WO2024093468A1 - 风偏闪络风险评价方法、系统、设备、可读存储介质 - Google Patents

风偏闪络风险评价方法、系统、设备、可读存储介质 Download PDF

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WO2024093468A1
WO2024093468A1 PCT/CN2023/115169 CN2023115169W WO2024093468A1 WO 2024093468 A1 WO2024093468 A1 WO 2024093468A1 CN 2023115169 W CN2023115169 W CN 2023115169W WO 2024093468 A1 WO2024093468 A1 WO 2024093468A1
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risk
cloud
risk assessment
weight value
transmission line
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PCT/CN2023/115169
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English (en)
French (fr)
Inventor
周超
刘辉
秦佳峰
孙晓斌
贾然
李丹丹
耿博
张洋
刘嵘
沈浩
刘传彬
于传维
杨杰
蔡英明
陈星延
高成成
韦立坤
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国网山东省电力公司电力科学研究院
国家电网有限公司
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Publication of WO2024093468A1 publication Critical patent/WO2024093468A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Definitions

  • the present application relates to the technical field of power systems, and for example, to a method, system, computer equipment, and computer-readable storage medium for evaluating wind-induced flashover risks of transmission lines.
  • the power transmission lines and facilities of the power grid are widely distributed in different geographical locations. Their operating environment is greatly affected by the different meteorological conditions in different geographical locations. Among them, line tripping caused by wind deflection flashover of the transmission line is one of the important factors affecting the normal operation of the transmission line.
  • the current wind deviation flashover risk assessment technology still has the following shortcomings: 1. Most of the above risk assessment wind deviation studies calculate the wind deviation angle of a single device, and rarely calculate and analyze the probability and risk value of wind deviation tripping, making the risk assessment not universal; 2. The weighting ratio between subjective and objective is unbalanced, with some weightings leaning towards subjective expert opinions and some towards objective reality conditions; 3. The above risk assessment does not fully consider the ambiguity of the boundaries of different evaluation indicators.
  • the embodiments of the present application provide a method, system, computer device and computer-readable storage medium for evaluating the risk of wind-induced flashover in a power transmission line to solve the above-mentioned technical problems in the background technology, so that the evaluation of wind-induced flashover is more universal and the evaluation results are more reasonable.
  • a method for evaluating wind-induced flashover risk of a power transmission line is provided.
  • the transmission line wind deflection flashover risk assessment method includes:
  • a two-dimensional similarity calculation is performed between the risk cloud and the standard cloud to obtain the two-dimensional similarity between the risk cloud and the standard cloud, and the risk level of wind deflection flashover of the transmission line is determined according to the two-dimensional similarity.
  • a transmission line wind deflection flashover risk assessment system is provided.
  • the transmission line wind deflection flashover risk assessment system includes:
  • An index setting module configured to determine the influencing factors of wind deflection flashover of the transmission line and set the risk assessment index according to the influencing factors
  • a standard cloud generation module configured to generate a standard cloud of the risk assessment indicator according to the accident probability and consequence level of the risk assessment indicator
  • a risk cloud generation module configured to score the influencing factors according to the risk assessment index, and generate a risk cloud of the risk assessment index according to the scoring result;
  • the risk level determination module is configured to perform two-dimensional similarity calculation on the risk cloud and the standard cloud to obtain the two-dimensional similarity between the risk cloud and the standard cloud, and determine the risk level of wind deflection flashover of the transmission line according to the two-dimensional similarity.
  • a computer device is provided.
  • the computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.
  • a computer-readable storage medium is provided.
  • the computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the above method are implemented.
  • FIG1 is a schematic flow chart of a method for evaluating wind-induced flashover risk of a transmission line according to an exemplary embodiment
  • FIG2 is a schematic diagram of a transmission line wind deflection flashover risk assessment index system architecture according to an exemplary embodiment
  • FIG3 is a flow chart of a SWARA method according to an exemplary embodiment
  • FIG4 is a schematic diagram of a two-dimensional cloud according to an exemplary embodiment
  • FIG5 is a structural block diagram of a transmission line wind deflection flashover risk assessment system according to an exemplary embodiment
  • Fig. 6 is a schematic diagram showing the structure of a computer device according to an exemplary embodiment.
  • the terms "include”, “comprise” or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one" do not exclude the existence of other identical elements in the structure, device or equipment including the elements.
  • Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.
  • the terms “installed”, “connected” and “connected” should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, or it can be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium.
  • installed should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, or it can be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium.
  • the term “plurality” means two or more than two.
  • A/B means: A or B.
  • a and/or B means: A or B, or, A and B.
  • Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof.
  • the above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.
  • FIG1 shows an embodiment of the transmission line wind deflection flashover risk assessment method of the present application.
  • the transmission line wind deflection flashover risk assessment method includes:
  • Step S101 determining the influencing factors of wind deflection flashover of the transmission line, and setting risk assessment indicators according to the influencing factors;
  • Step S103 generating a standard cloud of the risk assessment indicator according to the accident probability and consequence level of the risk assessment indicator
  • Step S105 scoring the influencing factors according to the risk assessment index, and generating a risk cloud of the risk assessment index according to the scoring result;
  • Step S107 performing two-dimensional similarity calculation on the risk cloud and the standard cloud to obtain the two-dimensional similarity between the risk cloud and the standard cloud, and determining the risk level of wind deflection flashover of the transmission line according to the two-dimensional similarity.
  • the transmission line wind deflection flashover risk assessment method further includes: determining the influencing factors of the transmission line wind deflection flashover based on the historical fault information, operation and maintenance information and meteorological information of the transmission line; and the risk assessment index determined based on the influencing factors can be shown in Figure 2, and the wind deflection flashover risk assessment index is divided into three types of first-level indicators and eight types of second-level indicators.
  • scoring the influencing factors according to the risk assessment index and generating a risk cloud of the risk assessment index according to the scoring result includes: generating a risk cloud of the risk assessment index according to the risk assessment index.
  • An expert evaluation system is formed, and the weight value of the risk assessment indicator is calculated according to the expert evaluation system; the risk assessment indicator is evaluated according to the expert evaluation system, and an initial risk cloud of the risk assessment indicator is generated according to the evaluation result; and a risk cloud of the risk assessment indicator is generated according to the weight value and the initial risk cloud.
  • calculating the weight value of the risk assessment indicator includes: sorting the risk assessment indicators according to the expert evaluation system, and comparing the risk assessment indicators of adjacent numbers to obtain a comparison result; calculating the subjective weight value of the risk assessment indicator based on the comparison result and based on the stepwise weighted evaluation analysis ratio method; evaluating the risk assessment indicator according to the expert evaluation system to obtain an evaluation result; converting the evaluation result into an intuitive fuzzy number, and calculating the objective weight value of the risk assessment indicator based on the direct fuzzy entropy weight method; determining the comprehensive weight value of the risk assessment indicator based on the combined weighting method of game theory according to the subjective weight value and the objective weight value; and using the comprehensive weight value as the weight value of the risk assessment indicator.
  • the SWARA method has great advantages in terms of expert composition and weight value calculation. Its main operating steps are shown in Figure 3.
  • the main process is as follows: First, the preliminary list of risk assessment indicators is distributed to experts, and the experts sort the selected risk assessment indicators in descending order of importance. The most important criterion is defined as the first level, and the least important criterion is defined as the last level (or the nth level); the serial number of each risk assessment indicator is the median of the ranking serial numbers of all experts for the risk assessment indicator, that is, criterion j in Figure 3. Secondly, the risk assessment indicators with adjacent serial numbers are compared pairwise, and the results of the pairwise comparison are determined after multiple cycles to achieve the evaluation of the relative importance of each risk assessment indicator. Finally, the weight value of each risk assessment indicator is determined by calculation.
  • Pj is the recalculated weight value of the risk assessment index
  • Sj is the relative weight value of the risk assessment index
  • Kj is the mutual influence value of the risk assessment index
  • FWj is the final weight value of the risk assessment indicator
  • Pj is the recalculated weight value of the risk assessment indicator
  • n is the total number of all risk assessment indicators.
  • the intuitionistic fuzzy decision matrix of each expert is:
  • IFWA is the intuitionistic fuzzy weighted average operator; represents the intuitionistic fuzzy matrix obtained by the Kth expert, i and j represent the i-th row and j-th column in the intuitionistic fuzzy matrix.
  • ⁇ K represents the weight of expert DM k , the sum of which is 1, and each value belongs to [0, 1].
  • K is the total number of experts.
  • the aggregated intuitionistic fuzzy decision matrix Expressed as:
  • the objective weight calculation formula is as follows:
  • Ej is the intuitionistic fuzzy entropy of the jth secondary indicator
  • ⁇ ij is the membership degree of the intuitionistic fuzzy number of the jth secondary indicator corresponding to the i-th primary indicator of the aggregated fuzzy matrix
  • ⁇ ij and ⁇ ij are the non-membership degree and hesitation degree respectively
  • m is the total number of primary indicators
  • W' is the objective indicator weight.
  • Wq is the indicator vector determined by the qth method
  • wql represents the lth indicator weight determined by the qth method.
  • ⁇ and ⁇ are the subjective and objective weighting coefficients, respectively.
  • the game theory combination principle is used to solve ⁇ and ⁇ , and the formula is as follows:
  • a two-dimensional similarity calculation is performed on the risk cloud and the standard cloud to obtain the two-dimensional similarity between the risk cloud and the standard cloud, including: generating a two-dimensional normal cloud model of the standard cloud and a two-dimensional normal cloud model of the risk cloud according to the occurrence probability level, consequence level and membership degree of wind flashover of the standard cloud and the risk cloud, respectively; and cloud model similarity calculation is performed according to the two-dimensional normal cloud model of the standard cloud and the two-dimensional normal cloud model of the risk cloud to obtain the two-dimensional similarity between the risk cloud and the standard cloud.
  • the cloud model is a qualitative and quantitative analysis method that can better analyze the randomness, fuzziness and uncertainty of the evaluation object.
  • U In the quantitative domain U, there are N qualitative concepts. If the quantitative value x ⁇ N, then x is said to be a random probability embodiment of the qualitative concept N.
  • the membership of x to N, ⁇ (x) ⁇ [0,1], is a random number with a stable tendency.
  • x is called a cloud droplet, and the distribution of x in the U domain is called a cloud.
  • the digital characteristics of a cloud can reflect the overall characteristics of a cloud. The digital characteristics include expectation Ex, entropy En, and super entropy He.
  • Ex can reflect the location of the cloud's center of gravity; En can reflect the degree of discreteness of cloud droplets and the uncertainty of qualitative concepts. The larger it is, the higher the uncertainty, the greater the fuzziness and randomness; He is directly related to the distance between cloud droplets and the thickness of cloud droplets.
  • the two-dimensional cloud is developed on the basis of the one-dimensional cloud.
  • the one-dimensional cloud can combine qualitative and quantitative, and integrate randomness and fuzziness, it provides a more systematic and higher-level tool, and can provide methods to deal with more complex problems.
  • the two-dimensional cloud can be used to describe the problems under the joint action of two factors, and the severity of the wind deflection flashover of the transmission line is determined by the consequence level of the risk factor and the probability level of the accident, which conforms to the concept of the two-dimensional cloud. Therefore, it is scientific and meaningful to use the two-dimensional cloud to establish the wind deflection flashover evaluation model of the transmission line.
  • the model uses three numerical features: expectation Ex, entropy En, and super entropy He to represent a qualitative concept.
  • Expectation Ex is the ideal mean point for quantifying qualitative problems and the center point of the cloud droplet distribution in the domain.
  • the probability and consequence of each indicator are divided into five levels according to the risk level classification standard, and the risk level interval is quantified.
  • the maximum value R max and the minimum value R min of each risk level quantification interval are used to establish a standard cloud composed of five two-dimensional clouds as the reference standard of the risk cloud.
  • the description and numerical characteristics of each level of risk are shown in Table 2:
  • k is a constant, which is selected according to the fuzzy threshold of the variable, and is usually 0.01, 0.5, or 1. In this application, k is 0.01. If there is only a unilateral constraint, such as [R min , + ⁇ ) or (- ⁇ , R max ], the maximum or minimum value of the sample data should be used as the threshold, and the unilateral constraint should be supplemented and then calculated using the above formula. [L min , L max ] corresponds to the risk level threshold interval of this application.
  • the data used is the value of each expert's score for each indicator, and the calculation formula is as follows:
  • Ex is the expectation of the risk cloud
  • En is the entropy of the risk cloud
  • He is the super entropy of the risk cloud
  • M1 and S are the sample mean, sample first-order absolute central moment and sample variance of the expert scores for each indicator.
  • Xi is the sample observation value, corresponding to the expert score;
  • n is the number of samples, corresponding to the number of experts.
  • Entropy En is a quantitative interval representing a qualitative concept, which is the deviation between reality and expectation, reflecting the ambiguity of information.
  • Super entropy He is the entropy of En , which reflects the randomness of information and is reflected in the cloud map as the tightness of cloud droplet condensation.
  • the comprehensive risk cloud it reflects the overall risk level of the evaluation object.
  • the weights of indicators at all levels obtained by the game theory method are used to realize the conversion from secondary indicators to primary indicators, thereby obtaining the comprehensive risk cloud of the evaluation object.
  • the calculation formula is as follows:
  • C is the digital feature of the comprehensive risk cloud
  • E'x , E'n , H'e are the expectation, entropy, and super entropy of the comprehensive cloud respectively.
  • the normal cloud model is divided into one-dimensional normal cloud model, From the two-dimensional normal cloud model to the n-dimensional normal cloud model. Different from the one-dimensional normal cloud model, the two-dimensional normal cloud model is constructed by two sets of digital features ( Ex , En , He ) representing two qualitative concepts, which are used to describe the randomness and fuzziness under the joint action of two factors.
  • the function of this model is to convert the three data expectations, entropy, and super entropy obtained from the previous standard cloud and risk cloud into cloud drops by repeating the following formula:
  • ( pxi , pyi ) is the random number generated by the ith cloud droplet.
  • a cloud drop ( xi , yi , ⁇ i ) that satisfies the above formula becomes a cloud droplet. Repeat the above formula.
  • a two-dimensional cloud model is formed. Set the probability level of wind deflection flashover to the Y axis and the consequence level to the X axis. Set the membership to the Z axis.
  • the schematic diagram is shown in Figure 4.
  • cloud model similarity determination When determining the similarity of cloud models, the quantitative comparison of the similarity between two cloud images is called cloud model similarity determination.
  • the risk level is determined using the two-dimensional cloud similarity calculation method. The calculation formula is as follows:
  • L is the similarity, Ex, Ex′, They are respectively the expected value of the standard cloud for the probability of wind flashover accidents, the expected value of the actual cloud for the probability of accidents, the expected value of the standard cloud for the level of consequences of accidents, and the expected value of the actual cloud for the level of consequences of accidents.
  • the technical solution provided by the embodiment of the present application may include the following beneficial effects: the present application determines the risk assessment index according to the influencing factors, so that the evaluation of wind flashover is more universal; and a two-dimensional normal cloud model is generated by the standard cloud and the risk cloud, and the risk value is determined according to the two-dimensional similarity of the two-dimensional normal cloud model, thereby taking into account both the possibility of occurrence and the consequences of occurrence, and fully considering the fuzziness of the boundary of the indicator risk level, the evaluation result is more reasonable, and the result is output in the form of a cloud map, and the risk level has a high degree of visualization.
  • this application weights the evaluation objectives through SWARA, intuitive fuzzy entropy weight method and combined weighting method based on game theory, so that the evaluation results are more balanced, and fully combine expert opinions and objective conditions, so that the two-dimensional cloud model drawn on this basis is more accurate and has a lower deviation rate.
  • FIG5 shows an embodiment of a transmission line wind deflection flashover risk assessment system of the present application.
  • the transmission line wind deflection flashover risk assessment system includes:
  • An index setting module 501 is configured to determine the influencing factors of wind deflection flashover of a transmission line and set risk assessment indicators according to the influencing factors;
  • a standard cloud generation module 503 is configured to generate a standard cloud of the risk assessment indicator according to the accident probability and consequence level of the risk assessment indicator;
  • a risk cloud generation module 505 is configured to score the influencing factors according to the risk assessment index, and generate a risk cloud of the risk assessment index according to the scoring result;
  • the risk level determination module 507 is configured to perform a two-dimensional similarity calculation on the risk cloud and the standard cloud to obtain the two-dimensional similarity between the risk cloud and the standard cloud, and determine the risk level of wind deflection flashover of the transmission line according to the two-dimensional similarity.
  • the index setting module 501 is configured to determine the influencing factors of wind deflection flashover of the transmission line according to the historical fault information, operation and maintenance information and meteorological information of the transmission line when determining the influencing factors of wind deflection flashover of the transmission line.
  • the risk cloud generation module 505 includes: a weight value calculation submodule, an initial risk cloud generation submodule and a risk cloud generation submodule, wherein the weight value calculation submodule is configured to generate an expert evaluation system according to the risk evaluation index, and calculate the weight value of the risk evaluation index according to the expert evaluation system; the initial risk cloud generation submodule is configured to evaluate the risk evaluation index according to the expert evaluation system, and generate the initial risk cloud of the risk evaluation index according to the evaluation result; the risk cloud generation submodule is configured to generate the risk cloud of the risk evaluation index according to the weight value and the initial risk cloud.
  • the weight value calculation submodule includes: a subjective weight value calculation unit, which is configured to sort the risk assessment indicators according to the expert evaluation system, and compare the risk assessment indicators of adjacent serial numbers to obtain a comparison result; based on the comparison result, the subjective weight value of the risk assessment indicator is calculated based on the stepwise weighted evaluation analysis ratio method.
  • An objective weight value calculation submodule which is configured to evaluate the risk assessment indicator according to the expert evaluation system to obtain an evaluation result; convert the evaluation result into an intuitive fuzzy number, and calculate the objective weight value of the risk assessment indicator based on the direct fuzzy entropy weight method.
  • a comprehensive weight value calculation submodule which is configured to determine the comprehensive weight value of the risk assessment indicator based on the combined weighting method of game theory according to the subjective weight value and the objective weight value; and use the comprehensive weight value as the weight value of the risk assessment indicator.
  • the risk level determination module 507 includes: a two-dimensional normal cloud model generation submodule and a two-dimensional similarity calculation submodule, wherein the two-dimensional normal cloud model generation submodule is configured to generate a standard cloud two-dimensional normal cloud model and a risk cloud two-dimensional normal cloud model according to the occurrence probability level, consequence level and membership degree of the wind deflection flashover of the standard cloud and the risk cloud respectively;
  • the proximity calculation submodule is configured to perform cloud model proximity calculation based on the standard cloud two-dimensional normal cloud model and the risk cloud two-dimensional normal cloud model to obtain the two-dimensional proximity between the risk cloud and the standard cloud.
  • a computer device which may be a server, and its internal structure diagram may be shown in FIG6.
  • the computer device includes a processor, a memory, and a network interface connected via a system bus.
  • the processor of the computer device is used to provide computing and control capabilities.
  • the memory of the computer device includes a non-volatile storage medium and an internal memory.
  • the non-volatile storage medium stores an operating system, a computer program, and a database.
  • the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium.
  • the database of the computer device is used to store static information and dynamic information data.
  • the network interface of the computer device is used to communicate with an external terminal via a network connection.
  • FIG. 6 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.
  • the specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
  • a computer device including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.
  • a computer-readable storage medium on which a computer program is stored.
  • the steps in the above method embodiment are implemented.
  • Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
  • the probability level and consequence level of risk involve the concept of quantitative interval division, which is the core of qualitative and quantitative transformation. Whether the interval division is reasonable directly affects the accuracy of the transformation.
  • This application divides the risk level of the two uncertain factors (consequence level and probability level) of wind deflection flashover of transmission lines into five levels, and uses the formula to calculate the cloud model parameters of each level, and then substitutes them into the normal cloud model formula to obtain the risk standard cloud map.
  • the qualitative descriptions of the probability level and consequence level of each risk level in Table 2 correspond to their respective risk level thresholds and cloud model parameters.
  • the risk level threshold corresponding to the probability level I occurrence frequency is [0, 1.6).
  • the risk level threshold corresponding to the qualitative description of consequence level I is also [0, 1.6).
  • Each risk level threshold has a corresponding cloud model parameter, so that the probability level and consequence level in the risk matrix can be superimposed in the form of a two-dimensional cloud model, thereby realizing the quantification of the risk matrix using a two-dimensional cloud model.
  • the probability of an accident is determined by the order of magnitude of the probability of an accident.
  • Experts score the accident consequence level according to the corresponding risk level threshold based on their own actual experience and relevant theoretical analysis, and integrate the scores of the accident consequence level by the experts to generate the consequence level cloud model. Then, according to the risk level threshold corresponding to the probability of occurrence in Table 1, the score of the accident possibility is scored, and the scores of the probability of occurrence by the experts are integrated to generate the probability of occurrence cloud model.
  • the weights obtained by the improved weighting method are used to combine the consequence level cloud model and the occurrence probability cloud model. Then, the two types of cloud models are brought into the normal cloud model formula to generate the wind deflection flashover comprehensive cloud model, and then they are brought into the two-dimensional cloud similarity calculation to determine the risk level.

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Abstract

一种输电线路风偏闪络风险评价方法，包括：确定输电线路风偏闪络的影响因素，并根据影响因素设定风险评价指标(S101)；根据风险评价指标的发生事故概率和后果等级，生成风险评价指标的标准云(S103)；根据风险评价指标对影响因素进行打分，并根据打分结果生成风险评价指标的风险云(S105)；将风险云与标准云进行二维相近度计算，得到风险云和标准云的二维相近度，并根据二维相近度，确定输电线路风偏闪络的风险等级(S107)。

Description

风偏闪络风险评价方法、系统、设备、可读存储介质
本公开要求在2022年11月4日提交中国专利局、申请号为202211375747.6的中国专利的优先权，以上申请的全部内容通过引用结合在本申请中。
技术领域
本申请涉及电力系统技术领域，例如涉及一种输电线路风偏闪络风险评价方法、系统、计算机设备及计算机可读存储介质。
背景技术
电网输电线路和设施广泛分布在不同的地理位置上，其运行环境受不同地理位置的不同气象条件影响较大，其中，输电线路风偏闪络导致线路跳闸是影响输电线路正常运行的重要因素之一。
目前，输电线路风偏闪络风险评估提出了各种各样的风险评估方法，如：用蒙特卡洛法对气象区域和输电线路进行抽样，得出输电元件状态并进行电力系统风险评估，对于有较大随机性的气象状况有较好的适用性。又如：基于泊松分布模型与回归分析，建立了电力系统输电线路跳闸概率计算方法，要通过概率统计方法对线路进行分析。
但是目前的风偏闪络风险评估技术依旧存在以下不足之处：1、上述风险评估风偏研究大多是对单个设备的风偏角进行计算，较少对风偏跳闸的概率和风险值进行计算和分析，使得风险评价不具普适性；2、对于主客观之间的赋权比例不均衡，部分赋权偏向主观专家意见，部分偏向客观现实条件；3、上述风险评价未充分考虑不同评价指标边界的模糊性。
发明内容
本申请实施例提供了一种输电线路风偏闪络风险评价方法、系统、计算机设备及计算机可读存储介质，以解决背景技术中的上述技术问题，使风偏闪络的评价更具普适性，评价结果更加合理。
根据本申请实施例的第一方面，提供了一种输电线路风偏闪络风险评价方法。
该输电线路风偏闪络风险评价方法，包括：
确定输电线路风偏闪络的影响因素，并根据所述影响因素设定风险评价指标；
根据所述风险评价指标的发生事故概率和后果等级，生成所述风险评价指标的标准云；
根据所述风险评价指标对所述影响因素进行打分，并根据打分结果生成所述风险评价指标的风险云；
将所述风险云与所述标准云进行二维相近度计算，得到所述风险云和所述标准云的二维相近度，并根据所述二维相近度，确定所述输电线路风偏闪络的风险等级。
根据本申请实施例的第二方面，提供了一种输电线路风偏闪络风险评价系统。
该输电线路风偏闪络风险评价系统，包括：
指标设定模块，设置为确定输电线路风偏闪络的影响因素，并根据所述影响因素设定风险评价指标；
标准云生成模块，设置为根据所述风险评价指标的发生事故概率和后果等级，生成所述风险评价指标的标准云；
风险云生成模块，设置为根据所述风险评价指标对所述影响因素进行打分，并根据打分结果生成所述风险评价指标的风险云；
风险等级确定模块，设置为将所述风险云与所述标准云进行二维相近度计算，得到所述风险云和所述标准云的二维相近度，并根据所述二维相近度，确定所述输电线路风偏闪络的风险等级。
根据本申请实施例的第三方面，提供了一种计算机设备。
所述计算机设备包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现上述方法的步骤。
根据本申请实施例的第四方面，提供了一种计算机可读存储介质。
所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现上述方法的步骤。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本申请的实施例，并与说明书一起用于解释本申请。
图1是根据一示例性实施例示出的输电线路风偏闪络风险评价方法的流程示意图；
图2是根据一示例性实施例示出的输电线路风偏闪络风险评价指标体系结构示意图；
图3是根据一示例性实施例示出的SWARA方法的流程示意图；
图4是根据一示例性实施例示出的二维云示意图；
图5是根据一示例性实施例示出的输电线路风偏闪络风险评价系统的结构框图；
图6是根据一示例性实施例示出的计算机设备的结构示意图。
具体实施方式
以下描述和附图充分地示出了本文的具体实施方案，以使本领域的技术人员能够实践它们。一些实施方案的部分和特征可以被包括在或替换其他实施方案的部分和特征。本文的实施方案的范围包括权利要求书的整个范围，以及权利要求书的所有可获得的等同物。本文中，术语“第一”、“第二”等仅被用来将一个元素与另一个元素区分开来，而不要求或者暗示这些元素之间存在任何实际的关系或者顺序。实际上第一元素也能够被称为第二元素，反之亦然。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的结构、装置或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种结构、装置或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的结构、装置或者设备中还存在另外的相同要素。本文中各个实施例采用递进的方式描述，每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似部分互相参见即可。
本文中的术语“纵向”、“横向”、“上”、“下”、“前”、“后”、“左”、“右”、“竖直”、“水平”、“顶”、“底”“内”、“外”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本文和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作。在本文的描述中，除非另有规定和限定，术语“安装”、“相连”、“连接”应做广义理解，例如，可以是机械连接或电连接，也可以是两个元件内部的连通，可以是直接相连，也可以通过中间媒介间接相连，对于本领域的普通技术人员而言，可以根据具体情况理解上述术语的具体含义。
本文中，除非另有说明，术语“多个”表示两个或两个以上。
本文中，字符“/”表示前后对象是一种“或”的关系。例如，A/B表示：A或B。
本文中，术语“和/或”是一种描述对象的关联关系，表示可以存在三种关系。例如，A和/或B，表示：A或B，或，A和B这三种关系。
应该理解的是，虽然流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，图中的至少一部分步骤可以包括多个子步骤或者多个阶段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，这些子步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。
本申请的装置或系统中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于计算机设备中的处理器中，也可以以软件形式存储于计算机设备中的存储器中，以便于处理器调用执行以上各个模块对应的操作。
申请图1示出了本申请的输电线路风偏闪络风险评价方法的一个实施例。
在该可选实施例中，所述输电线路风偏闪络风险评价方法，包括：
步骤S101，确定输电线路风偏闪络的影响因素，并根据所述影响因素设定风险评价指标；
步骤S103，根据所述风险评价指标的发生事故概率和后果等级，生成所述风险评价指标的标准云；
步骤S105，根据所述风险评价指标对所述影响因素进行打分，并根据打分结果生成所述风险评价指标的风险云；
步骤S107，将所述风险云与所述标准云进行二维相近度计算，得到所述风险云和所述标准云的二维相近度，并根据所述二维相近度，确定输电线路风偏闪络的风险等级。
在一个实施例中，所述输电线路风偏闪络风险评价方法还包括：根据输电线路的历史故障信息、运维信息以及气象信息，确定输电线路风偏闪络的影响因素；而根据该影响因素确定的风险评价指标则可如图2所示，将风偏闪络风险评价指标分为三类一级指标和八类二级指标。
在一个实施例中，根据所述风险评价指标对所述影响因素进行打分，并根据打分结果生成所述风险评价指标的风险云包括：根据所述风险评价指标，生 成专家评价体系，并根据所述专家评价体系，计算所述风险评价指标的权重值；根据所述专家评价体系对所述风险评价指标进行评价，并根据评价结果，生成所述风险评价指标的初始风险云；根据所述权重值和所述初始风险云，生成所述风险评价指标的风险云。
在一实施例中，根据所述专家评价体系，计算所述风险评价指标的权重值包括：根据所述专家评价体系，对所述风险评价指标进行排序，并根据相邻序号的风险评价指标进行比较，得到比较结果；根据所述比较结果，基于逐步加权评估分析比率方法，计算所述风险评价指标的主观权重值；根据所述专家评价体系，对所述风险评价指标进行评价，得到评价结果；将所述评价结果转换为直觉模糊数，并基于直接模糊熵权方法，计算所述风险评价指标的客观权重值；根据所述主观权重值和所述客观权重值，基于博弈论的组合赋权法，确定所述风险评价指标的综合权重值；将所述综合权重值作为所述风险评价指标的权重值。
实际应用时，对于逐步加权评估分析比率(Stepwise Weight Assessment Ratio Analysis，SWARA)方法来说，SWARA方法在专家组成和权重值计算方面具有较大优势，其主要操作步骤如附图3所示，主要流程如下：首先，将初拟的风险评价指标列表发放给专家，由专家对筛选出的风险评价指标按照重要性降序排列，最重要的准则定义为第1级，最不重要的准则定义为最后一级(或第n级)；每个风险评价指标的序号为全部专家对该项风险评价指标排序序号的中位数，即附图3中的准则j。其次，对相邻序号的风险评价指标进行两两比较，经多次循环后确定两两比较的结果，以实现对各风险评价指标相对重要性程度的评估。最后，通过计算确定每项风险评价指标的权重值。
示例性的，首先，确定各风险评价指标之间的相互影响值，其计算公式：Kj＝1&Kj＝Sj+1，式中Kj为风险评价指标的相互影响值；Sj为风险评价指标的相对权重值；为离散数学中的符号，表示任意；n为全部风险评价指标的数量，j表示为第j个风险评价指标；1&Kj表示当j等于1时K1＝1。
再次，根据各风险评价指标之间的相互影响值，计算风险评价指标的再计算权重值，计算公式如下：
式中，Pj为风险评价指标的再计算权重值。Sj为风险评价指标的相对权重值，Kj风险评价指标的相互影响值，1&Pj表示当j等于1时P1＝1。
再次，根据风险评价指标的再计算权重值Pj，计算风险评价指标的最终权重值，计算公式如下：
式中，FWj为风险评价指标的最终权重值，Pj为风险评价指标的再计算权重值，n为所有风险评价指标的总数量。
而对于基于直觉模糊熵权法来说，其操作步骤流程可如下：
1)将专家对评价对象的语言转换为直觉模糊数；对评价对象进行评级的语言术语可如下表1所示：
表1对评价对象进行评级的语言术语
2)构建原始决策矩阵。针对历史故障，运维信息，气象信息三类引起风偏闪络的一级指标，构建m×8的原始决策矩阵，其中m为每个一级指标中所包含的二级指标个数，8为评价指标个数。邀请输电线路领域内专家(如k个人)对各项指标进行等级评价，采用直觉模糊理论将等级评价语言转换为直觉模糊数。
设一级指标Di(i＝1，...，m)，二级指标为Cj(j＝1，...，n)，专家为DMk(1，...，K)，则每位专家的直觉模糊决策矩阵为：
式中，为专家DMk对待一级指标m，二级指标n的直觉模糊数。其中μ、ν和π分别代表直觉模糊数的隶属度、非隶属度和犹豫度。三者关系为0≤μ+ν≤1，π＝1-μ-ν。
3)决策矩阵集结。将直觉模糊决策矩阵集结成一个群体意见，公式如下：
式中，IFWA为直觉模糊加权平均算子；表示第K位专家获得的直觉模糊矩阵，i和j表示直觉模糊矩阵中第i行第j列。λK表示专家DMk的权重，其值之和为1，且每个值在属于[0，1]，K为专家总人数。集结后的直觉模糊决策矩阵 表示为：
式中：为集结直觉模糊矩阵，(μmn,νmn,πmn)为集结后待评价技术m、评价指标n对应的直接模糊数。
4)客观权重计算。客观权重计算公式如下：

式中：Ej为第j个二级指标的直觉模糊熵，μij为集结模糊矩阵的第i个一级指标对应的第j个二级指标的直觉模糊数的隶属度，同理νij和πij分别为非隶属度和犹豫度，m为一级指标总数，W”为客观指标权重。
而对于基于博弈论的组合赋权法来说，其过程则可如下：采用两种方法确定8个指标的权重，将两种方法确定的指标权重表示为向量形式，如下式：
Wq＝(wq1，wq2，...，wql)，q＝1，2，...，l；
式中，Wq为第q个方法确定的指标向量，wql表示第q个方法确定的第l个指标权重。则2个权重向量Wq的线性组合的表示为：W＝αW'+βW”；其中，W'为SWARA方法构建的主观权重向量，W”为直觉模糊熵权法构建的客观权重向量。α和β分别是主、客观加权系数。
采用博弈论组合原理求解α和β，公式如下：
在一个实施例中，将所述风险云与所述标准云进行二维相近度计算，得到所述风险云和所述标准云的二维相近度包括：根据所述标准云和所述风险云的风偏闪络的发生概率等级、后果等级以及隶属度，分别生成标准云二维正态云模型和风险云二维正态云模型；根据所述标准云二维正态云模型和所述风险云二维正态云模型进行云模相近度计算，得到所述风险云和所述标准云的二维相近度。
实际应用时，云模型是一种定性与定量结合的分析方法，能更好地分析评价对象的随机性、模糊性和不确定性。在定量论域U中，有N个定性概念，若有 定量值x∈N，则称x是定性概念N的一次随机概率体现。x对N的隶属度μ(x)∈[0，1]是具有稳定倾向的随机数，称x为云滴，x在U论域的分布称为云。云的数字特征可以反映云的整体特性，数字特征包括期望Ex、熵En和超熵He。Ex能反映云重心的位置；En能反映云滴的离散程度和定性概念的不确定性，其越大，不确定性就越高，模糊性和随机性也就越大；He跟云滴之间的距离和云滴的厚度有直接关系。
二维云是在一维云的基础上发展起来的，在一维云能够定性与定量结合、随机性与模糊性能够融合的基础上，提供了更系统、更高层的工具，能够提供应对更加复杂问题的方法。二维云能够用来描述2个因素共同作用下的问题，而输电线路风偏闪络的严重程度是由风险因素的后果等级和发生事故的概率等级这两者来决定的，符合二维云的概念，所以运用二维云来建立输电线路风偏闪络评价模型是科学的、有意义的。
模型用期望Ex、熵En、超熵He三个数字特征表示一个定性的概念。期望Ex是定性问题量化的理想均值点，是云滴在论域内分布的中心点。
而对于标准云来说，对风险等级的划分标准，将各指标的发生概率和发生后果划分为5个级别，并对其风险等级区间进行量化。此外，采用各风险等级量化区间的最大值Rmax和最小值Rmin建立由五个二维云组成的标准云，作为风险云的对照标准。各级风险描述及数字特征见表2所示：
表2风险等级量化判据
故，绘制标准云模型3个参数的计算公式为：
式中，k为常数，根据变量的模糊阈度选取，常取0.01、0.5、1，在本申请中，k取0.01。如果仅有单边约束，如[Rmin，+∞)或(－∞，Rmax]，应以样本数据的最大值或最小值作为阈限，补充单边再利用上述公式计算，[Lmin，Lmax]等对应于本申请的风险等级阈值区间。
而对于风险云(初始风险云)来说，对于风险云，采用的数据为各专家对各指标打分的值，计算公式如下：


式中：Ex为风险云的期望，En为风险云的熵，He为风险云的超熵；M1和S分别为各专家对各指标打分的样本均值、样本一阶绝对中心矩和样本方差。xi为样本观测值，对应专家打分；n为样本个数，对应专家人数。熵En是表示定性概念的量化区间，是实际与期望之间的偏差，反映信息的模糊性。超熵He是En的熵，体现了信息的随机性，在云图中反映为云滴凝聚的松紧程度。
而对于综合风险云来说，综合风险云反映评价对象总体的风险等级，由基于博弈论方法调和得到的各级指标权重实现由二级指标向一级指标的转换，从而得到评价对象的综合风险云，计算公式如下：
式中，C为综合风险云数字特征，E'x,E'n,H'e分别为综合云的期望、熵、超熵。wn为经博弈法调和后的各指标权重，n为第n个指标。例如有3个指标综合权重分别为0.2，0.5，0.3，则此处w＝(0.2，0.5，0.3)。代表第n个二级指标的期望、熵、超熵。
而对于正态云模型来说，正态云模型按照论域维数又分为一维正态云模型、 二维正态云模型到n维正态云模型。区别于一维正态云模型，二维正态云模型是由代表两个定性概念的两组数字特征(Ex，En，He)构建起来的，用来描述两个因素共同作用下的随机性和模糊性。此模型的作用为将前序标准云以及风险云得到的三个数据期望、熵、超熵，通过重复以下公式转换为云滴，公式为：
式中，和分别为发生概率等级与发生后果等级风险云生成的期望值，和分别为发生概率等级与发生后果等级风险云的标准差，分别为发生概率等级与发生后果等级风险云的超熵，μi为二维云模型的隶属度函数，F为服从正态分布的二维随机函数。此处i为生成一个一个的云滴的序号，由数域内涌现的大量二维云滴凝聚成二维正态云，(xi，yi)为第i个云滴生成的x轴y轴坐标。(pxi,pyi)为第i个云滴所生成的随机数。满足上述公式成为一个云滴drop(xi，yi，μi)，重复上述公式，云滴数量足够多时，形成二维云模型。将发生风偏闪络概率等级设为Y轴，后果等级设为X轴。隶属度设为Z轴。示意图则如图4所示。
在进行云模型相似性判定时，两朵云图相似程度的定量比较称为云模型相似性判定，运用二维云相近度计算方法确定风险等级，计算公式如下：
式中，L为相近度、Ex、Ex′、分别为风偏闪络事故发生概率标准云的期望值、事故发生概率实际云的期望值、事故发生后果等级标准云的期望值、发生后果等级实际云的期望值。
本申请实施例提供的技术方案可以包括以下有益效果：本申请根据影响因素确定风险评价指标，使风偏闪络的评价更具普适性；而通过标准云和风险云生成二维正态云模型，并根据二维正态云模型的二维相近度确定风险值，从而兼顾了发生可能性和发生后果，充分考虑了指标风险等级边界模糊性，评价结果更加合理，同时以云图的形式输出结果，风险等级可视化程度较高。
此外，本申请在确定风险云时，通过SWARA、直觉模糊熵权法以及基于博弈论的组合赋权法对评价目标进行赋权，使得评价结果更加均衡，充分结合专家意见以及客观条件，使得在此基础上绘制二维云模型更加准确以及偏差率更小。
图5示出了本申请的输电线路风偏闪络风险评价系统的一个实施例。
在该可选实施例中，所述输电线路风偏闪络风险评价系统，包括：
指标设定模块501，设置为确定输电线路风偏闪络的影响因素，并根据所述影响因素设定风险评价指标；
标准云生成模块503，设置为根据所述风险评价指标的发生事故概率和后果等级，生成所述风险评价指标的标准云；
风险云生成模块505，设置为根据所述风险评价指标对所述影响因素进行打分，并根据打分结果生成所述风险评价指标的风险云；
风险等级确定模块507，设置为将所述风险云与所述标准云进行二维相近度计算，得到所述风险云和所述标准云的二维相近度，并根据所述二维相近度，确定所述输电线路风偏闪络的风险等级。
对应的，在一个实施例中，所述指标设定模块501设置为在确定输电线路风偏闪络的影响因素时，根据输电线路的历史故障信息、运维信息以及气象信息，确定所述输电线路风偏闪络的影响因素。而所述风险云生成模块505包括：权重值计算子模块、初始风险云生成子模块以及风险云生成子模块，其中，权重值计算子模块，设置为根据所述风险评价指标，生成专家评价体系，并根据所述专家评价体系，计算所述风险评价指标的权重值；初始风险云生成子模块，设置为根据所述专家评价体系对所述风险评价指标进行评价，并根据评价结果，生成所述风险评价指标的初始风险云；风险云生成子模块，设置为根据所述权重值和所述初始风险云，生成所述风险评价指标的风险云。
对应的，在一个实施例中，所述权重值计算子模块包括：主观权重值计算单元，设置为根据所述专家评价体系，对所述风险评价指标进行排序，并根据相邻序号的风险评价指标进行比较，得到比较结果；根据所述比较结果，基于逐步加权评估分析比率方法，计算所述风险评价指标的主观权重值。客观权重值计算子模块，设置为根据所述专家评价体系，对所述风险评价指标进行评价，得到评价结果；将所述评价结果转换为直觉模糊数，并基于直接模糊熵权方法，计算所述风险评价指标的客观权重值。综合权重值计算子模块，设置为根据所述主观权重值和所述客观权重值，基于博弈论的组合赋权法，确定所述风险评价指标的综合权重值；将所述综合权重值作为所述风险评价指标的权重值。
对应的，在一个实施例中，所述风险等级确定模块507包括：二维正态云模型生成子模块和二维相近度计算子模块，其中，二维正态云模型生成子模块，设置为根据所述标准云和所述风险云的风偏闪络的发生概率等级、后果等级以及隶属度，分别生成标准云二维正态云模型和风险云二维正态云模型；二维相 近度计算子模块，设置为根据所述标准云二维正态云模型和所述风险云二维正态云模型进行云模相近度计算，得到所述风险云和所述标准云的二维相近度。
在一个实施例中，提供了一种计算机设备，该计算机设备可以是服务器，其内部结构图可以如图6所示。该计算机设备包括通过系统总线连接的处理器、存储器和网络接口。其中，该计算机设备的处理器用于提供计算和控制能力。该计算机设备的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作系统、计算机程序和数据库。该内存储器为非易失性存储介质中的操作系统和计算机程序的运行提供环境。该计算机设备的数据库用于存储静态信息和动态信息数据。该计算机设备的网络接口用于与外部的终端通过网络连接通信。该计算机程序被处理器执行时以实现上述方法实施例中的步骤。
本领域技术人员可以理解，图6中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在一个实施例中，还提供了一种计算机设备，包括存储器和处理器，存储器中存储有计算机程序，该处理器执行计算机程序时实现上述方法实施例中的步骤。
在一个实施例中，提供了一种计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现上述方法实施例中的步骤。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一非易失性计算机可读取存储介质中，该计算机程序在执行时，可包括如上述各方法的实施例的流程。其中，本申请所提供的各实施例中所使用的对存储器、存储、数据库或其它介质的任何引用，均可包括非易失性和易失性存储器中的至少一种。非易失性存储器可包括只读存储器(Read-Only Memory，ROM)、磁带、软盘、闪存或光存储器等。易失性存储器可包括随机存取存储器(Random Access Memory，RAM)或外部高速缓冲存储器。作为说明而非局限，RAM可以是多种形式，比如静态随机存取存储器(Static RandomAccess Memory，SRAM)或动态随机存取存储器(Dynamic RandomAccess Memory，DRAM)等。
综上所述，风险的发生概率等级和后果等级涉及概念的数量区间划分问题，是定性与定量转化的核心。区间划分是否合理直接影响转化的准确度，本申请将输电线路风偏闪络两个不确定因素(后果等级和发生概率等级)的风险等级划分为五个等级，利用公式计算得到各等级的云模型参数，再代入正态云模型公式可得到风险标准云图。
由表2中各风险等级的发生概率等级和后果等级的定性描述均对应各自的风险等级阈值及云模型参数，如可能性等级I发生次数为几乎不发生对应的风险等级阈值为[0，1.6)，同理后果等级I的定性描述对应的风险等级阈值也为[0，1.6)，各风险等级阈值都有对应的云模型参数，使得风险矩阵中的概率等级与后果等级可以以二维云模型的方式叠加，从而实现了用二维云模型量化风险矩阵。事故发生可能性大小由事故发生可能性的数量级决定，专家根据自身实际经验及相关理论分析按照对应的风险等级阈值打出事故后果等级的分数，整合各位专家对事故后果等级的打分，生成后果等级云模型。再根据表1中发生概率吧对应的风险等级阈值打出事故可能性的分数，整合各位专家对发生概率的打分，生成发生概率云模型。
最后利用改进赋权法所获得的权重将后果等级云模型和发生概率云模型。接着将两种类型云模型带入正态云模型公式生成风偏闪络综合云模型，接着分别带入二维云相近度计算中，从而确定风险等级。

Claims (16)

  1. 一种输电线路风偏闪络风险评价方法，包括：
    确定输电线路风偏闪络的影响因素，并根据所述影响因素设定风险评价指标；
    根据所述风险评价指标的发生事故概率和后果等级，生成所述风险评价指标的标准云；
    根据所述风险评价指标对所述影响因素进行打分，并根据打分结果生成所述风险评价指标的风险云；
    将所述风险云与所述标准云进行二维相近度计算，得到所述风险云和所述标准云的二维相近度，并根据所述二维相近度，确定所述输电线路风偏闪络的风险等级。
  2. 根据权利要求1所述的输电线路风偏闪络风险评价方法，所述方法还包括：
    根据输电线路的历史故障信息、运维信息以及气象信息，确定所述输电线路风偏闪络的影响因素。
  3. 根据权利要求1所述的输电线路风偏闪络风险评价方法，其中，所述根据所述风险评价指标对所述影响因素进行打分，并根据打分结果生成所述风险评价指标的风险云，包括：
    根据所述风险评价指标，生成专家评价体系，并根据所述专家评价体系，计算所述风险评价指标的权重值；
    根据所述专家评价体系对所述风险评价指标进行评价，并根据评价结果，生成所述风险评价指标的初始风险云；
    根据所述权重值和所述初始风险云，生成所述风险评价指标的风险云。
  4. 根据权利要求3所述的输电线路风偏闪络风险评价方法，其中，所述根据所述专家评价体系，计算所述风险评价指标的权重值，包括：
    根据所述专家评价体系，对所述风险评价指标进行排序，并根据相邻序号的风险评价指标进行比较，得到比较结果；
    根据所述比较结果，基于逐步加权评估分析比率方法，计算所述风险评价指标的主观权重值。
  5. 根据权利要求4所述的输电线路风偏闪络风险评价方法，其中，所述根据所述专家评价体系，计算所述风险评价指标的权重值，还包括：
    根据所述专家评价体系，对所述风险评价指标进行评价，得到评价结果；
    将所述评价结果转换为直觉模糊数，并基于直接模糊熵权方法，计算所述风险评价指标的客观权重值。
  6. 根据权利要求5所述的输电线路风偏闪络风险评价方法，其中，所述根据所述专家评价体系，计算所述风险评价指标的权重值，还包括：
    根据所述主观权重值和所述客观权重值，基于博弈论的组合赋权法，确定所述风险评价指标的综合权重值；
    将所述综合权重值作为所述风险评价指标的权重值。
  7. 根据权利要求1所述的输电线路风偏闪络风险评价方法，其中，所述将所述风险云与所述标准云进行二维相近度计算，得到所述风险云和所述标准云的二维相近度，包括：
    根据所述标准云和所述风险云的风偏闪络的发生概率等级、后果等级以及隶属度，分别生成标准云二维正态云模型和风险云二维正态云模型；
    根据所述标准云二维正态云模型和所述风险云二维正态云模型进行云模相近度计算，得到所述风险云和所述标准云的二维相近度。
  8. 一种输电线路风偏闪络风险评价系统，包括：
    指标设定模块，设置为确定输电线路风偏闪络的影响因素，并根据所述影响因素设定风险评价指标；
    标准云生成模块，设置为根据所述风险评价指标的发生事故概率和后果等级，生成所述风险评价指标的标准云；
    风险云生成模块，设置为根据所述风险评价指标对所述影响因素进行打分，并根据打分结果生成所述风险评价指标的风险云；
    风险等级确定模块，设置为将所述风险云与所述标准云进行二维相近度计算，得到所述风险云和所述标准云的二维相近度，并根据所述二维相近度，确定所述输电线路风偏闪络的风险等级。
  9. 根据权利要求8所述的输电线路风偏闪络风险评价系统，其中，所述指标设定模块设置为在确定输电线路风偏闪络的影响因素时，根据输电线路的历史故障信息、运维信息以及气象信息，确定所述输电线路风偏闪络的影响因素。
  10. 根据权利要求8所述的输电线路风偏闪络风险评价系统，其中，所述风险云生成模块包括：权重值计算子模块、初始风险云生成子模块以及风险云生成子模块，
    其中，权重值计算子模块，设置为根据所述风险评价指标，生成专家评价体系，并根据所述专家评价体系，计算所述风险评价指标的权重值；
    初始风险云生成子模块，设置为根据所述专家评价体系对所述风险评价指标进行评价，并根据评价结果，生成所述风险评价指标的初始风险云；
    风险云生成子模块，设置为根据所述权重值和所述初始风险云，生成所述风险评价指标的风险云。
  11. 根据权利要求10所述的输电线路风偏闪络风险评价系统，其中，所述权重值计算子模块包括：主观权重值计算单元，设置为根据所述专家评价体系，对所述风险评价指标进行排序，并根据相邻序号的风险评价指标进行比较，得到比较结果；根据所述比较结果，基于逐步加权评估分析比率方法，计算所述风险评价指标的主观权重值。
  12. 根据权利要求11所述的输电线路风偏闪络风险评价系统，其中，所述权重值计算子模块还包括：客观权重值计算单元，设置为根据所述专家评价体系，对所述风险评价指标进行评价，得到评价结果；将所述评价结果转换为直觉模糊数，并基于直接模糊熵权方法，计算所述风险评价指标的客观权重值。
  13. 根据权利要求12所述的输电线路风偏闪络风险评价系统，其中，所述权重值计算子模块还包括：综合权重值计算单元，设置为根据所述主观权重值和所述客观权重值，基于博弈论的组合赋权法，确定所述风险评价指标的综合权重值；将所述综合权重值作为所述风险评价指标的权重值。
  14. 根据权利要求8所述的输电线路风偏闪络风险评价系统，其中，所述风险等级确定模块包括：二维正态云模型生成子模块和二维相近度计算子模块，
    其中，二维正态云模型生成子模块，设置为根据所述标准云和所述风险云的风偏闪络的发生概率等级、后果等级以及隶属度，分别生成标准云二维正态云模型和风险云二维正态云模型；
    二维相近度计算子模块，设置为根据所述标准云二维正态云模型和所述风险云二维正态云模型进行云模相近度计算，得到所述风险云和所述标准云的二维相近度。
  15. 一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现权利要求1至7中任一项所述的方法的步骤。
  16. 一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现权利要求1至7中任一项的方法的步骤。
PCT/CN2023/115169 2022-11-04 2023-08-28 风偏闪络风险评价方法、系统、设备、可读存储介质 WO2024093468A1 (zh)

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