Method, device and equipment for analyzing corona discharge risk of power transmission line

Abstract

The invention discloses a power transmission line corona discharge risk analysis method, device and equipment, and relates to the field of power transmission line safety analysis, and the key points of the technical scheme are as follows: establishing a dielectric model for representing the polarization characteristics of different types of bird nests on a power transmission tower, and calculating the power transmission line corona discharge risk based on the weighted sum of the dielectric model; obtaining a first mathematical model used for describing bird nest polarization characteristics, and introducing an environment correction factor of the power transmission line to correct the first mathematical model to obtain a second mathematical model; determining the polarization intensity of different types of bird nests on the power transmission line according to a second mathematical model; correcting the initial corona field intensity of the power transmission line containing the bird nest according to the electric field distortion rate and the polarization intensity to obtain the corrected initial corona field intensity; and constructing a corona discharge probability model according to the corrected initial corona field intensity, the electric field distortion rate and the polarization intensity, and analyzing the corona discharge risk of the power transmission line based on the corona discharge probability model.

Description

Technical Field

[0001] The present invention relates to the field of power transmission line safety analysis, and more specifically, to a method, device and equipment for analyzing the risk of corona discharge in a power transmission line. Background Art

[0002] Birds nesting on transmission towers not only affects the mechanical strength of the line, but more importantly, it may cause a series of electrical faults. The harm of bird nests to transmission lines is mainly reflected in three aspects: first, wet bird nests can easily absorb moisture, significantly changing their dielectric properties and increasing the local electric field strength; second, birds may use conductive materials such as metal wire to build nests, which directly increases the conductivity of the bird nests; finally, the easily dispersed bird nest materials may cause insulator flashover or phase-to-phase short circuit. The combined effect of these factors makes bird nests prone to polarization in high-voltage electric fields, which may in turn cause corona discharge, leading to a series of serious consequences, such as accelerated aging of insulators, equipment damage and line tripping, which seriously threatens the safe operation of transmission lines.

[0003] Regarding the safety issues of power transmission lines caused by bird nests, the relevant technologies are mainly divided into preventive measures and monitoring and analysis methods, but both have obvious limitations. Preventive measures are difficult to fully cover potential risk areas, and cannot solve the problem of bird nests that have already formed. The existing monitoring methods are based on phenomenon observation and cannot accurately analyze the impact of bird nests on electric field distribution and potential electrical risks. It is difficult to distinguish the actual degree of harm of different types of bird nests, and it is impossible to monitor the dynamic changes in the status of bird nests. It is easy to cause excessive alarms and waste of operation and maintenance resources. At present, the relevant technologies ignore the polarization characteristics of bird nest materials and their dynamic changes. Since the polarization behavior of bird nest materials in high-voltage electric fields is affected by multiple factors such as material composition, humidity, and temperature, it is a complex dynamic process. Therefore, it is difficult for relevant technologies to accurately analyze the risk of corona discharge in power transmission lines caused by bird nests. Summary of the invention

[0004] The purpose of the present invention is to provide a method, device and equipment for analyzing the risk of corona discharge in transmission lines, so as to solve the problem that the related art ignores the dynamic change process of the polarization characteristics of bird nests with time and environmental conditions, resulting in difficulty in accurately analyzing the risk of corona discharge in transmission lines caused by bird nests.

[0005] A first aspect of the present invention provides a method for corona discharge risk analysis of a power transmission line, the method comprising:

[0006] A dielectric model for characterizing the polarization characteristics of different types of bird nests on transmission towers is established, a first mathematical model for describing the polarization characteristics of bird nests is obtained based on the weighted sum of the dielectric model, and an environmental correction factor of the transmission line is introduced to correct the first mathematical model to obtain a second mathematical model;

[0007] Determine the polarization intensity of different types of bird nests on the transmission line according to the second mathematical model;

[0008] The initial corona field strength of the power transmission line containing the bird's nest is corrected according to the electric field distortion rate and the polarization intensity to obtain the corrected initial corona field strength;

[0009] A corona discharge probability model is constructed according to the corrected initial corona field strength, electric field distortion rate and polarization intensity. Based on the corona discharge probability model, the corona discharge risk of transmission lines is analyzed.

[0010] In one implementation, the different types of bird nests include wet bird nests, bird nests containing conductive materials, and scattered bird nests;

[0011] Among them, the dielectric model of the wet bird nest is expressed as: Among them, ε eff is the effective dielectric constant, ε m is the dielectric constant of the dry bird nest material, ε w is the dielectric constant of water, f is the volume fraction of water, α RH is the humidity sensitivity coefficient, RH is the relative humidity;

[0012] The dielectric model of a bird's nest containing conductive material is expressed as: Among them, ε * (ω) is the complex dielectric constant, ε ∞ is the high frequency limiting dielectric constant, ε s is the static dielectric constant, τ MWS is the Maxwell-Wagner-Sillars polarization characteristic time, α is the distribution parameter, σ is the equivalent conductivity, ε0 ​​is the vacuum dielectric constant, and ω is the angular frequency;

[0013] The dielectric model of the scattered bird nest is expressed as: in, is the complex permittivity tensor, is the high frequency limiting dielectric constant tensor, is the static dielectric constant tensor, τ ij is the relaxation time tensor.

[0014] In one implementation, the environmental correction factor is expressed as: K(T,RH,v)=(1+α T (T-T0))(1+β RH (RH-RH0))(1+γ v v), where α T ,β RH ,γ v are the sensitivity coefficients of temperature, humidity and wind speed respectively, T0 and RH0 are the reference temperature and humidity respectively.

[0015] In one implementation, the expression of the second mathematical model is: Among them, ω1, ω2, ω3 are the weight coefficients of the dielectric models of wet bird nests, bird nests containing conductive materials, and scattered bird nests, respectively. A dielectric model representing a wet bird's nest, A dielectric model representing a bird's nest containing conductive material, A dielectric model representing a scattered bird's nest.

[0016] In one implementation, the expression for correcting the initial corona field strength of a transmission line containing a bird nest is: E c′ =E c ·f T ·f RH ·f P ·f D ·f P , where E c′ is the corrected initial corona field strength, E c is the initial corona field strength, f T 、f RH 、f P are the correction factors for temperature, humidity and air pressure, respectively, D is the correction factor to consider the electric field distortion effect, f P is the correction factor to take into account the polarization effect.

[0017] In one implementation, the expression of the corona discharge probability model is: Among them, D c is the critical electric field distortion rate, P c is the critical polarization intensity, E c is the initial corona field strength, E is the local electric field strength, D is the electric field distortion rate, P corona is the probability of corona discharge, P is the polarization intensity, k represents the influence index of electric field intensity, n represents the influence index of electric field distortion rate, and m is the influence index of polarization intensity.

[0018] In one implementation, the method further includes:

[0019] A risk index factor for quantifying the degree of change of the local electric field is calculated based on the electric field distortion rate and the critical distortion rate;

[0020] A time-varying equation of polarization intensity is constructed, and a dynamic risk index factor that captures the dynamic characteristics of the polarization process is calculated based on the time-varying equation of polarization intensity;

[0021] The changing trend of corona discharge risk is analyzed based on risk index factor and dynamic risk index factor.

[0022] In one implementation, the expression of the time-varying equation is: Where τ is the relaxation time, χ is the electric susceptibility, α is the corona discharge feedback coefficient, P corona is the corona discharge probability.

[0023] In a second aspect of the present application, a transmission line corona discharge risk analysis device is provided, the device comprising:

[0024] A mathematical model building module is used to build a dielectric model for characterizing the polarization characteristics of different types of bird nests on transmission towers, obtain a first mathematical model for describing the polarization characteristics of bird nests based on a weighted sum of the dielectric model, and introduce an environmental correction factor of the transmission line to correct the first mathematical model to obtain a second mathematical model;

[0025] A polarization intensity determination module, used to determine the polarization intensity of different types of bird nests on the transmission line according to the second mathematical model;

[0026] A field strength correction module is used to correct the initial corona field strength of the power transmission line containing the bird's nest according to the electric field distortion rate and the polarization intensity to obtain the corrected initial corona field strength;

[0027] The discharge risk analysis module is used to construct a corona discharge probability model according to the corrected initial corona field strength, electric field distortion rate and polarization intensity, and analyze the corona discharge risk of the transmission line based on the corona discharge probability model.

[0028] The third aspect of the present application provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of a transmission line corona discharge risk analysis method provided in the first aspect of the present application are implemented.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. In a transmission line corona discharge risk analysis method provided by the present invention, first, a dielectric model is established to characterize the polarization characteristics of different types of bird nests on transmission towers. Based on the weighted sum of the dielectric model, a first mathematical model for describing the polarization characteristics of the bird nests is obtained, and the environmental correction factor of the transmission line is introduced to correct the first mathematical model to obtain a second mathematical model. In order to comprehensively describe the polarization characteristics of different types of bird nests, the present invention proposes a second mathematical model, which is a comprehensive model that integrates the polarization conditions of different types of bird nests and considers the influence of environmental factors. Through this comprehensive model, the polarization characteristics of bird nests in complex environments can be more accurately described, providing a solid theoretical basis for subsequent electric field distribution analysis and corona discharge risk analysis. Secondly, the initial corona field strength of the transmission line containing bird nests is corrected according to the electric field distortion rate and polarization intensity to obtain the corrected initial corona field strength. When there is a bird nest on the transmission tower, the influence of clothing intrusion is generated. Therefore, the correction of the initial corona field strength needs to consider both the electric field distortion and polarization effects, so as to more comprehensively understand the influence of foreign body intrusion on the electrical characteristics of the transmission line. Finally, a corona discharge probability model is constructed according to the corrected initial corona field strength, electric field distortion rate and polarization intensity, and the corona discharge risk of the transmission line is analyzed based on the corona discharge probability model; the corona discharge probability model reveals the complex relationship between electric field distortion and discharge probability. In this way, the current corona discharge risk can be accurately quantified to provide a basis for real-time monitoring; the dynamic changes of risks can be predicted to provide guidance for preventive maintenance; and multiple factors can be comprehensively considered to improve the accuracy and comprehensiveness of risk analysis. This corona discharge probability model successfully combines static electric field distortion analysis with dynamic polarization process, providing a more comprehensive and accurate risk analysis tool for the safe operation of transmission lines. It can not only identify current high-risk areas, but also predict possible problems in the future, thereby achieving a shift from passive response to active prevention, significantly improving the safety and reliability of transmission lines.

[0031] 2. In a transmission line corona discharge risk analysis method provided by the present invention, a risk index factor for quantifying the degree of change of the local electric field is calculated based on the electric field distortion rate and the critical distortion rate; a time-varying equation of polarization intensity is constructed, and a dynamic risk index factor for capturing the dynamic characteristics of the polarization process is calculated based on the time-varying equation of polarization intensity; finally, the changing trend of the corona discharge risk is analyzed based on the risk index factor and the dynamic risk index factor. That is, the risk index factor of the electric field distortion rate can better reflect the abnormal changes of the local electric field, improve the accuracy and sensitivity of the risk analysis, and the dynamic risk index factor allows tracking the changes of polarization intensity over time, thereby more accurately analyzing the dynamic evolution of the corona discharge risk. It takes into account the polarization response characteristics and the influence of environmental factors, and provides a theoretical basis for real-time risk analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0033] Figure 1 A schematic diagram of a process flow of a transmission line corona discharge risk analysis method provided by an embodiment of the present invention;

[0034] Figure 2 The electric field distribution cloud diagram in the absence of a bird's nest provided by an embodiment of the present invention;

[0035] Figure 3 An electric field distribution cloud diagram in the presence of a bird's nest provided by an embodiment of the present invention;

[0036] Figure 4 A three-dimensional spatial sampling effect diagram provided by an embodiment of the present invention;

[0037] Figure 5 A region selection diagram of a heat map of the spatial distribution of field distortion rate provided by an embodiment of the present invention;

[0038] Figure 6 A thermal diagram of the XOY plane electric field distortion rate distribution provided by an embodiment of the present invention;

[0039] Figure 7 A region selection diagram of a heat map of the spatial distribution of the electric field distortion rate provided in an embodiment of the present invention;

[0040] Figure 8 A field strength variation diagram of a characteristic line segment in a case where there is no bird nest provided by an embodiment of the present invention;

[0041] Fig. 9 A field strength variation diagram of a characteristic line segment in the presence of a bird's nest provided by an embodiment of the present invention;

[0042] Fig.10 A graph showing a change in electric field distortion rate provided by an embodiment of the present invention;

[0043] Fig.11 A heat map and contour map of the electric field intensity distribution in region A provided by an embodiment of the present invention;

[0044] Fig.12 A scatter plot of the probability distribution of corona discharge in region A provided by an embodiment of the present invention;

[0045] Fig.13 A heat map and contour map of the electric field intensity distribution in region B provided by an embodiment of the present invention;

[0046] Fig.14 A scatter plot of the probability distribution of corona discharge in region B provided by an embodiment of the present invention;

[0047] Fig.15 A graph showing the change of polarization intensity over time provided by an embodiment of the present invention;

[0048] Fig.16 A graph showing the change of polarization intensity over time under different temperature and humidity conditions provided by an embodiment of the present invention;

[0049] Fig.17 A principle block diagram of a transmission line corona discharge risk analysis device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0051] It should be noted that the terms "include" or "may include" used in various embodiments of the present application indicate the presence of the function, operation or element applied for, and do not limit the addition of one or more functions, operations or elements. In addition, as used in various embodiments of the present application, the terms "include", "have" and their cognates are only intended to indicate specific features, numbers, steps, operations, elements, components or a combination of the foregoing items, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components or a combination of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, elements, components or a combination of the foregoing items.

[0052] In various embodiments of the present application, the expression "or" or "at least one of B or / and C" includes any combination or all combinations of the words listed at the same time. For example, the expression "B or C" or "at least one of B or / and C" may include B, may include C, or may include both B and C.

[0053] It should be understood that terms such as "first" and "second" are only used for descriptive purposes and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0054] Please refer to Figure 1 , Figure 1 A schematic diagram of a method for analyzing the risk of corona discharge in a power transmission line provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method includes:

[0055] S101, establishing a dielectric model for characterizing the polarization characteristics of different types of bird nests on transmission towers, obtaining a first mathematical model for describing the polarization characteristics of bird nests based on a weighted sum of the dielectric model, and introducing an environmental correction factor of the transmission line to correct the first mathematical model to obtain a second mathematical model.

[0056] In this example, the electric field distribution around the transmission line is the basis for understanding the risk of corona discharge. In an ideal situation, the electric field strength around a single cylindrical conductor can be expressed by the following formula: Where Q is the line charge density, ε0 ​​is the vacuum dielectric constant, and r is the distance to the center of the conductor. However, the electric field distribution of actual transmission lines is more complicated and is affected by factors such as multi-conductor arrangement, tower structure, and ground effect.

[0057] When a bird's nest enters the transmission line environment, the local electric field distribution will be significantly changed. In order to quantify this change, the concept of electric field distortion is introduced. The electric field distortion factor D is defined as: Where E max and E min are the maximum and minimum electric field strengths around the foreign object, and E0 is the background electric field strength when there is no foreign object.

[0058] In order to more comprehensively describe the electric field distortion, the electric field distortion rate δ is introduced: The electric field distribution of actual transmission lines presents a complex spatial variation law: in the vertical direction, the electric field strength decreases with the increase of the height from the ground, which can be approximately expressed as an exponential decay function. In the horizontal direction, due to the influence of adjacent conductors, the electric field strength usually presents a "double peak" distribution. Due to the existence of three-phase current, the electric field strength shows periodic changes in both time and space. In addition, local electric field enhancement occurs in geometric discontinuities such as the ends of conductors and the tips of towers, which is the so-called edge effect. Under high voltage conditions, the accumulation of space charge will also change the electric field distribution, causing it to deviate from the prediction of electrostatic field theory.

[0059] The polarization behavior of a bird's nest in a high-voltage electric field is a key factor affecting the electric field distribution. In order to accurately describe this complex polarization characteristic, it is necessary to establish a comprehensive mathematical model that takes into account the characteristics of different types of bird nests and the influence of environmental factors. Specifically, different types of bird nests include wet bird nests, bird nests containing conductive materials, and scattered bird nests. This embodiment will start from the basic wet bird nest model, gradually expand to more complex situations, and finally build a comprehensive model.

[0060] Wetness is the most common state of a bird's nest and one of the main factors affecting its polarization characteristics. The improved Maxwell-Garnett mixed model is used to describe the dielectric properties of a wet bird's nest. The dielectric model of a wet bird's nest is expressed as: Among them, εeff is the effective dielectric constant, ε m is the dielectric constant of the dry bird nest material, ε w is the dielectric constant of water, f is the volume fraction of water, α RH is the humidity sensitivity coefficient, and RH is the relative humidity. This model not only considers the influence of moisture content, but also introduces the humidity sensitivity coefficient α RH To reflect the dynamic effect of relative humidity on dielectric properties.

[0061] Birds may use conductive materials such as metal wire when building nests, which will significantly change the electrical properties of the bird's nest. To describe this situation, this embodiment proposes a new composite dielectric model. The dielectric model of the bird's nest containing conductive materials is expressed as: Among them, ε * (ω) is the complex dielectric constant, ε ∞ is the high frequency limiting dielectric constant, ε s is the static dielectric constant, τ MWS is the characteristic time of Maxwell-Wagner-Sillars polarization, α is the distribution parameter, σ is the equivalent conductivity, ε0 ​​is the vacuum dielectric constant, and ω is the angular frequency. This model combines Debye relaxation and Maxwell-Wagner-Sillars polarization to more accurately describe the dielectric behavior of complex bird nest materials containing conductive materials.

[0062] Taking into account the heterogeneity and possible anisotropy of the bird nest material, this embodiment introduces a dielectric model in tensor form, that is, the expression of the dielectric model of the floating bird nest is: in, is the complex permittivity tensor, is the high frequency limiting dielectric constant tensor, is the static dielectric constant tensor, τ ij is the relaxation time tensor. This tensor representation can capture the polarization differences of the material in different directions, providing a more accurate input for subsequent electric field analysis.

[0063] In order to fully describe the polarization characteristics of the bird's nest, this embodiment proposes a comprehensive mathematical model that integrates the above three types of bird's nests and considers the influence of environmental factors.

[0064] The comprehensive mathematical model is the second mathematical model, and its expression is:

[0065] Among them, ω1, ω2, ω3 are the weight coefficients of the dielectric models of wet bird nests, bird nests containing conductive materials, and scattered bird nests, respectively. A dielectric model representing a wet bird's nest, A dielectric model representing a bird's nest containing conductive material, A dielectric model representing a scattered bird's nest.

[0066] The expression of environmental correction factor is: K(T,RH,v)=(1+α T (T-T0))(1+β RH (RH-RH0))(1+γ v v), where α T ,β RH ,γ v are the sensitivity coefficients of temperature, humidity and wind speed respectively, T0 and RH0 are the reference temperature and humidity respectively.

[0067] S102, determining the polarization intensity of different types of bird nests on the transmission line according to the second mathematical model.

[0068] In this embodiment, the polarization intensity of different types of bird nests on the transmission line is determined in combination with the second mathematical model of step S101. Based on the second mathematical model, the polarization characteristics of bird nests in complex environments can be more accurately described, providing a basis for subsequent corona discharge risk analysis.

[0069] S103, correcting the initial corona field strength of the power transmission line containing the bird's nest according to the electric field distortion rate and the polarization intensity to obtain a corrected initial corona field strength.

[0070] In this embodiment, considering the impact of foreign objects such as bird nests, the correction of the initial corona field strength needs to consider both the electric field distortion and polarization effect. The expression for correcting the initial corona field strength of the transmission line containing bird nests is: E c′ =E c ·f T ·f RH ·f P ·f D ·f P , where E c′ is the corrected initial corona field strength, E c is the initial corona field strength, f T 、f RH 、f P are the correction factors for temperature, humidity and air pressure, respectively, D is the correction factor to consider the electric field distortion effect, f P is the correction factor to take into account the polarization effect.

[0071] Each correction factor can be expressed as:

[0072] f T =1-α T (T-T0)

[0073] f RH =1-βRH (RH-RH0)

[0074] f P =P / P0

[0075] f D =1 / (1+k D ·D)

[0076] f P =1 / (1+k P ·P / P c ), where D is the electric field distortion rate, P is the polarization intensity, P c is the critical polarization strength, k D and k P are the influence coefficients of distortion and polarization respectively. These parameters directly reflect the influence of foreign matter intrusion on the initial conditions of corona discharge.

[0077] There is a mutual influence between electric field distortion and polarization effect, and this coupling relationship has an important influence on the initiation and development of corona discharge.

[0078] The influence of electric field distortion on polarization: The electric field distortion caused by foreign matter intrusion will change the local electric field strength, thereby affecting the polarization degree of the material. This relationship can be simplified as: P = x e ε0E(1+αD), where P is the polarization intensity, χ e is the electric susceptibility, ε0 ​​is the vacuum dielectric constant, E is the electric field strength, D is the electric field distortion rate, and α is the influence coefficient of distortion on polarization.

[0079] Polarization feedback on electric field distortion: The polarization of the material will in turn affect the electric field distribution, further changing the electric field distortion. This feedback can be expressed as: D = D0 + βP / P k , where D0 is the initial distortion rate, β is the feedback coefficient of polarization on distortion, and P k is the critical polarization intensity.

[0080] Dynamic coupling process: Both electric field distortion and polarization intensity vary with time, and the interaction between them is a dynamic process. By considering the coupling relationship between electric field distortion and polarization characteristics, we can more comprehensively understand the impact of foreign body intrusion on the electrical characteristics of transmission lines. This provides a theoretical basis for subsequent foreign body identification and fault prediction. In practical applications, the risk of corona discharge can be indirectly analyzed by observing the changes in electric field distribution and the polarization response of the material.

[0081] S104, constructing a corona discharge probability model according to the corrected initial corona field strength, electric field distortion rate and polarization intensity, and analyzing the corona discharge risk of the transmission line based on the corona discharge probability model.

[0082] The expression of the corona discharge probability model is: Among them, D c is the critical electric field distortion rate, P c is the critical polarization intensity, E c is the initial corona field strength, E is the local electric field strength, D is the electric field distortion rate, P corona is the probability of corona discharge, P is the polarization intensity, k represents the influence index of electric field intensity, n represents the influence index of electric field distortion rate, and m is the influence index of polarization intensity.

[0083] In some embodiments, the method further comprises:

[0084] A risk index factor for quantifying the degree of change of the local electric field is calculated based on the electric field distortion rate and the critical distortion rate;

[0085] A time-varying equation of polarization intensity is constructed, and a dynamic risk index factor that captures the dynamic characteristics of the polarization process is calculated based on the time-varying equation of polarization intensity;

[0086] The changing trend of corona discharge risk is analyzed based on risk index factor and dynamic risk index factor.

[0087] Specifically, although the traditional static polarization model is simple and intuitive, it has obvious deficiencies in describing the actual behavior of bird nest materials in complex electromagnetic environments. The static model usually uses a linear relationship to describe the relationship between polarization intensity P and electric field intensity E:

[0088] P = ε0χE, where ε0 is the vacuum dielectric constant and χ is the electrical susceptibility. This model ignores the time dependence of the polarization process and the influence of environmental factors, which may lead to large errors in practical applications, especially in complex media such as bird nests.

[0089] Based on the above considerations, the following time-varying equation of polarization intensity is proposed: Where τ is the relaxation time, χ is the electric susceptibility, α is the corona discharge feedback coefficient, P corona is the corona discharge probability.

[0090] The terms of this equation represent the time rate of change of polarization intensity, the decay term of polarization, the polarization enhancement term driven by the external electric field, and the feedback effect of corona discharge on polarization. Among them, τ is the relaxation time, which represents the characteristic time scale of the polarization process; χ is the electric susceptibility, which reflects the response ability of the material to the external electric field; α is the corona discharge feedback coefficient, which describes the influence intensity of corona discharge on the polarization process; P corona is the corona discharge probability, which represents the possibility of local corona discharge.

[0091] Based on the above time-varying equation, the time evolution characteristics of the polarization intensity can be analyzed. In the fast response stage (t<<τ), the polarization intensity increases approximately linearly in a short period of time after the external electric field suddenly changes, which can be expressed as: P(t)≈ε0χEt / τ.

[0092] In the transition stage (t≈τ), the polarization intensity growth rate gradually slows down and begins to show nonlinear characteristics. In the quasi-steady-state stage (t>>τ), the polarization intensity approaches the steady-state value, but due to the influence of corona discharge, there may be small fluctuations, which can be approximately expressed as: P(∞)≈ε0χE / (1+ατP corona ).

[0093] Based on the kinetic equation, a prediction model for polarization state and discharge risk can be constructed:

[0094] P(t+Δt)≈P(t)+[ε0χE / τ-P(t) / τ-αP corona (t)P(t)]Δt, based on this prediction model, the change of polarization intensity in the short term can be predicted according to the current state. Among them, P(t) is the polarization intensity at the current moment, Δt is the predicted time interval, E is the external electric field intensity, τ, χ, α and P corona The meaning is the same as before. Combined with the corona discharge probability model, the discharge risk in the future can be predicted: Risk(t+Δt)=f(P(t+Δt),E(t+Δt)), where f is a risk analysis function based on physical mechanism, which takes the predicted polarization intensity P(t+Δt) and electric field intensity E(t+Δt) as input and outputs the corresponding discharge risk analysis results.

[0095] In order to quantify the degree of change of the local electric field, the electric field distortion rate D is introduced and the risk index factor R is proposed. D :

[0096] Among them, D c is the critical distortion rate, α and n are fitting parameters.

[0097] Electric field distortion plays a key role in risk analysis. It can locate high-risk areas more accurately and is more sensitive to small changes in electric field distribution, which helps to detect potential risks early. By setting different distortion rate thresholds, risk hierarchical management can be achieved, providing a scientific basis for preventive maintenance. Compared with the use of electric field strength alone, distortion rate can better reflect abnormal changes in local electric fields, improving the accuracy and sensitivity of risk analysis.

[0098] In order to capture the dynamic characteristics of the polarization process, the time-varying polarization intensity P(t) is introduced, and based on P(t), the dynamic risk index factor R is defined P (t): Pc is the critical polarization intensity, β and m are fitting parameters.

[0099] The dynamic model allows tracking the change of polarization intensity over time, thus more accurately analyzing the dynamic evolution of corona discharge risk. It takes into account the polarization response characteristics and the influence of environmental factors, providing a theoretical basis for real-time risk analysis.

[0100] Based on the 110kV transmission line environment, a 1:27 scaled-down 3D scene model was constructed. The model comprehensively considers key factors such as transmission tower structure, conductor configuration, insulators, bird nest characteristics, etc., aiming to provide an environment close to the actual situation and convenient for simulation analysis.

[0101] The transmission tower structure adopts the common 110kV voltage level single-circuit tower type. The actual tower height is about 30 meters, which is reduced to 705 mm in the model. The distance between adjacent towers is about 200 meters in reality, which is reduced to 959 mm in the model. The cross arm length is reduced from about 3.5 meters in reality to 130 mm in the model.

[0102] In terms of conductor configuration, a triangular arrangement is used, which is conducive to reducing phase interference and maintaining good electrical clearance. The phase distance is reduced from the actual 2 meters (vertical direction) to 74 mm in the model. The conductor uses a scaled-down version of the LGJ-240 / 30 steel-core aluminum stranded wire, and the actual diameter of 21.6 mm is reduced to 0.8 mm in the model.

[0103] The length of the insulator string in the model is about 37 mm, which corresponds to an actual length of about 1 meter. The material is still ceramic (Ceramic5) to maintain its insulation performance and mechanical strength characteristics.

[0104] The bird's nest model is designed based on the nesting characteristics of medium-sized birds. It adopts a bowl shape and the location of the bird's nest is set above the crossarm on the right side of the tower top, simulating the common situation of birds building nests on transmission towers.

[0105] Taking into account the extreme environmental conditions, the electrical properties of the bird's nest were maintained: relative permittivity was 30 and bulk conductivity was 1 S / m.

[0106] The reasons for these parameter settings are as follows: a. High relative dielectric constant (30), high humidity significantly increases the relative dielectric constant of the material. The dielectric constant of normal dry materials is usually between 2-5, but high humidity can increase it significantly.

[0107] The dielectric constant of wet bricks can reach 15-20. Considering that the bird's nest may be more humid and may contain various conductive impurities, it is set higher. This value reflects the worst case and helps to analyze the electric field enhancement effect under extreme conditions.

[0108] b. High bulk conductivity (1S / m): The conductivity of common insulating materials is usually very low (<10^-6S / m), but it is set relatively high due to the presence of conductive materials (such as metal wires) and high humidity conditions. In order to illustrate the impact of bird nests on the electric field distribution of transmission lines, the baseline electric field distribution without bird nests is first analyzed. Figure 2 The electric field intensity distribution cloud diagram when there is no bird nest is shown. Figure 2 It can be obtained that in the absence of bird nests, the electric field distribution presents the following characteristics: Symmetry: the electric field distribution presents a relatively symmetrical elliptical distribution around the wire; Gradient characteristics: the electric field intensity gradually weakens from the surface of the wire to the outside, forming an obvious gradient; Maximum electric field intensity: appears on the surface of the wire, with a value of about 1.37×10^6V / m.

[0109] After the bird's nest was introduced, the electric field distribution changed significantly. Figure 3 The electric field intensity distribution cloud diagram in the presence of a bird nest is shown. Figure 3 It can be obtained that: electric field distortion: the existence of the bird's nest breaks the original symmetrical distribution, and an obvious electric field distortion area is formed around the bird's nest; the maximum electric field strength increases: the maximum electric field strength rises to about 1.83×10^6V / m, an increase of 33.6%; local electric field enhancement: a significant electric field enhancement phenomenon occurs in the area between the bird's nest and the conductor; electric field redistribution: the distribution of electric field lines around the bird's nest becomes more dense, indicating that the electric field gradient in this area increases; far-field influence: the influence of the bird's nest is not limited to its immediate surroundings, but also extends to a farther distance, changing the overall electric field distribution pattern; lower tower area: after the presence of the bird's nest, the electric field strength around the lower tower is slightly weakened, which may be caused by the redistribution effect of the bird's nest on the electric field. These observations reveal the significant impact of the bird's nest on the electric field distribution of the transmission line, providing an important basis for analyzing potential electrical risks.

[0110] The electric field contours with and without bird nests were generated. This overall visualization provides a preliminary overview of the electric field distribution, but due to the complexity of three-dimensional data, it is difficult to clearly show the detailed changes.

[0111] like Figure 4As shown in the figure, in order to fully capture the changes in the electric field distribution, a sampling area with a step length of 100mm is defined in the simulation cloud map: starting coordinates: (400mm, 500mm, 400mm); sampling area size: 500mm×500mm×1200mm; sampling step length: 100mm. The sampling area covers the area around the bird's nest and part of the wire area to ensure that the key electric field changes can be captured. The field strength data of 468 sampling points are evenly collected in this area.

[0112] In order to fully understand the impact of bird nests on the electric field distribution around transmission lines, a detailed spatial analysis of the electric field distortion rate was conducted. The electric field distortion rate distribution examples on different planes revealed the complex spatial characteristics of the electric field distortion caused by bird nests. In order to analyze the electric field distortion more accurately, a layered thermal map method was used:

[0113] (1) XOY plane heat map

[0114] Fixed different Z values ​​and generated the corresponding XOY plane heat map. Figure 5 and Figure 6 As shown, the heat map shows the two-dimensional distribution of the electric field distortion rate at different heights.

[0115] The electric field distortion analysis in the XOY plane reveals the complex spatial distribution characteristics that vary with the Z value. Table 1 summarizes the main distortion characteristics at different Z values:

[0116] Table 1 Distortion characteristics summary

[0117]

[0118] As can be seen from Table 1, the electric field distortion shows obvious nonlinear characteristics with the change of Z value. In the low Z value area (400-500mm), the distortion is mainly affected by the wire and shows a single center feature. As the Z value increases to 600-900mm, the distortion intensity reaches the maximum, forming a complex multi-center structure, which may be caused by the combined effect of the wire and the bird's nest. In the higher Z value area (1000-1500mm), although the overall distortion intensity begins to weaken, an obvious dual-center structure appears, reflecting that the interaction between the wire and the bird's nest has reached the strongest.

[0119] It is particularly noteworthy that the electric field distortion presents an obvious three-peak characteristic, as shown in Table 2:

[0120] Table 2 Peak characteristics table

[0121]

[0122]

[0123] As the Z value increases, the shape of the distortion area also undergoes significant changes. At Z = 400-600mm, it presents a large ellipse; at Z = 700-1000mm, it forms an irregular multi-center shape; at Z = 1100-1300mm, it becomes an offset ellipse; and finally at Z = 1400-1500mm, it is concentrated into a small local area. This shape evolution reflects the complex change process of the electric field distribution with height.

[0124] (2) XOY plane heat map

[0125] like Figure 7 As shown in Figure 1, by changing the Y value, a series of thermal images of the XOZ plane are generated, which provides the distribution of the electric field distortion in the longitudinal section. The analysis of the XOZ plane further reveals the distribution characteristics of the electric field distortion in the vertical direction.

[0126] Table 3 summarizes the main distortion features under different Y values, and Table 4 deepens the understanding of the distribution of high-risk areas.

[0127] Table 3 Distortion characteristics summary

[0128]

[0129] Table 4 Peak characteristics

[0130]

[0131]

[0132] The analysis results of the XOZ plane show that the electric field distortion presents a complex variation pattern in the vertical direction. Particularly noteworthy are the distortion characteristics at Y = 600 mm and Y = 700 mm. At Y = 600 mm, a large range of strong distortion occurs, forming a double-center structure, reflecting the combined influence of the wire and the bird's nest.

[0133] Near the bird's nest, it was observed that although the overall distortion intensity was weakened, a small distortion region appeared near Z≈1500mm (the height of the bird's nest). This observation supports the hypothesis that the bird's nest does have a significant impact on the surrounding electric field.

[0134] In this embodiment, based on the electric field distortion analysis results of the XOY plane and the XOZ plane, the electric field distortion characteristics and their significance to the corona discharge risk analysis are discussed in depth.

[0135] By analyzing the electric field distortion characteristics in the XOY plane, it was found that the strongest distortion occurred in the Z value range of 600-900mm, with a distortion intensity of up to 0.8. This finding is of great significance because it not only points out possible high-risk discharge areas, but also provides key clues for understanding the dynamic changes in the electric field distribution. It is particularly noteworthy that at Z values ​​of 1000-1500mm, although the distortion intensity decreases slightly (0.8-0.7), a dual-center structure appears. This complex distortion structure implies the nonlinear characteristics of the electric field distribution, which may lead to abnormally high discharge risks in local areas.

[0136] Further analysis of the distortion characteristics of the XOZ plane revealed that strong distortion areas were formed at Y = 600 mm and Y = 700 mm, especially the appearance of a double-center "dumbbell" structure. This unique structure not only identifies potential high-risk areas, but also reveals the complex distribution pattern of electric field distortion in space. This finding is of great value for optimizing corona discharge prediction models because it shows that more complex spatial correlations and inhomogeneities need to be considered.

[0137] It is worth noting that even in the range of Y = 800-900 mm where the overall distortion intensity is weakened, a local strong distortion area is still found at Z ≈ 1500 mm. This phenomenon emphasizes the multi-scale characteristics of electric field distortion, suggesting that when analyzing discharge risks, we cannot rely solely on macroscopic analysis, but also need to consider local anomalies at the microscopic scale.

[0138] Based on these in-depth distortion analyses, risk areas can be divided more accurately. For example, the area with Z = 600-900mm, Y = 600-700mm may face the highest discharge risk, and the expected discharge probability may exceed 80%. In the area with Z = 1000-1500mm, Y = 500-600mm and 700-800mm, the discharge risk may be at a medium level, with a probability between 50-80%. This risk zoning method based on distortion characteristics provides more targeted guidance for the monitoring and maintenance of transmission lines.

[0139] More importantly, these distortion analysis results provide new ideas for improving the discharge probability model. Traditional models may be oversimplified and fail to fully consider the spatial inhomogeneity and multi-center structure of the electric field distribution. By introducing distortion intensity and structure factors, the accuracy and applicability of the model can be significantly improved. For example, one can consider using distortion intensity as a key input variable of the discharge probability model, or develop a new spatial correlation function based on the observed dual-center structure.

[0140] In addition, the distortion analysis also reveals the dynamic characteristics of the electric field distribution. It is observed that the distortion intensity and structure show a complex nonlinear relationship with the change of Z value and Y value. When analyzing the risk of corona discharge, a dynamic and adaptive method is needed instead of a static and unchanging model. By deeply analyzing the electric field distortion characteristics, not only can high-risk areas be identified more accurately, but also the accuracy of discharge probability prediction can be significantly improved.

[0141] like Figure 8 and Fig. 9 As shown, a representative line segment analysis was performed on areas of strong distortion that may exist around the bird's nest. This method allows the electric field distortion on a specific path to be examined at a higher resolution, thereby capturing details that may have been missed by previous sampling methods. A representative line segment was selected that passes through the location of the bird's nest with coordinates from (716, 757, 417) to (716, 757, 1617). Along this line segment, high-resolution sampling was performed at intervals of 1 mm to calculate the electric field strength in both cases.

[0142] like Fig.10 As shown in the figure, at a distance of about 1.1m, the third peak (near the bird's nest) is different from what expected. The distortion rate here not only does not increase significantly, but instead becomes negative, indicating that the electric field strength decreases. This phenomenon may be due to the specific settings of the bird's nest model (such as uniform smooth surface, specific conductivity settings, etc.). Based on these results, it is recommended that when selecting high-risk areas for subsequent analysis, the first two distortion peak areas should be focused on, and the area directly around the bird's nest (the third peak area in this case) should not be automatically regarded as a high-risk area. The first two peaks may correspond to the two obvious distortion areas observed in the XOY and XOZ thermal maps.

[0143] Based on the modified Peek formula mentioned above, the following parameters are used for calculation: E c′ =E c ·f T ·f RH ·f P ·f β , the following parameters are used: wire radius r = 0.04m (model size), δ = 1 under standard conditions. The environmental correction factor (mildly harsh conditions) is set to: f T =0.985, f RH =0.967, f P =1 (assuming the air pressure remains constant). Electric field enhancement coefficient β = 5.735 (based on simulation results).

[0144] Substituting these parameters into the modified Peek formula, we can obtain the initial corona field strength: E c′ ≈13.07kV / m.

[0145] The corona discharge probability is calculated using the following formula: where E is the local electric field strength, E c′ is the corrected initial corona field strength. The model shows that when the local electric field strength exceeds the initial corona field strength, the discharge probability increases sharply.

[0146] In order to comprehensively analyze the risk of corona discharge and determine the best measurement location, a two-dimensional plane high-resolution numerical simulation method was used. This method not only simplifies the data visualization process, but also effectively captures the key points with large electric field intensity. Two key areas were selected for detailed analysis: Area A (Area 1 near the conductor):

[0147] Starting coordinates: (567mm, 600mm, 507mm);

[0148] Sampling area size: 0mm×200mm×1050mm; step length: 50mm.

[0149] Area B (Area 2 near the wire):

[0150] Starting coordinates: (835mm, 600mm, 507mm);

[0151] Sampling area size: 200mm×200mm×1050mm;

[0152] Step length: 50 mm. In order to comprehensively analyze and intuitively display the distribution of corona discharge risks in areas A and B, a probability model based on electric field strength was used for calculation, and the results were presented through a series of visual charts.

[0153] The calculation of the corona discharge probability is based on the following formula: When the local field strength exceeds the starting field strength, the discharge probability increases significantly.

[0154] Based on this calculation method, three types of visualization charts are generated:

[0155] (1) Electric field intensity distribution heat map: It intuitively shows the spatial distribution of electric field intensity and uses color gradient to represent intensity changes.

[0156] (2) Corona onset field strength contour map: The corona onset field strength contour maps are superimposed on the thermal map to clearly identify the boundaries of the potential discharge risk area.

[0157] (3) Discharge probability distribution scatter plot: Discrete points are used to represent the discharge probability at different positions. The color and size of the points reflect the probability.

[0158] Fig.11The heat map of the electric field intensity distribution in area A and the contour lines of the corona onset field intensity are shown. From the figure, four high field intensity areas can be clearly seen, corresponding to the positions of the conductors. The red contour lines outline the boundaries of the corona onset field intensity. In the areas within these lines, the risk of corona discharge increases significantly.

[0159] Fig.12 It is a scatter plot of the probability distribution of corona discharge in region A. Each point represents a sampling location, and the color of the point changes from blue to red, reflecting the change of discharge probability from low to high. It can be observed that the high probability points (red and orange) are mainly concentrated in the region with Fig.11 Positions corresponding to medium and high field strength areas.

[0160] By analyzing Fig.12 The electric field distortion rate distribution heat map in the figure can quantitatively estimate the discharge probability of area A. According to the color distribution, the area is roughly divided into three probability levels: high, medium, and low. The high probability area is mainly concentrated in the red and orange parts, the medium probability area corresponds to the yellow and green parts, and the probability is about 0.5 to 0.8; the blue area represents the low probability area, and the discharge probability is close to 0.

[0161] In order to obtain the average discharge probability of the entire area A, the weighted average method is used for calculation. Assume that the high probability area occupies 10% of the total area, with an average probability of 0.9; the medium probability area occupies 20%, with an average probability of 0.65; and the low probability area occupies the remaining 70%, with an average probability of 0.1.

[0162] Through this weighted calculation method, the average discharge probability of area A is about 29%. It is worth noting that this 29% is the average value of the entire area, and in fact some local areas (such as Fig.11 The discharge probability of the red and orange points in the figure is much higher than this average level, even close to 100%.

[0163] Next, we will analyze region B to compare the similarities and differences between the two regions.

[0164] Similarly, the weighted average method is used to calculate the average discharge probability of area B, assuming that the high probability area accounts for 12% of the total area, with an average probability of 0.95; the medium probability area accounts for 18%, with an average probability of 0.7; the low probability area accounts for 70%, with an average probability of 0.1. The average discharge probability of area B is calculated to be about 0.31 or 31%.

[0165] like Fig.13 and Fig.14 As shown in the figure, by comparing and analyzing the electric field intensity distribution and discharge probability distribution in area A and area B, it can be found that the two areas have both similarities and significant differences. Specifically:

[0166] Electric field strength distribution: Area A is unevenly distributed, with the high field strength area in the lower left corner being particularly prominent, while area B is more evenly distributed, especially the two high field strength areas at the bottom have similar strengths. This difference may be due to different wire arrangements or environmental conditions.

[0167] Discharge probability distribution: Both regions show an obvious four-layer structure (Y = 775mm, 725mm, 675mm, 625mm), corresponding to the four main conductors of the transmission line. However, the high probability discharge points in region B are more concentrated and regular, especially at Y = 775mm and Y = 675mm.

[0168] Bird's nest impact: There is an independent high probability point ("Bird's nest") in the upper right corner of both regions, but the location is different. The suspected bird's nest in region A is at Z≈1400mm, while in region B it is at Z≈1550mm. This difference reflects the significant impact of bird's nests on partial discharge risk.

[0169] Characteristics of the central area: The field strength distribution in the central area of ​​area B is more uniform, while the field strength in the central area of ​​area A varies greatly. This may lead to differences in the stability and predictability of the overall discharge risk distribution between the two areas.

[0170] Through an in-depth comparison of the discharge probability distributions of area A and area B, it was found that the impact of local anomalies on the overall electric field distribution was far greater than expected. In particular, the presence of foreign objects such as bird nests will affect the discharge risk in a wider range by changing the surrounding electric field distribution. This discovery provides a new idea for the detection of foreign object intrusion on transmission lines: foreign object intrusion can be indirectly identified by monitoring abnormal changes in the electric field distribution, even if the foreign object itself is difficult to observe directly.

[0171] This static analysis method, combined with the dynamic simulation discussed in Chapter 3, can greatly improve the sensitivity and accuracy of foreign object detection. In the dynamic model, special attention can be paid to high-risk points, and their polarization characteristics under different weather conditions can be used to more accurately analyze their dynamic impact on the overall discharge risk.

[0172] Static electric field distortion analysis is extended to the dynamic domain. By introducing polarization characteristics, a connection between static analysis and dynamic behavior is established, allowing for a more comprehensive analysis of the potential threat of bird nests to the safety of transmission lines. Polarization characteristics serve as a key bridge, enabling the transition from static analysis to dynamic simulation, exploring the temporal changes in the polarization characteristics of bird nest materials, and analyzing the impact of these changes on the risk of corona discharge.

[0173] Although the dynamic changes of the electric field cannot be observed directly, the dynamic behavior of the electric field can be captured indirectly by simulating the polarization process. This method can not only infer instantaneous electric field changes, but also predict long-term risk trends. This chapter will first establish a dynamic model based on polarization characteristics, and then perform numerical simulations to reflect the time evolution characteristics of the electric field. Finally, these simulation results will be analyzed to analyze the dynamic changes of corona discharge risks.

[0174] The polarization intensity dynamics equation described above is converted into a computable numerical model:

[0175] In order to solve this nonlinear differential equation, the fourth-order Runge-Kutta method (RK4) is used. The RK4 method is selected based on its high accuracy and good numerical stability, which is particularly suitable for dealing with such complex equations involving the coupling of multiple physical processes.

[0176] In the specific implementation, the function describing the rate of change of polarization intensity is first defined, and then the RK4 algorithm is used for iterative calculation. In order to improve the calculation efficiency and accuracy, adaptive step control is also implemented. The initial time step is set to 1 / 1000 of the total simulation time, and then dynamically adjusted according to the error estimate.

[0177] τ0=1×10 -6 S

[0178] E a =0.1eV

[0179] k B =8.617×10 -5 eV / K

[0180] T=308K

[0181] χ e0 =29

[0182] β RH =0.01

[0183] RH=80%

[0184] E = 5.77 × 10 6 V / m

[0185] The following parameters were used: α = 0.1.

[0186] Given, initial conditions and boundary conditions Initial conditions

[0187] Boundary conditions: Since the embodiment is a time evolution problem, no spatial boundary conditions are involved. However, the time range needs to be defined, and 0 to 1 ms is selected here to capture fast polarization processes and possible steady-state behaviors.

[0188] Using the above numerical method, the variation of polarization intensity P(t) with time is simulated, and odeint is used to solve the differential equation:

[0189] like Fig.15 As shown, according to the results: characteristic time: 44.04μs, steady-state polarization intensity is 2.18e+08C / m 2 , rise time (10% to 90%): 95.10μs.

[0190] like Fig.16 This indicates that the polarization process occurs very quickly and can respond to changes in the electric field within microseconds. High temperature and high humidity conditions accelerate the polarization process, causing the polarization intensity to reach a stable state faster and with a higher steady-state value. The results show that high temperature and high humidity conditions accelerate the polarization process, causing the polarization intensity to reach a stable state faster and with a higher steady-state value. This explains why the risk of corona discharge increases significantly under severe weather conditions.

[0191] The dynamic changes of the electric field distribution are calculated: E total (t) = E background +k·P(t);

[0192] The electric field distortion rate is defined as:

[0193] The dynamic model can capture the time evolution characteristics of the electric field distribution, while the static model can only give a fixed result. The accuracy can be compared by comparing the electric field distribution of the two models at key time points (such as 0.5μs, 2μs, and 5μs).

[0194] Based on the dynamic changes of polarization intensity and electric field distribution, the time evolution of corona discharge probability is calculated:

[0195]

[0196] By analyzing the temporal evolution of the probability of corona discharge, the following high-risk periods and key influencing factors are identified:

[0197] (1) Initial rapid rise stage (0-1μs): At this time, the discharge risk increases sharply and requires special attention.

[0198] (2) Moments of sudden environmental changes: Sudden changes in temperature or humidity may cause a sharp increase in discharge risk.

[0199] (3) Long-term cumulative effect: Long-term repeated polarization may cause material performance degradation and increase the risk of discharge.

[0200] The simulation analysis of the rainstorm process simulated a rainstorm process lasting 6 hours, which is divided into three stages: before the rainstorm, during the rainstorm and after the rainstorm: Before the rainstorm (0-2 hours): the temperature slowly dropped from 28℃ to 24℃, and the relative humidity rose from 65% to 85%. During the rainstorm (2-4 hours): the temperature dropped sharply to 20℃, and the relative humidity rose rapidly and maintained at 95-100%. After the rainstorm (4-6 hours): the temperature slowly rose to 22℃, and the relative humidity gradually dropped to 80%. Changes in polarization characteristics and discharge risks during the rainstorm.

[0201] Based on the simulated rainstorm conditions, the changes in polarization intensity and corona discharge probability are calculated. The calculation process uses the fourth-order Runge-Kutta method with a time step of 1 second to ensure that the rapidly changing dynamic characteristics are captured. The results show that:

[0202] (1) The polarization intensity increases rapidly at the beginning of the rainstorm (2 hours), from the initial value of 0.5×10^-4C / m 2 Increased to 1.8×10^-4C / m 2 .

[0203] (2) During the duration of the heavy rain, the polarization intensity remained at a high level but showed small fluctuations, reflecting the response of the material to the continued high humidity.

[0204] (3) The discharge probability increases significantly when the humidity reaches 90% (about 2.5 hours), rising rapidly from the initial 5% to 25%.

[0205] (4) After the rainstorm ended, the polarization intensity and discharge probability both showed a slow downward trend, but were still significantly higher than the initial levels at the end of the observation period.

[0206] In summary, this embodiment proposes a transmission line corona discharge risk analysis, which provides an innovative solution to the transmission line safety problem caused by bird nests, and has the following beneficial effects:

[0207] A mathematical model was established that comprehensively considers the polarization characteristics of wet bird nests, bird nests containing conductive materials, and bird nests that are easy to disperse. This model not only considers the essential characteristics of the material, but also incorporates the influence of environmental factors, providing more accurate input for subsequent electric field analysis.

[0208] The electric field distortion rate is introduced as a quantitative evaluation index, and its spatial distribution characteristics are deeply analyzed. The electric field distortion rate presents a complex three-peak distribution pattern, which provides a theoretical basis for the identification of high-risk areas.

[0209] The time-varying dynamic equation of polarization intensity was constructed, realizing the analysis upgrade from static to dynamic. This innovation enables a more accurate description of the response process of bird nest materials in electric fields, providing theoretical support for real-time monitoring and early warning.

[0210] A corona discharge probability model that comprehensively considers electric field distortion and polarization characteristics is proposed. This model can not only more accurately assess the discharge risk, but also capture the dynamic impact of changing environmental conditions on the risk.

[0211] The effectiveness of this method in practical applications has been verified through the simulation cases described above. Under extreme weather conditions such as heavy rain, the risk of corona discharge shows obvious hysteresis effects and nonlinear response characteristics, which provides an important basis for formulating more targeted preventive measures. Combining the polarization characteristics of bird nest materials with electric field distortion analysis has achieved an important breakthrough from static analysis to dynamic evaluation. This method not only improves the accuracy of risk assessment, but also provides a new perspective for real-time monitoring and early warning technology. It has important theoretical and practical significance for improving the safety and reliability of transmission lines.

[0212] The embodiment of the present invention also provides a transmission line corona discharge risk analysis device, which can be used to perform each step of a transmission line corona discharge risk analysis method described in the above embodiment of the present invention. Fig.17 , Fig.17 The principle block diagram of a transmission line corona discharge risk analysis device provided by an embodiment of the present invention is shown, and the device includes:

[0213] The mathematical model building module 1710 is used to build a dielectric model for characterizing the polarization characteristics of different types of bird nests on the transmission tower, obtain a first mathematical model for describing the polarization characteristics of the bird nest based on the weighted sum of the dielectric model, and introduce the environmental correction factor of the transmission line to correct the first mathematical model to obtain a second mathematical model;

[0214] A polarization intensity determination module 1720, configured to determine the polarization intensity of different types of bird nests on the transmission line according to the second mathematical model;

[0215] A field strength correction module 1730 is used to correct the initial corona field strength of the power transmission line containing the bird's nest according to the electric field distortion rate and the polarization intensity to obtain a corrected initial corona field strength;

[0216] The discharge risk analysis module 1740 is used to construct a corona discharge probability model according to the corrected initial corona field strength, electric field distortion rate and polarization intensity, and analyze the corona discharge risk of the transmission line based on the corona discharge probability model.

[0217] A transmission line corona discharge risk analysis device in an embodiment of the present application is similar to the above Figure 1 The transmission line corona discharge risk analysis method shown is an invention based on the same concept. Through the detailed description of a transmission line corona discharge risk analysis device provided in the above embodiment, those skilled in the art can clearly understand the implementation process of a transmission line corona discharge risk analysis device in this embodiment, so for the sake of brevity of the specification, it will not be repeated here.

[0218] Accordingly, the present embodiment provides a transmission line corona discharge risk analysis device. First, a dielectric model is established to characterize the polarization characteristics of different types of bird nests on transmission towers. Based on the weighted sum of the dielectric model, a first mathematical model for describing the polarization characteristics of the bird nests is obtained. The environmental correction factor of the transmission line is introduced to correct the first mathematical model to obtain a second mathematical model. In order to comprehensively describe the polarization characteristics of different types of bird nests, the present invention proposes a second mathematical model. The second mathematical model is a comprehensive model that integrates the polarization conditions of different types of bird nests and takes into account the influence of environmental factors. Through this comprehensive model, the polarization characteristics of bird nests in complex environments can be more accurately described, providing a solid theoretical basis for subsequent electric field distribution analysis and corona discharge risk analysis. Secondly, the initial corona field strength of the transmission line containing bird nests is corrected according to the electric field distortion rate and polarization intensity to obtain the corrected initial corona field strength. When there is a bird nest on the transmission tower, the influence of clothing intrusion is generated. Therefore, the correction of the initial corona field strength needs to consider both the electric field distortion and polarization effects, so as to more comprehensively understand the influence of foreign body intrusion on the electrical characteristics of the transmission line. Finally, a corona discharge probability model is constructed according to the corrected initial corona field strength, electric field distortion rate and polarization intensity, and the corona discharge risk of the transmission line is analyzed based on the corona discharge probability model; the corona discharge probability model reveals the complex relationship between electric field distortion and discharge probability. In this way, the current corona discharge risk can be accurately quantified to provide a basis for real-time monitoring; the dynamic changes of risks can be predicted to provide guidance for preventive maintenance; and multiple factors can be comprehensively considered to improve the accuracy and comprehensiveness of risk analysis. This corona discharge probability model successfully combines static electric field distortion analysis with dynamic polarization process, providing a more comprehensive and accurate risk analysis tool for the safe operation of transmission lines. It can not only identify current high-risk areas, but also predict possible problems in the future, thereby achieving a shift from passive response to active prevention, significantly improving the safety and reliability of transmission lines.

[0219] An embodiment of the present application also provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of a transmission line corona discharge risk analysis method described in the above embodiment are implemented.

[0220] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for analyzing the risk of corona discharge in a transmission line, characterized in that: include: A dielectric model for characterizing the polarization characteristics of different types of bird nests on transmission towers is established, a first mathematical model for describing the polarization characteristics of bird nests is obtained based on the weighted sum of the dielectric model, and an environmental correction factor of the transmission line is introduced to correct the first mathematical model to obtain a second mathematical model; Determine the polarization intensity of different types of bird nests on the transmission line according to the second mathematical model; The initial corona field strength of the power transmission line containing the bird's nest is corrected according to the electric field distortion rate and the polarization intensity to obtain the corrected initial corona field strength; A corona discharge probability model is constructed according to the corrected initial corona field strength, electric field distortion rate and polarization intensity. Based on the corona discharge probability model, the corona discharge risk of transmission lines is analyzed.

2. A method for analyzing the risk of corona discharge in a power transmission line according to claim 1, characterized in that: The different types of bird nests include wet bird nests, bird nests containing conductive materials and scattered bird nests; Among them, the dielectric model of the wet bird nest is expressed as: Among them, ε eff is the effective dielectric constant, ε m is the dielectric constant of the dry bird nest material, ε w is the dielectric constant of water, f is the volume fraction of water, α RH is the humidity sensitivity coefficient, RH is the relative humidity; The dielectric model of a bird's nest containing conductive material is expressed as: Among them, ε * (ω) is the complex dielectric constant, ε ∞ is the high frequency limiting dielectric constant, ε s is the static dielectric constant, τ MWS is the Maxwell-Wagner-Sillars polarization characteristic time, α is the distribution parameter, σ is the equivalent conductivity, ε0 ​​is the vacuum dielectric constant, and ω is the angular frequency; The dielectric model of the scattered bird nest is expressed as: in, is the complex permittivity tensor, is the high frequency limiting dielectric constant tensor, is the static dielectric constant tensor, τ ij is the relaxation time tensor.

3. A method for analyzing the risk of corona discharge in a power transmission line according to claim 2, characterized in that: The expression of the environmental correction factor is: K(T,RH,v)=(1+α T (T-T0))(1+β RH (RH-RH0))(1+γ v v), where α T ,β RH ,γ v are the sensitivity coefficients of temperature, humidity and wind speed respectively, T0 and RH0 are the reference temperature and humidity respectively.

4. A method for analyzing the risk of corona discharge in a power transmission line according to claim 3, characterized in that: The expression of the second mathematical model is: Among them, ω1, ω2, ω3 are the weight coefficients of the dielectric models of wet bird nests, bird nests containing conductive materials, and scattered bird nests, respectively. A dielectric model representing a wet bird's nest, A dielectric model representing a bird's nest containing conductive material, A dielectric model representing a scattered bird's nest.

5. A method for analyzing the risk of corona discharge in a power transmission line according to claim 1, characterized in that: The expression for correcting the initial corona field strength of a transmission line containing bird nests is: E c′ =E c ·f T ·f RH ·f P ·f D ·f P , where Ec ′ is the corrected initial corona field strength, E c is the initial corona field strength, f T 、f RH 、f P are the correction factors for temperature, humidity and air pressure, respectively, D is the correction factor to consider the electric field distortion effect, f P is the correction factor to take into account the polarization effect.

6. A method for analyzing the risk of corona discharge in a power transmission line according to claim 1, characterized in that: The expression of the corona discharge probability model is: Among them, D c is the critical electric field distortion rate, P c is the critical polarization intensity, E c is the initial corona field strength, E is the local electric field strength, D is the electric field distortion rate, P corona is the probability of corona discharge, P is the polarization intensity, k represents the influence index of electric field intensity, n represents the influence index of electric field distortion rate, and m is the influence index of polarization intensity.

7. A method for analyzing the risk of corona discharge in a power transmission line according to claim 1, characterized in that: The method further comprises: A risk index factor for quantifying the degree of change of the local electric field is calculated based on the electric field distortion rate and the critical distortion rate; A time-varying equation of polarization intensity is constructed, and a dynamic risk index factor that captures the dynamic characteristics of the polarization process is calculated based on the time-varying equation of polarization intensity; The changing trend of corona discharge risk is analyzed based on risk index factor and dynamic risk index factor.

8. A method for analyzing the risk of corona discharge in a power transmission line according to claim 7, characterized in that: The expression of the time-varying equation is: Where τ is the relaxation time, χ is the electric susceptibility, α is the corona discharge feedback coefficient, P corona is the corona discharge probability.

9. A transmission line corona discharge risk analysis device, characterized in that: The device includes: A mathematical model building module is used to build a dielectric model for characterizing the polarization characteristics of different types of bird nests on transmission towers, obtain a first mathematical model for describing the polarization characteristics of bird nests based on a weighted sum of the dielectric model, and introduce an environmental correction factor of the transmission line to correct the first mathematical model to obtain a second mathematical model; A polarization intensity determination module, used to determine the polarization intensity of different types of bird nests on the transmission line according to the second mathematical model; A field strength correction module is used to correct the initial corona field strength of the power transmission line containing the bird's nest according to the electric field distortion rate and the polarization intensity to obtain the corrected initial corona field strength; The discharge risk analysis module is used to construct a corona discharge probability model according to the corrected initial corona field strength, electric field distortion rate and polarization intensity, and analyze the corona discharge risk of the transmission line based on the corona discharge probability model.

10. An electronic device, characterized in that: The electronic device includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of a transmission line corona discharge risk analysis method as described in any one of claims 1 to 8 are implemented.

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