Grounding status assessment method for power transmission and distribution lines in seasonally frozen areas

Abstract

The present invention relates to the field of information technology for transmission line measurement data, and in particular to a method for evaluating the grounding state of power transmission and distribution lines in seasonal frozen soil areas, including: collecting relevant data in seasonal frozen soil areas; building a state evaluation model for power transmission and distribution lines in seasonal frozen soil areas; optimizing the parameters in the state evaluation model; and evaluating the grounding state of power transmission and distribution lines in seasonal frozen soil areas through the state evaluation model obtained through optimization. The present invention detects factors that affect the grounding state of transmission line towers in seasonal frozen soil areas, and optimizes and evaluates the grounding state of transmission lines in seasonal frozen soil areas through an improved fruit fly algorithm, thereby improving the evaluation accuracy. Generate a corresponding operation and inspection strategy, which is differentiated and formulated based on the evaluation level of the grounding state of transmission line towers in seasonal frozen soil areas and the risk classification, and can improve the work efficiency and economy of grounding operation and maintenance of transmission lines in seasonal frozen soil areas.

Description

Technical Field

[0001] The invention relates to the technical field of information technology for transmission line measurement data, in particular to a method for evaluating the grounding state of a transmission and distribution line in a seasonally frozen soil region. Background Art

[0002] The evaluation and verification of the grounding performance of transmission line towers is an issue that must be considered during the design stage of overhead transmission lines. When a line fault occurs, good tower grounding performance can quickly discharge the fault current to the ground through the tower grounding device, effectively reduce the fault tripping rate of the transmission line, and improve the line's counter-strike lightning resistance level. Therefore, the grounding resistance of the transmission line tower is an important indicator to characterize the operating status of the transmission circuit. Therefore, in the operation and maintenance of transmission lines, it is very important to measure and ensure that the grounding resistance of the tower is within a reasonable range. However, the distribution area of ​​seasonal frozen soil accounts for more than half of my country's territory, and the standard freezing depth of seasonal frozen soil in most areas is 0.6 to 3m. Seasonal frozen soil melts in summer and freezes in winter, alternating all year round. The temperature and humidity changes in seasonal frozen soil areas have different effects on transmission line towers than those in ordinary areas, and the grounding status of transmission line towers in seasonal frozen soil areas cannot be evaluated by ordinary evaluation methods. Summary of the invention

[0003] The purpose of the present invention is to solve the defects mentioned in the above background technology by proposing a method for evaluating the grounding status of power transmission and distribution lines in seasonally frozen areas.

[0004] The present invention proposes a method for evaluating the grounding state of power transmission and distribution lines in seasonal frozen soil areas, detects factors affecting the grounding state of power transmission line towers in seasonal frozen soil areas, and optimizes and evaluates the grounding state of power transmission lines in seasonal frozen soil areas through an improved fruit fly algorithm, which can automatically optimize and adjust according to changes in influencing factors to improve the accuracy of the evaluation. In addition, a corresponding operation and inspection strategy is generated, which is differentiated and formulated in combination with the evaluation level and risk classification of the grounding state of power transmission line towers in seasonal frozen soil areas, which can improve the work efficiency and economy of grounding operation and maintenance of power transmission lines in seasonal frozen soil areas.

[0005] The technical solution adopted by the present invention is as follows:

[0006] Provides a method for evaluating the grounding status of transmission and distribution lines in seasonally frozen areas, including:

[0007] S1: Collect relevant data in seasonally frozen areas;

[0008] S2: Build a condition assessment model for transmission and distribution lines in seasonally frozen areas;

[0009] S3: Optimizing the parameters in the state assessment model;

[0010] S4: The grounding status of the transmission and distribution lines in seasonally frozen ground areas is evaluated by using the status assessment model obtained through optimization.

[0011] As a preferred technical solution of the present invention: the relevant data collected in S1 include rainfall, rainfall time, soil temperature, ambient temperature and humidity in seasonal frozen soil areas, and measurement data of power transmission and distribution lines.

[0012] As a preferred technical solution of the present invention: the state assessment model is built in S2 according to the collected measurement data of the power transmission and distribution line as follows:

[0013] in, is the state evaluation value, is the ground resistance value measured most recently, Design parameters.

[0014] As a preferred technical solution of the present invention: the design parameter value corresponds to a variation range of soil resistivity, and in S3, the soil resistivity corresponding to the design parameter value is optimized.

[0015] As a preferred technical solution of the present invention: the factors affecting the change of soil resistivity include rainfall, rainfall time, soil temperature, and ambient temperature and humidity in seasonal frozen soil areas.

[0016] As a preferred technical solution of the present invention: in S3, the soil resistivity is optimized based on the improved fruit fly algorithm.

[0017] As a preferred technical solution of the present invention: the steps of the improved fruit fly algorithm are as follows:

[0018] The pair-finding space of the standard fruit fly algorithm is expanded from two dimensions to three dimensions, and the initial position of the fruit fly is , the initial scale is set to , the maximum number of iterations is ;

[0019] Each time a fruit fly flies, it generates a random number, which is assigned to the initialized fruit fly individual to find the specific position of the next fruit fly. The search distance

[0020] The specific calculation formula is as follows:

[0021]

[0022]

[0023]

[0024] in, Indicates The specific location of the fruit fly, is the step size control factor, is the number of iterations,

[0025] Determine the distance between the current fruit fly and the coordinate origin The final taste concentration judgment value is the inverse of the initial position of the fruit fly and the distance to the origin and the taste concentration judgment value of the best fruit fly individual in the previous generation. ,

[0026]

[0027]

[0028] Will The value of is substituted into the fitness function, and the root mean square error function between the predicted value and the true value of the training sample in the seasonal frozen soil area transmission and distribution line based on the fruit fly algorithm is used. Determine the current fruit fly :

[0029]

[0030] in, for fruit flies to judge taste concentration;

[0031] Analyze the concentration of flavors and find the fruit fly individuals with the best flavor concentration in the fruit fly population:

[0032]

[0033] in, Indicates the optimal flavor concentration in the current fruit fly population. Fruit fly individuals, Indicates flavor intensity;

[0034] Find and save the optimal flavor concentration value and the specific position of the current optimal fruit fly. At this time, let all fruit flies fly to the optimal position of the current fruit fly individual:

[0035]

[0036]

[0037]

[0038]

[0039] in, Indicates the optimal flavor concentration value, represents the current optimal fruit fly The specific location.

[0040] As a preferred technical solution of the present invention: in the improved fruit fly algorithm, iterative optimization is performed again to analyze and determine the number of iterations Whether the maximum number of iterations has been reached If it is not reached, then the fruit fly individual with the best taste concentration in the fruit fly population is searched again according to the initialization data, and the current best taste concentration value is analyzed again, and the current best taste concentration value is compared with the best taste concentration value of the previous iteration. If the best taste concentration value of the previous iteration is better than the current best taste concentration value, the best taste concentration value and the specific position of the fruit fly with the best taste concentration are calculated and saved, and all fruit flies are ordered to fly to the optimal position of the current fruit fly individual. Otherwise, the fruit fly individual with the best taste concentration in the fruit fly population is searched again according to the initialization data until the number of iterations reaches the maximum number of iterations, and the optimization search is terminated.

[0041] As a preferred technical solution of the present invention: the state assessment model obtained by optimization in S4 divides the line grounding state risk level according to the design parameter value and generates a differentiated operation and inspection plan.

[0042] As a preferred technical solution of the present invention: the differentiated operation and inspection solution adjusts the measurement interval duration according to the risk level of the line grounding status.

[0043] Compared with the prior art, the method for evaluating the grounding status of power transmission and distribution lines in seasonally frozen areas provided by the present invention has the following beneficial effects:

[0044] The present invention detects factors affecting the grounding state of transmission line towers in seasonal frozen soil areas, and optimizes and evaluates the grounding state of transmission lines in seasonal frozen soil areas through an improved fruit fly algorithm, which can automatically optimize and adjust according to changes in influencing factors to improve the evaluation accuracy. At the same time, the corresponding operation and inspection strategy can be generated, and the operation and inspection strategy is differentiated and formulated in combination with the evaluation level and risk classification of the grounding state of transmission line towers in seasonal frozen soil areas, which can improve the work efficiency and economy of grounding operation and maintenance of transmission lines in seasonal frozen soil areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The figure is a flow chart of a method according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0046] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of this embodiment can be combined with each other. The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Reference Figure 1 The preferred embodiment of the present invention provides a method for evaluating the grounding status of power transmission and distribution lines in seasonally frozen areas, comprising:

[0048] S1: Collect relevant data in seasonally frozen areas;

[0049] S2: Build a condition assessment model for transmission and distribution lines in seasonally frozen areas;

[0050] S3: Optimizing the parameters in the state assessment model;

[0051] S4: The grounding status of the transmission and distribution lines in seasonally frozen ground areas is evaluated by using the state assessment model obtained through optimization.

[0052] The relevant data collected in S1 include rainfall, rainfall time, soil temperature, ambient temperature and humidity in seasonal frozen soil areas, and measurement data of power transmission and distribution lines.

[0053] In S2, a state assessment model is built based on the collected measurement data of the transmission and distribution lines as follows:

[0054] in, is the state evaluation value, is the ground resistance value measured most recently, Design parameters.

[0055] The design parameter value corresponds to a variation range of soil resistivity. In S3, the soil resistivity corresponding to the design parameter value is optimized.

[0056] The factors affecting the change of soil resistivity include rainfall, rainfall time, soil temperature, and ambient temperature and humidity in seasonally frozen soil areas.

[0057] In S3, soil resistivity is optimized based on an improved fruit fly algorithm.

[0058] The improved fruit fly algorithm steps are as follows:

[0059] The pair-finding space of the standard fruit fly algorithm is expanded from two dimensions to three dimensions, and the initial position of the fruit fly is , the initial scale is set to , the maximum number of iterations is ;

[0060] Each time a fruit fly flies, it generates a random number, which is assigned to the initialized fruit fly individual to find the specific position of the next fruit fly. The search distance

[0061] The specific calculation formula is as follows:

[0062] in, Indicates The specific location of the fruit fly, is the step size control factor, is the number of iterations,

[0063] Determine the distance between the current fruit fly and the coordinate origin The final taste concentration judgment value is the inverse of the initial position of the fruit fly and the distance to the origin and the taste concentration judgment value of the best fruit fly individual in the previous generation. ,

[0064]

[0065] Will The value of is substituted into the fitness function, and the root mean square error function between the predicted value and the true value of the training sample in the seasonal frozen soil area transmission and distribution line based on the fruit fly algorithm is used. Determine the current fruit fly :

[0066]

[0067] in, for fruit flies to judge taste concentration;

[0068] Analyze the concentration of flavors and find the fruit fly individuals with the best flavor concentration in the fruit fly population:

[0069]

[0070] in, Indicates the optimal flavor concentration in the current fruit fly population. Fruit fly individuals, Indicates flavor intensity;

[0071] Find and save the optimal flavor concentration value and the specific position of the current optimal fruit fly. At this time, let all fruit flies fly to the optimal position of the current fruit fly individual:

[0072]

[0073]

[0074]

[0075]

[0076] in, Indicates the optimal flavor concentration value, represents the current optimal fruit fly The specific location.

[0077] In the improved fruit fly algorithm, iterative optimization is performed again to analyze and determine the number of iterations. Whether the maximum number of iterations has been reached If it is not reached, then the fruit fly individual with the best taste concentration in the fruit fly population is searched again according to the initialization data, and the current best taste concentration value is analyzed again, and the current best taste concentration value is compared with the best taste concentration value of the previous iteration. If the best taste concentration value of the previous iteration is better than the current best taste concentration value, the best taste concentration value and the specific position of the fruit fly with the best taste concentration are calculated and saved, and all fruit flies are ordered to fly to the optimal position of the current fruit fly individual. Otherwise, the fruit fly individual with the best taste concentration in the fruit fly population is searched again according to the initialization data until the number of iterations reaches the maximum number of iterations, and the optimization search is terminated.

[0078] The state assessment model obtained by optimal search in S4 divides the line grounding state risk level according to the design parameter value and generates a differentiated operation and inspection plan.

[0079] The differentiated operation and inspection scheme adjusts the measurement interval duration according to the risk level of the line grounding status.

[0080] In this embodiment, the measurement data of rainfall, rainfall time, soil temperature, ambient temperature and humidity, and power transmission and distribution in seasonal frozen soil areas are collected, and a state assessment model is built based on the collected data:

[0081] in, is the state evaluation value, is the ground resistance value measured most recently, Design parameters.

[0082] Design parameters correspond to the soil resistivity variation range,

[0083] The change of soil resistivity corresponds to different grounding resistance values. For example, when the soil resistivity is lower than 100, the corresponding grounding resistance value is 10Ω. When the corresponding node resistance is 15Ω, when the soil resistivity is in the interval When the corresponding grounding resistance is 20Ω, when the soil resistivity is in the range When the soil resistivity is greater than 2000, the corresponding node resistance is 25Ω. When the soil resistivity is greater than 2000, the corresponding grounding resistance is 30Ω. In addition, the soil resistivity is affected by the rainfall, rainfall time, soil temperature, and ambient temperature and humidity in seasonal frozen soil areas. Different values ​​lead to different soil resistivity. Therefore, the improved fruit fly algorithm is used to optimize the soil resistivity according to the influencing factors, and the design parameters, that is, the corresponding grounding resistance value, are obtained according to the soil resistivity obtained by optimization.

[0084] The improved fruit fly algorithm expands the pair-finding space of the standard fruit fly algorithm from two dimensions to three dimensions, which can improve the global optimization ability of the fruit fly algorithm and increase the search range and search space of the fruit fly. Assume that the initial position of the fruit fly is , the initial scale is set to , the maximum number of iterations is ;

[0085] Each time a fruit fly flies, it generates a random number, which is assigned to the initialized fruit fly individual to find the specific position of the next fruit fly. is the search distance.

[0086] As the number of iterations of the fruit fly increases, its search step length is reduced. The larger the search step length, the larger the search space of the fruit fly, and the faster the search process, which improves the global performance of the fruit fly, but reduces the local search ability; the opposite is true, and a search step length that is too small will cause the fruit fly algorithm to fall into a local optimal solution, so it is extremely important to choose a reasonable search step length. This paper adopts the method of adaptively reducing the search step length every generation to ensure that the search range is large during the early iterations, improve its global optimization ability, and reduce the search step length in the later stage to improve its local optimization ability, thereby achieving a dynamic balance. The specific calculation formula is as follows:

[0087] in, Indicates The specific location of the fruit fly, is the step size control factor, is the number of iterations. As the number of iterations of the fruit fly algorithm increases, the search range of the fruit fly will decrease exponentially.

[0088] Determine the distance between the current fruit fly and the coordinate origin The reciprocal of the distance between the fruit fly's initial position and the origin and the taste concentration judgment value of the best fruit fly individual of the previous generation are used as the taste concentration judgment value This can expand the range of flavor concentration judgment values, effectively avoiding the situation where the fruit fly population falls into a local minimum value due to its small search range during the optimization process;

[0089]

[0090] Find After that, we further determined the current , usually The value of is substituted into the fitness function. The dimension concentration judgment function is usually uncertain, and the function to be used needs to be determined according to the specific situation. Therefore, the root mean square error function between the predicted value and the true value of the training sample is selected. Determine the current fruit fly :

[0091]

[0092] in, is the root mean square error function between the predicted value and the true value of the training sample, for fruit flies to judge taste concentration;

[0093] Analyze the concentration of flavors and find the fruit fly individuals with the best flavor concentration in the fruit fly population:

[0094]

[0095] in, Indicates the optimal flavor concentration in the current fruit fly population. Fruit fly individuals, Indicates flavor intensity;

[0096] Find and save the optimal flavor concentration value and the specific position of the current optimal fruit fly. At this time, let all fruit flies fly to the optimal position of the current fruit fly individual:

[0097]

[0098]

[0099]

[0100]

[0101] in, Indicates the optimal flavor concentration value, represents the current optimal fruit fly The specific location.

[0102] Then perform iterative optimization and analyze the number of iterations Whether the maximum number of iterations has been reached If it is not reached, then the fruit fly individual with the best taste concentration in the fruit fly population is searched again according to the initialization data, and the current best taste concentration value is analyzed again, and the current best taste concentration value is compared with the best taste concentration value of the previous iteration. If the best taste concentration value of the previous iteration is better than the current best taste concentration value, the best taste concentration value and the specific position of the fruit fly with the best taste concentration are calculated and saved, and all fruit flies are ordered to fly to the optimal position of the current fruit fly individual. Otherwise, the fruit fly individual with the best taste concentration in the fruit fly population is searched again according to the initialization data until the number of iterations reaches the maximum number of iterations, and the optimization search is terminated.

[0103] The current soil resistivity is obtained by optimizing the improved fruit fly algorithm, and the corresponding grounding resistance value, that is, the design parameter value, is obtained. If the grounding resistance values ​​corresponding to the five soil resistivity intervals correspond to the five risk levels of A, B, C, D, and E, the corresponding operation and inspection plans are: A: The grounding state is good, and it is measured every 5 years; B: The grounding state is relatively good, and it is measured every 3 years; C: The grounding state is normal, but there are hidden dangers of failure, and it is measured every 1 year; D: The grounding state is relatively poor, and there may be corrosion, connection or other faults, and it is measured every 6 months; E: The grounding state is poor, and there are corrosion, connection or other faults, and it is immediately re-measured and the grounding transformation is carried out.

[0104] Assuming that the currently measured soil resistivity is 365, the corresponding grounding resistance value, that is, the design parameter value is 10Ω, it is classified as Class A operating state, the grounding state is good, and the adjustable interval time is once every 5 years.

[0105] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0106] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for evaluating the grounding status of power transmission and distribution lines in seasonally frozen areas, characterized in that: include: S1: Collect relevant data in seasonal frozen ground areas, including rainfall, rainfall time, soil temperature, ambient temperature and humidity, and measurement data of transmission and distribution lines in seasonal frozen ground areas; S2: Build a condition assessment model for power transmission and distribution lines in seasonally frozen areas. The constructed condition assessment model is as follows: in, is the state evaluation value, is the ground resistance value measured most recently, represents the design parameters; S3: The design parameters in the state assessment model are optimized in the following way: The factors affecting the change of soil resistivity corresponding to the design parameter value include rainfall, rainfall time, soil temperature, and ambient temperature and humidity in seasonal frozen soil areas. The steps for optimizing soil resistivity based on the improved fruit fly algorithm are as follows: The pair-finding space of the standard fruit fly algorithm is expanded from two dimensions to three dimensions, and the initial position of the fruit fly is , the initial scale is set to , the maximum number of iterations is ; Each time a fruit fly flies, it generates a random number, which is assigned to the initialized fruit fly individual to find the specific position of the next fruit fly. is the search distance, The specific calculation formula is as follows: ， ， ， in, Indicates The specific location of the fruit fly, is the step size control factor, is the number of iterations, Determine the distance between the current fruit fly and the coordinate origin The final taste concentration judgment value is the inverse of the initial position of the fruit fly and the distance to the origin and the taste concentration judgment value of the best fruit fly individual in the previous generation. , ， ， Will The value of is substituted into the fitness function, and the root mean square error function between the predicted value and the true value of the training sample in the seasonal frozen soil area transmission and distribution line based on the fruit fly algorithm is used. Determine the current fruit fly : ， in, for fruit flies to judge taste concentration; Analyze the concentration of flavors and find the fruit fly individuals with the best flavor concentration in the fruit fly population: ， in, Indicates the optimal flavor concentration in the current fruit fly population. Fruit fly individuals, Indicates flavor intensity; Find and save the optimal flavor concentration value and the specific position of the current optimal fruit fly. At this time, let all fruit flies fly to the optimal position of the current fruit fly individual: ， ， ， ， in, Indicates the optimal flavor concentration value, represents the current optimal fruit fly The specific location of Then perform iterative optimization and analyze the number of iterations Whether the maximum number of iterations has been reached , if it is not reached, then search for the fruit fly individual with the best taste concentration in the fruit fly group according to the initialization data, analyze the current best taste concentration value, compare the current best taste concentration value with the best taste concentration value of the previous iteration, if the best taste concentration value of the previous iteration is better than the current best taste concentration value, find and save the best taste concentration value and the specific position of the fruit fly with the best taste concentration at the current time, and let all fruit flies fly to the optimal position of the current fruit fly individual, otherwise continue to search for the fruit fly individual with the best taste concentration in the fruit fly group according to the initialization data until the number of iterations reaches the maximum number of iterations, and then end the optimization; S4: The grounding status of the transmission and distribution lines in seasonal frozen ground areas is evaluated through the state assessment model obtained through optimization. The state assessment model obtained through optimization divides the line grounding status risk level according to the design parameter value and generates a differentiated operation and inspection plan. The differentiated operation and inspection plan adjusts the measurement interval duration according to the line grounding status risk level.

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