CN109460602A - Overhead transmission line tripping rate with lightning strike calculation method based on big data and neural network - Google Patents
Overhead transmission line tripping rate with lightning strike calculation method based on big data and neural network Download PDFInfo
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Abstract
A kind of overhead transmission line tripping rate with lightning strike calculation method based on big data and neural network, neural network use three layers of BP neural network, including an input layer, a hidden layer and an output layer；Tripping rate with lightning strike is divided into counterattack trip-out rate and back flash-over rate two parts calculate；Probability of sustained arc is calculated according to regular method, calculates back flash-over rate and counterattack trip-out rate using probability of sustained arc and obtained output node layer；Calculating the sum of resulting back flash-over rate and counterattack trip-out rate is tripping rate with lightning strike.This method reduces destruction of the lightening activity to power grid, is of crucial importance to transmission line of electricity O&M level is improved.
Description
Technical field
The present invention relates to the engineering design of power overhead network and lightning protection field, it is specifically a kind of based on big data and
The overhead transmission line tripping rate with lightning strike calculation method of neural network.
Background technique
The influence for electric power netting safe running of being struck by lightning has been a concern, and statistics shows 330kV and the above ac transmission
Route, since failure rate caused by being struck by lightning is more than half.Especially when the complicated multiplicity of topography and geomorphology, different regions thunderstorm day difference
Obviously, the different climate characteristic in various regions also has a significant effect for lightening activity.Differentiated lightning protection is carried out for different regions
Demand becomes increasingly conspicuous, and the accurate lightning Protection Design calculated for overhead transmission line of tripping rate with lightning strike plays a significant role.
The method of traditional calculating tripping rate with lightning strike includes regular method and pilot model method.Regular method is based primarily upon single loop
Short tower statistical value derives, and with being continuously increased for shaft tower height, summarizes from the pole and tower design experience not higher than 30m in early days
And the inductor models come have been not applied for the lightning protection research of high shaft tower, there are large errors for the shielding calculating of high tower.First guided mode
Type method relies heavily on the data of thunder and lightning and long gap test observation for a long time, different parameters and criterion bring
Calculated result difference is larger, meanwhile, pilot model method does not account for having decreased below line levels when thunder and lightning descending leader
The lightning stroke route phenomenon of Shi Fasheng.Electric geometry method be consider electric discharge dispersibility, do not account for other factors for hit away from
Influence.
In short, since there are many tripping rate with lightning strike influence factor, and for these influence factors, there is no a kind of authoritys at present
Theory, specify its relationship with lightning stroke, for the calculation method of tripping rate with lightning strike, all there is large error.
Summary of the invention
The deficiency of present invention art technology mentioned in the background art provides a kind of based on big data and nerve net
The overhead transmission line tripping rate with lightning strike calculation method of network.
The technical scheme adopted by the invention is that: a kind of overhead transmission line tripping rate with lightning strike based on big data and neural network
Calculation method, the calculation method are based on big data and neural network, and the neural network uses three layers of BP neural network, wrap
Include an input layer, a hidden layer and an output layer；
Tripping rate with lightning strike is divided into counterattack trip-out rate and back flash-over rate two parts calculate:
A, counterattack trip-out rate is calculated
The included node of input layer: shaft tower height, cross-arm height, lightning conducter height, conductor height, shaft tower diverting coefficient, conducting wire
With the lightning conducter coefficient of coup, pole tower ground resistance, U50% discharge voltage；
Hidden layer according toIt calculates, according to formula, choosing lesser trained values is
4 start to test, and obtain number of nodes according to training result；
Before training, counterattack flashover strike is calculated first with regular method, the resistance to Lei Shuiping of lightning stroke rate counterattack is calculated, such as formula (1)
In formula:For shaft tower height, m；
For cross-arm distance away the ground, m；
For lightning conducter average height over the ground, m；
For conducting wire average height, m；
For shaft tower diverting coefficient；
The coefficient of coup between conducting wire and lightning conducter；
Geometrical coupling ratio between conducting wire and lightning conducter；
For Tower Impulse Grounding Resistance, Ω；
For the wave head time of thunder and lightning waveform, μ S；
For 50% impulse sparkover voltage of insulator chain positive polarity；
Then according to resistance to Lei Shuiping, lightning strike probability is calculated:
Finally calculate tripping rate with lightning strike such as formula (2):
According to calculated result, determines weight and neural network threshold value is constrained, pass through the storage system of electric system big data
System is selected the counterattack of lightning stroke in nearly 10 years flashover strike data, is trained using at least 20 groups of sample datas to neural network；
Only one node of output layer, i.e. counterattack flashover strike；
B, back flash-over rate is calculated
The included node of input layer: where lightning conducter height, conductor height, shielding angle, the height above sea level in shaft tower location, shaft tower
Ground elevation, U50% discharge voltage；
Hidden layer according toIt calculates, obtains number of nodes；
Improved Eriksson electric geometry method algorithm is utilized before training, shielding flashover strike is calculated, so that it is determined that neural network
Threshold value and weight；
The calculation method that Eriksson is proposed is as shown in formula (3):
In formula: h is conducting wire mean height, m；
I is amplitude of lightning current, kA；
When lightning leader develops to aerial condutor side, the influence of ground shape, the possibility that conducting wire and ground are struck by lightning will receive
Property all exists, lightning leader hit over the ground away from to conducting wire hit away from ratio (striking distance factor) subtract with the increase of conductor height
Conducting wire average height hit over the ground away from as shown in formula (4) in < 40 m small, that IEEE working group proposes:
For super extra high voltage line, since shaft tower height is higher, guide is hit to the earth away from hitting with it to lightning conducter and conducting wire
Away from being unequal, therefore as conductor height > 40m, hit over the ground away from as shown in formula (5):
As lightning current increases, the region of the conducting wire that is struck by lightning reduces, when lightning current reache a certain level what time, or hit lightning conducter,
Or the earth is hit, no longer generation shielding is then known as maximum shielding electric current, is hit accordingly away from referred to as maximum striking distance；
Related with the size of shaft tower, landform and landforms, general calculating formula is (6)
In formula:For lightning conducter and conducting wire average height；
It is averaged shielding angle for lightning conducter；
For the ground elevation at shaft tower；
For striking distance factor,
Pass through maximum striking distanceCalculate maximum shielding electric current；
According to electric geometry method, it is as follows that risk of shielding failure calculates the formula also needed:
According to calculated result, determines weight and neural network threshold value is constrained, pass through the storage system of electric system big data
System is selected the counterattack of lightning stroke in nearly 10 years flashover strike data, is trained using at least 20 groups of sample datas to neural network；
Only one node of output layer, i.e. shielding flashover strike；
C, probability of sustained arc is calculated according to regular method, calculates back flash-over rate and counterattack using probability of sustained arc and obtained output node layer
Trip-out rate；
D, calculating the sum of resulting back flash-over rate and counterattack trip-out rate is tripping rate with lightning strike.
When the BP neural network carries out sample training, each group of sample requires electric system long-term observation and record,
Regulation uses last decade data, uses relative error using the ERROR ALGORITHM of neural network, according to this principle, ERROR ALGORITHM is adopted
With formula (11):
Meanwhile choosing activation primitive such as formula (12):
。
During 3 calculate counterattack trip-out rate, the optimum efficiency number of nodes that hidden layer obtains is 6.Shielding is calculated to jump
During lock rate, the optimum efficiency number of nodes that hidden layer obtains is 6.
The big data is statistical data of the power department to the tripping rate with lightning strike of route.
The calculation method of this overhead transmission line tripping rate with lightning strike disclosed in this invention is mainly for existing tripping rate with lightning strike
Calculating and the larger situation of actual deviation, pass through research thunderstorm day, wire arrangements mode, pole tower forms and landform and climatic factor
Etc. the analysis of data and the collection of the data such as tripping rate with lightning strike, neural network is weighed in conjunction with regular method and electric geometry method
Value is constrained, and the calculating of overhead transmission line tripping rate with lightning strike has been carried out, horizontal to transmission line of electricity O&M is improved, and reduces lightening activity
Destruction to power grid, is of crucial importance.
Detailed description of the invention
Fig. 1 is conventional thunderbolt electric geometry method schematic diagram.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and examples.
Referring to attached drawing 1, this overhead transmission line tripping rate with lightning strike based on big data and neural network disclosed in this invention
Calculation method, big data are statistical data of the power department to the tripping rate with lightning strike of route.
This calculation method is based on big data and neural network, and the neural network uses three layers of BP neural network, wraps
Include an input layer, a hidden layer and an output layer.
Tripping rate with lightning strike is divided into counterattack trip-out rate and back flash-over rate two parts calculate:
A, counterattack trip-out rate is calculated
The included node of input layer: shaft tower height, cross-arm height, lightning conducter height, conductor height, shaft tower diverting coefficient, conducting wire
With the lightning conducter coefficient of coup, pole tower ground resistance, U50% discharge voltage；
Hidden layer according toIt calculates, according to formula, choosing lesser trained values is
4 start to test, and obtain number of nodes according to training result；
Before training, counterattack flashover strike is calculated first with regular method, the resistance to Lei Shuiping of lightning stroke rate counterattack is calculated, such as formula (1)
In formula:For shaft tower height, m；
For cross-arm distance away the ground, m；
For lightning conducter average height over the ground, m；
For conducting wire average height, m；
For shaft tower diverting coefficient；
The coefficient of coup between conducting wire and lightning conducter；
Geometrical coupling ratio between conducting wire and lightning conducter；
For Tower Impulse Grounding Resistance, Ω；
For the wave head time of thunder and lightning waveform, μ S；
For 50% impulse sparkover voltage of insulator chain positive polarity.
Then according to resistance to Lei Shuiping, lightning strike probability is calculated:
Finally calculate tripping rate with lightning strike such as formula (2):
According to calculated result, determines weight and neural network threshold value is constrained, pass through the storage system of electric system big data
System is selected the counterattack of lightning stroke in nearly 10 years flashover strike data, is trained using at least 20 groups of sample datas to neural network；
B, back flash-over rate is calculated
The included node of input layer: where lightning conducter height, conductor height, shielding angle, the height above sea level in shaft tower location, shaft tower
Ground elevation, U50% discharge voltage；
Hidden layer according toIt calculates, obtains number of nodes；
Improved Eriksson electric geometry method algorithm is utilized before training, shielding flashover strike is calculated, so that it is determined that neural network
Threshold value and weight；
The calculation method that Eriksson is proposed is as shown in formula (3):
In formula: h is conducting wire mean height, m；
I is amplitude of lightning current, kA；
When lightning leader develops to aerial condutor side, the influence of ground shape, the possibility that conducting wire and ground are struck by lightning will receive
Property all exists.Lightning leader hit over the ground away from to conducting wire hit away from ratio (striking distance factor) subtract with the increase of conductor height
Conducting wire average height hit over the ground away from as shown in formula (4) in < 40 m small, that IEEE working group proposes:
For super extra high voltage line, since shaft tower height is higher, guide is hit to the earth away from hitting with it to lightning conducter and conducting wire
Away from being unequal, therefore as conductor height > 40m, hit over the ground away from as shown in formula (5):
As lightning current increases, the region of the conducting wire that is struck by lightning reduces, when lightning current reache a certain level what time, or hit lightning conducter,
Or the earth is hit, no longer generation shielding is then known as maximum shielding electric current, is hit accordingly away from referred to as maximum striking distance；Thunderbolt
Electric geometry method is as shown in Figure 1.
Related with the size of shaft tower, landform and landforms, general calculating formula is (6)
In formula:For lightning conducter and conducting wire average height；
It is averaged shielding angle for lightning conducter；
For the ground elevation at shaft tower；
For striking distance factor,
Pass through maximum striking distanceCalculate maximum shielding electric current；
According to electric geometry method, it is as follows that risk of shielding failure calculates the formula also needed:
According to calculated result, determines weight and neural network threshold value is constrained, pass through the storage system of electric system big data
System is selected the counterattack of lightning stroke in nearly 10 years flashover strike data, is trained using at least 20 groups of sample datas to neural network；
Only one node of output layer, i.e. shielding flashover strike；
C, probability of sustained arc is calculated according to regular method, calculates back flash-over rate and counterattack using probability of sustained arc and obtained output node layer
Trip-out rate；
D, calculating the sum of resulting back flash-over rate and counterattack trip-out rate is tripping rate with lightning strike.
Only one node of output layer, i.e. counterattack flashover strike；
When BP network carries out sample training, because each group of sample requires electric system long-term observation and record, this patent rule
Surely last decade data are used, therefore number of training is relatively on the low side, relative error, root is used using the ERROR ALGORITHM of neural network
According to this principle, ERROR ALGORITHM uses formula (11):
Meanwhile choosing activation primitive such as formula (12):
After obtaining tripping rate with lightning strike, corresponding safeguard measure is taken according to tripping rate with lightning strike.
As a preferred solution: hidden layer needs to obtain that 6 nodes are optimum efficiency section during calculating counterattack trip-out rate
Point quantity.During calculating back flash-over rate, hidden layer needs to obtain that 6 nodes are optimum efficiency number of nodes.
This calculation method disclosed in this invention calculates biggish with actual deviation mainly for existing tripping rate with lightning strike
Situation, by studying the analysis in thunderstorm day, wire arrangements mode, pole tower forms and the data such as landform and climatic factor, Yi Jilei
The collection for hitting the data such as trip-out rate constrains neural network weight in conjunction with regular method and electric geometry method, has carried out frame
The calculating of empty lightning outage rate reduces destruction of the lightening activity to power grid, has extremely to transmission line of electricity O&M level is improved
Close important role.
Claims (5)
1. a kind of overhead transmission line tripping rate with lightning strike calculation method based on big data and neural network, it is characterised in that: the meter
Calculation method is based on big data and neural network, and the neural network uses three layers of BP neural network, including input layer, one
A hidden layer and an output layer；
Tripping rate with lightning strike is divided into counterattack trip-out rate and back flash-over rate two parts calculate:
A, counterattack trip-out rate is calculated
The included node of input layer: shaft tower height, cross-arm height, lightning conducter height, conductor height, shaft tower diverting coefficient, conducting wire
With the lightning conducter coefficient of coup, pole tower ground resistance, U50% discharge voltage；
Hidden layer according toIt calculates, according to formula, choosing lesser trained values is 4
Start to test, number of nodes is obtained according to training result；
Before training, counterattack flashover strike is calculated first with regular method, the resistance to Lei Shuiping of lightning stroke rate counterattack is calculated, such as formula (1)
In formula:For shaft tower height, m；
For cross-arm distance away the ground, m；
For lightning conducter average height over the ground, m；
For conducting wire average height, m；
For shaft tower diverting coefficient；
The coefficient of coup between conducting wire and lightning conducter；
Geometrical coupling ratio between conducting wire and lightning conducter；
For Tower Impulse Grounding Resistance, Ω；
For the wave head time of thunder and lightning waveform, μ S；
For 50% impulse sparkover voltage of insulator chain positive polarity；
Then according to resistance to Lei Shuiping, lightning strike probability is calculated:
Finally calculate tripping rate with lightning strike such as formula (2):
According to calculated result, determines weight and neural network threshold value is constrained, pass through the storage system of electric system big data
System is selected the counterattack of lightning stroke in nearly 10 years flashover strike data, is trained using at least 20 groups of sample datas to neural network；
Only one node of output layer, i.e. counterattack flashover strike；
B, back flash-over rate is calculated
The included node of input layer: where lightning conducter height, conductor height, shielding angle, the height above sea level in shaft tower location, shaft tower
Ground elevation, U50% discharge voltage；
Hidden layer according toIt calculates, obtains number of nodes；
Improved Eriksson electric geometry method algorithm is utilized before training, shielding flashover strike is calculated, so that it is determined that neural network
Threshold value and weight；
The calculation method that Eriksson is proposed is as shown in formula (3):
In formula: h is conducting wire mean height, m；
I is amplitude of lightning current, kA；
When lightning leader develops to aerial condutor side, the influence of ground shape, the possibility that conducting wire and ground are struck by lightning will receive
Property all exists, lightning leader hit over the ground away from to conducting wire hit away from ratio (striking distance factor) subtract with the increase of conductor height
Conducting wire average height hit over the ground away from as shown in formula (4) in < 40 m small, that IEEE working group proposes:
For super extra high voltage line, since shaft tower height is higher, guide is hit to the earth away from hitting with it to lightning conducter and conducting wire
Away from being unequal, therefore as conductor height > 40m, hit over the ground away from as shown in formula (5):
As lightning current increases, the region of the conducting wire that is struck by lightning reduces, when lightning current reache a certain level what time, or hit lightning conducter,
Or the earth is hit, no longer generation shielding is then known as maximum shielding electric current, is hit accordingly away from referred to as maximum striking distance；
Related with the size of shaft tower, landform and landforms, general calculating formula is (6)
In formula:For lightning conducter and conducting wire average height；
It is averaged shielding angle for lightning conducter；
For the ground elevation at shaft tower；
For striking distance factor,
Pass through maximum striking distanceCalculate maximum shielding electric current；
According to electric geometry method, it is as follows that risk of shielding failure calculates the formula also needed:
According to calculated result, determines weight and neural network threshold value is constrained, pass through the storage system of electric system big data
System is selected the counterattack of lightning stroke in nearly 10 years flashover strike data, is trained using at least 20 groups of sample datas to neural network；
Only one node of output layer, i.e. shielding flashover strike；
C, probability of sustained arc is calculated according to regular method, calculates back flash-over rate and counterattack using probability of sustained arc and obtained output node layer
Trip-out rate；
D, calculating the sum of resulting back flash-over rate and counterattack trip-out rate is tripping rate with lightning strike.
2. the overhead transmission line tripping rate with lightning strike calculation method according to claim 1 based on big data and neural network,
Be characterized in that: when the BP neural network carries out sample training, each group of sample requires electric system long-term observation and record,
Regulation uses last decade data, uses relative error using the ERROR ALGORITHM of neural network, according to this principle, ERROR ALGORITHM is adopted
With formula (11):
Meanwhile choosing activation primitive such as formula (12):
。
3. the overhead transmission line tripping rate with lightning strike calculation method according to claim 1 based on big data and neural network,
Be characterized in that: during calculating counterattack trip-out rate, the optimum efficiency number of nodes that hidden layer obtains is 6.
4. the overhead transmission line tripping rate with lightning strike calculation method according to claim 1 based on big data and neural network,
Be characterized in that: during calculating back flash-over rate, the optimum efficiency number of nodes that hidden layer obtains is 6.
5. the overhead transmission line tripping rate with lightning strike calculation method according to claim 1 based on big data and neural network,
Be characterized in that: the big data is statistical data of the power department to the tripping rate with lightning strike of route.
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|---|---|---|---|---|
| CN112529398A (en) * | 2020-12-07 | 2021-03-19 | 华能新能源股份有限公司 | Method for estimating lightning trip-out rate of current collecting line of wind power plant in high-altitude mountain area |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108108520A (en) * | 2017-11-29 | 2018-06-01 | 海南电网有限责任公司电力科学研究院 | A kind of transmission line of electricity damage to crops caused by thunder risk forecast model based on Artificial neural network ensemble |
-
2018
- 2018-11-07 CN CN201811316228.6A patent/CN109460602A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108108520A (en) * | 2017-11-29 | 2018-06-01 | 海南电网有限责任公司电力科学研究院 | A kind of transmission line of electricity damage to crops caused by thunder risk forecast model based on Artificial neural network ensemble |
Non-Patent Citations (5)
| Title |
|---|
| L. EKONOMOU ET AL: "Application of artificial neural network methods for the lightning performance evaluation of Hellenic high voltage transmission lines", 《ELECTRIC POWER SYSTEMS RESEARCH》 * |
| WEIXIN_30627381: "如何提高神经网络学习算法的效果", 《HTTPS://BLOG.CSDN.NET/WEIXIN_30627381/ARTICLE/DETAILS/99264293?》 * |
| 周浩等: "《高电压技术》", 31 July 2007, 浙江大学出版社 * |
| 李瑞芳: "雷电活动及地形地貌对输电线路绕击特性的影响研究", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 柳炳祥等: "《智能优化方法及应用》", 31 August 2017, 江苏凤凰美术出版社 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112529398A (en) * | 2020-12-07 | 2021-03-19 | 华能新能源股份有限公司 | Method for estimating lightning trip-out rate of current collecting line of wind power plant in high-altitude mountain area |
| CN112529398B (en) * | 2020-12-07 | 2023-10-03 | 华能新能源股份有限公司 | Estimation method for lightning trip-out rate of collecting line of wind power plant in high-altitude mountain area |
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Application publication date: 20190312 |