lines, specifically comprising:

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

[0010] S2 determines a reference date based on weather data of the line abnormality area on different dates within a preset period, determines deviations 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 filters designated dates from the reference dates based on the deviations;

[0011] S3: determining the change of weather data on the specified date, and combining the change of line loss data in each transmission power interval on the specified date to determine 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 an associated date, and when it is determined that there is an icing risk in the abnormal line loss area based on the matching results of different associated dates and the weather data of 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] Based on the number of associated dates and the matching results of the weather data of the associated dates and 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 and the current date. This achieves an accurate assessment of the icing risk in the line loss abnormal area from multiple dimensions, and lays the foundation for further differentiated icing risk assessment.

[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 the method for screening the line loss abnormal area in the line area is:

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

[0020] Determining line loss deviation moments at different times based on deviations between line loss data of the line area at different times 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 icing risk, specifically including:

[0025] Obtaining the number of the associated dates, and determining a basic risk factor using the proportion of the associated dates in the number of the specified dates;

[0026] Determine the deviation rates of the weather data of the different associated dates and the current date in different dimensions based on the matching results of the weather data of the different associated dates and the current date, determine the deviation coefficients of the different associated dates based on the average value of the deviation rates of the weather data in different dimensions, and determine the average value of the deviation coefficients based on the average value of the deviation coefficients of the different associated dates;

[0027] An 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 the abnormal line loss area has an icing risk.

[0028] A further technical solution is to use the estimated risk coefficient to determine whether there is an icing risk in the abnormal line loss 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 from different time periods are used as input, and a preset simulation model is used to obtain the icing risk in the abnormal line loss area.

[0033] Other features and advantages will be described in the following description. The objectives 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 accompanying drawings.

[0036] Figure 1 It is a flow chart of a method for sensing and identifying ice cover risks in ultra-high-speed transmission lines;

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

[0038] Figure 3 is a flowchart of a method for filtering a specified date among reference dates;

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

[0040] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this specification without creative work should fall within the scope of protection of this specification.

[0041] Line loss abnormal area: The line area where the average deviation of the line loss data from other line areas is greater than the preset deviation is regarded as the line abnormal area.

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

[0043] Icing risk perception: Weather data from areas with abnormal line loss is used as input, and icing risk is determined using a preset prediction model 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, according to the Bayesian formula, the prior information about the unknown parameter is combined with the sample information to obtain the posterior information, and then the unknown parameter is inferred based on the posterior information. The prior information comes from previous statistical conclusions, experience or assumptions. The formula is expressed as follows:

[0046]

[0047] In the formula, A1, ...An are mutually exclusive and represent a complete set of events in the sample space, P(Ai)>0, and P(B)>0. This formula is called the Bayesian formula, where P(Ai) represents the probability of Ai occurring, i.e., the prior probability. This probability is a known probability, and from this, the conditional probability of B occurring under the condition of Ai occurring can be calculated based on the sample information, i.e., P(B|Ai). Then, based on the Bayesian formula, the probability of Ai occurring under the condition of the outcome event B occurring, P(Ai|B), can be calculated. This probability is determined after the experiment, i.e., 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 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 reference 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 i-th risk factor, Pa(X i ) as its parent node set.

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

[0053] ② Risk 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 = {w1, w2, ..., 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] Among them L i Indicates the weight of the risk level.

[0056] The risk level is divided into five levels (Level I to Level V), and the classification is achieved by setting thresholds {T1, T2, …, t4}:

[0057]

[0058] ③The 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 that evidence E appears 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 cover risks of ultra-high transmission lines is provided, which specifically includes:

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

[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, such as 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 averaging the line loss data of other line areas at different times on the current date;

[0071] Determining line loss deviation moments at different times based on deviations between line loss data of the line area at different times 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 preset number of 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 averaging the line loss data of other line areas at different times on the current date;

[0077] Determining line loss deviations at different times based on 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 based on the transmission powers corresponding to the different moments, determines deviation amounts at different moments based on the deviations between the line loss data and the preset line loss amounts, and determines a screening deviation moment among the moments based on the deviations;

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

[0083] S111: Determine preset line loss amounts at different times based on the transmission power corresponding to different times, and determine deviation amounts at different times based on the deviations between the line loss data and the preset line loss amounts. If it is determined by the deviations that no screening deviation moments exist at the times, then determine that the line area does not belong to an abnormal line loss area. If it is determined by the deviations that a screening deviation moment exists at the times, proceed to step S112.

[0084] Step S112: obtaining the number of screening deviation moments. If 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. If 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 baseline line loss amounts at different screening deviation moments based on the average value of line loss data of other line areas at different screening deviation moments on the current date, and determines line loss deviation amounts at different screening deviation moments based on deviations 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 baseline line loss amounts at different screening deviation moments based on average values ​​of line loss data of other line areas at different screening deviation moments on the current date, and determines line loss deviation amounts at different screening deviation moments based on deviations between the line loss data of the line area at different screening deviation moments and the baseline line loss amount;

[0089] S122: If there is no screening deviation moment at which the line loss deviation does not meet the requirements, it is determined that the line area belongs to the line loss abnormal area. If there is a screening deviation moment at which the line loss deviation does not meet the requirements, 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 and the average values ​​of the deviation amounts at different screening deviation moments, 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 a line loss abnormal area.

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

[0093] S131 determines the deviation coefficients at different screening deviation moments based on the line loss deviations at different screening deviation moments and the average value of the deviations. If the average value of the deviation coefficients at different screening deviation moments does not meet the requirement, the process proceeds to step S132. If the average value of the deviation coefficients at different screening deviation moments meets the requirement, the process proceeds to step S133.

[0094] S132: When the number of the screened deviation moments is within the preset deviation moment number range, 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 range, the process proceeds to step S133;

[0095] S133 uses the deviation coefficient to determine the deviation moments in the screened deviation moments, and uses the number of deviation moments to determine whether the line area is an abnormal line loss 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 and the ratio of the average value of the deviation to the preset line loss at the corresponding moment.

[0098] S2 determines a reference date based on weather data of the line abnormality area on different dates within a preset period, determines deviations 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 filters designated dates from the reference dates based on the deviations;

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

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

[0101] Determining a line loss deviation in each power transmission interval based on the line loss data in each power transmission interval on the reference date and a preset line loss amount in each power transmission interval, and using the line loss deviation to determine an average value of the line loss deviations in different time periods on the reference date, and using the average value as the deviation average;

[0102] Whether the reference date is a designated date is determined based on the average value of the deviations.

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

[0104] When the average deviation value is greater than a preset deviation threshold value, 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] Determining a line loss deviation in each power transmission interval based on the line loss data in each power transmission interval on the reference date and a preset line loss amount in each power transmission interval, and determining a line loss deviation moment on the reference date using the line loss deviation;

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

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

[0109] S21: determining a line loss deviation in each power transmission interval based on the line loss data in each power transmission interval on the reference date and a preset line loss amount in each power transmission interval, determining a line loss deviation moment in each power transmission interval based on the line loss deviation, and determining a line loss deviation coefficient based on a 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 amount 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. If it is determined that there is no line loss deviation moment using the line loss deviation amount, it is determined that the reference date does not belong to the specified date. If it is determined that there is a line loss deviation moment using the line loss deviation amount, the process proceeds to step S212.

[0112] In step S212, when the number of the 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; and when the number of the 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 based on the proportion of the number 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 amount at different line loss deviation moments. When the average value of the line loss deviation amounts at different line loss deviation moments is greater than the deviation setting value, the process proceeds to step S21. When the average value of the line loss deviation amounts at different line loss deviation moments 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 based on the line loss deviation coefficient.

[0116] Furthermore, the line loss deviation coefficient of the reference date has a value range of 0 to 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 in each transmission power interval on the specified date to determine the change correlation factor between line loss data and weather data on different specified dates;

[0119] Specifically, such as 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] Determining weather temperatures between different adjacent time periods based on changes in weather data on a specified date, and using the weather temperatures to determine periods of increased ice coverage and periods of decreased ice coverage;

[0121] Determine the variation of the line loss data in each transmission power interval based on 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 using the average 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 correlation factor between the line loss data and the weather data on the specified date is:

[0125] Determining weather temperatures between different adjacent time periods based on changes in weather data on a specified date, and using the weather temperatures to determine periods of increased ice coverage and periods of decreased ice coverage;

[0126] Determining a variation of the line loss data in each transmission power interval based on variation data of the line loss data in each transmission power interval between different adjacent time periods, and determining a variation of the line loss between different adjacent time periods using an average of the variation of the line loss data in each transmission power interval; and determining that the specified date is not a related date when the variation of the line loss between different adjacent time periods is within a preset variation range.

[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 whether there is no line loss change period during the period of increased ice coverage, and the line loss change between the period of decreased ice coverage and the adjacent period is used to determine whether there is no line loss change period during the period of decreased ice coverage:

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

[0130] Obtaining a ratio of the number of time periods in which line loss changes during the time period of increased ice coverage and a ratio of the number of time periods in which line loss changes during the time period of decreased ice coverage. When both the ratio of the number of time periods in which line loss changes during the time period of increased ice coverage and the ratio of the number of time periods in which line loss changes during the time period of decreased ice coverage are less than a set value of the time period ratio, 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 coverage increase period and the proportion of the line loss change period in the ice coverage decrease period is not less than the set period proportion value:

[0132] A 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 an associated date, and when it is determined that there is an icing risk in the abnormal line loss area based on the matching results of different associated dates and the weather data of the current date, the icing risk is perceived based on the weather data of the abnormal line loss area.

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

[0135] Obtaining the number of the associated dates, and determining a basic risk factor using the proportion of the associated dates in the number of the specified dates;

[0136] Determine the deviation rates of the weather data of the different associated dates and the current date in different dimensions based on the matching results of the weather data of the different associated dates and the current date, determine the deviation coefficients of the different associated dates based on the average value of the deviation rates of the weather data in different dimensions, and determine the average value of the deviation coefficients based on the average value of the deviation coefficients of the different associated dates;

[0137] An 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 the abnormal line loss area has an icing risk.

[0138] Specifically, determining whether there is an icing risk in the abnormal line loss area using the estimated risk coefficient 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 is understandable 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 from different time periods are used as input, and a preset model is used to obtain the icing risk in the abnormal line loss area.

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

[0144] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0145] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0146] The foregoing description is merely one or more embodiments of this specification and is not intended to limit this specification. It will be apparent to those skilled in the art that various modifications and variations may be made to one or more embodiments of this specification. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of one or more embodiments of this specification are intended to be within the scope of the claims of this specification.

Claims

1. A method for sensing and identifying ice risk in ultra-high-speed transmission lines, characterized in that: Specifically include: Dividing the ultra-high transmission channel into multiple line areas, and screening abnormal line loss areas in the line areas based on the analysis results of line loss data of the line areas; Determining a reference date based on weather data of the abnormal line loss area on different dates within a preset period, determining deviations 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 screening designated dates from the reference dates based on the deviations; Determine the change of weather data on a specified date, and combine the change of line loss data of each transmission power interval on the specified date to determine the change correlation factor between line loss data and weather data on different specified dates; When the variable correlation factor is used to determine the existence of an associated date, 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-speed transmission 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-speed transmission lines according to claim 1, 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 averaging the line loss data of other line areas at different times on the current date; Determining line loss deviation moments at different times based on deviations between line loss data of the line area at different times 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-speed transmission lines according to claim 3, 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-speed transmission 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-speed transmission lines according to claim 1, characterized in that: The deviation of the line loss data in the transmission power interval is determined based on the deviation of the line loss data in the transmission power interval from a preset line loss threshold value in the transmission power interval and the deviation from the line loss data of other line areas at the corresponding moment.

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

8. The method for sensing and identifying ice risk of ultra-high-speed transmission 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 average deviation value is greater than a preset deviation threshold value, the reference date is determined to be a designated date.

9. The method for sensing and identifying ice risk of ultra-high-speed transmission lines according to claim 1, wherein: Determine whether the area with abnormal line loss has icing risk, specifically including: Obtaining the number of the associated dates, and determining a basic risk factor using the proportion of the associated dates in the number of the specified dates; Determine the deviation rates of the weather data of the different associated dates and the current date in different dimensions based on the matching results of the weather data of the different associated dates and the current date, determine the deviation coefficients of the different associated dates based on the average value of the deviation rates of the weather data in different dimensions, and determine the average value of the deviation coefficients based on the average value of the deviation coefficients of the different associated dates; An 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 the abnormal line loss area has an icing risk.

10. The method for sensing and identifying ice risk of ultra-high-speed transmission lines according to claim 9, characterized in that: Determining whether there is an icing risk in the abnormal line loss 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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