CN113742973A - Post-earthquake failure probability analysis method for strut equipment - Google Patents
Post-earthquake failure probability analysis method for strut equipment Download PDFInfo
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Abstract
The utility model provides a post equipment failure probability analysis method after earthquake, includes: selecting a clustering index of the pillar equipment sample; calculating by adopting Sturges empirical formula and elbow discrimination method to obtain the optimal classification number; classifying the pillar equipment samples by adopting a K-means clustering algorithm according to the optimal classification number to obtain a clustering result; selecting a typical strut type equipment sample; establishing a finite element model of a typical strut equipment sample; selecting earthquake motion parameters, and carrying out amplitude modulation on PGA in the earthquake motion parameters; sequentially inputting the amplitude-modulated PGA into a finite element model, and calculating to obtain the failure probability of a typical pillar type equipment sample; and fitting the failure probability of the typical strut equipment by adopting a logarithm positive-phase distribution function to obtain a vulnerability curve of the strut equipment. According to the method, a small number of typical equipment samples are selected by a k-means clustering method to replace all samples to calculate the vulnerability, so that the calculation cost is reduced, and meanwhile, the result accuracy and the universal applicability of the vulnerability curve can be ensured.
Description
Technical Field
The application relates to the field of power equipment earthquake resistance evaluation, in particular to a post equipment post-earthquake failure probability analysis method.
Background
The earthquake is easy to damage the power system, if the earthquake is not enough, part of lines are interrupted, and if the earthquake is not enough, the regional power system is in full-line breakdown, so that normal social operation is seriously influenced, earthquake rescue and post-disaster reconstruction work are more delayed, and the economic loss is difficult to estimate.
In the power system, the damage of the substation equipment in the earthquake is extremely high. The bottom of the transformer substation equipment is provided with the pillar equipment, so that the transformer substation equipment can be insulated and supported, and if the bottom of the transformer substation equipment is provided with the pillar insulator, the pillar equipment can be used for insulating and supporting the transformer substation equipment. The structure of the support column equipment is simple, and certain lateral force resistance can be provided for the substation equipment in an earthquake. But at the same time will also cause toppling or displacement of the substation equipment when the post type equipment is damaged or broken in earthquakes. It can be seen that whether the substation equipment is damaged or not is related to the damage condition of the post equipment.
In the prior art, the damage condition of the strut equipment in the epicenter is predicted by calculating the failure probability of the strut equipment, and the method specifically comprises the following steps: selecting a sample of the strut equipment to form a sample set; establishing a finite element model of each strut equipment sample; calculating the number of samples of the failed column equipment in the sample set under different vibration parameters through a finite element model, and further calculating the failure probability of the sample set; and fitting the failure probability of the sample set by adopting a logarithm positive-phase distribution function to obtain a vulnerability curve of the strut equipment. In the prior art, a method for modeling and calculating based on all strut equipment samples in a sample set has the problems of large calculation amount and high modeling calculation time cost.
Disclosure of Invention
The application provides a post equipment post-earthquake failure probability analysis method, which aims to solve the problems of large calculation amount and high modeling calculation time cost in the prior art.
The technical scheme adopted by the application is as follows:
a post-earthquake failure probability analysis method for strut equipment comprises the following steps:
selecting a sample set formed by pillar equipment samples and selecting a clustering index of the pillar equipment samples, wherein the clustering index is formed by a plurality of key structure parameters of the pillar equipment;
calculating by adopting Sturges empirical formula and elbow discrimination method according to the clustering index to obtain the optimal classification number of the sample set;
classifying the strut equipment samples by adopting a K-means clustering algorithm according to the optimal classification number to obtain a clustering result;
selecting a typical pillar type equipment sample from the clustering result;
establishing a finite element model of the typical strut-type equipment sample;
selecting earthquake motion parameters, and carrying out amplitude modulation on peak ground acceleration in the earthquake motion parameters;
inputting the amplitude-modulated peak ground acceleration into the finite element model in sequence, and calculating to obtain the failure probability of the typical strut equipment sample;
and fitting the failure probability of the typical strut equipment by adopting a logarithm positive-phase distribution function to obtain a vulnerability curve of the strut equipment.
Further, according to the clustering index, calculating by using a Sturges empirical formula and an elbow discrimination method to obtain the optimal classification number of the sample set, including:
sequentially setting classification numbers k to be 1-N, wherein N is a positive integer, and calculating the clustering distortion degree corresponding to each classification number;
drawing a line graph by taking the classification number as a horizontal axis and the clustering distortion degree corresponding to each classification number as a vertical axis;
two classification numbers corresponding to two ends of the elbow in the line graph are first candidate classification numbers kaThe second candidate classification number kb；
Classifying the first candidate class number kaThe second candidate classification number kbAnd a third candidate classification number k calculated by Sturges empirical formulacComparing, and selecting the candidate classification number with the minimum value as the optimal classification number k.
Further, calculating the cluster distortion degree corresponding to each classification number comprises:
clustering the sample set into k classification subsets G according to classification number k1，G2，…，GkEach of the classification subsets is composed of the strut-class device samples g;
selecting a cluster center point e of each classification subset1，e2，…，ek；
Calculating the cluster distortion degree of each classification number according to the cluster center point:
wherein D represents the degree of cluster distortion, D (g, e)i) Representing the strut-like device samples g to a cluster center point eiThe index of l of the sample g of the strut-type device is g ═ p1，p2，…，pl)。
Further, the Sturges empirical formula is:
wherein m represents the total number of samples of the pillar-like device in the sample set, kcClassify the third candidateAnd (4) counting.
Further, classifying the pillar equipment samples by adopting a K-means clustering algorithm according to the optimal classification number to obtain a clustering result, wherein the clustering result comprises the following steps:
clustering the sample set into k ' classification subsets according to the optimal classification number k ', and generating k ' initial clustering center points which are uniformly distributed;
calculating the Euclidean distance: calculating the Euclidean distance from each strut-class device sample to each initial clustering center point;
comparing the Euclidean distance from each support column type equipment sample to each initial clustering center point, and selecting the minimum Euclidean distance from each support column type equipment sample to each initial clustering center point;
dividing the pillar equipment samples corresponding to the minimum Euclidean distance into classification subsets corresponding to the initial clustering center points to form k' new classification subsets;
calculating a new clustering center point of the new classification subset, and judging whether the new clustering center point is converged;
if the new clustering center point is converged, outputting a clustering result containing k' new classification subsets, otherwise, continuing to execute the step of calculating the Euclidean distance and the step after calculating the Euclidean distance.
Further, selecting a representative pillar class device sample from the clustering result, including:
respectively selecting z from the i-th to k' -th new classification subsets at uniform intervals according to the Euclidean distance from each pillar equipment sample to the clustering central pointiThe pillar equipment samples form a typical pillar equipment sample library and are expressed by the following formula:
in the formula, m' represents the number of the pillar class device samples in the new classification subset, m represents the total number of the pillar class device samples in the sample set, and n represents the preset number of typical pillar class device samples.
Further, the failure probability of the typical strut-type device is formulated as:
p is the failure probability of a typical strut-like specimen, nfAnd n is the preset number of the typical strut type equipment samples.
The technical scheme of the application has the following beneficial effects:
the application discloses a post equipment failure probability analysis method after earthquake. The method classifies the pillar equipment according to key structure parameters by a k-means clustering method, uses a small amount of typical equipment samples to replace all samples to calculate the vulnerability, reduces the calculation cost, and simultaneously ensures the result accuracy and the universal applicability of the general vulnerability curve of the equipment.
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In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a post-earthquake failure probability analysis method for a strut-type device according to an embodiment of the present disclosure;
FIG. 2 is a diagram illustrating a relationship between classification number k and cluster distortion degree D according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram illustrating a vulnerability curve fitting of a post-type device according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
In an earthquake, the vulnerability of the substation equipment is extremely high, and most of the vulnerability is caused by the root fracture of the post insulator, so that the equipment is toppled or displaced. From the perspective of electric power, the post insulator is a supporting and external insulating member in a transformer substation, does not directly participate in the conversion or transportation of electric power, but is a component of the most common transformer substation and transformer equipment, and ensures the stable operation of the transformer substation. From the structural point of view, the post insulator is simple in structure, a single body is similar to a cantilever member, and is similar to the stress of a post member when being used as a supporting component in substation equipment, but no complex coil is provided, no simple component for important power conversion is provided, the single body is a source of the lateral resistance of a plurality of important equipment in an earthquake, sometimes the lateral resistance rigidity of the post insulator determines whether the upper equipment main body part can be safely and carelessly used in the earthquake, sometimes the displacement of the top of the post insulator determines the displacement of the whole equipment and cannot be mutually pulled with other equipment to form continuous damage, or the lateral resistance limit strength of the post insulator determines whether the equipment can normally operate, the substation in which the post insulator is positioned can continuously operate in the earthquake, and further determines whether the power supply of an area is normal, whether the earthquake rescue can be facilitated or not can be achieved, and the earthquake rescue takes seconds for life.
Through analysis and demonstration, the main reasons for damage of the substation equipment in earthquake are as follows:
since most of substation equipment is used for current processing and transmission, and it is necessary to take into consideration the electrical insulation distance to the ground, the main body of the equipment in the substation cannot be directly placed on the ground, and it is necessary to provide a post insulator (i.e., post-type equipment) which is an insulating material to support the insulation.
The support column type equipment positioned at the bottom supports the substation equipment to form an integral structure, most of weight is concentrated on the upper part of the integral structure, and the phenomenon that the weight distribution of the integral structure is uneven and the weight is heavy is light is obvious. Therefore, under the action of an earthquake, the upper part of the integral structure is subjected to a larger earthquake action, so that the strain of the strut equipment at the bottom is increased rapidly, and the strut equipment is supported by a brittle material, so that the strut equipment is easy to break brittle easily and further the transformer substation equipment is easy to damage; meanwhile, the displacement response of the top of the substation equipment is also extremely large, and the substation equipment is very easy to pull to form continuous damage under the condition of limited redundancy among the substation equipment.
The theoretical research foundation shows that: the power grid earthquake disaster prevention and quick response technical research is developed, the safety of the power system in the earthquake and the quick evaluation capability after the earthquake are improved, and the guarantee of power supply is an irreparable task and challenge of the current power system. By combining the real-time earthquake monitoring technology and the anti-seismic technology of the strut equipment, after the acceleration and the displacement of the key parts of the strut equipment of the transformer substation are monitored in real time, when an earthquake comes in the future, real-time data of earthquake waves and real-time response data of the key parts of the strut equipment are obtained. By combining the data, the failure probability of the post equipment with the similar structure after the earthquake is evaluated to guide the emergency repair of disaster relief.
The post equipment post-earthquake failure probability analysis method can evaluate post equipment post-earthquake failure probability with an approximate structure.
Referring to fig. 1, a flowchart of a post-earthquake failure probability analysis method for strut-type equipment provided in the embodiment of the present application is shown; referring to fig. 2, a graph of a relation between classification number k and cluster distortion degree D provided in the embodiment of the present application is shown; fig. 3 is a schematic diagram of a vulnerability curve fitting of the strut-type device according to an embodiment of the present application.
As shown in fig. 1, the method for analyzing post-earthquake failure probability of a strut-type device according to the embodiment of the present application includes the following steps:
step 1: selecting a sample set formed by the pillar equipment samples and selecting a clustering index of the pillar equipment samples, wherein the clustering index is formed by a plurality of key structure parameters of the pillar equipment.
Parameters of the strut equipment comprise structural fundamental frequency, height, ratio of the height of the highest point to the diameter of the root fixing part, material strength and the like of the strut equipment, wherein the material strength comprises the critical part such as the root which is a brittle material and suggests the ultimate tensile strength; the critical part such as the root part is the elastic material, and the mises limit value is recommended. Selecting l parameters as key structure parameters to form a clustering index, wherein clustering with l too large is uneconomical, the efficiency is not high, and clustering with l too small is too rough. It is recommended to take l as 3. When l is 3, preferably three key structural parameters of the structural fundamental frequency, the height of the highest point and the diameter of the root fixing part of the strut equipment are used as clustering indexes.
Step 2: and calculating by adopting Sturges empirical formula and elbow discrimination method according to the clustering index to obtain the optimal classification number of the sample set.
Euclidean distances are used to describe the similarity between samples. Setting the l-dimensional clustering index of a certain device sample g as g ═ p (p)1，p2，…，pl) The l-dimensional clustering index of the sample g 'is g' ═ p1’，p2’，…，pl') wherein p is1，p2，…，plThe parameters are the I key structure parameters and are called clustering indexes. The euclidean distance between g and g' is then:
wherein d (g, g ') represents the Euclidean distance between g and g'.
S21 sets the classification number k to 1 to N in order, where N is a positive integer, and preferably N is 10. Calculating the cluster distortion degree corresponding to each classification number, specifically:
clustering the sample set G into k classification subsets G according to the classification number k1，G2，…，GkEach classification subset comprises a plurality of pillar class device samples g.
Selecting a cluster center point e of each classification subset1，e2，…，ek；
And calculating the clustering distortion degree corresponding to each classification number according to the clustering center point, wherein the clustering distortion degree is used for evaluating the aggregation degree of the classification subsets, and the clustering distortion degree is expressed by using a square error sum criterion function.
Wherein D represents the degree of cluster distortion, D (g, e)i) Representing pillar class device samples g to a cluster center point eiThe euclidean distance between the two devices, i of the strut-type device sample g is g ═ p1，p2，…，pl)。
S22 is a line graph drawn with the classification numbers 1 to N as the horizontal axis and the cluster distortion degrees corresponding to each classification number as the vertical axis, as shown in fig. 2.
The two classification numbers corresponding to the two ends of the elbow in the line drawing of S23 are the first candidate classification number kaThe second candidate classification number kb。
As shown in fig. 2, as the classification number k increases, the sample division becomes finer, the aggregation degree of each classification subset also increases, and the clustering distortion degree naturally becomes smaller. When k is smaller than the real classification number, the aggregation degree of each classification subset is greatly increased due to the increase of k, so that the reduction range of the clustering distortion degree is large; when k reaches the real classification number, the clustering distortion degree obtained when k is increased is rapidly reduced, so that the reduction amplitude of the clustering distortion degree is reduced rapidly and then becomes gentle with the continuous increase of the k value. Although the classification effect is relatively good when the k value is larger than the real classification number, the classification is not economical and too many classifications result in too few samples of a certain class, which is not practical. As shown in FIG. 2, the relation graph of the cluster distortion degree and k shows the shape of an elbow, and the two classification numbers corresponding to the two ends of the elbow are the first candidate classification number kaThe second candidate classification number kb。
S24 calculated by Sturges empirical formulaThree candidate classification number kcThe Sturges empirical formula is:
in the formula, m represents the total number of samples of the pillar-type device in the sample set, kcIs the third candidate classification number.
S25 classifying the first candidate number kaThe second candidate classification number kbAnd a third candidate classification number kcAnd comparing, and selecting the candidate classification number with the minimum value as the optimal classification number k'.
And step 3: and classifying the pillar equipment samples by adopting a K-means clustering algorithm according to the optimal classification number K 'to obtain K' clustering results. The method specifically comprises the following steps:
s31, clustering the sample set into k ' classification subsets according to the optimal classification number k ', and generating k ' initial clustering center points which are uniformly distributed.
S32 calculates the euclidean distance: and calculating the Euclidean distance from each pillar type equipment sample to each initial clustering center point.
S33, comparing the Euclidean distance from each pillar type equipment sample to each initial clustering center point, and selecting the minimum Euclidean distance from each pillar type equipment sample to each initial clustering center point.
S34, the pillar class device samples corresponding to the minimum Euclidean distance are divided into classification subsets corresponding to the initial clustering center points, and k' new classification subsets are formed.
S35, calculating a new cluster center point of the new classification subset, and judging whether the new cluster center point converges.
Judging whether the new cluster center point converges or not, including: judging whether the new clustering center point is changed or whether the variation is smaller than a preset value compared with the initial clustering center point:
if the new clustering center point changes or the variation amount is smaller than the preset value, the new clustering center point is converged, otherwise, the new clustering center point is not converged.
S36, if the new cluster center point is converged, outputting the cluster result containing k' new classification subsets, otherwise, continuing to execute S32-S36.
And 4, step 4: and selecting a typical pillar type equipment sample from the clustering result.
Respectively selecting z from the i-th to k' -th new classification subsets at uniform intervals according to the Euclidean distance from each pillar type equipment sample to the cluster central point of the pillar type equipment sampleiThe pillar equipment samples form a pillar equipment typical sample library, and are expressed by the following formula:
in the formula, m' represents the number of the pillar class device samples in the new classification subset, m represents the total number of the pillar class device samples in the sample set, and n represents the preset number of typical pillar class device samples. And selecting a small number of typical equipment samples to replace all samples to calculate the general vulnerability curve of the equipment, wherein the number of the typical strut type equipment samples is selected according to the requirement, and when the number of the samples exceeds 100, generally selecting 30 typical strut type equipment samples can have good representativeness.
And 5: and establishing a finite element model of the typical strut-type equipment sample.
Step 6: seismic oscillation parameters meeting the requirements are selected from the PEER website. And amplitude modulation is carried out on peak ground acceleration in the seismic motion parameters to obtain PGA (0.1, 0.2 and 0.3 … … 1.0.0 g).
The earthquake motion parameters refer to ground motion caused by earthquake waves released by the earthquake source, the inertia force caused by the earthquake motion serves as earthquake action, and physical parameters representing the earthquake motion are called earthquake motion parameters and comprise numerical parameters such as peak ground acceleration PGA, response spectrum and duration.
Selecting earthquake motion of which earthquake waves need to meet the earthquake design specification of electric power facilities and the earthquake design specification of buildings simultaneously for vulnerability analysis.
And 7: and sequentially inputting the amplitude-modulated peak ground acceleration into a finite element model, and calculating to obtain the failure probability of the typical column equipment sample.
S71 sequentially inputs the amplitude-modulated peak ground acceleration PGA of 0.1, 0.2, and 0.3 … … 1.0.0 g into the finite element model.
And S72, judging the number of samples of the failed typical strut type equipment in the typical strut type samples according to the failure criterion.
The failure criteria include:
and when the root stress of the strut equipment exceeds a limit value, the strut equipment fails. Determining a limit value according to the material of the strut equipment, specifically: when the strut equipment is made of brittle materials (such as ceramics and cast aluminum), the second strength theory is adopted to know that the main tensile stress exceeds the ultimate tensile strength and is a failure mode, so that the ultimate tensile strength is recommended to be limited by the brittle materials at key parts such as root parts; when the material of the strut equipment is elastic material (such as steel and common composite material), the fourth strength theory shows that the mises stress exceeds the ultimate strength, which is a failure mode, so that the critical part, such as the root part, of the elastic material suggests that the limit value of the mises is taken as a limit value.
And when the relative displacement of the tops of the support columns exceeds the insulation requirement, the support column equipment fails.
And when the displacement between the strut equipment exceeds the limit value, the strut equipment fails.
S73, calculating the failure probability of the typical strut type sample based on the number of the failed typical strut type equipment samples.
p is the failure probability of a typical strut-like specimen, nfThe number of samples of the failed strut type equipment is n, and the number of samples of the preset typical strut type equipment is n.
And 8: as shown in fig. 3, the probability of failure of a typical strut-type device is fitted by using a logarithmic positive-phase distribution function, so as to obtain a vulnerability curve of the strut-type device. The failure probability of the strut type equipment with similar structure under different PGAs can be predicted according to the vulnerability curve of the strut type equipment.
The application discloses a post equipment failure probability analysis method after earthquake. The method classifies the pillar equipment according to key structure parameters by a k-means clustering method, uses a small amount of typical equipment samples to replace all samples to calculate the vulnerability, reduces the calculation cost, and simultaneously ensures the result accuracy and the universal applicability of the general vulnerability curve of the equipment.
The embodiments provided in the present application are only a few examples of the general concept of the present application, and do not limit the scope of the present application. Any other embodiments extended according to the scheme of the present application without inventive efforts will be within the scope of protection of the present application for a person skilled in the art.
Claims (7)
1. A post-earthquake failure probability analysis method for strut equipment is characterized by comprising the following steps:
selecting a sample set formed by pillar equipment samples and selecting a clustering index of the pillar equipment samples, wherein the clustering index is formed by a plurality of key structure parameters of the pillar equipment;
calculating by adopting Sturges empirical formula and elbow discrimination method according to the clustering index to obtain the optimal classification number of the sample set;
classifying the strut equipment samples by adopting a K-means clustering algorithm according to the optimal classification number to obtain a clustering result;
selecting a typical pillar type equipment sample from the clustering result;
establishing a finite element model of the typical strut-type equipment sample;
selecting earthquake motion parameters, and carrying out amplitude modulation on peak ground acceleration in the earthquake motion parameters;
inputting the amplitude-modulated peak ground acceleration into the finite element model in sequence, and calculating to obtain the failure probability of the typical strut equipment sample;
and fitting the failure probability of the typical strut equipment by adopting a logarithm positive-phase distribution function to obtain a vulnerability curve of the strut equipment.
2. The method for analyzing the post-earthquake failure probability of the strut equipment according to claim 1, wherein the step of calculating by using a Sturges empirical formula and an elbow discrimination method according to the clustering index to obtain the optimal classification number of the sample set comprises the following steps:
sequentially setting classification numbers k to be 1-N, wherein N is a positive integer, and calculating the clustering distortion degree corresponding to each classification number;
drawing a line graph by taking the classification number as a horizontal axis and the clustering distortion degree corresponding to each classification number as a vertical axis;
two classification numbers corresponding to two ends of the elbow in the line graph are first candidate classification numbers kaThe second candidate classification number kb；
Classifying the first candidate class number kaThe second candidate classification number kbAnd a third candidate classification number k calculated by Sturges empirical formulacAnd comparing, and selecting the candidate classification number with the minimum value as the optimal classification number k'.
3. The post-earthquake failure probability analysis method of claim 2,
calculating the cluster distortion degree corresponding to each classification number comprises the following steps:
clustering the sample set into k classification subsets G according to classification number k1，G2，…，GkEach of the classification subsets is composed of the strut-class device samples g;
selecting a cluster center point e of each classification subset1，e2，…，ek；
Calculating the cluster distortion degree of each classification number according to the cluster center point:
wherein D represents the degree of cluster distortion, D (g, e)i) Representing the strut-like device samples g to a cluster center point eiThe index of l of the sample g of the strut-type device is g ═ p1，p2，…，pl)。
4. The post-earthquake failure probability analysis method of claim 2,
the Sturges empirical formula is:
wherein m represents the total number of samples of the pillar-like device in the sample set, kcIs the third candidate classification number.
5. The post-earthquake failure probability analysis method of claim 1,
classifying the pillar equipment samples by adopting a K-means clustering algorithm according to the optimal classification number to obtain a clustering result, wherein the clustering result comprises the following steps:
clustering the sample set into k ' classification subsets according to the optimal classification number k ', and generating k ' initial clustering center points which are uniformly distributed;
calculating the Euclidean distance: calculating the Euclidean distance from each strut-class device sample to each initial clustering center point;
comparing the Euclidean distance from each support column type equipment sample to each initial clustering center point, and selecting the minimum Euclidean distance from each support column type equipment sample to each initial clustering center point;
dividing the pillar equipment samples corresponding to the minimum Euclidean distance into classification subsets corresponding to the initial clustering center points to form k' new classification subsets;
calculating a new clustering center point of the new classification subset, and judging whether the new clustering center point is converged;
if the new clustering center point is converged, outputting a clustering result containing k' new classification subsets, otherwise, continuing to execute the step of calculating the Euclidean distance and the step after calculating the Euclidean distance.
6. The post-earthquake failure probability analysis method of claim 1,
selecting a typical pillar class device sample from the clustering result, wherein the typical pillar class device sample comprises the following steps:
respectively selecting z from the i-th to k' -th new classification subsets at uniform intervals according to the Euclidean distance from each pillar equipment sample to the clustering central pointiThe pillar equipment samples form a typical pillar equipment sample library and are expressed by the following formula:
in the formula, m' represents the number of the pillar class device samples in the new classification subset, m represents the total number of the pillar class device samples in the sample set, and n represents the preset number of typical pillar class device samples.
7. The method for analyzing post-earthquake failure probability of post-earthquake equipment according to claim 1, wherein the typical post-earthquake failure probability of post-earthquake equipment is formulated as follows:
p is the failure probability of a typical strut-like specimen, nfAnd n is the preset number of the typical strut type equipment samples.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111507027A (en) * | 2019-09-10 | 2020-08-07 | 邓晨 | Method for judging integral power failure time of steel truss tower structure based on finite element software |
| CN112001070A (en) * | 2020-08-10 | 2020-11-27 | 国网山东省电力公司滨州供电公司 | Modeling method for outage probability of power transmission line affected by external environment |
| CN112613093A (en) * | 2020-11-25 | 2021-04-06 | 中国电力科学研究院有限公司 | Method and system for evaluating anti-seismic performance of extra-high voltage transmission tower structure |
| CN112990379A (en) * | 2021-05-10 | 2021-06-18 | 广东电网有限责任公司湛江供电局 | Clustering-based method and equipment for rapidly analyzing wind load vulnerability of power transmission tower |
-
2021
- 2021-09-07 CN CN202111058773.1A patent/CN113742973A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111507027A (en) * | 2019-09-10 | 2020-08-07 | 邓晨 | Method for judging integral power failure time of steel truss tower structure based on finite element software |
| CN112001070A (en) * | 2020-08-10 | 2020-11-27 | 国网山东省电力公司滨州供电公司 | Modeling method for outage probability of power transmission line affected by external environment |
| CN112613093A (en) * | 2020-11-25 | 2021-04-06 | 中国电力科学研究院有限公司 | Method and system for evaluating anti-seismic performance of extra-high voltage transmission tower structure |
| CN112990379A (en) * | 2021-05-10 | 2021-06-18 | 广东电网有限责任公司湛江供电局 | Clustering-based method and equipment for rapidly analyzing wind load vulnerability of power transmission tower |
Non-Patent Citations (2)
| Title |
|---|
| 程东东等: "基于自然邻居的聚类分析和离群检测算法研究", 30 November 2019, 上海交通大学出版社, pages: 14 - 15 * |
| 边晓旭等: "基于地震动聚类的变电站设备易损性分析", 中国电机工程学报, vol. 41, no. 8, 20 April 2021 (2021-04-20), pages 2671 - 2681 * |
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