CN107239651A - A kind of method that power network birds droppings class failure risk grade is assessed - Google Patents
A kind of method that power network birds droppings class failure risk grade is assessed Download PDFInfo
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- CN107239651A CN107239651A CN201710249946.5A CN201710249946A CN107239651A CN 107239651 A CN107239651 A CN 107239651A CN 201710249946 A CN201710249946 A CN 201710249946A CN 107239651 A CN107239651 A CN 107239651A
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Abstract
The present invention relates to grid power transmission Line technology field, more particularly to a kind of method that power network birds droppings class failure risk grade is assessed, a kind of method that power network birds droppings class failure risk grade based on fuzzy cluster analysis is assessed is specifically referred to.Comprise the following steps：Related data is collected, data matrix is set up, data normalization sets up fuzzy similarity matrix, and clustering divides risk.Cluster result of the present invention is relatively reasonable, and reliable data can be provided for related personnel；Be conducive to dividing the practical application of birds droppings class failure risk grade, be that there is provided actual directive significance for the development of Transmission Line Design maintenance work.This method degree of accuracy is higher, it is easy to use, you can to take into account power system security, while power network tripping fault rate caused by bird pest can also be safeguarded effectively, the safe and stable operation of power system has effectively been ensured, has been reduced because of the unnecessary economic loss that bird pest is brought.
Description
Technical field
Assessed the present invention relates to grid power transmission Line technology field, more particularly to a kind of power network birds droppings class failure risk grade
Method, specifically refer to a kind of method that power network birds droppings class failure risk grade based on fuzzy cluster analysis is assessed.
Background technology
In recent years, country's increasingly attaching importance to environmental protection, perfect relevant laws and regulations, the living environment of birds is more and more good, birds
Population, quantity are also more and more more, and its scope of activities is also expanding.But because the environmental consciousness of former people is very poor, it is substantial amounts of
Vegetation is destroyed, and trees are cut down, and the space of nesting of birds greatly reduces, so birds prefer to stop on electric power line pole tower
Stay, which results in the birds motion frequency near shaft tower is higher, power network tripping fault is also more and more more caused by bird pest,
Power system security stable operation is seriously endangered.The number of times of the bird pest failure of the transmission line of electricity of national grid is annual all to be increased
Plus.In order to improve the security and stability of power system, the work of anti-bird pest has been very urgent.According to statistics, birds activity causes
Line tripping fault time digit column the 3rd, only less than line tripping fault number of times caused by damage to crops caused by thunder and external force destruction.And according to system
Meter shows that the main Types of bird trouble on transmission line failure are birds droppings class failure.
Threat of the bird pest to the safe operation of transmission line of electricity is growing day by day, and an emphasis of associate power department of China is exactly
How effectively anti-bird pest.Although electric power relevant departments take multinomial anti-bird pest measure for a long time, from artificial bird repellent to installation
Bird-proof cover, anti-birds droppings baffle plate, bird-resistant, then to install high-pressure side sleeve pipe, deep bid footpath full skirt, electronics bird scarer.But it is existing anti-
Either protection unit threatens to the life security of birds, or protection is not enough, it is impossible to take into account power system security and birds peace
The problem of the two full aspects, cause unnecessary economic loss.
The content of the invention
The purpose of the present invention is in order to overcome above weak point of the prior art, and the present invention provides a kind of power network birds droppings class
A kind of method that failure risk grade is assessed, in order to which it is high, easy to use to provide degree of accuracy, can be Transmission Line Design
Maintenance work provides the side that the power network birds droppings class failure risk grade based on fuzzy cluster analysis effectively instructed is estimated
Method.
A kind of method that power network birds droppings class failure risk grade is assessed, comprises the following steps：
(1) related data is collected；(2) data matrix is set up；(3) data normalization；(4) fuzzy similarity matrix is set up；
(5) clustering；(6) risk is divided.
It is described to collect related data, refer to the related money of survey region transmission line of electricity birds droppings class failure bird pest risk class
Material, including geographic data, birds data and relate to bird failure related data.
River of the geographic data including research area, lake, reservoir, forest, ocean, wetland distribution situation；By this
A little geographic datas obtain the distance between the distance between shaft towers and the nearest river of surrounding, shaft tower and the nearest lake of surrounding,
The distance between shaft tower reservoir distance, shaft tower and the nearest ocean of surrounding nearest with surrounding, shaft tower and the nearest wetland of surrounding
The distance between, the distance between shaft tower and the nearest forest zone of surrounding information.
The birds data is mainly the migratory bird moving passage of survey region；Nearest by data acquisition shaft tower and surrounding
The distance between migration of birds passage information.
The bird failure related data that relates to refers to that research area relates to bird failure situation over the years, it follows that survey region shaft tower
With the distance of nearest birds droppings class trouble point around.
The data matrix of setting up refers to collecting sample, determines Main Factors, sets up data matrix；If domain U={ l1,
l2,l3,...,lnBe some regional collection data (n sample), each sample describes by 8 leading indicators, i.e. xi
=(li1,li2,...,li8) (i=1,2 ..., n)；The implication of 8 leading indicators is between shaft tower and the nearest river of surrounding
Apart from li1, the distance between shaft tower and the nearest lake of surrounding li2, shaft tower and the nearest reservoir of surrounding be apart from li3, shaft tower with week
Enclose the distance between nearest ocean li4, the distance between shaft tower and the nearest wetland of surrounding li5, shaft tower and the nearest woods of surrounding
The distance between area li6, the distance between shaft tower and the nearest migration of birds passage of surrounding li7, shaft tower and the nearest birds droppings of surrounding
Class trouble point apart from li8。
The data normalization refers to data matrix being compressed in interval [0,1]；Real data typically has different
Dimension, in order to which the data different to dimension are compared, it is necessary to carry out proper transformation to data；This is not meant to that data are certain
In interval [0,1], standardization is exactly the requirement according to fuzzy matrix by data compression to interval [0,1].
The fuzzy similarity matrix of setting up refers to determine similarity factor according to clustering method, sets up and obscure similar square
Battle array；Use index similarity factor calculate i-th of sample and j-th of sample similarity factor for：
Wherein, k=1 ..., 8,It is the variance of k-th of factor, rijFor fuzzy similarity matrix R element, R=
(rij)m×n,likRepresent the value of k-th of factor of i-th of sample, ljkRepresent the value of k-th of factor of j-th of sample.
The clustering refers to ask for transitive closure with quadratic method, obtains fuzzy equivalent matrix；Asked with quadratic method
Take transitive closure t (R), R → R2→R4→R8, R8oR8=R8..., obtain fuzzy equivalent matrix t (R)=R*；
Wherein R o R=(cij)m×nFor R and R synthesis；
λ is the number between arbitrary 1 to 1, when λ is constantly reduced to value a by 1, obtains the Boolean matrix of a series of equivalent
Rλ *；For matrix R λ Level Matrix；
λ=a is taken, if Rij>=a, takes Rij=1；Rij≤ a, takes Rij=0, obtain Ra, original sample is divided into the class of A, B, C tri-.
The division risk class refers to the comprehensive evaluation value by calculating Different categories of samples, judges risk class；Root
According to the size of each factor values, the same class statistical indicator for being belonging respectively to A, B, C sample is taken arithmetic mean of instantaneous value, according to these because
The significance level that son influences on risk class, determines the weight of each factor；Further according to value of statistical indicant and weighted value to risk etc.
Level carries out overall merit, all kinds of comprehensive evaluation values of summing, sector-style of going forward side by side danger grade classification.
The present invention has following beneficial effect：
1st, the power network birds droppings class failure risk grade based on fuzzy cluster analysis is assessed, and cluster result is relatively reasonable, can
Reliable data is provided for related personnel；
2nd, the method being estimated using fuzzy cluster analysis to power network birds droppings class failure risk grade has been formulated, has been conducive to
The practical application of birds droppings class failure risk grade is divided, is that there is provided actual guidance for the development of Transmission Line Design maintenance work
Meaning.
3rd, this method degree of accuracy is higher, is easy to use.Power system security can be taken into account, while can also effectively tie up
Power network tripping fault rate caused by protecting bird pest, has effectively ensured the safe and stable operation of power system, has reduced because of bird pest band
The unnecessary economic loss come.
Brief description of the drawings
Fig. 1 is that the present invention is the flow being estimated using fuzzy cluster analysis to power network birds droppings class failure risk grade
Figure.
Embodiment
The present invention is described in further detail with reference to example and accompanying drawing.
The present invention is a kind of method that power network birds droppings class failure risk grade is assessed, as shown in figure 1, this method can pass through
Write program code realization.Comprise the following steps：
(1) related data is collected；
(2) data matrix is set up；
(3) data normalization；
(4) fuzzy similarity matrix is set up；
(5) clustering；
(6) risk etc. is divided.
1st, related data is collected：
Related data refers to the related data of survey region transmission line of electricity birds droppings class failure bird pest risk class.
Collecting related data includes：Geographic data, birds data and relate to bird failure related data.
1.1 geographic data：
River of the geographic data including research area, lake, reservoir, forest, ocean, wetland equal distribution situation.
The distance between shaft tower and the nearest river of surrounding, shaft tower and the nearest lake of surrounding can be obtained by these geographic datas
The distance between pool, shaft tower and the distance between the nearest reservoir distance of surrounding, shaft tower and the nearest ocean of surrounding, shaft tower and week
Enclose the information such as the distance between the distance between nearest wetland, shaft tower and the nearest forest zone of surrounding.
1.2 birds data：
Birds data is mainly the migratory bird moving passage of survey region.Shaft tower and the nearest bird of surrounding can be obtained by the data
Class migrates the distance between passage information.
1.3 relate to bird failure related data：
Research area relates to bird failure situation over the years.It can thus be concluded that survey region shaft tower and the nearest birds droppings class trouble point of surrounding
Distance.
2nd, data matrix is set up：
Set up data matrix and refer to collecting sample, determine Main Factors, set up data matrix.
If domain U={ l1,l2,l3,...,lnBe some regional collection data (n sample), each sample is by 8
Individual leading indicator is described, i.e. xi=(li1,li2,...,li8) (i=1,2 ..., n).
The implication of 8 leading indicators is the distance between shaft tower and the nearest river of surrounding li1, shaft tower and surrounding it is nearest
The distance between lake li2, shaft tower and the nearest reservoir of surrounding be apart from li3, the distance between shaft tower and the nearest ocean of surrounding
li4, the distance between shaft tower and the nearest wetland of surrounding li5, the distance between shaft tower and the nearest forest zone of surrounding li6, shaft tower with
The distance between the nearest migration of birds passage of surrounding li7, shaft tower is with the nearest birds droppings class trouble point of surrounding apart from li8.Each
The sample data of shaft tower position is as shown in table 1.
3rd, data normalization：
Data normalization refers to data matrix being compressed in interval [0,1].
Real data typically has different dimensions.In order to which the data different to dimension are compared, it is necessary to enter to data
Row is suitably converted.This is not meant to that data one are scheduled in interval [0,1].Standardization is exactly will according to the requirement of fuzzy matrix
Data compression is interior to interval [0,1].
4th, fuzzy similarity matrix is set up：
Set up fuzzy similarity matrix to refer to determine similarity factor according to clustering method, set up fuzzy similarity matrix R.
Use index similarity factor calculate i-th of sample and j-th of sample similarity factor for：
Wherein, k=1 ..., 8,It is the variance of k-th of factor.rijFor fuzzy similarity matrix R element.R=
(rij)m×n, likRepresent the value of k-th of factor of i-th of sample, ljkRepresent the value of k-th of factor of j-th of sample.
5th, clustering：
Clustering refers to ask for transitive closure with quadratic method, obtains fuzzy equivalent matrix.
Transitive closure t (R), R → R are asked for quadratic method2→R4→R8, R8oR8=R8..., obtain fuzzy equivalent matrix t
(R)=R*。
Wherein R o R=(cij)m×nFor R and R synthesis.
λ is the number between arbitrary 0 to 1.When λ is constantly reduced to value a by 1, the Boolean matrix of a series of equivalent is obtained
Rλ *。
For matrix R λ Level Matrix.
λ=a is taken, if Rij>=a, takes Rij=1；Rij≤ a, takes Rij=0, obtain Ra, original sample is divided into the class of A, B, C tri-.
6th, risk class is divided：
Division risk class refers to the comprehensive evaluation value by calculating Different categories of samples, judges risk class.
According to the size of each factor values, the same class statistical indicator for being belonging respectively to A, B, C sample is taken arithmetic mean of instantaneous value.Root
The significance level influenceed according to these factor pair risk class, determines the weight of each factor.Further according to value of statistical indicant and weight
Value carries out overall merit to risk class, all kinds of comprehensive evaluation values of summing, sector-style of going forward side by side danger grade classification.
Table 1
| l1(km) | l2(km) | l3(km) | l4(km) | l5(km) | l6(km) | l7(km) | l8(km) |
| 7 | 8 | 12 | 182 | 32 | 22 | 39 | 5 |
Claims (10)
1. a kind of method that power network birds droppings class failure risk grade is assessed, it is characterized in that：Comprise the following steps：
(1) related data is collected；
(2) data matrix is set up；
(3) data normalization；
(4) fuzzy similarity matrix is set up；
(5) clustering；
(6) risk is divided.
2. the method that a kind of power network birds droppings class failure risk grade according to claim 1 is assessed, it is characterized in that：It is described to receive
Collect related data, refer to the related data of survey region transmission line of electricity birds droppings class failure bird pest risk class, including geographical money
Material, birds data and relate to bird failure related data.
3. the method that a kind of power network birds droppings class failure risk grade according to claim 2 is assessed, it is characterized in that：Describedly
River of the reason data including research area, lake, reservoir, forest, ocean, wetland distribution situation；Obtained by these geographic datas
The distance between the distance between shaft tower river nearest with surrounding, shaft tower and the nearest lake of surrounding, shaft tower and surrounding are nearest
Reservoir distance, the distance between shaft tower and the nearest ocean of surrounding, the distance between shaft tower and the nearest wetland of surrounding, shaft tower
With the distance between nearest forest zone around information.
4. the method that a kind of power network birds droppings class failure risk grade according to claim 2 is assessed, it is characterized in that：The bird
Class data is mainly the migratory bird moving passage of survey region；By the data obtain shaft tower and the nearest migration of birds passage of surrounding it
Between range information.
5. the method that a kind of power network birds droppings class failure risk grade according to claim 2 is assessed, it is characterized in that：It is described to relate to
Bird failure related data refers to that research area relates to bird failure situation over the years, it follows that survey region shaft tower and the nearest bird of surrounding
The distance of excrement class trouble point.
6. the method that a kind of power network birds droppings class failure risk grade according to claim 1 is assessed, it is characterized in that：It is described to build
Vertical data matrix refers to collecting sample, determines Main Factors, sets up data matrix；If domain U={ l1,l2,l3,...,lnBe
The data (n sample) of some regional collection, each sample is described by 8 leading indicators, i.e. xi=(li1,li2,...,
li8) (i=1,2 ..., n)；The implication of 8 leading indicators is the distance between shaft tower and the nearest river of surrounding li1, shaft tower with
The distance between the nearest lake of surrounding li2, shaft tower and the nearest reservoir of surrounding be apart from li3, shaft tower and the nearest ocean of surrounding it
Between apart from li4, the distance between shaft tower and the nearest wetland of surrounding li5, the distance between shaft tower and the nearest forest zone of surrounding
li6, the distance between shaft tower and the nearest migration of birds passage of surrounding li7, shaft tower and the nearest birds droppings class trouble point of surrounding away from
From li8。
7. the method that a kind of power network birds droppings class failure risk grade according to claim 1 is assessed, it is characterized in that：The number
Refer to data matrix being compressed in interval [0,1] according to standardization；Real data typically has different dimensions, in order to dimension
Different data are compared, it is necessary to carry out proper transformation to data；This is not meant to that data one are scheduled in interval [0,1],
Standardization is exactly the requirement according to fuzzy matrix by data compression to interval [0,1].
8. the method that a kind of power network birds droppings class failure risk grade according to claim 1 is assessed, it is characterized in that：It is described to build
Vertical fuzzy similarity matrix refers to determine similarity factor according to clustering method, sets up fuzzy similarity matrix；It is similar using index
The similarity factor of i-th of sample and j-th of sample that coefficient is calculated is：
<mrow>
<msub>
<mi>r</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mn>8</mn>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>8</mn>
</munderover>
<msup>
<mi>e</mi>
<mrow>
<mo>&lsqb;</mo>
<mo>-</mo>
<mfrac>
<mn>3</mn>
<mn>4</mn>
</mfrac>
<mo>&CenterDot;</mo>
<mfrac>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>l</mi>
<mrow>
<mi>i</mi>
<mi>k</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>l</mi>
<mrow>
<mi>j</mi>
<mi>k</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<msubsup>
<mi>s</mi>
<mi>k</mi>
<mn>2</mn>
</msubsup>
</mfrac>
<mo>&rsqb;</mo>
</mrow>
</msup>
<mo>;</mo>
</mrow>
Wherein, k=1 ..., 8,It is the variance of k-th of factor, rijFor fuzzy similarity matrix R element, R=(rij)m×n,lik
Represent the value of k-th of factor of i-th of sample, ljkRepresent the value of k-th of factor of j-th of sample.
9. the method that a kind of power network birds droppings class failure risk grade according to claim 1 is assessed, it is characterized in that：It is described poly-
Alanysis refers to ask for transitive closure with quadratic method, obtains fuzzy equivalent matrix；Transitive closure t (R) is asked for quadratic method,Obtain fuzzy equivalent matrix t (R)=R*；
WhereinFor R and R synthesis；
λ is the number between arbitrary 1 to 1, when λ is constantly reduced to value a by 1, obtains the Boolean matrix R of a series of equivalentλ *；For matrix R λ Level Matrix；
λ=a is taken, if Rij>=a, takes Rij=1；Rij≤ a, takes Rij=0, obtain Ra, original sample is divided into the class of A, B, C tri-.
10. the method that a kind of power network birds droppings class failure risk grade according to claim 1 is assessed, it is characterized in that：It is described
Division risk class refers to the comprehensive evaluation value by calculating Different categories of samples, judges risk class；According to each factor values
The same class statistical indicator for being belonging respectively to A, B, C sample, is taken arithmetic mean of instantaneous value by size, according to these factor pair risk class
The significance level of influence, determines the weight of each factor；Risk class integrate to comment further according to value of statistical indicant and weighted value
Valency, all kinds of comprehensive evaluation values of summing, sector-style of going forward side by side danger grade classification.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108388993A (en) * | 2018-03-05 | 2018-08-10 | 广东电网有限责任公司韶关供电局 | A kind of power grid birds failure risk appraisal procedure |
| CN108399503A (en) * | 2018-03-08 | 2018-08-14 | 云南电网有限责任公司电力科学研究院 | A kind of overhead transmission line bird pest fault early warning method |
| CN108921452A (en) * | 2018-07-27 | 2018-11-30 | 国网河北能源技术服务有限公司 | A kind of compound method for early warning of transmission line of electricity risk assessment based on fuzzy algorithmic approach |
| CN109214419A (en) * | 2018-07-26 | 2019-01-15 | 南京航空航天大学 | A kind of bird based on space clustering hits event risk Synthetical prevention method |
| CN109583683A (en) * | 2018-10-15 | 2019-04-05 | 广东工业大学 | A kind of efficiency evaluation method of bird trouble on transmission line protective device |
| CN110020779A (en) * | 2019-02-23 | 2019-07-16 | 国网江西省电力有限公司电力科学研究院 | A kind of transmission line of electricity relates to bird failure bird kind hazard rating division methods |
| CN111598309A (en) * | 2020-04-27 | 2020-08-28 | 国网山东省电力公司电力科学研究院 | Differential bird damage risk level evaluation method and system |
| CN115879775A (en) * | 2023-02-27 | 2023-03-31 | 国网江西省电力有限公司电力科学研究院 | Three-dimensional transformer substation bird-involved fault risk level evaluation method and system |
| CN117392551A (en) * | 2023-12-12 | 2024-01-12 | 国网江西省电力有限公司电力科学研究院 | Power grid bird damage identification method and system based on bird droppings image features |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101470779A (en) * | 2007-12-24 | 2009-07-01 | 北京启明星辰信息技术股份有限公司 | Fuzzy risk evaluation system and method for computer information security |
| CN103198175A (en) * | 2013-03-04 | 2013-07-10 | 辽宁省电力有限公司鞍山供电公司 | Transformer fault diagnosis method based on fuzzy cluster |
| CN105023283A (en) * | 2015-06-24 | 2015-11-04 | 云南电网有限责任公司电力科学研究院 | Power grid bird trouble map drawing method and system based on winter visitor migration route and habitat |
| CN105184668A (en) * | 2015-08-24 | 2015-12-23 | 国家电网公司 | Forest fire risk area dividing method for power transmission line based on cluster analysis |
| CN105335898A (en) * | 2015-11-11 | 2016-02-17 | 国网内蒙古东部电力有限公司电力科学研究院 | Bird-caused damage risk distribution map drawing system |
-
2017
- 2017-04-17 CN CN201710249946.5A patent/CN107239651A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101470779A (en) * | 2007-12-24 | 2009-07-01 | 北京启明星辰信息技术股份有限公司 | Fuzzy risk evaluation system and method for computer information security |
| CN103198175A (en) * | 2013-03-04 | 2013-07-10 | 辽宁省电力有限公司鞍山供电公司 | Transformer fault diagnosis method based on fuzzy cluster |
| CN105023283A (en) * | 2015-06-24 | 2015-11-04 | 云南电网有限责任公司电力科学研究院 | Power grid bird trouble map drawing method and system based on winter visitor migration route and habitat |
| CN105184668A (en) * | 2015-08-24 | 2015-12-23 | 国家电网公司 | Forest fire risk area dividing method for power transmission line based on cluster analysis |
| CN105335898A (en) * | 2015-11-11 | 2016-02-17 | 国网内蒙古东部电力有限公司电力科学研究院 | Bird-caused damage risk distribution map drawing system |
Non-Patent Citations (3)
| Title |
|---|
| 刘丽华: "基于模糊聚类分析的网络安全综合评价", 《陕西理工学院学报》 * |
| 陈又星 等，: "《管理科学研究方法 数据模型决策》", 28 February 2013 * |
| 陈小志: "输电线路鸟害预警模型及系统的设计与实现", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
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| CN108388993A (en) * | 2018-03-05 | 2018-08-10 | 广东电网有限责任公司韶关供电局 | A kind of power grid birds failure risk appraisal procedure |
| CN108399503A (en) * | 2018-03-08 | 2018-08-14 | 云南电网有限责任公司电力科学研究院 | A kind of overhead transmission line bird pest fault early warning method |
| CN109214419A (en) * | 2018-07-26 | 2019-01-15 | 南京航空航天大学 | A kind of bird based on space clustering hits event risk Synthetical prevention method |
| CN109214419B (en) * | 2018-07-26 | 2021-06-22 | 南京航空航天大学 | Bird strike event risk comprehensive prevention and control method based on spatial clustering |
| CN108921452A (en) * | 2018-07-27 | 2018-11-30 | 国网河北能源技术服务有限公司 | A kind of compound method for early warning of transmission line of electricity risk assessment based on fuzzy algorithmic approach |
| CN109583683A (en) * | 2018-10-15 | 2019-04-05 | 广东工业大学 | A kind of efficiency evaluation method of bird trouble on transmission line protective device |
| CN109583683B (en) * | 2018-10-15 | 2023-05-02 | 广东工业大学 | Effectiveness evaluation method of bird damage protection device of power transmission line |
| CN110020779A (en) * | 2019-02-23 | 2019-07-16 | 国网江西省电力有限公司电力科学研究院 | A kind of transmission line of electricity relates to bird failure bird kind hazard rating division methods |
| CN111598309A (en) * | 2020-04-27 | 2020-08-28 | 国网山东省电力公司电力科学研究院 | Differential bird damage risk level evaluation method and system |
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