CN112529395A - Power transmission line typical ice wind disaster analysis method based on fusion of FCE and SVM - Google Patents
Power transmission line typical ice wind disaster analysis method based on fusion of FCE and SVM Download PDFInfo
- Publication number
- CN112529395A CN112529395A CN202011414791.4A CN202011414791A CN112529395A CN 112529395 A CN112529395 A CN 112529395A CN 202011414791 A CN202011414791 A CN 202011414791A CN 112529395 A CN112529395 A CN 112529395A
- Authority
- CN
- China
- Prior art keywords
- evaluation
- power transmission
- ice wind
- transmission line
- index
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000005540 biological transmission Effects 0.000 title claims abstract description 38
- 238000004458 analytical method Methods 0.000 title claims abstract description 32
- 230000004927 fusion Effects 0.000 title claims abstract description 20
- 238000011156 evaluation Methods 0.000 claims abstract description 81
- 239000013598 vector Substances 0.000 claims abstract description 24
- 238000000034 method Methods 0.000 claims abstract description 16
- 238000013178 mathematical model Methods 0.000 claims abstract description 8
- 238000010219 correlation analysis Methods 0.000 claims abstract description 7
- 238000012706 support-vector machine Methods 0.000 claims description 19
- 239000011159 matrix material Substances 0.000 claims description 5
- 238000012937 correction Methods 0.000 claims description 4
- 238000012549 training Methods 0.000 claims description 3
- 230000005494 condensation Effects 0.000 claims description 2
- 238000009833 condensation Methods 0.000 claims description 2
- 230000005684 electric field Effects 0.000 claims description 2
- OVBPIULPVIDEAO-LBPRGKRZSA-N folic acid Chemical compound C=1N=C2NC(N)=NC(=O)C2=NC=1CNC1=CC=C(C(=O)N[C@@H](CCC(O)=O)C(O)=O)C=C1 OVBPIULPVIDEAO-LBPRGKRZSA-N 0.000 claims description 2
- 230000007246 mechanism Effects 0.000 claims description 2
- 239000000725 suspension Substances 0.000 claims description 2
- 230000002194 synthesizing effect Effects 0.000 claims description 2
- 230000009466 transformation Effects 0.000 claims description 2
- 239000004020 conductor Substances 0.000 claims 1
- 230000002265 prevention Effects 0.000 abstract description 7
- 230000009467 reduction Effects 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 230000007257 malfunction Effects 0.000 description 3
- 238000012502 risk assessment Methods 0.000 description 3
- 230000001174 ascending effect Effects 0.000 description 1
- 238000007635 classification algorithm Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002844 melting Methods 0.000 description 1
- 230000008018 melting Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Economics (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Probability & Statistics with Applications (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a power transmission line typical ice wind disaster analysis method based on fusion of FCE and SVM. The method comprises the steps of determining an evaluation index set according to analysis of a disaster causing factor of the ice wind disaster of the power transmission line; determining the weight of the evaluation index type and the weight of each subtype index; for the evaluation of the ice wind disaster of the line, determining an evaluation grade according to the correlation analysis of the influence indexes and the fault occurrence; establishing a fuzzy comprehensive evaluation mathematical model; analyzing and correcting an FCE evaluation result; establishing a nonlinear SVM regression model; and taking the typical parameter indexes determined by the fuzzy comprehensive evaluation method as input vectors of the SVM regression model, taking the probability of possible occurrence of the fault as output vectors, and grading the fault probability for judging the ice wind disaster. The method can effectively analyze and judge the occurrence probability of the ice wind disaster, realize the reliable analysis of the small sample data of the ice wind disaster, and play a role in early warning the ice wind disaster of the power transmission line so as to implement disaster prevention and reduction measures in advance and avoid the loss of the power grid.
Description
Technical Field
The invention belongs to the technical field of ice wind disaster analysis of a power transmission line, and particularly relates to a typical ice wind disaster analysis method of the power transmission line based on fusion of FCE and SVM.
Background
The ice wind disaster of the power transmission line is a big problem facing a power supply system. The ice wind disasters of different degrees occur all over the world, the power grid system in China also has the ice wind disasters for many times, and the occurrence frequency of the disasters is in an ascending trend. The method warns people of huge economic loss and social influence caused by ice wind disasters of the power transmission line, and developing disaster prevention research of the power system and ensuring safe and stable operation of the power system in the ice wind disasters are challenges and difficult tasks which are not slow at present. When the ice wind disaster of the power transmission line is coped with, besides the ice covering prevention technology, the ice melting technology and the deicing technology, the method has very important significance for effectively identifying and predicting the ice wind disaster.
The mature current risk assessment of the power transmission line and the wide application are equipment risk assessment, and the risk assessment refers to the comprehensive consideration of risks in the aspects of equipment safety, economy, social influence and the like and the determination of equipment risk degree. However, in the field of power transmission line disaster prevention and reduction, the influence of a specific disaster type on a line is faced, evaluation factors relate to aspects such as historical records, operation and maintenance live conditions, body states, meteorological monitoring and forecasting, the related range is wide, and the correlation among the factors is unclear, so that the work in the field of power transmission line disaster prevention and reduction mainly aims at performing prevention and post-disaster maintenance work on a certain kind of events at present. Therefore, a method for analyzing the ice wind disaster caused disaster by coupling different influence factors by analyzing the key influence indexes of the ice wind disaster is very urgent.
Disclosure of Invention
The invention aims to provide a typical ice wind disaster analysis method of a power transmission line based on fusion of a Fuzzy Comprehensive Evaluation (FCE) and a Support Vector Machine (SVM), which can construct a line ice wind disaster analysis model based on the FCE and the SVM and provide theoretical and practical bases for analysis and judgment of typical ice wind disasters by analyzing key influence indexes of ice wind disasters.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
a power transmission line typical ice wind disaster analysis method based on FCE and SVM fusion is characterized in that: the method comprises the following steps:
the first step is as follows: determining an evaluation index set according to analysis of the factors causing the ice wind disaster of the power transmission line;
the second step is as follows: determining the weight of the evaluation index type and the weight of each subtype index;
the third step: for the evaluation of the ice wind disaster of the line, determining an evaluation grade according to the correlation analysis of the influence indexes and the fault occurrence;
the fourth step: establishing a fuzzy comprehensive evaluation mathematical model;
the fifth step: analyzing and correcting an FCE evaluation result;
a sixth step: establishing a nonlinear SVM regression model;
a seventh step of: and taking the typical parameter indexes determined by the fuzzy comprehensive evaluation method as input vectors of the SVM regression model, taking the probability of possible occurrence of the fault as output vectors, and grading the fault probability for judging the ice wind disaster.
In the second step, the evaluation index types include meteorological conditions, terrain and geographical conditions, and line structures and parameters;
in the second step, the subtype indexes comprise temperature, relative humidity, rainfall, wind speed, wind direction, altitude, landform, slope direction, condensation height, line trend, ground wire suspension height, wire diameter, load current and electric field, wire rigidity and wire split number.
In the seventh step, the grade is classified into a high possibility of failure, a low possibility of failure, and no failure.
The invention also provides a typical ice wind disaster analysis device of the power transmission line based on the fusion of the FCE and the SVM, which comprises the following components:
an index collection unit: determining an evaluation index set according to analysis of the factors causing the ice wind disaster of the power transmission line;
a weighting unit: determining the weight of the evaluation index type and the weight of each subtype index;
an evaluation unit: determining the evaluation level of the ice wind disaster of the line according to the correlation analysis of the influence indexes and the fault occurrence;
a mathematical model unit: establishing a fuzzy comprehensive evaluation mathematical model;
an analysis and correction unit: analyzing and correcting an FCE evaluation result;
a regression model unit: establishing a nonlinear SVM regression model;
a judging unit: and taking the typical parameter indexes determined by the fuzzy comprehensive evaluation method as input vectors of the SVM regression model, taking the probability of possible occurrence of the fault as output vectors, and grading the fault probability for judging the ice wind disaster.
Compared with the prior art, the invention has the advantages that:
according to the typical ice wind disaster analysis method for the power transmission line based on the fusion of the FCE and the SVM, the correlation degree of ice wind disaster influence indexes of the power transmission line is analyzed by using a fuzzy comprehensive evaluation method, key disaster influence indexes are extracted, meanwhile, a non-linear SVM small sample disaster analysis model adopting a radial basis RBF kernel function is provided, the key influence indexes are used as input quantity, the dimensionality of a classification algorithm is reduced, the problems of complex structure, low training speed, low efficiency and the like are effectively solved, the ice wind disaster of the power transmission line can be reliably analyzed under the condition of small sample data, the early warning effect of the ice wind disaster of the power transmission line is achieved, the disaster prevention and reduction measures are implemented in advance, and the loss of a power grid is avoided.
Drawings
FIG. 1 is a model structure diagram of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following specific examples:
a power transmission line typical ice wind disaster analysis method based on FCE and SVM fusion is characterized in that:
step 1: and determining an evaluation index set.
Setting the evaluation index set as U-M11,M12,M13…MijIn the formula: m is an evaluation index; i is the total number of evaluation index types; j is the total number of each subtype index.
Evaluation indexes shown in table 1 are constructed by researching and analyzing the influence indexes of the ice wind disaster and combining with the expert experience of operation and maintenance.
TABLE 1
Step 2: and confirming the index weight.
Determining the weight A of the evaluation index type by combining the expert experience and the historical ice wind disaster fault information1And the weight A of each subtype index2Comprises the following steps:
A1＝(M1,M2,M3)＝(0.5,0.3,0.2)
A2＝(M11,M12,M13…M36)
＝(0.3,0.15,0.1,0.3,0.15,0.25,0.35,0.15,0.05,0.2,0.25,0.25,0.1,0.15,0.1,0.15)
and step 3: and determining the evaluation grade.
Let the evaluation grade be V ═ V (V)1,V2…Vj…Vn) In the formula: n is the total number of evaluation results; vjJ is 1,2, … n as the jth evaluation result. For the evaluation of the ice wind disaster of the line, the correlation analysis between the influence indexes and the fault occurrence is carried out, namely V1，V2，V3Respectively, high, middle and low 3 grades.
And 4, step 4: and establishing a fuzzy comprehensive evaluation mathematical model.
Performing single-factor fuzzy evaluation, and determining an evaluation matrix as follows:
in the formula: r isijFuzzy subset V of grade from factor U for a certain evaluated objectjDegree of membership.
The fuzzy comprehensive evaluation result vector B of each evaluated object is obtained by synthesizing the weight A of the evaluation index and the evaluation matrix R, and can be expressed as follows:
in the formula: bjFuzzy subset V of rating for rated object as a wholejDegree of membership.
Under meteorological conditions M1For example, the evaluation matrix is established as follows:
then B is1(0.475,0.37,0.155) in terms of maximum membership, M1The correlation with the occurrence of a failure is high.
And 5: and analyzing and correcting the FCE evaluation result.
And analyzing the membership degree and the weight coefficient of each graded index and the comprehensive evaluation index result to obtain an index with the highest correlation with the occurrence of the ice wind fault, and then providing a correction factor of the index needing to be corrected by combining with the analysis of the occurrence mechanism of the ice wind fault.
Step 6: and establishing a nonlinear SVM regression model.
Assume that the training sample set is: s { (x)i,yi) 1,2 … N, wherein: x is the number ofiIs an input vector; y isiIs an output vector; and N is the number of samples.
The nonlinear SVM uses functional regression estimation: (x) ω · Φ (x) + b, wherein: omega is an m-dimensional vector; phi (x) is a non-linear transformation function; b is a threshold value.
The structure of the established typical ice wind disaster model of the power transmission line based on the fusion of the fuzzy comprehensive evaluation method and the support vector machine method is shown in fig. 1.
And 7: taking the typical parameter index determined by the fuzzy comprehensive evaluation method as an input vector of the SVM regression model, wherein the probability of possible fault occurrence is an output vector yiThe failure probability is classified for use in determining the ice wind disaster, as shown in table 2.
TABLE 2
| Influence of faults | yiRange of (1) |
| Is likely to malfunction | 0.85≤yi<1 |
| Is likely to malfunction | 0.5≤yi<0.85 |
| The possibility of failure is less | 0.2≤yi<0.5 |
| Will not malfunction | 0≤yi<0.2 |
Therefore, the possibility of ice and wind disaster faults of the power transmission line in different states can be analyzed.
Those not described in detail in this specification are within the knowledge of those skilled in the art.
Claims (9)
1. A power transmission line typical ice wind disaster analysis method based on FCE and SVM fusion is characterized in that: the method comprises the following steps:
the first step is as follows: determining an evaluation index set according to analysis of the factors causing the ice wind disaster of the power transmission line;
the second step is as follows: determining the weight of the evaluation index type and the weight of each subtype index;
the third step: determining the evaluation level of the ice wind disaster of the line according to the correlation analysis of the influence indexes and the fault occurrence;
the fourth step: establishing a fuzzy comprehensive evaluation mathematical model;
the fifth step: analyzing and correcting an FCE evaluation result;
a sixth step: establishing a nonlinear SVM regression model;
a seventh step of: and taking the typical parameter indexes determined by the fuzzy comprehensive evaluation method as input vectors of the SVM regression model, taking the probability of possible occurrence of the fault as output vectors, and grading the fault probability for judging the ice wind disaster.
2. The FCE and SVM fusion-based power transmission line typical ice wind disaster analysis method according to claim 1, characterized in that: the specific process of the first step is as follows:
setting the evaluation index set as U-M11,M12,M13…MijIn the formula: m is an evaluation index; i is the total number of evaluation index types; j is the total number of each subtype index;
the evaluation index comprises an evaluation index type, and the evaluation index type comprises a meteorological condition M1Topographic and geographical conditions M2And line structure and parameter M3At least one of; the meteorological condition M1Index name of (1) includes temperature M11Relative humidity M12Rainfall M13Wind speed M14And the wind direction M15At least one of; the terrain and geographical conditions M2Index name of (1) includes altitude M21Landform M22Slope M23In the direction of slope M24And a condensation height M25At least one of; the line structure and the parameter M3The index name includes the line trend M31Suspension height M of ground wire32Diameter of wire M33Load current and electric field M34Wire stiffness M35And number of split conductors M36At least one of (a).
3. The FCE and SVM fusion-based power transmission line typical ice wind disaster analysis method according to claim 1, characterized in that: the specific process of the second step is as follows:
combining expert experience with historyDetermining the weight A of the evaluation index type according to the ice wind disaster fault information1And the weight A of each subtype index2Comprises the following steps:
A1＝(M1,M2,M3)＝(0.5,0.3,0.2)
A2＝(M11,M12,M13…M36)
＝(0.3,0.15,0.1,0.3,0.15,0.25,0.35,0.15,0.05,0.2,0.25,0.25,0.1,0.15,0.1,0.15)。
4. the FCE and SVM fusion-based power transmission line typical ice wind disaster analysis method according to claim 1, characterized in that: the third step comprises the following specific processes:
let the evaluation grade be V ═ V (V)1,V2…Vj…Vn) In the formula: n is the total number of evaluation results; vjJ is 1,2, … n for the jth evaluation result; and for the evaluation of the ice wind disaster of the line, determining an evaluation grade according to the correlation analysis of the influence indexes and the fault occurrence.
5. The FCE and SVM fusion-based power transmission line typical ice wind disaster analysis method according to claim 1, characterized in that: the concrete process of the fourth step is as follows:
performing single-factor fuzzy evaluation, and determining an evaluation matrix as follows:
in the formula: r isijFuzzy subset V of grade from factor U for a certain evaluated objectjDegree of membership of;
synthesizing the weight A of the evaluation index and the evaluation matrix R to obtain a fuzzy comprehensive evaluation result vector B of each evaluated object, wherein the fuzzy comprehensive evaluation result vector B is expressed as follows:
in the formula: bjFuzzy subset V of rating for rated object as a wholejDegree of membership.
6. The FCE and SVM fusion-based power transmission line typical ice wind disaster analysis method according to claim 1, characterized in that: the concrete process of the fifth step is as follows:
and analyzing the membership degree, the weight coefficient and the comprehensive evaluation index result of each graded index to obtain an index with the highest correlation with the occurrence of the ice wind fault, and providing a correction factor of the index needing to be corrected by combining with the analysis of the occurrence mechanism of the ice wind fault.
7. The FCE and SVM fusion-based power transmission line typical ice wind disaster analysis method according to claim 1, characterized in that: the concrete process of the sixth step is as follows:
setting a training sample set as follows: s { (x)i,yi) 1,2 … N, wherein: x is the number ofiIs an input vector; y isiIs an output vector; n is the number of samples;
the nonlinear SVM uses functional regression estimation: (x) ω · Φ (x) + b, wherein: omega is m-dimensional vector, phi (x) is nonlinear transformation function, and b is threshold;
and establishing a typical ice wind disaster model structure of the power transmission line based on fusion of a fuzzy comprehensive evaluation method and a support vector machine method.
8. The FCE and SVM fusion-based power transmission line typical ice wind disaster analysis method according to claim 1, characterized in that: the concrete process of the seventh step is as follows:
taking the typical parameter index determined by the fuzzy comprehensive evaluation method as an input vector of the SVM regression model, wherein the probability of possible fault occurrence is an output vector yiGrading the fault probability for judging the ice wind disaster, and outputting the vector yiThe degree of influence of the fault, which is worth of scale, is: y is not less than 0.85i<1 indicates that a fault is likely to occur, 0.5 ≦ yi<0.85 indicates that there is a possibility of failure, and 0.2. ltoreq. yi<0.5 means that the probability of failure is small, and 0 is less than or equal to yi<0.2 means no failure occurred;
therefore, the possibility of ice and wind disaster faults of the power transmission line in different states can be analyzed.
9. A typical ice wind disaster analysis device of a power transmission line based on the fusion of FCE and SVM is characterized in that: the device comprises:
an index collection unit: determining an evaluation index set according to analysis of the factors causing the ice wind disaster of the power transmission line;
a weighting unit: determining the weight of the evaluation index type and the weight of each subtype index;
an evaluation unit: determining the evaluation level of the ice wind disaster of the line according to the correlation analysis of the influence indexes and the fault occurrence;
a mathematical model unit: establishing a fuzzy comprehensive evaluation mathematical model;
an analysis and correction unit: analyzing and correcting an FCE evaluation result;
a regression model unit: establishing a nonlinear SVM regression model;
a judging unit: and taking the typical parameter indexes determined by the fuzzy comprehensive evaluation method as input vectors of the SVM regression model, taking the probability of possible occurrence of the fault as output vectors, and grading the fault probability for judging the ice wind disaster.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011414791.4A CN112529395A (en) | 2020-12-04 | 2020-12-04 | Power transmission line typical ice wind disaster analysis method based on fusion of FCE and SVM |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011414791.4A CN112529395A (en) | 2020-12-04 | 2020-12-04 | Power transmission line typical ice wind disaster analysis method based on fusion of FCE and SVM |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112529395A true CN112529395A (en) | 2021-03-19 |
Family
ID=74997773
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011414791.4A Pending CN112529395A (en) | 2020-12-04 | 2020-12-04 | Power transmission line typical ice wind disaster analysis method based on fusion of FCE and SVM |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112529395A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114021844A (en) * | 2021-11-17 | 2022-02-08 | 国网浙江省电力有限公司经济技术研究院 | Power transmission line operation and maintenance investment optimization method and system based on meteorological disaster prediction |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105956934A (en) * | 2016-05-05 | 2016-09-21 | 国网湖南省电力公司防灾减灾中心 | Power grid forest fire and icing disaster safety evaluation method based on fuzzy comprehensive evaluation approach |
-
2020
- 2020-12-04 CN CN202011414791.4A patent/CN112529395A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105956934A (en) * | 2016-05-05 | 2016-09-21 | 国网湖南省电力公司防灾减灾中心 | Power grid forest fire and icing disaster safety evaluation method based on fuzzy comprehensive evaluation approach |
Non-Patent Citations (2)
| Title |
|---|
| 司鹄等: "输电线路雷击灾害风险评估", 《重庆电力高等专科学校学报》 * |
| 谷凯凯等: "基于FCE和SVM融合的线路典型冰风灾害算法分析", 《中国电力》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114021844A (en) * | 2021-11-17 | 2022-02-08 | 国网浙江省电力有限公司经济技术研究院 | Power transmission line operation and maintenance investment optimization method and system based on meteorological disaster prediction |
| CN114021844B (en) * | 2021-11-17 | 2024-06-04 | 国网浙江省电力有限公司经济技术研究院 | Power transmission line operation and maintenance investment optimization method and system based on meteorological disaster prediction |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111582700B (en) | Method for calculating fault rate of power distribution network equipment | |
| CN110097297A (en) | A kind of various dimensions stealing situation Intellisense method, system, equipment and medium | |
| CN112287018B (en) | 10kV pole tower damage risk assessment method and system under typhoon disaster | |
| CN110533331B (en) | Fault early warning method and system based on transmission line data mining | |
| CN102789447B (en) | Based on the icing of grey multiple linear regression and the analytical approach of meteorological relation | |
| CN107742040A (en) | A kind of power transmission line comprehensive methods of risk assessment based on TOPSIS and optimum combination weight | |
| CN114723285B (en) | Power grid equipment safety evaluation prediction method | |
| CN114254818B (en) | Low-voltage distribution network cascading failure early warning method based on risk assessment model | |
| CN104657822A (en) | Power system disaster warning grading method and system based on risk evaluation result | |
| CN113689053B (en) | Strong convection weather overhead line power failure prediction method based on random forest | |
| CN112734204B (en) | Power distribution network fault line risk assessment method and system | |
| CN112632148B (en) | Intelligent early warning method for power distribution network components under extreme weather condition | |
| CN116050599A (en) | Line icing fault prediction method, system, storage medium and equipment | |
| CN111461921A (en) | Load modeling typical user database updating method based on machine learning | |
| CN112668821A (en) | Distribution line risk analysis method based on insulator fault probability of sand blown region | |
| CN113191585A (en) | Typhoon disaster risk assessment method for power transmission line | |
| Sun et al. | An ensemble system to predict the spatiotemporal distribution of energy security weaknesses in transmission networks | |
| CN112529395A (en) | Power transmission line typical ice wind disaster analysis method based on fusion of FCE and SVM | |
| CN115224684A (en) | Intelligent power distribution network risk state identification method and system based on immune hazard theory | |
| CN117113157B (en) | Platform district power consumption fault detection system based on artificial intelligence | |
| CN112784744B (en) | Mechanical component vibration signal preprocessing method with missing value | |
| CN103928923B (en) | A kind of network stationary power quality method for early warning based on sensitivity analysis | |
| CN117200223A (en) | Day-ahead power load prediction method and device | |
| CN117131414A (en) | Energy storage safety early warning method and system based on rough set and neural network | |
| CN111815137A (en) | Comprehensive assessment method for vulnerability of power system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210319 |
|
| RJ01 | Rejection of invention patent application after publication |