CN109948925A - Consider the power communication system reliability estimation method that weather influences - Google Patents
Consider the power communication system reliability estimation method that weather influences Download PDFInfo
- Publication number
- CN109948925A CN109948925A CN201910188652.5A CN201910188652A CN109948925A CN 109948925 A CN109948925 A CN 109948925A CN 201910188652 A CN201910188652 A CN 201910188652A CN 109948925 A CN109948925 A CN 109948925A
- Authority
- CN
- China
- Prior art keywords
- power communication
- weather
- communication system
- variable
- system element
- 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.)
- Withdrawn
Links
Abstract
The invention discloses a kind of power communication system reliability estimation methods that consideration weather influences, include the following steps: (1) according to historical weather data and history power communication system element fault historical data, using grey relevant degree method, the related coefficient and importance values of weather variable and power communication system element fault are calculated；(2) critical correlation coefficients ξ and importance values η is set, relative coefficient is chosen and is greater than the weather variable of ξ and importance greater than η as crucial weather variable；(3) the state of weather model for constructing crucial weather variable, establishes the power communication system element fault probability model for considering that weather influences；(4) reliability index of power communication system is calculated in conjunction with historical weather data based on power communication system element fault probability model.The present invention can be directed to influence of the different weather variable to power communication system element fault probability, assess power communication system reliability, to improve the accuracy of fail-safe analysis.
Description
Technical field
The present invention relates to a kind of power communication system reliability estimation method more particularly to a kind of electricity for considering weather and influencing
Power reliability of communication system appraisal procedure.
Background technique
As more and more communication technologys are applied to electric system, the alternative mechanism of electric system and communication network is increasingly
Complexity, converged communication network develops into power communication system to modern power systems.Electric system to communication system according to
Degree of depositing be continuously improved, new threat is also introduced to electric system, once communication system occur integrity problem, can directly or
What is connect has an impact the reliability of power grid, causes the electrification of using of large area, and Ukraine's major break down event in 2015 is exactly electricity
Power communication device failure leads to the typical case of power grid major break down.Therefore, evaluation process is being carried out to the reliability of electric system
In, need to fully consider the influence that communication system may generate electric system.
Influence power communication system reliability key factor first is that element fault.The reason of causing element fault is many
It is more, such as component ageing, artificial destruction, natural calamity, weather conditions, wherein influence of the weather conditions to element is frequent, and
Relevance is relatively clear, therefore, it is significant to study influence of the weather conditions to power communication system reliability.Communication device exists
Under the conditions of different weather, the probability and recovery time to break down is widely different, and the element failure rate under severe weather conditions can
Can be more much higher than the failure rate under normal weather, the recovery time during winter storm is many more than normal weather.
Power communication reliability estimation method can be divided into two major classes: one kind is based on mutual between communication system and physical system
The qualitative analysis of effect, the parameters such as analytic modification electric network element availability, using common Power System Analysis method to power grid member
Part failure carries out consequences analysis, obtains Reliability evaluation index；Another kind of is with power communication system element concrete function
Or business scenario is point of penetration, is obtained according to the power communication system element annual number of stoppages and annual fault time
The reliability index of power communication system.
Summary of the invention
Goal of the invention: in view of the above problems, the present invention proposes a kind of power communication system reliability that consideration weather influences
Appraisal procedure, to further increase the accuracy of power communication system fail-safe analysis.
Technical solution: to achieve the purpose of the present invention, the technical scheme adopted by the invention is that: a kind of consideration weather influence
Power communication system reliability estimation method, comprising steps of
(1) it is based on weather variable historical data and power communication system element fault historical data, using grey relational grade
Method calculates the related coefficient and importance values of weather variable and communication device failure；
(2) critical correlation coefficients ξ and importance values η is set, the phase relation of weather variable and communication device failure is selected
It is crucial weather variable that number, which is greater than the weather variable of ξ and importance values greater than η,；
(3) the state of weather model for constructing crucial weather variable, analyzes power communication system element fault probability and weather
The relationship of state establishes the power communication system element fault probability model for considering state of weather；
(4) it is based on power communication system element fault probability model, in conjunction with historical weather data, calculates power communication system
Reliability index.
Further, in the step (1), weather variable includes temperature, relative air humidity, wind speed, lightning stroke and frost
Degree.
Further, anti-lightning strike electric current and peak value thunder-strike current is selected to indicate lightning stroke variation；It selects gust velocity, close
Air speed influence variable is indicated at demeanour and mean wind speed；The frost degree variable is calculated by following formula:
Wherein, RS is the maximum number of days of sleet assessment, FRS is rainfall and snowfall, CLT are continuous low temperature number of days；Under c
It is designated as current value, is designated as average value under av.
Further, in the step (2), the degree of association r of weather variable and communication device probability of malfunction0i:
Wherein, X0For the probability of certain year a certain weather occurrences, XiFor this year, the failure of the element under the weather variable is general
Rate.
Further, in the step (2), the importance values are calculated by P value method, are indicated pair with 1-p
Answer weather variable to power communication system element fault probability importance values:
P=2P (z > | zc|)
Wherein, z is the statistic that weather variable corresponds to power communication system element fault probability, zcIt is to be obtained from sample data
The weather variable obtained corresponds to the statistic of power communication system element fault probability.
Further, in the step (4), when the reliability index of power communication system includes that system mean failure rate continues
Between index, system mean failure rate frequency index and power communication system element reliability index.
Further, step is specifically included:
(4.1) the power communication system element total failare time is defined:
FT=∑i∈nUiNi
In formula, UiAnd NiIt is fault time in year and the number of elements of power communication node i respectively；N is power communication number of nodes
Amount.
(4.2) total user malfunction number is calculated:
FF=∑i∈nλiNi
In formula, λiAnd NiIt is the number of stoppages and number of elements of power communication node i respectively；N is power communication number of nodes
Amount.
(4.3) computing system average failure duration index:
Computing system mean failure rate frequency index:
Calculate the reliability index of power communication system element:
(4.4) pass through system average failure duration index, system mean failure rate frequency index and power communication system
The reliability index of element assesses the reliability of power communication system.
The utility model has the advantages that the present invention can preferably adapt to the reliable of the practical power communication system of various different grid structures
Property assessment, power supply unit can be according to reliability assessment as a result, instructing the planning and day-to-day operation maintenance work of power communication system.
The present invention can also be directed to influence of the different weather variable to power communication system element failure rate, reliable to power communication system
Property is more accurately assessed.
Detailed description of the invention
Fig. 1 is power communication system reliability estimation method flow chart of the invention；
Fig. 2 is the statistical chart of Nanjing Suburb state of weather in 2017 in the embodiment of the present invention；
Fig. 3 is that weather variable influences statistical chart to power communication system element fault probability in the embodiment of the present invention；
Fig. 4 is Nanjing Suburb power communication system reliability assessments in 2017 and real reliability number in the embodiment of the present invention
According to its difference figure.
Specific embodiment
Further description of the technical solution of the present invention with reference to the accompanying drawings and examples.
As shown in Figure 1, the power communication system reliability estimation method for considering weather and influencing of the present invention, including step
It is rapid:
(1) based on weather variable historical data and electricity such as temperature, relative air humidity, wind speed, lightning stroke and frost degree
Power communication system component malfunction history data, using grey relevant degree method, analytical calculation weather variable and communication device failure
Related coefficient and importance values.
It specifically includes:
(1.1) anti-lightning strike electric current and peak value thunder-strike current is selected to indicate the variable of effects of lightning；
(1.2) variable of gust velocity, synthesis demeanour and mean wind speed as research windage is selected；
(1.3) in maximum number of days (RS), rainfall and the snowfall (FRS) and continuous low temperature number of days (CLT) of sleet assessment
Any one or it is several come to assess ice condition be one sided, therefore the present invention indicates frost using equal weight phase Calais
The variable that situation influences, by the equal weight phase of maximum number of days, rainfall and the snowfall of sleet assessment and continuous low temperature number of days
Calais indicates the variable that frost situation influences:
Wherein, c is current state value, and av is average value.
(1.4) correlation level of the analysis different weather variable to power communication system element fault probability；
(1.5) degree of association r of every kind of weather variable Yu communication device probability of malfunction is calculated with the following formula0i:
Wherein, X0For the probability of certain year a certain weather occurrences, XiFor this year, the failure of the element under the weather variable is general
Rate.
(1.6) value of related coefficient is higher, then shows influence of the weather variable to power communication system element fault probability
It is bigger.
(2) critical correlation coefficients ξ and importance values η is set, the phase relation of weather variable and communication device failure is selected
It is crucial weather variable that number, which is greater than the weather variable of ξ and importance values greater than η,.
Critical correlation coefficients 0.5 and importance values 0.8 are set, the phase relation of weather variable and communication device failure is selected
Number is greater than 0.5 and weather variable of the importance values greater than 0.8 is crucial weather variable.
Influence about different weather variable to communication device probability of malfunction can pass through the P value method pair in statistics
The historical data of multiple weather variables is analyzed, to calculate separately out the p value for different weather variable:
P=2P (z > | zc|)
Wherein, z is the statistic that weather variable corresponds to power communication system element fault probability, zcIt is to be obtained from sample data
The weather variable obtained corresponds to the statistic of power communication system element fault probability.
Each p value is ranked up after the completion of calculating.In general, p value just illustrates the weather variable to electricity less than 0.05
Power communication system component probability of malfunction influences significantly, just more significant if it is less than 0.01.In other words, it can indicate corresponding with 1-p
Weather variable is to power communication system element fault probability importance values.Model enforceability and model accuracy in order to balance,
Related coefficient be can choose greater than 0.5 and weather variable of the importance values (i.e. 1-p) greater than 0.8 is as crucial weather variable.
(3) the state of weather model being made of crucial weather variable GS, NLPL, analysis power communication system element event are constructed
Hinder the relationship between probability and state of weather, establishes the power communication system element fault probability model for considering state of weather.
Failure rate of the element under all possible state of weather can be described as:
Wherein, WiIt is the failure rate that the every annual meeting of power communication system element under i-th of state of weather occurs, SiIt is
The probability of stability of i state of weather.Indeed, it is difficult to be directly determined from the fault statistics data of collection electric under certain state of weather
The failure rate of power communication system component.In fact, element fault number is a part of failure sum under i-th kind of state of weather, because
This has:
Wherein, NiIt is the total failare number under i-th of state of weather.
The failure rate of power communication system element is metastable under long time scale, can pass through the event of collection
Hinder data direct estimation, and then obtains element fault probability under i-th kind of state of weather.
Therefore, the failure recovery time of power communication system element can be used institute faulty flat under i-th kind of weather conditions
Equal recovery time is estimated:
Wherein, TiIt is the recovery time of failure under the i-th state of weather, TijIt is the recovery of jth time failure under the i-th state of weather
Time.
(4) it is based on power communication system element fault probability model, in conjunction with historical weather data, calculates power communication system
Reliability index.
Define the power communication system element total failare time: FT=∑i∈nUiNi, in formula, UiAnd NiIt is power communication respectively
The fault time in year of node i and number of elements；N is power communication number of nodes.
Total user malfunction number: FF=∑i∈nλiNi, in formula, λiAnd NiBe respectively power communication node i the number of stoppages and
Number of elements；N is power communication number of nodes.
System average failure duration index:
System mean failure rate frequency index:
The reliability index of power communication system element:
Pass through system average failure duration index, system mean failure rate frequency index and power communication system element
Reliability index assesses the reliability of power communication system.
Weather range of variables within the scope of certain system realm is as shown in table 1, wherein GS is gust velocity, RS is resultant wind
Speed, AS are mean wind speed, NLAL is the natural logrithm of total thunder-strike current, NLPL is the natural logrithm of peak value thunder-strike current, CI is
Ice condition composite index, HT are maximum temperature, LT is minimum temperature, AT is mean temperature, RH is air humidity.
Table 1
Nanjing Suburb weather conditions in 2017 as shown in Figure 2, every 12h carry out one-shot measurement, obtain the line power
730 kinds of weather conditions corresponding to communication system component.Assuming that first section of GS and first section of NLPL are weather
State 1, first section of GS and second section of NLPL are state of weather 2, five sections of GS and five areas of NLPL
Between combine one by one, and so on, the 5th section of the 5th section of GS and NLPL are state of weather 25.
By element fault database and weather data, the probability of malfunction of power communication system element can be respectively obtained,
As shown in Figure 3.Using the reliability of present invention assessment Nanjing Suburb power communication system in 2017, and provided with power supply company
Truthful data compares, as shown in Figure 4.A kind of power communication system reliability assessment for considering weather and influencing according to the present invention
Embodiment of the method can be seen that be not much different using the method for the present invention and actual value.
Claims (7)
1. a kind of power communication system reliability estimation method for considering weather and influencing, which is characterized in that comprising steps of
(1) it is based on weather variable historical data and power communication system element fault historical data, using grey relevant degree method,
Calculate the related coefficient and importance values of weather variable and communication device failure；
(2) critical correlation coefficients ξ and importance values η is set, weather variable is selected and the related coefficient of communication device failure is big
It is greater than the weather variable of η in ξ and importance values for crucial weather variable；
(3) the state of weather model for constructing crucial weather variable, analyzes power communication system element fault probability and state of weather
Relationship, establish consider state of weather power communication system element fault probability model；
(4) it is based on power communication system element fault probability model, in conjunction with historical weather data, calculate power communication system can
By property index.
2. the power communication system reliability estimation method according to claim 1 for considering weather and influencing, which is characterized in that
In the step (1), weather variable includes temperature, relative air humidity, wind speed, lightning stroke and frost degree.
3. the power communication system reliability estimation method according to claim 2 for considering weather and influencing, which is characterized in that
Anti-lightning strike electric current and peak value thunder-strike current is selected to indicate lightning stroke variation；Select gust velocity, synthesis demeanour and mean wind speed
Indicate air speed influence variable；The frost degree variable is calculated by following formula:
Wherein, RS is the maximum number of days of sleet assessment, FRS is rainfall and snowfall, CLT are continuous low temperature number of days；It is designated as working as under c
Preceding value is designated as average value under av.
4. the power communication system reliability estimation method according to claim 1 for considering weather and influencing, which is characterized in that
In the step (2), the degree of association r of weather variable and communication device probability of malfunction0i:
Wherein, X0For the probability of certain year a certain weather occurrences, XiFor the probability of malfunction of this year element under the weather variable.
5. the power communication system reliability estimation method according to claim 1 for considering weather and influencing, which is characterized in that
In the step (2), the importance values are calculated by P value method, indicate that corresponding weather variable is logical to electric power with 1-p
Believe system element probability of malfunction importance values:
P=2P (z > | zc|)
Wherein, z is the statistic that weather variable corresponds to power communication system element fault probability, zcIt is to be obtained from sample data
Weather variable corresponds to the statistic of power communication system element fault probability.
6. the power communication system reliability estimation method according to claim 1 for considering weather and influencing, which is characterized in that
In the step (4), the reliability index of power communication system includes system average failure duration index, the average event of system
Hinder the reliability index of frequency index and power communication system element.
7. the power communication system reliability estimation method according to claim 6 for considering weather and influencing, which is characterized in that
Specifically include step:
(4.1) the power communication system element total failare time is defined:
FT=∑i∈nUiNi
In formula, UiAnd NiIt is fault time in year and the number of elements of power communication node i respectively；N is power communication number of nodes；
(4.2) total user malfunction number is calculated:
FF=∑i∈nλiNi
In formula, λiAnd NiIt is the number of stoppages and number of elements of power communication node i respectively；N is power communication number of nodes；
(4.3) computing system average failure duration index:
Computing system mean failure rate frequency index:
Calculate the reliability index of power communication system element:
(4.4) pass through system average failure duration index, system mean failure rate frequency index and power communication system element
Reliability index the reliability of power communication system assessed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910188652.5A CN109948925A (en) | 2019-03-13 | 2019-03-13 | Consider the power communication system reliability estimation method that weather influences |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910188652.5A CN109948925A (en) | 2019-03-13 | 2019-03-13 | Consider the power communication system reliability estimation method that weather influences |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109948925A true CN109948925A (en) | 2019-06-28 |
Family
ID=67009797
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910188652.5A Withdrawn CN109948925A (en) | 2019-03-13 | 2019-03-13 | Consider the power communication system reliability estimation method that weather influences |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109948925A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111027827A (en) * | 2019-11-27 | 2020-04-17 | 广东信通通信有限公司 | Method and device for analyzing operation risk of bottom-preserving communication network and computer equipment |
| CN112613684A (en) * | 2020-12-31 | 2021-04-06 | 上海交通大学 | Special differentiation operation and maintenance method based on distribution network fault prediction |
-
2019
- 2019-03-13 CN CN201910188652.5A patent/CN109948925A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111027827A (en) * | 2019-11-27 | 2020-04-17 | 广东信通通信有限公司 | Method and device for analyzing operation risk of bottom-preserving communication network and computer equipment |
| CN111027827B (en) * | 2019-11-27 | 2023-12-19 | 广东信通通信有限公司 | Method and device for analyzing operation risk of bottom-protecting communication network and computer equipment |
| CN112613684A (en) * | 2020-12-31 | 2021-04-06 | 上海交通大学 | Special differentiation operation and maintenance method based on distribution network fault prediction |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102629294B (en) | Probability evaluation method of failure caused by typhoon to power transmission line | |
| US7920997B2 (en) | Electric power distribution interruption risk assessment calculator | |
| CN116256602B (en) | Method and system for identifying state abnormality of low-voltage power distribution network | |
| CN109842373A (en) | Diagnosing failure of photovoltaic array method and device based on spatial and temporal distributions characteristic | |
| CN105117602A (en) | Metering apparatus operation state early warning method | |
| Wang et al. | Time‐varying failure rate simulation model of transmission lines and its application in power system risk assessment considering seasonal alternating meteorological disasters | |
| CN108133225A (en) | A kind of icing flashover fault early warning method based on support vector machines | |
| CN110210701A (en) | A kind of grid equipment risk perceptions method | |
| CN107273273A (en) | A kind of distributed type assemblies hardware fault method for early warning and system | |
| CN106251045A (en) | Distribution network reliability appraisal procedure based on multiple leading factor | |
| CN112215305A (en) | Virtual laser radar system and method based on mutual early warning of fans | |
| CN103246939A (en) | Security and stability margin based on-line identification method for power network operating safety risk incidents | |
| CN109948925A (en) | Consider the power communication system reliability estimation method that weather influences | |
| CN108694479A (en) | Consider the distribution network reliability prediction technique that weather influences time between overhaul | |
| CN107239899A (en) | Sensitivity analysis method of the network load to meteorologic factor under a kind of typhoon weather | |
| Gao et al. | Heuristic failure prediction model of transmission line under natural disasters | |
| CN106355308A (en) | Method for identifying key equipment of wind power access system based on decision tree | |
| CN111126672A (en) | High-voltage overhead transmission line typhoon disaster prediction method based on classification decision tree | |
| CN106327071A (en) | Power line communication risk analysis method and power line communication risk analysis system | |
| Billinton et al. | Consideration of multi-state weather models in reliability evaluation of transmission and distribution systems | |
| CN109066651A (en) | The calculation method of wind-powered electricity generation-load scenarios limit transmitted power | |
| CN110084403B (en) | Multi-period joint probability density prediction method and system for current-carrying capacity of overhead conductor | |
| CN113609637B (en) | Multi-disaster power distribution network elasticity assessment method considering fault linkage | |
| CN104574211A (en) | Power grid dispatching operating risk early warning method and system based on risk source | |
| CN113988655A (en) | Power transmission line running state evaluation method considering multiple meteorological factors |
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 | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20190628 |