Risk assessment method and device for power transmission line deicing jump, electronic equipment and computer readable storage medium

Abstract

The invention belongs to the technical field of power grid disaster prevention and reduction, and particularly relates to a risk assessment method and device for deicing jump of a power transmission line, electronic equipment and a computer readable storage medium. The method comprises the following steps: determining risk influence factors influencing the deicing jump of the power transmission line, and constructing an original parameter data set containing a plurality of deicing working conditions; establishing a conductor deicing model under a plurality of deicing working conditions and carrying out analogue simulation to obtain a risk consequence index; constructing a standardized decision matrix, respectively calculating the comparison intensity and conflict of each risk influence factor, and calculating the comprehensive information amount and objective weight of each risk influence factor to obtain an objective weight vector; and identifying the key dominant factors based on the objective weight vector and carrying out working condition optimization in combination with the risk consequence indexes under each deicing working condition to obtain the optimal deicing working condition with the lowest comprehensive risk. According to the method, the CRITIC method is applied to power transmission line deicing jump risk assessment, and the deicing working condition with small risk can be selected.

Description

Technical Field

[0001] This application belongs to the field of power grid disaster prevention and mitigation technology, specifically relating to a risk assessment method, device, electronic equipment, and computer-readable storage medium for transmission line de-icing and jumping. Background Technology

[0002] As my country's power grid develops towards longer distances, larger capacities, and higher voltage levels, an increasing number of transmission lines need to traverse high-altitude and heavy icing areas with complex climates. In these regions, conductor icing in winter is a common and extremely dangerous natural disaster. When temperatures rise or natural winds blow, the ice on the conductors can suddenly and unevenly detach, triggering a violent de-icing jump. This process causes an instantaneous release of energy within the conductor system, generating dynamic loads and large displacements far exceeding design conditions, which can easily lead to a series of serious safety accidents, including but not limited to: electrical clearance breakdown between conductors or between conductors and ground wires, causing short circuits and tripping; damage to hardware and broken conductor strands; and damage to the tower structure due to excessive dynamic tension, even leading to catastrophic consequences such as tower collapse. Therefore, scientific and accurate assessment and early warning of the risk of transmission line de-icing jumps have become a major technical requirement for ensuring the safe and stable operation of power grids in icy areas.

[0003] Currently, the industry primarily relies on finite element numerical simulations and engineering experience to assess and control the risk of ice-breaking jumps. By establishing detailed finite element models of the line-tower system, the dynamic response under specific operating conditions such as ice thickness, ice-breaking rate, and ice-breaking method can be simulated. However, the actual ice-breaking process is influenced by a complex interplay of factors, making it a typical multivariate, nonlinear dynamic problem. Numerous key influencing factors exist, including: ice parameters (such as ice thickness, ice-breaking location, and ice-breaking rate), environmental parameters (such as wind speed and radiation intensity), and line-specific parameters (such as span, elevation difference, splitting pattern, and conductor diameter). Traditional methods typically only simulate and analyze a few pre-defined typical operating conditions or rely on expert experience to discuss the sensitivity of individual factors. This approach has significant limitations: firstly, it is difficult to exhaust all possible combinations of operating conditions, resulting in insufficient representativeness of the analysis results; secondly, it lacks an objective and quantitative mechanism to identify and weigh the relative importance of each influencing factor to the final risk. In existing studies, the determination of factor weights is often subjective, making it impossible to automatically and scientifically extract the intrinsic relationships and independent contributions between indicators from massive simulation data. This limits the universality of risk assessment conclusions and their guidance for decision-making. Summary of the Invention

[0004] To address the aforementioned issues, there is an urgent need to introduce a decision analysis tool capable of deeply mining the intrinsic structure of multi-factor data and objectively assigning weights. This application applies the CRITIC method to the risk assessment of transmission line de-icing jumps, which helps to select low-risk de-icing conditions and reduce the risk of de-icing jumps.

[0005] In a first aspect, this application provides a risk assessment method for de-icing jumps in transmission lines, including: Identify the risk factors affecting the de-icing jump of transmission lines and construct a raw parameter dataset containing multiple de-icing conditions; Establish conductor de-icing models under multiple de-icing conditions, conduct conductor de-icing simulation, and obtain risk consequence indicators under each de-icing condition; Based on the original parameter datasets for each de-icing condition, a standardized decision matrix is ​​constructed. Based on the standardized decision matrix, the comparative strength and conflict of each risk factor are calculated, as well as the comprehensive information content and objective weight of each risk factor are calculated, to obtain the objective weight vector of all risk factors. Based on the objective weight vector, key dominant factors are identified, and the working conditions are optimized by combining the risk consequence indicators under each de-icing condition, so as to obtain the best de-icing condition with the lowest comprehensive risk.

[0006] Secondly, this application also provides a risk assessment device for de-icing jumps of transmission lines, the device comprising: The factor definition unit is used to determine the risk factors affecting the de-icing jump of transmission lines and to construct a raw parameter dataset containing multiple de-icing conditions. The simulation unit is used to establish conductor de-icing models under multiple de-icing conditions, conduct conductor de-icing simulation, and obtain risk consequence indicators under each de-icing condition. The matrix construction unit is used to construct a standardized decision matrix based on the original parameter dataset for each de-icing condition; The parameter calculation unit is used to calculate the comparative strength and conflict of each risk influencing factor based on the standardized decision matrix, as well as to calculate the comprehensive information content and objective weight of each risk influencing factor, and obtain the objective weight vector of all risk influencing factors. The working condition optimization unit is used to identify key dominant factors based on the objective weight vector, and to optimize the working condition by combining the risk consequence indicators under each de-icing working condition, so as to obtain the best de-icing working condition with the lowest comprehensive risk.

[0007] Thirdly, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the risk assessment method for de-icing and jumping of transmission lines described above.

[0008] Fourthly, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the risk assessment method for de-icing and jumping of transmission lines described above.

[0009] The above-mentioned at least one technical application used in the embodiments of this application can achieve the following beneficial effects: (1) The evaluation dimensions are comprehensive and objective, overcoming subjective bias. This invention adopts the CRITIC objective weighting method, whose weight calculation is not only based on the self-variation of a single influencing factor under different working conditions (comparative strength), but also takes into account the correlation (conflict) between factors. This mechanism can automatically identify and reduce the weight of information redundancy factors, thereby mathematically ensuring that the evaluation results are entirely derived from the inherent structure of the data itself, completely avoiding the subjectivity and inconsistency brought about by traditional reliance on expert experience scoring, making the ranking and classification of risk influencing factors more scientific and credible.

[0010] (2) Accurately identify key risk factors to achieve targeted prevention and control. Traditional methods are difficult to quantify the contribution of different factors to the risk of de-icing jumps. This invention, through the calculation of objective weights, can clearly and quantitatively identify the key dominant factors that lead to the dynamic response of the line. This provides direct and accurate data support for the differentiated design, reinforcement and renovation of the line, as well as the deployment strategy of the online monitoring system, realizing the transformation from "general anti-icing" to "targeted prevention and control", which helps to optimize resource allocation and improve the efficiency of safety investment.

[0011] (3) Reverse guidance for de-icing operations, supporting scientific decision-making. The core application value of this invention lies in its reverse guidance capability. By analyzing the relationship between the weights of various factors and the risk value of the operating conditions, it can proactively seek optimization and select the optimal de-icing condition with the lowest overall risk. This provides a quantitative and operable safety operation guide for on-site de-icing and ice melting operations, enabling operators to formulate the optimal de-icing strategy while ensuring the safety of the power grid, effectively avoiding secondary accidents that may be caused by blind or non-standard operations.

[0012] (4) The method has strong universality and is easy to integrate and promote. The method described in this invention has a clear process, a high degree of standardization, and does not rely on individual experience of specific lines. Its input is general finite element simulation data or processed measured data, and the output is a clear weight and ranking. It can be easily integrated into existing transmission line disaster prevention and mitigation decision support systems or intelligent operation and maintenance platforms as a core risk analysis module. This method is applicable to the de-icing jump risk analysis of transmission lines of different voltage levels and different geographical environments, and has broad promotion value.

[0013] In summary, by introducing the CRITIC objective weighting method, this invention overcomes the subjectivity and limitations of traditional experience-based judgments or single simulations, and can automatically and quantitatively extract the relative importance of each risk influencing factor from massive de-icing simulation data. This method not only identifies the key factors affecting the risk of de-icing jumps, providing a basis for precise prevention and control, but also uses weighting analysis to guide de-icing operations in reverse, finding the optimal de-icing conditions within the safety boundary, thereby improving the scientific and proactive nature of transmission lines in responding to ice disasters and ensuring the safe and stable operation of the power grid. Attached Figure Description

[0014] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating a risk assessment method for de-icing jump of a transmission line according to an embodiment of this application is shown. Figure 2 A schematic diagram of a risk assessment device for de-icing jump of a transmission line according to an embodiment of this application is shown. Figure 3 A schematic diagram of the resulting electronic device according to an embodiment of this application is shown. Detailed Implementation

[0015] To make the objectives, technical claims, and advantages of this application clearer, the technical application of this application will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] The purpose of this application is to find an objective assessment method to help discuss the various factors affecting the risk of de-icing jumps, eliminate the influence of subjective factors, and make the analysis results representative and universal. Using the method of this application, a data-driven assessment framework can be established to scientifically identify the most unfavorable operating conditions with the highest risk from a large number of feasible operating conditions, providing a precise basis for line design and verification; moreover, it can be applied in reverse to guide de-icing operations, that is, given line parameters and meteorological conditions, it helps to screen out the optimal de-icing operating conditions that cause the least risk, providing key decision support for formulating safe and efficient proactive de-icing strategies, and ultimately achieving an upgrade from passive anti-icing to proactive anti-icing and precise ice control risk management model.

[0017] The main concept of this application lies in introducing a decision analysis tool capable of deeply mining the inherent structure of multi-factor data and objectively assigning weights—the CRITIC (Criteria Importance Through Intercriteria Correlation) objective weighting method, a classic method designed for such multi-criteria decision problems. Its core advantage lies in the fact that weight calculation not only considers the dispersion (information content) of individual indicator data but also fully takes into account the correlation (conflict) between indicators, thereby automatically reducing the weight of redundant indicators and highlighting key core indicators with strong independence and high volatility. Applying the CRITIC method to the risk assessment of transmission line de-icing jumps helps to select low-risk de-icing conditions and reduce the risk of de-icing jumps.

[0018] Example 1 Figure 1 A flowchart illustrating a risk assessment method for de-icing jumps of transmission lines according to an embodiment of this application is shown. Figure 1 As can be seen, this embodiment includes steps S110 to S150: Step S110: Identify the risk factors affecting the de-icing jump of the transmission line and construct an original parameter dataset containing multiple de-icing conditions.

[0019] In some embodiments of this application, the risk factors affecting the de-icing jump of transmission lines mainly include, but are not limited to: line structural parameters, icing parameters, and meteorological parameters; wherein, line structural parameters include, but are not limited to: span, elevation difference, splitting pattern, and conductor diameter; icing parameters include, but are not limited to: icing type, icing thickness, de-icing location, and de-icing rate; meteorological parameters include, but are not limited to: temperature, temperature rise rate, and radiation intensity.

[0020] Based on the above parameters, raw data of these risk factors under different de-icing conditions were collected to form raw parameter datasets for different de-icing conditions.

[0021] Step S120: Establish conductor de-icing models under multiple de-icing conditions, conduct conductor de-icing simulation, and obtain risk consequence indicators under each de-icing condition.

[0022] Existing technologies can be referenced for conductor de-icing models and simulations, such as establishing dynamic models of the conductor-tower system under various de-icing conditions, and obtaining the core risk consequences indicators for each condition through transient dynamic simulation.

[0023] In some embodiments of this application, the core risk assessment indicators for each de-icing condition simulation result include, but are not limited to: maximum jump height, minimum electrical clearance, maximum dynamic tension of the conductor, etc. It is assumed that each de-icing condition has p risk consequence indicators.

[0024] Step S130: Construct a standardized decision matrix based on the original parameter datasets for each de-icing condition.

[0025] Specifically, in some embodiments of this application, the original parameter values ​​of each risk influencing factor in all de-icing conditions are constructed into an original data matrix, and the range normalization method is used to standardize it to form a dimensionless standardized decision matrix.

[0026] Suppose there are m simulated operating conditions, and each condition contains n risk influencing factors. First, construct the original data matrix for each of the n influencing factors. ,in This represents the parameter value of the j-th influencing factor under the i-th working condition.

[0027] To eliminate the influence of dimensions, the range normalization method is used to standardize matrix X, resulting in a standardized matrix. .

[0028] Step S140: Based on the standardized decision matrix, calculate the comparative strength and conflict of each risk influencing factor, and calculate the comprehensive information content and objective weight of each risk influencing factor to obtain the objective weight vector of all risk influencing factors.

[0029] In some embodiments of this application, the following method is used to calculate the contrast intensity: using the standard deviation This measures the degree of variability of the j-th influencing factor across all de-icing conditions, i.e., its intrinsic information content. A larger standard deviation indicates more significant changes in the factor under different conditions, and a stronger potential discriminative power. The formula for calculating the contrast intensity is: .

[0030] In some embodiments of this application, the following method is used to calculate conflict: the degree of information overlap is quantified by calculating the correlation coefficient between each risk influencing factor.

[0031] Specifically, calculate the Pearson correlation coefficient between the j-th factor and the k-th factor. Using Pearson correlation coefficient This represents the correlation between the j-th factor and the k-th factor; for the j-th factor, its conflict with all other factors is accumulated, that is, the conflict with all other factors of the j-th factor is quantified as: The larger this value, the more independent information the j-th factor provides.

[0032] Furthermore, based on the comparative strength and conflict of each risk influencing factor, the comprehensive information content and objective weight of each risk influencing factor are calculated.

[0033] In some embodiments of this application, the comprehensive information content is calculated using the following method. Multiplying the contrast intensity and conflict level of the j-th influencing factor yields its comprehensive information content. ; The higher the value, the more important the factor is in the overall evaluation system.

[0034] In some embodiments of this application, the objective weight of each risk factor is calculated using the following method. The objective weight of each influencing factor is obtained by normalizing the overall information content of that factor. .

[0035] Summarizing the above results, we can obtain the objective weight vector of all n risk factors affecting the ice-breaking jump: .

[0036] Step S150: Based on the objective weight vector, identify key dominant factors and combine risk consequence indicators under each de-icing condition to optimize the working condition and obtain the best de-icing condition with the lowest comprehensive risk.

[0037] The identification of key dominant factors is based on objective weight vectors. Specifically, in some embodiments of this application, the calculated objective weights Wj of each risk influencing factor are ranked. Factors with significantly higher weights than other influencing factors are identified as key dominant factors leading to de-icing jump risks. Alternatively, a descending order method is used to select a predetermined number of risk influencing factors as key dominant factors. Or, if the weight of a risk influencing factor is greater than 1.5 times the average weight (this is a preset threshold that can be set as needed), then that risk influencing factor is determined to be a key dominant factor. This provides precise targets for line anti-icing design (such as specifically improving the tolerance standard for a certain factor) and risk monitoring (such as focusing on monitoring changes in key factors).

[0038] Finally, based on the simulation results obtained in step S120 above, the optimal de-icing conditions for the de-icing operation are sought.

[0039] The simulation results include risk consequence indicators under each de-icing condition. In some embodiments of this application, the search for the optimal de-icing condition includes: constructing a multi-factor evaluation system by combining the risk consequence factors and the objective weights of each risk influencing factor under each de-icing condition; calculating the comprehensive risk assessment value of each de-icing condition based on the multi-factor evaluation system, or clarifying the risk sensitivity of different de-icing strategies through weight analysis, where different de-icing strategies are reflected as combinations of different risk influencing factors; and selecting the condition with the lowest comprehensive risk assessment value or the lowest risk sensitivity as the optimal de-icing condition.

[0040] By combining the risk consequences (such as the maximum jump height) obtained under each working condition and the objective weights of each risk influencing factor obtained above, a multi-factor weighted evaluation model is constructed.

[0041] Based on this multi-factor weighted evaluation model, the comprehensive risk assessment value under each de-icing condition can be calculated, or the risk sensitivity of different de-icing strategies (reflected in different combinations of influencing factors) can be clarified through weight analysis.

[0042] Ultimately, the operating condition with the lowest comprehensive risk assessment value, or the condition where the risk is significantly reduced after proactively controlling key factors (such as controlling the de-icing rate and selecting specific de-icing times to match favorable meteorological parameters), can be recommended as the optimal de-icing condition. This condition can be used to guide the formulation of on-site de-icing operation plans and achieve efficient removal of line icing under controllable risks.

[0043] Example 2 1. A 500kV transmission line needs to cross the heavy icing area in Southwest China. The line management plans to carry out active thermal de-icing operations during the winter icing period. To avoid safety accidents caused by de-icing jumps, it is necessary to scientifically assess the de-icing risks and determine the safest weather conditions and de-icing rate for the operation.

[0044] 2. Detailed Implementation Steps Step 1: Identify influencing factors and obtain operating condition data List of influencing factors: Based on the characteristics of this line and engineering experience, the following six key influencing factors are selected for analysis: F1: Ice thickness (unit: mm); F2: De-icing rate (unit: %, refers to the percentage of ice load that instantly falls off within a single range); F3: De-icing position (at 1 / 10, 1 / 5, 3 / 10, 2 / 5, 1 / 2 of the gear); F4: Wind speed (unit: m / s); F5: Gear distance (unit: m); F6: Initial tension of the conductor (unit: kN).

[0045] Construction of working condition sets and simulation: Based on historical meteorological data and the range of parameters for ice melting operations, 20 representative combinations of ice removal working conditions (i.e., m=20) were designed. For example: Operating Condition 1: {Ice thickness 20mm, de-icing rate 50%, central de-icing, wind speed 0m / s, span 450m, tension 35kN}; Operating Condition 2: {Ice thickness 15mm, de-icing rate 80%, end de-icing, wind speed 5m / s, span 450m, tension 32kN}; ... (20 groups in total).

[0046] Using professional transmission line dynamic analysis software, transient dynamic simulation of de-icing jump was performed for each working condition, and the core risk consequence indicators for each working condition were extracted: Y1: maximum jump height (m) and Y2: conductor dynamic tension increment (%).

[0047] Step Two: Data Standardization The raw data of 6 influencing factors under 20 operating conditions were constructed into a 20×6 matrix X. Since the dimensions of each factor are different (such as thickness mm and wind speed m / s), the range method was used for standardization to transform all data into the interval [0, 1], resulting in a standardized matrix Z.

[0048] Step 3: Calculate the contrast intensity and conflict. Contrast strength: Calculate the standard deviation σ of each factor after standardization.

[0049] The calculations yielded the following results: σ(ice thickness) = 0.32, σ(ice removal rate) = 0.28, σ(ice removal location) = 0.25, σ(wind speed) = 0.18, σ(span) = 0.02 (close to a constant), and σ(initial tension) = 0.15.

[0050] Preliminary interpretation: The data on icing thickness showed the greatest fluctuation, indicating that it varied significantly under the designed operating conditions, which may lead to substantial differences in risk.

[0051] Conflictability: Calculate the Pearson correlation coefficient matrix R between each pair of the six factors, and calculate the conflict quantification value for each factor. .

[0052] For example, calculations revealed a certain negative correlation between "ice thickness" and "initial conductor tension" (r≈-0.6), because ice weight increases tension. However, "ice removal rate" showed a weaker correlation with several other factors.

[0053] The calculated conflict values ​​for each factor are: 5.2, 5.8, 4.9, 5.1, 5.5, and 4.7.

[0054] Step 4: Calculate the total information content and objective weights Comprehensive information content Multiply the contrast intensity by the conflict intensity.

[0055] For example, regarding "ice thickness": =0.32 × 5.2 = 1.664; Regarding "ice removal rate": =0.28 × 5.8 = 1.624; Regarding "gear spacing": =0.02 × 5.5 = 0.110; Objective weight :right Normalize.

[0056] The calculation results are shown in the following example: Ice thickness (F1): Weight ≈0.28; De-icing rate (F2): Weight ≈0.27; De-icing position (F3): Weight ≈0.18; Wind speed (F4): Weight ≈0.15; Gear spacing (F5): Weight ≈0.02; Initial tension (F6): Weight ≈0.10.

[0057] Step 5: Results Analysis and Application Key Factor Identification: Weighting analysis clearly shows that for this section of the line, ice thickness and de-icing rate are the two most critical factors affecting the risk of ice-free jumping, with their combined weight exceeding 50%. This suggests that on-site operations should prioritize accurate monitoring of ice thickness and strictly control the de-icing rate.

[0058] Optimal operating condition search: Review the risk consequences of 20 simulated operating conditions (Y1, Y2). Apply weights to aid decision-making: Option A (Comprehensive Scoring Method): For each working condition, calculate its weighted risk score (e.g., sum the standardized values ​​of each factor after multiplying them by their weights; the lower the score, the more controllable the risk). Ranking revealed that when the icing thickness was moderate (15mm), the de-icing rate was low (30%), and the conditions were windless or lightly windy, the comprehensive weighted score was the lowest, indicating the most controllable risk.

[0059] Option B (Key Factor Control Method): Based on the weighting results, the key factor "ice removal rate" is directly identified. Research in the simulation data reveals that when the ice removal rate is controlled within the 30%-50% range, the maximum jump height is generally 40%-60% lower than when the ice removal rate is above 80%. Therefore, the optimal ice removal condition can be determined as follows: when the ice thickness is <20mm, segmented, low-speed ice melting is employed to control the single ice removal rate below 50%, and operation should be prioritized during windless periods.

[0060] In summary, the technical solution of this application has the following significant advantages over the prior art: (1) The evaluation dimensions are comprehensive and objective, overcoming subjective bias. This invention adopts the CRITIC objective weighting method, whose weight calculation is not only based on the self-variation of a single influencing factor under different working conditions (comparative strength), but also takes into account the correlation (conflict) between factors. This mechanism can automatically identify and reduce the weight of information redundancy factors, thereby mathematically ensuring that the evaluation results are entirely derived from the inherent structure of the data itself, completely avoiding the subjectivity and inconsistency brought about by traditional reliance on expert experience scoring, making the ranking and classification of risk influencing factors more scientific and credible.

[0061] (2) Accurately identify key risk factors to achieve targeted prevention and control. Traditional methods are difficult to quantify the contribution of different factors to the risk of de-icing jumps. This invention, through the calculation of objective weights, can clearly and quantitatively identify the key dominant factors that lead to the dynamic response of the line. This provides direct and accurate data support for the differentiated design, reinforcement and renovation of the line, as well as the deployment strategy of the online monitoring system, realizing the transformation from "general anti-icing" to "targeted prevention and control", which helps to optimize resource allocation and improve the efficiency of safety investment.

[0062] (3) Reverse guidance for de-icing operations, supporting scientific decision-making. The core application value of this invention lies in its reverse guidance capability. By analyzing the relationship between the weights of various factors and the risk value of the operating conditions, it can proactively seek optimization and select the optimal de-icing condition with the lowest overall risk. This provides a quantitative and operable safety operation guide for on-site de-icing and ice melting operations, enabling operators to formulate the optimal de-icing strategy while ensuring the safety of the power grid, effectively avoiding secondary accidents that may be caused by blind or non-standard operations.

[0063] (4) The method has strong universality and is easy to integrate and promote. The method described in this invention has a clear process, a high degree of standardization, and does not rely on individual experience of specific lines. Its input is general finite element simulation data or processed measured data, and the output is a clear weight and ranking. It can be easily integrated into existing transmission line disaster prevention and mitigation decision support systems or intelligent operation and maintenance platforms as a core risk analysis module. This method is applicable to the de-icing jump risk analysis of transmission lines of different voltage levels and different geographical environments, and has broad promotion value.

[0064] In summary, by introducing the CRITIC objective weighting method, this invention overcomes the subjectivity and limitations of traditional experience-based judgments or single simulations, and can automatically and quantitatively extract the relative importance of each risk influencing factor from massive de-icing simulation data. This method not only identifies the key factors affecting the risk of de-icing jumps, providing a basis for precise prevention and control, but also uses weighting analysis to guide de-icing operations in reverse, finding the optimal de-icing conditions within the safety boundary, thereby improving the scientific and proactive nature of transmission lines in responding to ice disasters and ensuring the safe and stable operation of the power grid.

[0065] Figure 2 A risk assessment apparatus for de-icing jump of transmission lines according to an embodiment of this application is shown, from... Figure 2 It can be seen that the risk assessment device 200 for de-icing and tripping of transmission lines includes: Factor definition unit 210 is used to determine the risk factors affecting the de-icing jump of transmission lines and to construct a raw parameter dataset containing multiple de-icing conditions. The simulation unit 220 is used to establish conductor de-icing models under multiple de-icing conditions, perform conductor de-icing simulation, and obtain risk consequence indicators under each de-icing condition. Matrix building unit 230 is used to build a standardized decision matrix based on the original parameter dataset of each de-icing condition; The parameter calculation unit 240 is used to calculate the comparative strength and conflict of each risk influencing factor based on the standardized decision matrix, and to calculate the comprehensive information content and objective weight of each risk influencing factor, so as to obtain the objective weight vector of all risk influencing factors. The working condition optimization unit 250 is used to identify key dominant factors based on the objective weight vector, and to optimize the working condition by combining the risk consequence indicators under each de-icing working condition, so as to obtain the best de-icing working condition with the lowest comprehensive risk.

[0066] In some embodiments of this application, the risk influencing factors in the above-mentioned device mainly include: line structure parameters, icing parameters, and meteorological parameters; wherein, the line structure parameters include: span, elevation difference, splitting pattern, and conductor diameter; the icing parameters include, but are not limited to: icing type, icing thickness, de-icing location, and de-icing rate; the meteorological parameters include, but are not limited to: temperature, temperature rise rate, and radiation intensity.

[0067] In some embodiments of this application, in the above-described apparatus, the matrix construction unit 230 is used to construct an original data matrix from the original parameter values ​​of each risk influencing factor in all de-icing conditions; wherein, there are m simulated conditions, and each condition contains n risk influencing factors; for the n influencing factors, their original data matrices are... ,in Let represent the parameter value of the j-th influencing factor under the i-th working condition; standardize it using the range normalization method to form a dimensionless standardized decision matrix. .

[0068] In some embodiments of this application, in the above-described apparatus, the matrix construction unit 230 is used to calculate the matrix using standard deviation. To measure the degree of variability of the j-th influencing factor across all de-icing conditions, the formula for calculating the contrast intensity is: ; Conflict calculations include: Calculate the Pearson correlation coefficient between the j-th factor and the k-th factor. For the j-th factor, its conflict with all other factors is accumulated: .

[0069] In some embodiments of this application, in the above-described apparatus, the matrix construction unit 230 is used to multiply the contrast intensity and conflict level of the j-th influencing factor to obtain the comprehensive information contained therein: ; Objective weight of each risk factor The calculations include: The objective weight of each influencing factor is obtained by normalizing the overall information content of that factor. .

[0070] In some embodiments of this application, in the above-described apparatus, the working condition optimization unit 250 is used to sort the calculated objective weights Wj of each risk influencing factor in descending order, and select a preset number of risk influencing factors at the top of the sort as key dominant factors; or, if the weight value of a risk influencing factor is greater than the average weight preset multiple threshold, then the risk influencing factor is determined to be a key dominant factor.

[0071] In some embodiments of this application, in the above-mentioned device, the working condition optimization unit 250 is used to construct a multi-factor evaluation system by combining the risk consequence factors and the objective weights of each risk influencing factor under each de-icing working condition; calculate the comprehensive risk evaluation value of each de-icing working condition based on the multi-factor evaluation system, or clarify the risk sensitivity of different de-icing strategies through weight analysis, wherein different de-icing strategies are reflected as combinations of different risk influencing factors; and select the one with the lowest comprehensive risk evaluation value, or the one with the lowest risk sensitivity, as the optimal de-icing working condition.

[0072] It should be noted that the aforementioned risk assessment device for transmission line de-icing jump can implement the aforementioned risk assessment method for transmission line de-icing jump, which will not be elaborated further.

[0073] Figure 3 This invention illustrates a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 3 As shown, the electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used for communication with external devices via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a risk assessment method for transmission line de-icing and tripping.

[0074] In one embodiment, the electronic device provided in this application includes a memory and a processor. The memory stores a database and a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the aforementioned risk assessment method for de-icing and jumping of transmission lines.

[0075] The above is as stated in this application. Figure 2The method for performing risk assessment of transmission line de-icing and tripping as disclosed in the illustrated embodiments can be applied to a processor or implemented by a processor. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The steps of the method disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0076] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the aforementioned risk assessment method for transmission line de-icing and jumping.

[0077] It should be noted that the functions or steps that the above-mentioned electronic devices or computer-readable storage media can achieve can be referred to the relevant descriptions in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0078] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0079] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0080] The above-described embodiments are only used to illustrate the technical application of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical applications described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical applications to deviate from the spirit and scope of the technical applications of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A risk assessment method for de-icing jumps in transmission lines, characterized in that, include: Identify the risk factors affecting the de-icing jump of transmission lines and construct a raw parameter dataset containing multiple de-icing conditions; Establish conductor de-icing models under multiple de-icing conditions, conduct conductor de-icing simulation, and obtain risk consequence indicators under each de-icing condition; Based on the original parameter datasets for each de-icing condition, a standardized decision matrix is ​​constructed; Based on the standardized decision matrix, the comparative strength and conflict of each risk factor are calculated, as well as the comprehensive information content and objective weight of each risk factor are calculated, to obtain the objective weight vector of all risk factors. Based on the objective weight vector, key dominant factors are identified, and the working conditions are optimized by combining the risk consequence indicators under each de-icing condition, so as to obtain the best de-icing condition with the lowest comprehensive risk.

2. The method according to claim 1, characterized in that, The risk factors mainly include: line structure parameters, icing parameters, and meteorological parameters; The line structure parameters include: span, elevation difference, splitting pattern, and conductor diameter; The icing parameters include, but are not limited to: icing type, icing thickness, de-icing location, and de-icing rate; The meteorological parameters include, but are not limited to: temperature, rate of temperature rise, and radiation intensity.

3. The method according to claim 1, characterized in that, Based on the original parameter dataset for each de-icing condition, a standardized decision matrix is ​​constructed, including: The original parameter values ​​of each risk influencing factor in all de-icing conditions are constructed into an original data matrix; where there are m simulation conditions, and each condition contains n risk influencing factors; for each of the n influencing factors, the original data matrix is... ,in This represents the parameter value of the j-th influencing factor under the i-th working condition; The range normalization method is used to standardize the matrix, resulting in a dimensionless standardized decision matrix. .

4. The method according to claim 3, characterized in that, The calculation of contrast intensity includes: With standard deviation To measure the degree of variability of the j-th influencing factor across all de-icing conditions, the formula for calculating the contrast intensity is: ； Conflict calculations include: Calculate the Pearson correlation coefficient between the j-th factor and the k-th factor. For the j-th factor, its conflict with all other factors is accumulated: 。 5. The method according to claim 3, characterized in that, Comprehensive information content The calculations include: Multiplying the contrast intensity and conflict level of the j-th influencing factor yields the total amount of information implied: ； Objective weight of each risk factor The calculations include: The objective weight of each influencing factor is obtained by normalizing the overall information content of that factor. 。 6. The method according to claim 1, characterized in that, The identification of key dominant factors based on the objective weight vector includes: The objective weights Wj of each risk influencing factor calculated are sorted in descending order, and a predetermined number of risk influencing factors at the top of the sort are selected as key dominant factors. or, If the weight of a risk factor is greater than the preset multiple threshold of the average weight, then the risk factor is identified as a key dominant factor.

7. The method according to claim 1, characterized in that, Finding the optimal de-icing conditions includes: A multi-factor evaluation system is constructed by combining the risk consequences factors and the objective weights of each risk influencing factor under various de-icing conditions. The comprehensive risk assessment value of each de-icing condition is calculated based on a multi-factor evaluation system, or the risk sensitivity of different de-icing strategies is clarified through weight analysis. Different de-icing strategies are reflected as combinations of different risk influencing factors. The lowest comprehensive risk assessment value, or the lowest risk sensitivity, is selected as the optimal de-icing condition.

8. A risk assessment device for de-icing jumps in transmission lines, characterized in that, The device includes: The factor definition unit is used to determine the risk factors affecting the de-icing jump of transmission lines and to construct a raw parameter dataset containing multiple de-icing conditions. The simulation unit is used to establish conductor de-icing models under multiple de-icing conditions, conduct conductor de-icing simulation, and obtain risk consequence indicators under each de-icing condition. The matrix construction unit is used to construct a standardized decision matrix based on the original parameter dataset for each de-icing condition; The parameter calculation unit is used to calculate the comparative strength and conflict of each risk influencing factor based on the standardized decision matrix, as well as to calculate the comprehensive information content and objective weight of each risk influencing factor, and obtain the objective weight vector of all risk influencing factors. The working condition optimization unit is used to identify key dominant factors based on the objective weight vector, and to optimize the working condition by combining the risk consequence indicators under each de-icing working condition, so as to obtain the best de-icing working condition with the lowest comprehensive risk.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the risk assessment method for de-icing and jumping of transmission lines as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the risk assessment method for de-icing and jumping of transmission lines as described in any one of claims 1 to 7.

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