CN107292415A - A kind of Forecasting Methodology of intelligent meter rotation time - Google Patents
A kind of Forecasting Methodology of intelligent meter rotation time Download PDFInfo
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Abstract
The invention discloses a kind of Forecasting Methodology of intelligent meter rotation time, belong to equipment life prediction field, solve the technical problem of intelligent meter rotation time forecasting inaccuracy in the prior art, the Forecasting Methodology of intelligent meter rotation time of the invention, including：Obtain the rate of qualified voltage and temperature value, humidity value and altitude value under current operating environment of intelligent meter；Obtain the resultant fault rate of intelligent meter；By rate of qualified voltage, resultant fault rate, temperature value, humidity value and altitude value reduction to predeterminated voltage qualification rate benchmark, preset comprehensive fault rate benchmark, the poor benchmark of preset temperature, default humidity benchmark and default height above sea level benchmark, the perunit value of rate of qualified voltage, resultant fault rate, temperature, humidity and height above sea level is determined；The weight parameter of rate of qualified voltage, resultant fault rate, temperature, humidity and height above sea level is obtained, and intelligent meter rotation time is determined according to the perunit value of rate of qualified voltage, resultant fault rate, temperature, humidity and height above sea level.
Description
【Technical field】
Field is predicted the present invention relates to equipment life, and in particular to a kind of Forecasting Methodology of intelligent meter rotation time.
【Background technology】
The determination of the physical life of equipment has two methods：One is provided by equipment manufacturers.For producing a certain throughout the year
For the manufacturer of equipment because there is knowhow and substantial amounts of data accumulation for many years, and user feedback information, can be with
Determine its equipment life.Equipment for newly developing, also can make correct estimation to life value according to the equipment of same model；Two
It is that equipment life is predicted using Delphi method, detailed process is the relevant of equipment and its main parts size by demander
Associated specialist is sent in background material and the requirement of the purpose of prediction respectively, it is desirable to which they are entered with the experience of oneself to equipment life on time
Row is assessed, and every expertise then is carried out into induction-arrangement, and sends every expert again by result is arranged, and asks them to enter
Row secondary evaluation, it is then comprehensive again to conclude.Above two method is subjective, to consider a variety of objective factors, pair sets
The degree of accuracy of standby life prediction is weaker.
【The content of the invention】
When the technical problems to be solved by the invention are to overcome the deficiencies in the prior art and provide a kind of intelligent meter rotation
Between Forecasting Methodology, the influence of objective factor can be taken into full account, so as to improve the life prediction precision of intelligent meter.
In order to solve the above technical problems, the present invention is adopted the following technical scheme that：
A kind of Forecasting Methodology of intelligent meter rotation time, the Forecasting Methodology includes：
Obtain the rate of qualified voltage and temperature value, humidity value and altitude value under current operating environment of intelligent meter；
Obtain the resultant fault rate of intelligent meter；
By rate of qualified voltage, resultant fault rate, temperature value, humidity value and altitude value reduction to predeterminated voltage qualification rate base
Standard, the poor benchmark of preset comprehensive fault rate benchmark, preset temperature, default humidity benchmark and default height above sea level benchmark, determine that voltage is qualified
Rate, resultant fault rate, temperature, the perunit value of humidity and height above sea level；
Obtain rate of qualified voltage, resultant fault rate, temperature, the weight parameter of humidity and height above sea level, and according to rate of qualified voltage,
Resultant fault rate, temperature, the perunit value of humidity and height above sea level determine intelligent meter rotation time.
Further, the resultant fault rate for obtaining intelligent meter includes：
Obtain the clock failure rate α of the intelligent meter1, burn table fault rate α2, communication failure rate α3, watchcase damage or wiring
Post damages fault rate α4With liquid crystal display failure and display fault rate α5；
According to clock failure rate α1, burn table fault rate α2, communication failure rate α3, watchcase damage or binding post damage fault rate
α4, liquid crystal display failure and display fault rate α5, and the weight coefficient of default clock failure rate h1, burn table fault rate
Weight coefficient h2, communication failure rate weight coefficient h3, watchcase damage or binding post damage fault rate weight coefficient h4, liquid
Crystalline substance screen failure and the weight coefficient h for showing fault rate5, meet h1+h2+h3+h4+h5=1, resultant fault is determined by formula one
Rate α,
α=h1*α1+h2*α2+h3*α3+h4*α4+h5*α5Formula one.
Further, the poor benchmark of the preset temperature is 4, and the perunit value of the temperature isIt is described default
Humidity benchmark is 40%, and the perunit value of the humidity isThe default height above sea level benchmark is 300 meters, the height above sea level
Perunit value isThe predeterminated voltage qualification rate benchmark is 70%, and the perunit value of the rate of qualified voltage isThe preset comprehensive fault rate benchmark is 10%, and the perunit value of the resultant fault rate is
Further, the weight parameter for obtaining rate of qualified voltage, resultant fault rate, temperature, humidity and height above sea level, and
Intelligent meter rotation time is determined according to the perunit value of rate of qualified voltage, resultant fault rate, temperature, humidity and height above sea level, including
Intelligent meter rotation time y is determined according to formula two,
Wherein, k1For the weight parameter of temperature, k2For the weight parameter of humidity, k3For the weight parameter of height above sea level, k4For voltage
The weight parameter of qualification rate, k5For the weight parameter of resultant fault rate, y is intelligent meter rotation time；
The weight parameter k of the temperature1, humidity weight parameter k2, height above sea level weight parameter k3, rate of qualified voltage power
Weight parameter k4And the weight parameter k of resultant fault rate5Meet：k1+k2+k3+k4+k5=1.
Beneficial effects of the present invention：
The technical scheme is that considered the temperature value of the actually located working environment of intelligent meter, humidity value,
The rotation time prediction carried out on the basis of altitude value and intelligent meter rate of qualified voltage in itself and resultant fault rate objective factor,
The interference of personnel's subjectivity is reduced, precision of prediction is higher, provide effective preparation for the rotation of intelligent meter, reduction is due to changing intelligence
Can table and the loss that causes.
The present invention these features and advantage will be detailed in following embodiment, accompanying drawing exposure.
【Brief description of the drawings】
The present invention is described further below in conjunction with the accompanying drawings：
Fig. 1 is flow chart of the invention.
【Embodiment】
The technical scheme of the embodiment of the present invention is explained and illustrated with reference to the accompanying drawing of the embodiment of the present invention, but under
State embodiment only the preferred embodiments of the present invention, and not all.Based on the embodiment in embodiment, people in the art
Member obtains other embodiments on the premise of creative work is not made, and belongs to protection scope of the present invention.
As shown in figure 1, the invention discloses a kind of Forecasting Methodology of intelligent meter rotation time, the Forecasting Methodology includes：
11st, the rate of qualified voltage β and temperature value T, humidity value P and altitude value under current operating environment of intelligent meter are obtained
H；
12nd, the resultant fault rate of intelligent meter is obtained；
13rd, by rate of qualified voltage, resultant fault rate, temperature value, humidity value and altitude value reduction to predeterminated voltage qualification rate
The poor benchmark of benchmark, preset comprehensive fault rate benchmark, preset temperature, default humidity benchmark and default height above sea level benchmark, determine that voltage is closed
Lattice rate, resultant fault rate, temperature, the perunit value of humidity and height above sea level；
14th, the weight parameter of rate of qualified voltage, resultant fault rate, temperature, humidity and height above sea level is obtained, and it is qualified according to voltage
Rate, resultant fault rate, temperature, the perunit value of humidity and height above sea level determine intelligent meter rotation time.
The resultant fault rate of intelligent meter is obtained wherein in step 12 to be included：
Obtain the clock failure rate α of intelligent meter1, burn table fault rate α2, communication failure rate α3, watchcase damage or binding post damage
Bad fault rate α4With liquid crystal display failure and display fault rate α5；
According to clock failure rate α1, burn table fault rate α2, communication failure rate α3, watchcase damage or binding post damage fault rate
α4, liquid crystal display failure and display fault rate α5, and the weight coefficient of default clock failure rate h1, burn table fault rate
Weight coefficient h2, communication failure rate weight coefficient h3, watchcase damage or binding post damage fault rate weight coefficient h4, liquid
Crystalline substance screen failure and the weight coefficient h for showing fault rate5, meet h1+h2+h3+h4+h5=1, resultant fault is determined by formula one
Rate α,
α=h1*α1+h2*α2+h3*α3+h4*α4+h5*α5Formula one.
The poor benchmark of the preset temperature is 4, and the perunit value of the temperature isThe default humidity benchmark is
40%, the perunit value of the humidity isThe default height above sea level benchmark is 300 meters, and the perunit value of the height above sea level isThe predeterminated voltage qualification rate benchmark is 70%, and the perunit value of the rate of qualified voltage isIt is described pre-
If resultant fault rate benchmark is 10%, the perunit value of the resultant fault rate is
The weight parameter of rate of qualified voltage, resultant fault rate, temperature, humidity and height above sea level is obtained in step 14, and according to electricity
The perunit value of qualification rate, resultant fault rate, temperature, humidity and height above sea level is pressed to determine intelligent meter rotation time, including
Intelligent meter rotation time y is determined according to formula two,
Wherein, k1For the weight parameter of temperature, k2For the weight parameter of humidity, k3For the weight parameter of height above sea level, k4For voltage
The weight parameter of qualification rate, k5For the weight parameter of resultant fault rate, y is intelligent meter rotation time；
The weight parameter k of the temperature1, humidity weight parameter k2, height above sea level weight parameter k3, rate of qualified voltage power
Weight parameter k4And the weight parameter k of resultant fault rate5Meet：k1+k2+k3+k4+k5=1.
In order to verify the Forecasting Methodology of the present invention, it is further described below in conjunction with instantiation.
It is 18 degrees Celsius that the intelligent meter of such as batch, which is arranged on ambient temperature value T, is typically averaged, humidity value P is
50%, altitude value H are 10 meters of place, according to account of the history, and the rate of qualified voltage β of this batch of electric energy meter is 80%.
Wherein weight coefficient hiNumerical value using entropy assessment determine, i is 1,2,3,4,5.Gather n group intelligent meters, every group of intelligence
Can the fault category of table be m classes, in the present embodiment is that 6, m is 5 for n,
If the Evaluations matrix being made up of m indexs of n scheme is X=(xji)n×m, j=1,2 ..., n；I=1,2 ..., m.
Criterion method is as follows：
P in formulajiFor the achievement data of standardization.Standardization effectively eliminates the incommensurability between index.Respectively refer to
Target entropy is：
Especially, P is worked asjiWhen=0, P is madejilnPji=0.wiIt is each index without preference weight.
With θiRepresent that power grid enterprises to i-th of fault indices or the preference of influence factor, then assess i-th of failure of sample
The preference entropy weight weight of index or influence factor is (with hiRepresent), then preference entropy weight coefficient is：
The h of the weight coefficient of the clock failure rate calculated in the present embodiment1For 0.4, the weight system of burning table fault rate
Number h2For the weight coefficient h of 0.1, communication failure rate3The weight coefficient h of fault rate is damaged for the damage of 0.2, watchcase or binding post4For
0.1st, the weight coefficient h of liquid crystal display failure and display fault rate5For 0.2.
Frequency and weight according to the conventional Faulty Analysis of equipment, each major failure occur set as shown in table 1：
The fault rate of all categories of table 1 is counted and weight coefficient sets table
By the data α of upper table1、、α2、、α3α4α5And h1、h2、h、3h4h5Substituting into formula one can obtain, and resultant fault rate α is
0.2638。
The perunit value x of temperature1, humidity perunit value x2, height above sea level perunit value x3, rate of qualified voltage perunit value x4, it is comprehensive
The perunit value x of fault rate5And the weight parameter k of temperature1, humidity weight parameter k2, height above sea level weight parameter k3, voltage it is qualified
The weight parameter k of rate4With the weight parameter k of resultant fault rate5As shown in table 2, wherein kiCalculation with reference to hiCalculating side
Formula,
Each perunit value of table 2 and weight parameter set table
Each above-mentioned data are substituted into formula two, the batch intelligent meter rotation time are obtained for 4.3 years.
From formula two, the absolute value of temperature actual temperature and optimum temperature difference is bigger, and the perunit value of temperature is bigger,
Meter rotation time is shorter；Actual humidity is bigger, and the perunit value of humidity is also bigger, and meter rotation time is shorter；Actual height above sea level is got over
Height, the perunit value of height above sea level is bigger, and meter rotation time is shorter；Rate of qualified voltage is higher, and the perunit value of rate of qualified voltage is bigger, table
Count rotation time longer；Resultant fault rate is higher, and the perunit value of resultant fault rate is bigger, and meter rotation time is shorter.
It follows that the technical scheme is that considering the temperature of the actually located working environment of intelligent meter
The wheel carried out on the basis of value, humidity value, altitude value and intelligent meter rate of qualified voltage in itself and resultant fault rate objective factor
Time prediction is changed, the interference of personnel's subjectivity is reduced, precision of prediction is higher, effective preparation, drop are provided for the rotation of intelligent meter
The low loss caused due to replacing intelligent meter.
The foregoing is only a specific embodiment of the invention, but protection scope of the present invention is not limited thereto, and is familiar with
The those skilled in the art should be understood that the present invention includes but is not limited to accompanying drawing and interior described in embodiment above
Hold.The modification of any function and structure principle without departing from the present invention is intended to be included in the scope of claims.
Claims (4)
1. a kind of Forecasting Methodology of intelligent meter rotation time, it is characterised in that the Forecasting Methodology includes：
Obtain the rate of qualified voltage and temperature value, humidity value and altitude value under current operating environment of intelligent meter；
Obtain the resultant fault rate of intelligent meter；
By rate of qualified voltage, resultant fault rate, temperature value, humidity value and altitude value reduction to predeterminated voltage qualification rate benchmark, in advance
If resultant fault rate benchmark, preset temperature difference benchmark, default humidity benchmark and default height above sea level benchmark, rate of qualified voltage is determined, comprehensive
Close the perunit value of fault rate, temperature, humidity and height above sea level；
The weight parameter of rate of qualified voltage, resultant fault rate, temperature, humidity and height above sea level is obtained, and according to rate of qualified voltage, synthesis
Fault rate, temperature, the perunit value of humidity and height above sea level determine intelligent meter rotation time.
2. the Forecasting Methodology of intelligent meter rotation time according to claim 1, it is characterised in that the acquisition intelligent meter
Resultant fault rate includes：
Obtain the clock failure rate α of the intelligent meter1, burn table fault rate α2, communication failure rate α3, watchcase damage or binding post damage
Bad fault rate α4With liquid crystal display failure and display fault rate α5；
According to clock failure rate α1, burn table fault rate α2, communication failure rate α3, watchcase is damaged or binding post damages fault rate α4, liquid
Crystalline substance screen failure and display fault rate α5, and the weight coefficient of default clock failure rate h1, burn table fault rate weight
Coefficient h2, communication failure rate weight coefficient h3, watchcase damage or binding post damage fault rate weight coefficient h4, liquid crystal display therefore
The weight coefficient h of barrier and display fault rate5, meet h1+h2+h3+h4+h5=1, resultant fault rate α is determined by formula one,
α=h1*α1+h2*α2+h3*α3+h4*α4+h5*α5Formula one.
3. the Forecasting Methodology of intelligent meter rotation time according to claim 2, it is characterised in that the poor base of the preset temperature
Standard is 4, and the perunit value of the temperature isThe default humidity benchmark is 40%, and the perunit value of the humidity isThe default height above sea level benchmark is 300 meters, and the perunit value of the height above sea level isThe predeterminated voltage qualification rate
Benchmark is 70%, and the perunit value of the rate of qualified voltage isThe preset comprehensive fault rate benchmark is 10%, described
The perunit value of resultant fault rate is
4. the Forecasting Methodology of intelligent meter rotation time according to claim 3, it is characterised in that the acquisition voltage is qualified
Rate, resultant fault rate, temperature, the weight parameter of humidity and height above sea level, and according to rate of qualified voltage, resultant fault rate, temperature, humidity
Intelligent meter rotation time is determined with the perunit value of height above sea level, including
Intelligent meter rotation time y is determined according to formula two,
Wherein, k1For the weight parameter of temperature, k2For the weight parameter of humidity, k3For the weight parameter of height above sea level, k4It is qualified for voltage
The weight parameter of rate, k5For the weight parameter of resultant fault rate, y is intelligent meter rotation time；
The weight parameter k of the temperature1, humidity weight parameter k2, height above sea level weight parameter k3, rate of qualified voltage weight ginseng
Number k4And the weight parameter k of resultant fault rate5Meet：k1+k2+k3+k4+k5=1.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108268076A (en) * | 2018-01-23 | 2018-07-10 | 余绍志 | A kind of computer room operational safety assessment system based on big data |
| CN109359896A (en) * | 2018-12-10 | 2019-02-19 | 国网福建省电力有限公司 | A kind of Guangdong power system method for prewarning risk based on SVM |
| CN109583697A (en) * | 2018-10-29 | 2019-04-05 | 中国电力科学研究院有限公司 | A kind of electric energy metering device intelligent rotation method and system based on big data cluster analysis |
| CN110927654A (en) * | 2019-08-23 | 2020-03-27 | 国网天津市电力公司电力科学研究院 | Batch running state evaluation method for intelligent electric energy meters |
| CN113607413A (en) * | 2021-08-26 | 2021-11-05 | 上海航数智能科技有限公司 | Bearing component fault monitoring and predicting method based on controllable temperature and humidity |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103218752A (en) * | 2013-03-13 | 2013-07-24 | 云南电力试验研究院（集团）有限公司电力研究院 | Current and short-term reliability assessment method of high-voltage transformer |
| CN103310051A (en) * | 2013-06-05 | 2013-09-18 | 天泽信息产业股份有限公司 | Method for predicting failure rate of vehicle-mounted information terminal in entire life cycle |
| CN103426030A (en) * | 2013-04-24 | 2013-12-04 | 华北电力大学 | Power equipment fault rate prediction method considering aging factors |
| CN103714493A (en) * | 2014-01-10 | 2014-04-09 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | SF6 breaker remaining life assessment method |
| CN104914327A (en) * | 2015-05-06 | 2015-09-16 | 北京航空航天大学 | Transformer fault maintenance prediction method based on real-time monitoring information |
| US20160154061A1 (en) * | 2014-11-28 | 2016-06-02 | State Grid Corporation Of China | Method of assessing risk of power system with high penetration of wind power |
| CN105740582A (en) * | 2016-03-16 | 2016-07-06 | 华北电力大学 | Prediction method for ageing state of composite insulator |
-
2017
- 2017-05-02 CN CN201710302053.2A patent/CN107292415B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103218752A (en) * | 2013-03-13 | 2013-07-24 | 云南电力试验研究院（集团）有限公司电力研究院 | Current and short-term reliability assessment method of high-voltage transformer |
| CN103426030A (en) * | 2013-04-24 | 2013-12-04 | 华北电力大学 | Power equipment fault rate prediction method considering aging factors |
| CN103310051A (en) * | 2013-06-05 | 2013-09-18 | 天泽信息产业股份有限公司 | Method for predicting failure rate of vehicle-mounted information terminal in entire life cycle |
| CN103714493A (en) * | 2014-01-10 | 2014-04-09 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | SF6 breaker remaining life assessment method |
| US20160154061A1 (en) * | 2014-11-28 | 2016-06-02 | State Grid Corporation Of China | Method of assessing risk of power system with high penetration of wind power |
| CN104914327A (en) * | 2015-05-06 | 2015-09-16 | 北京航空航天大学 | Transformer fault maintenance prediction method based on real-time monitoring information |
| CN105740582A (en) * | 2016-03-16 | 2016-07-06 | 华北电力大学 | Prediction method for ageing state of composite insulator |
Non-Patent Citations (2)
| Title |
|---|
| ZHOU YANG等: "Smart electricity meter reliability prediction based on accelerated degradation testing and modeling", 《INTERNATIONAL JOURNAL OF ELECTRICAL POWER&ENERGY SYSTEMS》 * |
| 梁博渊等: "变压器健康状况评估与剩余寿命预测", 《电网与清洁能源》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108268076A (en) * | 2018-01-23 | 2018-07-10 | 余绍志 | A kind of computer room operational safety assessment system based on big data |
| CN109583697A (en) * | 2018-10-29 | 2019-04-05 | 中国电力科学研究院有限公司 | A kind of electric energy metering device intelligent rotation method and system based on big data cluster analysis |
| CN109359896A (en) * | 2018-12-10 | 2019-02-19 | 国网福建省电力有限公司 | A kind of Guangdong power system method for prewarning risk based on SVM |
| CN109359896B (en) * | 2018-12-10 | 2021-11-12 | 国网福建省电力有限公司 | SVM-based power grid line fault risk early warning method |
| CN110927654A (en) * | 2019-08-23 | 2020-03-27 | 国网天津市电力公司电力科学研究院 | Batch running state evaluation method for intelligent electric energy meters |
| CN113607413A (en) * | 2021-08-26 | 2021-11-05 | 上海航数智能科技有限公司 | Bearing component fault monitoring and predicting method based on controllable temperature and humidity |
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