Icing galloping monitoring device evaluation method based on fuzzy comprehensive evaluation

Abstract

The invention relates to the technical field of power system online monitoring, and particularly discloses an icing galloping monitoring device evaluation method based on fuzzy comprehensive evaluation. In order to solve the problems that an existing evaluation method depends on experience judgment, standards are not uniform, evaluation dimensions are single, and a scientific basis cannot be provided for device type selection and arrangement, the method comprises the steps that a three-level evaluation index system of a tree structure is constructed, and weights of indexes of all levels are determined in a grading mode; and then dynamically adjusting the weight by adopting multiple strategies according to the actual configuration condition of the device, and finally calculating a comprehensive evaluation value and grade by applying a multi-level fuzzy comprehensive evaluation method. According to the method, comprehensive and accurate quantitative evaluation of the icing galloping monitoring device from technical parameters to overall performance is realized, the accuracy and comparability of an evaluation result are remarkably improved, and a scientific and reliable decision basis is provided for an electric power department to perform device type selection, purchase and optimized layout.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, and in particular to an icing galloping monitoring device evaluation method based on fuzzy comprehensive evaluation. BACKGROUND

[0002] With the influence of global climate change, the range of icing belts of power transmission lines is expanding, and the threat of icing galloping disasters to power systems is becoming increasingly serious. In order to effectively respond to icing galloping disasters, the power department has installed online monitoring devices for icing, galloping, etc. on some key lines and regions, and by collecting key parameters such as icing thickness and galloping amplitude in real time, line state information is provided to operating personnel. However, the reliability and stability of existing monitoring devices in harsh environments need to be improved, and there are significant differences in measurement accuracy, response speed, etc. among devices of different manufacturers and different technical principles, the value of multi-source sensing data cannot be fully utilized, and there is a lack of scientific and reasonable monitoring device evaluation methods.

[0003] In the prior art, the evaluation of icing galloping monitoring devices mainly relies on experience judgment and simple comparison of technical parameters, and lacks a systematic evaluation method. Traditional evaluation methods usually only focus on a single technical indicator, such as measurement accuracy or operating temperature range, while ignoring the comprehensive performance and reliability of the device and other key factors, and have the defects of strong subjectivity, non-uniform standards, poor comparability of results, etc., and cannot provide scientific basis for the selection, procurement and layout of monitoring devices. SUMMARY

[0004] The present application provides an icing galloping monitoring device evaluation method and system based on fuzzy comprehensive evaluation, which solves the technical problem that existing icing galloping monitoring device evaluation methods based on fuzzy comprehensive evaluation rely on experience judgment and simple comparison of technical parameters, and cannot provide scientific basis for the selection, procurement and layout of monitoring devices.

[0005] To solve the above technical problems, the present application provides an icing galloping monitoring device evaluation method based on fuzzy comprehensive evaluation, comprising:

[0006] Based on the power industry standards and the characteristics of the icing galloping monitoring device, a three-level evaluation index system with a tree structure is constructed;

[0007] According to the different characteristics of each index of the three-level evaluation index system, the weight of each index at each level is determined;

[0008] Based on the configuration requirements of the icing galloping monitoring device in actual application, the weights of the indexes at each level are dynamically adjusted;

[0009] Based on the weights and index values of the currently configured indexes at each level, the multi-level fuzzy comprehensive evaluation method is used to calculate the comprehensive evaluation value of the three-level evaluation index system, and the evaluation grade is determined according to the comprehensive evaluation value.

[0010] Furthermore, in the three-level evaluation index system, the first-level index includes at least the basic technical parameters of the device and the overall performance of the device; the second-level index includes at least multiple technical units that affect the basic technical parameters of the device and multiple performance units that affect the overall performance of the device; and the third-level index includes at least multiple influencing factors that affect each of the technical units and each of the performance units.

[0011] Furthermore, for the primary indicators, the weights of each primary indicator are determined based on the analytic hierarchy process. The determination process is as follows:

[0012] A judgment matrix for primary indicators is constructed based on pairwise comparisons of the relative importance of the primary indicators.

[0013] Calculate the eigenvector corresponding to the largest eigenvalue of the primary indicator judgment matrix, and obtain the primary indicator weight vector after normalization.

[0014] The consistency ratio is calculated based on the maximum eigenvalue. If the consistency ratio is less than the preset ratio, the weight of each primary indicator is determined according to the current primary indicator weight vector. Otherwise, the process returns to the first step to adjust the primary indicator judgment matrix.

[0015] Furthermore, for the secondary indicators, the weights of each secondary indicator are determined based on the analytic hierarchy process and objective data. The determination process is as follows:

[0016] The analytic hierarchy process (AHP) was used to determine the subjective weight matrix of each secondary indicator.

[0017] The objective weight matrix of each secondary indicator is calculated using the entropy weight method.

[0018] The subjective weight matrix of the secondary indicators is fused with the objective weight matrix of the secondary indicators to obtain the comprehensive weight of each secondary indicator.

[0019] Furthermore, the subjective weight matrix of the secondary indicators is fused with the objective weight matrix of the secondary indicators, specifically including:

[0020] Calculate the membership degree of each secondary indicator in the subjective weight matrix and the objective weight matrix of the secondary indicators, respectively;

[0021] The membership degrees of each secondary indicator are summed to obtain the corresponding fuzzy frequencies;

[0022] The fuzzy frequencies of each secondary indicator are normalized to obtain the comprehensive weight of each secondary indicator, resulting in a comprehensive weight vector of secondary indicators that includes the comprehensive weight of all secondary indicators.

[0023] Furthermore, for the tertiary indicators, both objective and subjective algorithms are used to determine the weights of each tertiary indicator. The determination process is as follows:

[0024] The weights of the three-level indicators for strategy selection were determined using the analytic hierarchy process.

[0025] The weights of the third-level indicators for monitoring accuracy range were determined by combining the improved entropy weight method with the objective weighting method.

[0026] The weights of log-normally distributed tertiary indicators, uniformly distributed tertiary indicators, and technical specification tertiary indicators in the category of technical parameter tertiary indicators were determined by the log-normal coefficient of variation method, the standard coefficient of variation method, and the technical specification comparison method, respectively.

[0027] The weights of each tertiary indicator are uniformly normalized to obtain the comprehensive weight vector of the tertiary indicators.

[0028] Furthermore, strategies for dynamically adjusting the weights of indicators at each level include:

[0029] Secondary indicator main function weight allocation strategy: dynamically allocate the weight of primary indicators related to icing and galloping monitoring according to the type of icing and galloping monitoring device;

[0030] Secondary indicator optional function weight redistribution strategy: For optional secondary indicators, if they are not configured, their preset standard weights are proportionally compensated to the other mandatory secondary indicators.

[0031] The strategy for redistributing the weights of optional tertiary indicators is as follows: If an optional tertiary indicator under a secondary indicator is not configured, its standard weight will be proportionally compensated to other configured tertiary indicators within that secondary indicator.

[0032] The three-level indicator structure weight switching strategy is as follows: based on whether the device protection structure is single-layer or double-layer, the weights of the corresponding three-level indicators are switched and assigned.

[0033] Furthermore, based on the currently configured weights and values ​​of indicators at all levels, a multi-level fuzzy comprehensive evaluation method is used to calculate the comprehensive evaluation value of the three-level evaluation indicator system, and the evaluation level is determined based on the comprehensive evaluation value, specifically including:

[0034] Based on the correlation between the values ​​of the three-level indicators and the evaluation results, the currently configured three-level indicators are divided into positively correlated indicators where the larger the indicator value, the better the evaluation result; negatively correlated indicators where the smaller the indicator value, the better the evaluation result; and qualitative or quantitative indicators with finite discrete points.

[0035] Design corresponding membership functions for positively correlated indicators, negatively correlated indicators, and qualitative or quantitative indicators;

[0036] The membership degree of the currently configured third-level indicators is calculated using the corresponding membership function;

[0037] Based on the membership degree of each configured third-level indicator, a fuzzy evaluation matrix for the third-level indicators is constructed;

[0038] Multiplying the fuzzy evaluation matrix of the third-level indicator with its weight matrix yields the evaluation vector of the second-level indicator;

[0039] The primary indicator evaluation vector is obtained by multiplying the secondary indicator evaluation vector by its weight matrix.

[0040] The comprehensive evaluation vector is obtained by multiplying the primary indicator evaluation vector by its weight matrix.

[0041] Calculate the comprehensive evaluation value based on the comprehensive evaluation vector;

[0042] The assessment level is determined based on the comprehensive assessment value.

[0043] Furthermore, the primary indicators include at least the basic technical parameters of the device and the overall performance of the device; the secondary indicators include at least two of the following: icing monitoring unit, galloping monitoring unit, micro-meteorological monitoring unit, image monitoring unit, communication unit, and power supply unit, which affect the basic technical parameters of the device; and at least two of the following: environmental adaptability, protection performance, safety performance, and reliability performance, which affect the overall performance of the device.

[0044] Furthermore, the three-level indicators affecting the icing monitoring unit include at least one of the following: icing thickness measurement range and icing thickness measurement accuracy.

[0045] The three-level indicators affecting the gooseing monitoring unit include at least one of the following: gooseing amplitude measurement range, gooseing amplitude measurement accuracy, gooseing frequency measurement range, and gooseing frequency measurement accuracy.

[0046] The three-level indicators affecting the micro-meteorological monitoring unit include at least one of the following: wind speed measurement range, wind speed measurement accuracy, wind direction measurement range, wind direction measurement accuracy, air temperature measurement range, air temperature measurement accuracy, humidity measurement range, humidity measurement accuracy, air pressure measurement range, air pressure measurement accuracy, rainfall measurement range, rainfall measurement accuracy, and light radiation measurement range and light radiation measurement accuracy.

[0047] The three-level indicators affecting the image monitoring unit include at least one of the following: camera device type, image format, number of pixels, lens zoom rate, and number of preset positions;

[0048] The three-level indicators affecting the communication unit include at least one of the following: data acquisition method, data communication method, and data loop storage time;

[0049] The three-level indicators affecting the power supply unit include at least one of the following: power supply method, power supply duration, and battery life.

[0050] The three-level indicators affecting environmental adaptability should include at least the working condition level;

[0051] The three levels of indicators that affect protective performance include at least one of the following: single-layer shell protection requirements, outer layer protection requirements, and inner layer protection requirements;

[0052] The three levels of indicators that affect safety performance include at least one of the following: the weight of the monitoring unit and the weight of the power supply chassis.

[0053] The three-level indicators affecting reliability performance include at least one of the following: mean time between failures (MTBF) and device lifespan.

[0054] The present invention provides an evaluation method for icing galloping monitoring devices based on fuzzy comprehensive evaluation, specifically comprising: constructing a tree-shaped three-level evaluation index system covering the basic technical parameters and overall performance of the device, based on power industry standards and the actual characteristics of the icing galloping monitoring device; in terms of weight determination, a hierarchical combination weighting strategy is adopted according to the characteristics of different levels of indicators: the first-level indicators are determined based on the analytic hierarchy process (AHP) to determine subjective weights; the second-level indicators combine the AHP and entropy weight method to achieve a combination of subjective and objective weights; and the third-level indicators are precisely allocated weights for each influencing factor using various algorithms such as the AHP, improved entropy weight method, CRITIC method, and log-normal coefficient of variation method, according to the characteristics of the indicators; further, a dynamic weight adjustment mechanism is introduced to flexibly redistribute the weights at each level according to the actual configuration of the device, significantly enhancing the adaptability and practicality of the evaluation system; finally, based on the multi-level fuzzy comprehensive evaluation method, combined with three types of membership functions, an evaluation model is constructed and a comprehensive evaluation value and grade are output. This invention constructs a systematic and multi-level evaluation framework, combining a subjective and objective weighting method with a dynamic adjustment mechanism, to achieve a comprehensive and scientific quantitative evaluation of the icing and galloping monitoring device from functional units to overall performance. It effectively overcomes the problems of strong subjectivity, inconsistent standards, and single dimensions in traditional methods, providing a reliable and accurate decision-making basis for device selection, procurement, and deployment. Attached Figure Description

[0055] Figure 1 This is a flowchart of the evaluation method for an ice-covering galloping monitoring device based on fuzzy comprehensive evaluation provided in an embodiment of the present invention;

[0056] Figure 2 This is an example diagram of the three-level evaluation index system provided in the embodiments of the present invention. Detailed Implementation

[0057] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. The embodiments are given for illustrative purposes only and should not be construed as limiting the present invention. The accompanying drawings are for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0058] The embodiments of the present invention fully consider multiple factors such as the technical performance and reliability of the device, and establish a scientific, reasonable and systematic evaluation method for ice accretion and galloping monitoring devices based on fuzzy comprehensive evaluation. This method can comprehensively and effectively evaluate different types of ice accretion and galloping monitoring devices, and provide decision support for the rational selection, procurement and deployment of different types of ice accretion and galloping monitoring devices.

[0059] Figure 1 This is a flowchart illustrating the evaluation method for icing galloping monitoring devices based on fuzzy comprehensive evaluation. Figure 1 As shown in the figure, the evaluation method for an ice-covering galloping monitoring device based on fuzzy comprehensive evaluation provided by the present invention specifically includes the following steps:

[0060] S1: Based on power industry standards and the characteristics of icing and galloping monitoring devices, a tree-structured three-level evaluation index system is constructed.

[0061] In the three-level evaluation index system, the first-level index includes at least the basic technical parameters of the device and the overall performance of the device; the second-level index includes at least multiple technical units that affect the basic technical parameters of the device and multiple performance units that affect the overall performance of the device; and the third-level index includes at least multiple influencing factors that affect each technical unit and each performance unit.

[0062] Figure 2 This is a preferred example of a three-level evaluation indicator system. For example... Figure 2 As shown:

[0063] The primary indicators include the basic technical parameters of the device and the overall performance of the device.

[0064] Secondary indicators include the icing monitoring unit, galloping monitoring unit, micrometeorological monitoring unit, image monitoring unit, communication unit, and power supply unit, which affect the basic technical parameters of the device; and,

[0065] Environmental adaptability, protection performance, safety performance, and reliability performance that affect the overall performance of the device.

[0066] The third-level indicators include the range of ice thickness measurement and the accuracy of ice thickness measurement that affect the ice monitoring unit; and,

[0067] The range, accuracy, frequency, and range of motion measurement of the motion monitoring unit are all affected by these factors.

[0068] The following factors affect the micro-meteorological monitoring unit: wind speed measurement range, wind speed measurement accuracy, wind direction measurement range, wind direction measurement accuracy, air temperature measurement range, air temperature measurement accuracy, humidity measurement range, humidity measurement accuracy, air pressure measurement range, air pressure measurement accuracy, rainfall measurement range, rainfall measurement accuracy, and solar radiation measurement range and accuracy.

[0069] The camera device type, image format, number of pixels, lens zoom rate, and number of preset positions affect the image monitoring unit;

[0070] The data acquisition method, data communication method, and data loop storage time of the communication unit are all affected.

[0071] Affects the power supply method, power supply duration, and battery life of the power supply unit;

[0072] Working condition levels that affect environmental adaptability;

[0073] Requirements for single-layer outer shell protection, outer layer protection, and inner layer protection that affect protective performance;

[0074] The weight of the monitoring unit and the power supply chassis, which affect safety performance;

[0075] Mean time between failures (MTBF) and device lifespan are factors that affect reliability.

[0076] The above indicators are merely a list and not exhaustive. Relevant indicators can be added or removed based on the actual layout of the power system.

[0077] During the evaluation, some indicators are optional, such as icing detection units and galloping detection units, and single-layer shell protection requirements and inner / outer layer protection requirements (outer layer protection requirements and inner layer protection requirements) under the protection performance criteria; some indicators are optional (marked with "*"), such as some tertiary indicators under the image monitoring unit. The specific indicators included in the evaluation must be adapted to the actual laying conditions of the power system.

[0078] Traditional assessments often focus only on individual technical parameters (such as measurement accuracy), neglecting the overall performance of the device, such as reliability and environmental adaptability, leading to a one-sided assessment. The tree structure designed in this method covers all dimensions, from the bottom-level influencing factors (third-level indicators), to intermediate technical / performance units (second-level indicators), and finally to the top-level comprehensive performance (first-level indicators), ensuring that the assessment comprehensively reflects the device's strengths and weaknesses. This method organizes scattered assessment points into an organic whole, clarifying the logical relationships (subordination, influence) between indicators, making the assessment process systematic and the results more convincing. The clear hierarchical structure facilitates understanding and operation. Furthermore, the system is open; indicators can be easily added, deleted, or modified at the appropriate levels based on technological developments or new industry standards without reconstructing the entire assessment framework.

[0079] S2: Based on the different characteristics of each level of the three-level evaluation indicator system, determine the weight of each indicator at each level.

[0080] For the primary indicators, the weights of each primary indicator are determined using the analytic hierarchy process (AHP). The determination process is as follows:

[0081] S211. Construct a primary indicator judgment matrix based on pairwise comparisons (empirical values) of the relative importance of primary indicators;

[0082] S212. Calculate the eigenvector corresponding to the largest eigenvalue of the first-level indicator judgment matrix, and obtain the first-level indicator weight vector after normalization.

[0083] S213. Calculate the consistency ratio based on the largest eigenvalue. If the consistency ratio is less than the preset ratio, determine the weight of each primary indicator according to the current primary indicator weight vector. Otherwise, return to step S211 to adjust the primary indicator judgment matrix.

[0084] In step S211, the relative importance values ​​of the m primary indicators compared pairwise by different comparison subjects are first obtained. The comparison subjects can be automatic comparison modules (such as using different automatic comparison mechanisms based on big data) or power system experts. Taking power system experts as an example, n power system experts use the 1-9 scaling method shown in Table 1 to perform pairwise comparisons of the m primary indicators of the indicator system and give the relative importance values.

[0085] Table 1: 1-9 Scale Method

[0086]

[0087] Then, a judgment matrix is ​​generated based on the evaluation results of each power system expert. , This represents the relative importance value of indicator i and j given by the k-th expert.

[0088] Finally, the geometric mean method is used to synthesize the judgment matrices corresponding to the n experts to obtain the first-level indicator judgment matrix. ,in This represents the average relative importance of indicators i and j given by n experts.

[0089] In step S212, the maximum eigenvalue λ of the first-level index judgment matrix A is calculated. max and its corresponding eigenvectors And then Normalization is performed to obtain the first-level indicator weight vector. The weight of the i-th primary indicator , For feature vectors The i-th eigenvalue.

[0090] In step S213, to verify the logical rationality of the judgment matrix, a consistency check is required. First, based on the largest eigenvalue λ... max Calculate the consistency index Then query the random consistency index RI and calculate the consistency ratio. Finally, if the CR is less than a preset ratio (e.g., 0.1), the judgment matrix is ​​considered to have passed the consistency test, and the weight vector... If valid, otherwise, after obtaining a new judgment matrix by adjusting the evaluation results of different power system experts (adding or deleting results), the matrix is ​​recalculated until it passes the consistency test.

[0091] For the secondary indicators, the weights of each secondary indicator are determined based on the analytic hierarchy process and objective data. The determination process is as follows:

[0092] S221. Using the Analytic Hierarchy Process (AHP) (consistent with the AHP used for the primary indicators), determine the subjective weight matrix of each secondary indicator. ;

[0093] S222. Calculate the objective weight matrix of each secondary indicator using the entropy weight method. ;

[0094] S223. Use the fuzzy Borda method to convert the subjective weight matrix of the secondary indicators. With the objective weight matrix of secondary indicators The data is then integrated to obtain the combined weights of each secondary indicator.

[0095] In step S222, the objective weights are calculated by collecting and analyzing information such as device fault statistics, operation and maintenance feedback, and manufacturing costs. First, data such as failure rate and maintenance costs are collected to form a sample matrix. Where M is the number of samples and N is the number of secondary indicators. Then, the objective weights are calculated using the entropy weight method, specifically including:

[0096] For the sample matrix Each element Standardize to obtain , These represent the maximum and minimum values ​​in the j-th column, respectively.

[0097] The information entropy of the j-th index is calculated using the improved entropy weight method. , express The weight of the j-th indicator;

[0098] Sure Objective weight vector of each secondary indicator The objective weight of its j-th indicator .

[0099] In step S223, the fuzzy Borda method is used to convert the subjective weight matrix of the secondary indicators. With the objective weight matrix of secondary indicators The integration process specifically includes:

[0100] Calculate the subjective weight matrix of the secondary indicators respectively With the objective weight matrix of secondary indicators Membership degree of each secondary indicator , Represents the subjective weight matrix of secondary indicators Or the objective weight matrix of secondary indicators The j-th weight value in the data. These represent the subjective weight matrices of the secondary indicators. Or the objective weight matrix of secondary indicators The maximum value in;

[0101] For each secondary indicator Summing yields the corresponding fuzzy frequencies. ;

[0102] Fuzzy frequency of each secondary indicator Normalization is performed to obtain the comprehensive weight of each secondary indicator. This yields a comprehensive weight vector for the secondary indicators, which includes the combined weights of all secondary indicators. .

[0103] For the tertiary indicators, both objective and subjective algorithms are used to determine the weights of each tertiary indicator. The determination process is as follows:

[0104] S231. Use the Analytic Hierarchy Process (AHP) (consistent with the AHP used for the first-level and second-level indicators) to determine the weights of the third-level indicators for strategy selection.

[0105] S232. The weights of the third-level indicators of the monitoring accuracy range are determined by an objective algorithm that combines the improved entropy weight method with the objective weighting method (CRITIC method).

[0106] S233. The weights of the log-normal distribution type tertiary indicators, the uniform distribution type tertiary indicators, and the technical specification type tertiary indicators are determined by the log-normal coefficient of variation method, the standard coefficient of variation method, and the technical specification comparison method, respectively.

[0107] S234. Normalize the weights of each tertiary indicator to obtain the comprehensive weight vector of the tertiary indicators. .

[0108] Technical parameter indicators are determined based on the characteristics of their subcategories.

[0109] In step S231, for the third-level indicators of strategy selection, which are highly subjective, the weights are determined by the analytic hierarchy process. First, a judgment matrix is ​​constructed, and then the eigenvectors are calculated and passed through a consistency check.

[0110] In step S232, for the third-level indicators of monitoring accuracy range, which have clear numerical characteristics and comparability, the weights are calculated using an improved entropy weight method combined with the CRITIC method, specifically including:

[0111] The information entropy of the j-th precision range class of third-level indicators is calculated using an improved entropy weight method. ;

[0112] The information content of the j-th precision range class of third-level indicators is calculated using the CRITIC method. , This represents the correlation coefficient between the j-th precision range category tertiary index and the k-th precision range category tertiary index. Let be the standard deviation of the third-level index of the j-th precision range class. The number of third-level indicators representing the precision range;

[0113] The information entropy and information content of each precision range category tertiary indicator are fused to obtain the weight of each precision range category tertiary indicator. ,in and These are the combination coefficients.

[0114] In step S233, for the three-level indicators of technical parameters, they are divided into log-normal distribution type, uniform distribution type and technical specification type according to their data distribution characteristics.

[0115] For log-normally distributed tertiary indicators, the log-normal coefficient of variation method is used to determine the weight of the j-th tertiary indicator. .

[0116] For uniformly distributed tertiary indicators, the standard coefficient of variation method is used to determine the weight of the j-th tertiary indicator. , This represents the mean of the j-th tertiary indicator.

[0117] For technical specification-type tertiary indicators, the weight of the j-th tertiary indicator is determined using the technical specification comparison method. , Indicates the standard reference value. This represents the technical specification value of the j-th tertiary indicator. This means taking the largest of the three values.

[0118] Finally, the weights of all tertiary indicators are unified and normalized to obtain the comprehensive weight vector of the tertiary indicators. .

[0119] Because the characteristics of indicators at different levels differ, a single weighting method cannot simultaneously consider both subjective experience and objective data, easily leading to weight distortion. This method uses the Analytic Hierarchy Process (AHP) to determine weights for first-level indicators, focusing on integrating domain expert experience to ensure that the strategic direction of the assessment aligns with industry consensus. For second-level indicators, a combination of subjective and objective methods is used to determine weights. Based on expert opinions, operational data (entropy weighting method) is introduced for verification and correction, effectively reducing the arbitrariness of purely subjective judgments and making the weights closer to actual operational conditions. For third-level indicators, a categorized and precise weighting approach is adopted. For different types of indicators (such as strategy selection, precision range, and technical parameter types), the most suitable algorithm (such as the CRITIC method and the coefficient of variation method) is selected based on the data characteristics, maximizing the extraction of information from the data itself and ensuring the refinement and accuracy of weight allocation.

[0120] S3. Based on the configuration requirements of the icing and dancing monitoring device in practical applications, the weights of each level of indicators are dynamically adjusted.

[0121] Dynamic adjustment strategies include:

[0122] Secondary indicator main function weight allocation strategy: dynamically allocate the weight of primary indicators related to icing and galloping monitoring according to the type of icing and galloping monitoring device;

[0123] Secondary indicator optional function weight redistribution strategy: For optional secondary indicators (such as image monitoring units), if they are not configured, their preset standard weights are proportionally compensated to the other mandatory secondary indicators.

[0124] The strategy for redistributing the weights of optional functions under the secondary indicators is as follows: If an optional tertiary indicator under the secondary indicator (such as an optional sub-indicator within the micro-meteorological monitoring unit) is not configured, its standard weight will be proportionally compensated to other configured tertiary indicators within the secondary indicator.

[0125] The three-level indicator structure weight switching strategy is as follows: based on whether the device protection structure is single-layer or double-layer, the weights of the corresponding three-level indicators are switched and assigned.

[0126] In the secondary indicator main function weight allocation strategy, the device type identifier T∈{Ice Covering and Dancing Monitoring Primary Indicator Pool} is defined, and the benchmark weight of the main monitoring unit is set to... This strategy performs weight allocation based on conditional judgment: if the device type has X ≥ 1 primary indicator, then the weights of these X primary indicators are... The weight of the remaining primary indicators in the primary indicator pool for monitoring ice accretion and dancing is 0.

[0127] See Figure 2 For example, T∈{Icing Monitoring Unit, Dancing Monitoring Unit}, if the device type is icing monitoring unit, then the weight of the icing monitoring unit is... The weight of the motion monitoring unit is 0. Conversely, if the device type is a motion monitoring unit, the weight of the motion monitoring unit is... The weight of the icing monitoring unit is 0.

[0128] In the secondary indicator optional function weight redistribution strategy, refer to Figure 2 Example: Standard weights of image monitoring units under a preset full configuration. ,definition ∈{Icing / Galloping Monitoring Functional Unit, Micro-meteorological Monitoring Functional Unit, Communication Unit, Power Supply Unit}, if the image monitoring unit is enabled, then The weights of the remaining secondary indicators remain unchanged. If the image monitoring unit is not activated, the weight of the u-th mandatory secondary indicator must be within its standard weight. Add to .

[0129] In the strategy for redistributing the optional functional weights of the three-level indicators, refer to Figure 2 For example, the complete weight set of the three-level indicators under the preset micro-meteorological monitoring unit is as follows: and (That is, the standard weight of each tertiary indicator is 1) When there is If no tertiary indicator is selected, a compensation mechanism is triggered, adjusting the weight of each remaining configured tertiary indicator according to its standard weight. Add to , This indicates the preset weight of each unselected tertiary indicator. .

[0130] In the three-level indicator structure weight switching strategy, the preset weights are called according to the structure type of the device protection structure. When the device structure type is single-layer, the protection performance weight is the weight of the single-layer shell protection requirement index. When the device structure type is double-layer, the protection performance weight is the sum of the weights of the outer layer protection requirement and the inner layer protection requirement index.

[0131] In practical applications, the configurations of monitoring devices (such as whether imaging functions are optional, and whether they have single or double-layer protection) vary greatly. Using a fixed set of weights to evaluate all devices is unfair and unscientific. This method, through strategies such as the redistribution of optional function weights and the switching of structural weights, enables the evaluation system to automatically adapt to devices with different configurations. For functions that are not configured, their weights are reasonably transferred to the core functions that are already configured, ensuring that the evaluation always focuses on the actual capabilities of the device, thereby making a fair comparison of devices with different configurations. This mechanism allows this method not only to evaluate ideal devices with full configurations, but also to accurately evaluate practical devices with simplified configurations to meet specific budget or scenario requirements, greatly expanding the applicability of the method.

[0132] S4: Based on the weights and values ​​of the currently configured indicators at all levels, the multi-level fuzzy comprehensive evaluation method is used to calculate the comprehensive evaluation value of the three-level evaluation indicator system, and the evaluation level is determined based on the comprehensive evaluation value.

[0133] Step S4 specifically includes:

[0134] S41. Based on the correlation between the index values ​​of the three-level indicators and the evaluation results, the currently configured three-level indicators are divided into positively correlated indicators where the larger the index value, the better the evaluation result; negatively correlated indicators where the smaller the index value, the better the evaluation result; and qualitative or quantitative indicators with finite discrete points.

[0135] S42. Design corresponding membership functions for positively correlated indicators, negatively correlated indicators, and qualitative or quantitative indicators;

[0136] S43. Calculate the membership degree of the currently configured third-level indicators using the corresponding membership function;

[0137] S44. Based on the membership degree of each configured third-level indicator, construct a fuzzy evaluation matrix for the third-level indicators;

[0138] S45. Multiply the fuzzy evaluation matrix of the third-level indicator with its weight matrix to obtain the evaluation vector of the second-level indicator;

[0139] S46. Multiply the secondary indicator evaluation vector by its weight matrix to obtain the primary indicator evaluation vector.

[0140] S47. Multiply the primary indicator evaluation vector by its weight matrix to obtain the comprehensive evaluation vector;

[0141] S48. Calculate the comprehensive evaluation value based on the comprehensive evaluation vector;

[0142] S49. Determine the assessment level based on the comprehensive assessment value.

[0143] In step S42, the membership functions designed for positively correlated indicators, negatively correlated indicators, and qualitative or quantitative indicators are all incremental membership functions. Decreasing membership function Discrete value mapping function ( ), This represents the membership degree of the i-th configured third-level indicator. This represents the value of the i-th configured level-three indicator. These represent the maximum and minimum values ​​of the positively or negatively correlated indicators, respectively.

[0144] In step S47, due to the three-level index division and the three-category division of the three-level index, the final comprehensive evaluation vector is a 9-level structure.

[0145] In step S48, the component values ​​of the corresponding level of the comprehensive evaluation vector are multiplied by the standard score of that level, and then the sum of all elements is performed to obtain the comprehensive evaluation value.

[0146] In step S49, the final comprehensive evaluation value is converted to a 100-point scale. The evaluation levels given in this embodiment are shown in Table 2, which are divided into 9 levels from excellent to poor based on the size of the evaluation value.

[0147] Table 2: Assessment Level Table

[0148]

[0149] Because evaluation indicators contain numerous fuzzy and uncertain factors, and are diverse in type (correlated positive and negative, qualitative and quantitative), traditional scoring methods struggle to handle them scientifically. This method, by designing incremental, decremental, and discrete membership functions, successfully transforms various types of quantitative and qualitative indicators into measurable membership degrees, solving the problem of inconsistent calculation for different qualitative indicators. Through layer-by-layer fuzzy calculations from tertiary to primary indicators, the complex multidimensional evaluation is ultimately condensed into an intuitive comprehensive evaluation value and grade. This process scientifically integrates all underlying information, transforming the evaluation result from a vague qualitative description into a clear quantitative conclusion, greatly facilitating decision-makers' horizontal comparisons and judgments of merit.

[0150] It should be noted that the various processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved. This embodiment does not impose any limitations on these steps.

[0151] This invention provides an evaluation method for icing galloping monitoring devices based on fuzzy comprehensive evaluation. The system includes an evaluation index system construction module, an index weight determination module, an index weight dynamic adjustment module, and a comprehensive evaluation module. The evaluation index system construction module constructs a tree-structured three-level evaluation index system based on power industry standards and the characteristics of the icing galloping monitoring device. The index weight determination module determines the weight of each index at each level according to its characteristics. The index weight dynamic adjustment module dynamically adjusts the weights of each level of the index based on the configuration requirements of the icing galloping monitoring device in practical applications. The comprehensive evaluation module calculates the comprehensive evaluation value of the three-level evaluation index system using a multi-level fuzzy comprehensive evaluation method based on the currently configured weights and values ​​of each level of the index, and determines the evaluation level based on the comprehensive evaluation value.

[0152] The embodiments described in this invention can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with the implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0153] Computer programs for implementing the methods and systems of the present invention may be written in any combination of one or more programming languages ​​and stored in a computer-readable storage medium. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0154] Computer-readable storage media can be tangible media that may contain or store computer programs for use by or in conjunction with an instruction execution system, apparatus, or device. Computer-readable storage media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, optical disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0155] In summary, the evaluation method and system for icing galloping monitoring devices based on fuzzy comprehensive evaluation provided by this invention specifically include: constructing a tree-shaped three-level evaluation index system covering the basic technical parameters and overall performance of the device, based on power industry standards and the actual characteristics of the icing galloping monitoring device; in terms of weight determination, a hierarchical combination weighting strategy is adopted according to the characteristics of different levels of indicators: the subjective weight of the first-level indicators is determined based on the analytic hierarchy process (AHP); the second-level indicators combine the AHP and the entropy weight method to achieve a combination of subjective and objective weights; and the third-level indicators are precisely allocated weights for each influencing factor using various algorithms such as the AHP, the improved entropy weight method, the CRITIC method, and the log-normal coefficient of variation method, based on the characteristics of the indicators; further, a dynamic weight adjustment mechanism is introduced to flexibly redistribute the weights at each level according to the actual configuration of the device, significantly enhancing the adaptability and practicality of the evaluation system; finally, based on the multi-level fuzzy comprehensive evaluation method, combined with three types of membership functions, an evaluation model is constructed and a comprehensive evaluation value and grade are output. This invention constructs a systematic and multi-level evaluation framework, combining a subjective and objective weighting method with a dynamic adjustment mechanism, to achieve a comprehensive and scientific quantitative evaluation of the icing and galloping monitoring device from functional units to overall performance. It effectively overcomes the problems of strong subjectivity, inconsistent standards, and single dimensions in traditional methods, providing a reliable and accurate decision-making basis for device selection, procurement, and deployment.

[0156] This implementation plan, through its interconnected technical design, constitutes an advanced assessment method that is comprehensive yet refined, scientific yet flexible, and capable of handling both deterministic and fuzzy information. Its ultimate effect is to elevate the assessment of icing galloping monitoring devices from an experiential art to a data science, providing solid technical support for precise investment by the power sector and the safe and stable operation of the power grid.

[0157] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. An evaluation method for an icing galloping monitoring device based on fuzzy comprehensive evaluation, characterized in that, The method comprises the following steps: Based on the power industry standard and the characteristics of the icing vibration monitoring device, a three-level evaluation index system with a tree structure is constructed; According to the different characteristics of each level index of the three-level evaluation index system, the weight of each index at each level is determined; Based on the configuration requirements of the icing vibration monitoring device in actual application, the weight of each level index is dynamically adjusted; Based on the weight and index value of each level index currently configured, the multi-level fuzzy comprehensive evaluation method is used to calculate the comprehensive evaluation value of the three-level evaluation index system, and the evaluation grade is determined according to the comprehensive evaluation value.

2. The icing flutter monitoring device evaluation method based on fuzzy comprehensive evaluation according to claim 1, characterized in that: In the three-level evaluation index system, the first-level index at least includes device basic technical parameters and device overall performance, the second-level index at least includes multiple technical units affecting the device basic technical parameters and multiple performance units affecting the device overall performance, and the third-level index at least includes multiple influence factors affecting each technical unit and each performance unit.

3. The method according to claim 2, wherein, For the first-level index, the weight of each first-level index is determined based on the analytic hierarchy process, and the determination process is as follows: A first-level index judgment matrix is constructed based on the pairwise comparison results of the relative importance of the first-level index; The characteristic vector corresponding to the maximum eigenvalue of the first-level index judgment matrix is calculated, and the first-level index weight vector is obtained after normalization processing; Based on the maximum eigenvalue, the consistency ratio is calculated, if the consistency ratio is less than a preset ratio, the weight of each first-level index is determined according to the current first-level index weight vector, otherwise, the first-level index judgment matrix is adjusted.

4. The method according to claim 3, wherein, For the second-level index, the weight of each second-level index is determined based on the analytic hierarchy process combined with objective data, and the determination process is as follows: The second-level index subjective weight matrix of each second-level index is determined by using the analytic hierarchy process; The second-level index objective weight matrix of each second-level index is calculated by using the entropy weight method; The second-level index subjective weight matrix and the second-level index objective weight matrix are fused to obtain the comprehensive weight of each second-level index.

5. The method according to claim 4, wherein, The second-level index subjective weight matrix and the second-level index objective weight matrix are fused, specifically including: The membership degrees of each second-level index in the second-level index subjective weight matrix and the second-level index objective weight matrix are calculated respectively; The membership degrees of each second-level index are summed to obtain the corresponding fuzzy frequency; The fuzzy frequency of each second-level index is normalized to obtain the comprehensive weight of each second-level index, and the second-level index comprehensive weight vector including the comprehensive weights of all second-level indexes is obtained.

6. The method according to claim 5, wherein, For the third-level index, the weight of each third-level index is determined by using objective algorithm and subjective algorithm, and the determination process is as follows: The weight of the strategy selection type third-level index is determined by using the analytic hierarchy process; The weight of the monitoring precision range type third-level index is determined by using the improved entropy weight method combined with the objective weight method; The weight of the lognormal distribution type third-level index, the uniform distribution type third-level index and the technical specification type third-level index in the technical parameter type third-level index is determined by using the lognormal variation coefficient method, the standard variation coefficient method and the technical specification comparison method respectively; The weight of each third-level index is uniformly normalized to obtain the third-level index comprehensive weight vector.

7. The method according to claim 2, wherein the method is characterized by, The strategy for dynamically adjusting the weight of each level index includes: The secondary index main function weight distribution strategy is: dynamically distributing the weight of the primary index related to the ice-dancing monitoring according to the type of the ice-dancing monitoring device; The secondary index optional function weight redistribution strategy is: for the optional secondary index, if it is not configured, the standard weight of the optional secondary index is proportionally compensated to the rest of the mandatory secondary index; The tertiary index optional function weight redistribution strategy is: for the optional tertiary index under the secondary index, if it is not configured, the standard weight of the optional tertiary index is proportionally compensated to the other configured tertiary index in the secondary index; The tertiary index structure weight switching strategy is: switching and distributing the weight of the corresponding tertiary index according to whether the device protection structure is a single-layer or double-layer type. 8.The method of claim 2, wherein, Based on the currently configured weight and index value of each index, the comprehensive evaluation value of the three-level evaluation index system is calculated by using the multi-level fuzzy comprehensive evaluation method, and the evaluation grade is determined according to the comprehensive evaluation value, which specifically includes: According to the correlation between the index value of the tertiary index and the evaluation result, the currently configured tertiary index is divided into a positive correlation index with a better evaluation result as the index value increases, a negative correlation index with a better evaluation result as the index value decreases, and a qualitative or quantitative index with a limited discrete point; The corresponding membership function is designed for the positive correlation index, the negative correlation index, and the qualitative or quantitative index; The membership of the currently configured tertiary index is calculated by using the corresponding membership function; The fuzzy evaluation matrix of the tertiary index is constructed according to the membership of each configured tertiary index; The secondary index evaluation vector is obtained by multiplying the fuzzy evaluation matrix of the tertiary index and its weight matrix; The primary index evaluation vector is obtained by multiplying the secondary index evaluation vector and its weight matrix; The comprehensive evaluation vector is obtained by multiplying the primary index evaluation vector and its weight matrix; The comprehensive evaluation value is calculated according to the comprehensive evaluation vector; The evaluation grade is determined according to the comprehensive evaluation value.

9. The method according to any one of claims 2 to 8, wherein the method is a method for evaluating an icing galloping monitoring device based on fuzzy comprehensive evaluation. The primary index at least includes device basic technical parameters and device overall performance; the secondary index includes at least two of the ice monitoring unit, the dancing monitoring unit, the micro-meteorological monitoring unit, the image monitoring unit, the communication unit, and the power supply unit affecting the device basic technical parameters; and at least two of the environmental adaptation performance, the protection performance, the safety performance, and the reliability performance affecting the device overall performance.

10. The fuzzy comprehensive evaluation-based ice-dancing monitoring device evaluation method according to claim 9, wherein: The tertiary index affecting the ice monitoring unit includes at least one of the ice thickness measurement range and the ice thickness measurement accuracy; The tertiary index affecting the dancing monitoring unit includes at least one of the dancing amplitude measurement range, the dancing amplitude measurement accuracy, the dancing frequency measurement range, and the dancing frequency measurement accuracy; The tertiary index affecting the micro-meteorological monitoring unit includes at least one of the wind speed measurement range, the wind speed measurement accuracy, the wind direction measurement range, the wind direction measurement accuracy, the air temperature measurement range, the air temperature measurement accuracy, the humidity measurement range, the humidity measurement accuracy, the air pressure measurement range, the air pressure measurement accuracy, the rainfall measurement range, the rainfall measurement accuracy, the light radiation measurement range, and the light radiation measurement accuracy; The third-level indexes affecting the image monitoring unit include at least one of a camera device type, an image format, a pixel number, a lens zoom ratio, and a preset position number; The third-level indexes affecting the communication unit include at least one of a data acquisition mode, a data communication mode, and a data cycle storage time; The third-level indexes affecting the power supply unit include at least one of a power supply mode, a power supply duration, and a battery life; The third-level indexes affecting the environmental adaptation performance include at least a working condition level; The third-level indexes affecting the protection performance include at least one of a single-layer shell protection requirement, an outer layer protection requirement, and an inner layer protection requirement; The third-level indexes affecting the safety performance include at least one of a monitoring unit weight and a power supply case weight; The third-level indexes affecting the reliability performance include at least one of an average failure-free working time and a device life.

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