CN103971171B - A kind of transmission facility state evaluating method - Google Patents

A kind of transmission facility state evaluating method Download PDF

Info

Publication number
CN103971171B
CN103971171B CN201410157794.2A CN201410157794A CN103971171B CN 103971171 B CN103971171 B CN 103971171B CN 201410157794 A CN201410157794 A CN 201410157794A CN 103971171 B CN103971171 B CN 103971171B
Authority
CN
China
Prior art keywords
state
matrix
weight
parts
transmission facility
Prior art date
Application number
CN201410157794.2A
Other languages
Chinese (zh)
Other versions
CN103971171A (en
Inventor
宋云海
陈岳
王奇
李晋伟
常安
邓军
严英杰
Original Assignee
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
上海交通大学
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by 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, 上海交通大学 filed Critical 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
Priority to CN201410157794.2A priority Critical patent/CN103971171B/en
Publication of CN103971171A publication Critical patent/CN103971171A/en
Application granted granted Critical
Publication of CN103971171B publication Critical patent/CN103971171B/en

Links

Abstract

The invention discloses transmission facility state evaluating method, it comprises step S1, merges multi-source heterogeneous information, adopts the Model Establishment of multi-layer framework to play the state estimation parameter system of transmission facility; Step S2, determine the state grade assessed, and obtain fuzzy matrix for assessment according to the quantity of state limit value in design document and comment directive/guide; Step S3, employing information Entropy Method determine the objective weight of each quantity of state; Step S4, employing analytical hierarchy process determine the subjective weight of each quantity of state, and described supervisor's weight are combined with objective weight, calculate the comprehensive weight of each quantity of state; Step S5, according to the fuzzy matrix for assessment in step S2 and the comprehensive weight in step S4, calculate the state estimation matrix of each parts and the state estimation matrix of transmission facility entirety.Subjective weight, objective weight and fuzzy matrix for assessment combine by the present invention, problem less on integrality impact when avoiding indivedual index severely subnormal that fixed weight brings.

Description

A kind of transmission facility state evaluating method
Technical field
The present invention relates to a kind of Operation of Electric Systems safety technique, be specifically related to a kind of transmission facility state evaluating method.
Background technology
The safety of transmission facility is the basis of power grid security, reliable, stable operation, and carrying out effectively, accurately assessing, diagnose and predicting to equipment state, is the important channel of improving power supply reliability and operation of power networks intelligent level.
Fuzzy mathematics method is applicable to the state estimation of transmission facility, and it has used the theory of degree of membership and subordinate function in fuzzy set, carries out the abstract of mathematicization to the restricting relation of multimode amount in transmission facility.Blur method first carries out simple element evaluation to multi-Fuzzy sexual factor, then carries out fuzzy deduction according to predetermined rule set, makes an explanation to evaluation result according to certain principle.
The difficult point of transmission facility state estimation is the determination of comprehensive all kinds of status information and weight.Carry out comprehensive and accurate state estimation, need the multi-source heterogeneous information such as fusion device status information, operation of power networks information and environmental state information, in conjunction with history, the current and to-be of power equipment, draw condition evaluation results by certain standard and intelligent evaluation method.At present, study less both at home and abroad to the state estimation of transmission facility, concentrate on the one hand to transmission facility electrically or the detection of the concrete parameter of mechanical aspects, as measure traverse line tension force and inclination angle realize the icing monitoring of wire, measure the filth monitoring that the close and leakage current of the salt of insulator realizes insulator; Concentrating on the other hand the state analysis based on single or a small amount of parameter of some macroscopic views, as assessed shaft tower state according to shaft tower degree of tilt, the parameter such as antitheft, assessing Lead status according to icing, windage yaw, wave etc.Above-mentionedly all science cannot hold health status and the state development trend of transmission facility entirety.
In the determination of weight, because the characteristic quantity run for assessment of equipment is a lot, and each characteristic quantity role when assessing is different, accurately need determine the weight of this characteristic quantity different.Prior art aspect mainly contains subjective weight and objective weight two kinds of analytical approachs, wherein subjective weight analysis method mainly analytical hierarchy process, the weight of each parameter is determined according to expert opinion, objective weight analytic approach, it is the Changing Pattern utilizing different characteristic amount, dependence mathematical method determines its weight, mainly comprises entropy assessment, evidence theory etc.
The present invention, under National 863 planning item fund (2012AA050209) is subsidized, proposes " a kind of transmission facility state evaluating method ".
Summary of the invention
Propose a kind of transmission facility state evaluating method herein, on the basis having considered all kinds of status information of transmission facility, comprehensive weight is used effectively to combine current data and historical data, problem less on integrality impact when avoiding indivedual index severely subnormal that fixed weight brings.
Transmission facility state evaluating method of the present invention, comprises the following steps:
A kind of transmission facility state evaluating method, it comprises the following steps:
Step S1, merge multi-source heterogeneous information, the Model Establishment of multi-layer framework is adopted to play the state estimation parameter system of transmission facility, described multi-source heterogeneous information at least comprises equipment on-line monitoring and O&M service information, operation of power networks information and environmental state information, described state estimation parameter system comprise be made from multiple components transmission facility layer, for enumerate each unit status assessment corresponding to the state-detection layer of quantity of state and the sensor layer for enumerating various kinds of sensors parameter, described sensor parameters is the original vol of described quantity of state;
Step S2, determine the state grade assessed, and obtain fuzzy matrix for assessment according to the quantity of state limit value in design document and comment directive/guide;
Step S3, employing information Entropy Method determine the objective weight of each quantity of state;
Step S4, employing analytical hierarchy process determine the subjective weight of each quantity of state, and described subjective weight are combined with objective weight, calculate the comprehensive weight of each quantity of state;
Step S5, according to the fuzzy matrix for assessment in step S2 and the comprehensive weight in step S4, calculate the state estimation matrix of each parts and the state estimation matrix of transmission facility entirety.
Quantity of state in described step S1 comprises detection data and basic data.
Described step S2 comprises:
Step S2.1, the measured value of each quantity of state to be normalized, to describe the relative inferiority degree that each quantity of state departs from normal operating conditions;
Step S2.2, to transmission facility state demarcation be good, general, note, serious 4 state grades, determine the membership function of each quantity of state facing to 4 kinds of state grades respectively by trigonometric sum half is trapezoidal;
Step S2.3, the measured value of each quantity of state is normalized rear substitution membership function, what calculate that each quantity of state corresponds to 4 kinds of state grades is subordinate to angle value, and obtain fuzzy matrix for assessment, described fuzzy matrix for assessment is:
R = R 1 R 2 ... R n = r 11 r 12 r 13 r 14 r 21 r 22 r 23 r 24 ... ... ... ... r n 1 r n 2 r n 3 r n 4 - - - ( 1 )
Wherein, R is the fuzzy matrix for assessment of each parts, R ifor the fuzzy matrix for assessment of i-th quantity of state in the fuzzy matrix for assessment R of each parts, r ijrepresent each quantity of state u ito comment v jmembership; 0≤r ij≤ 1, j=4.
Described normalized comprises:
For large quantity of state, the larger state of its numerical value is more excellent, and its computing formula is:
f(x)=(x-a)/(b-a)(2)
For minimal type quantity of state, the less state of its numerical value is more excellent, and its computing formula is:
f(x)=(b-x)/(b-a)(3)
Wherein, f (x) is the relative inferiority degree of i-th quantity of state, and x is the measured value of i-th quantity of state; A is the ratings of i-th quantity of state, and b is the demand value of i-th quantity of state.
Described step S3 comprises the following steps:
Step S3.1, calculate each quantity of state u ientropy H i:
H i = - k Σ j = 1 4 r i j lnr i j - - - ( 4 )
Wherein, k=ln4, r ijmeet and work as r ijwhen=0, H i=0;
Step S3.2, calculate each quantity of state u icoefficient of variation g i:
g i=1-H i(5)
Step S3.3, calculate each quantity of state u iobjective weight e i:
e i = g i Σ i = 1 n g i - - - ( 6 )
Described step S4 comprises the following steps:
Step S4.1, obtain judgment matrix P according to expertise;
Step S4.2, consistency check is carried out to judgment matrix P:
CR=CI/RI(7)
Wherein, CR is the random Consistency Ratio of judgment matrix P, and CI is the general coincident indicator of judgment matrix, and the computing method of described CI are:
C I = 1 n - 1 ( λ m a x - n ) - - - ( 8 )
Wherein, RI is called the general coincident indicator of judgment matrix P, along with the exponent number of judgment matrix P gets fixed numbers, and λ maxfor the Maximum characteristic root of judgment matrix P;
As CR<0.1, namely think that judgment matrix P has satisfied consistance, the rationality that flexible strategy are distributed is described; Otherwise need to adjust judgment matrix, until by consistency check;
Step S4.3, when judgment matrix P by inspection after, obtain the proper vector C={c corresponding to Maximum characteristic root of judgment matrix P 1, c 2..., c n, required proper vector C is each quantity of state importance ranking, c iit is the subjective weighted value of i-th quantity of state;
Step S4.4, subjective weight and objective weight to be combined, calculate comprehensive weight:
w i = c i e i &part; &Sigma;c i e i &part; - - - ( 9 )
Wherein, w ibe the comprehensive weight of i-th quantity of state, for becoming weight coefficient.
Described &part; = 0.1.
Described parts are 9, are respectively basis, shaft tower, wire, ground wire, insulator, gold utensil, earthing device, affiliated facility, channel environment.
Described step S5 comprises the following steps:
Step S5.1, acquisition calculate the state estimation matrix of all parts:
Wherein, B represents the state estimation matrix of a certain parts, w={w 1, w 2..., w nrepresent the comprehensive weight of these parts, w ifor the comprehensive weight of i-th quantity of state in these parts, R = r 11 r 12 r 13 r 14 r 21 r 22 r 23 r 24 ... ... ... ... r n 1 r n 2 r n 3 r n 4 Represent the fuzzy evaluation matrix of these parts, b 1, b 2, b 3, b 4represent respectively these parts be under the jurisdiction of well, general, note, serious degree of membership;
Step S5.2, acquisition calculate the state estimation matrix of transmission facility entirety:
Wherein, W parts={ W 1, W 2..., W 9in W 1-W 9be respectively the comprehensive weight of 9 parts, the comprehensive weight computing formula of each parts is formula (9), in B 1-B 9be respectively the state estimation matrix of 9 parts, the computing formula of the state estimation matrix of each parts is formula (10), B overall={ d 1, d 2, d 3, d 4, wherein, d 1-d 4be respectively that transmission facility entirety belongs in good condition, general, notes, serious state estimation matrix;
Step S5.3, give score value 1,2,3,4 respectively to state grade, then average to the fuzzy set theory of 4 kinds of states according to evaluation result, draw the value of form factor:
f = &Sigma; j = 1 4 d j k h / &Sigma; j = 1 4 d j k - - - ( 12 )
Wherein f is form factor, and h is the score value of 4 state grades, and k is undetermined coefficient.
Described k=1.
The invention has the beneficial effects as follows: subjective weight, objective weight and fuzzy matrix for assessment combine by the present invention, and obtain the state estimation matrix of transmission facility entirety and each parts, more existing only to adopt supervisor's weight to carry out state estimation mode more accurate, problem less on integrality impact when avoiding indivedual index severely subnormal that fixed weight brings.
Accompanying drawing explanation
Fig. 1 is transmission line status evaluate parameter system;
Fig. 2 is triangle and the half trapezoidal distribution function in conjunction with membership function.
Embodiment
Below in conjunction with the drawings and specific embodiments, content of the present invention is described in further details.
According to Judgement Method herein, fuzzy comprehensive evoluation is carried out to the state of certain section of 500kV transmission line of electricity, and verifies in conjunction with the result of practical operation situation to fuzzy comprehensive evoluation.The state evaluation parameter system of this section of transmission line of electricity is as shown in table 1, and its state Comment gathers is that V={ is good, generally, notes, serious }.
Table 1 membership function
For the component leads of transmission facility, recording of this section of transmission line wire on-line monitoring class quantity of state and daily tour class quantity of state is as shown in table 2 below.The quantity of state ratings provided containing with good grounds relevant criterion, code and expert opinion in table 2 and demand value.
Table 2 on-line monitoring and daily tour class record
Component names Weight Well Note Generally Seriously
Shaft tower 0.125 0.175 0.341 0.384 0.100
Gold utensil 0.225 0.231 0.353 0.414 0.003
Insulator 0.275 0.16 0.620 0.280 0.003
Lead wire and earth wire 0.225 0.199 0.369 0.293 0.036
Basis 0.05 0.370 0.620 0.010 0.001
Affiliated facility 0.025 0.830 0.170 0.000 0.000
Channel environment 0.025 0.500 0.500 0.000 0.000
Earthing device 0.05 0.150 0.500 0.350 0.000
According to above information, as follows to the state estimation of wire and transmission facility entirety:
(1) set up the fuzzy matrix for assessment of on-line monitoring class and daily tour class, be respectively:
R 1 = 0 0.45 0.55 0 0.12 0.88 0 0 0 0.65 0.35 0 0 0.5 0.5 0 0.44 0.56 0 0 0.6 0.4 0 0 R 2 = 0 0.16 0.84 0 0 0.54 0.46 0 0 0.63 0.37 0 0.9 0.1 0 0 0.9 0.1 0 0
(2) for on-line monitoring class quantity of state, according to expertise, use analytical hierarchy process obtains its subjective weight and is:
C 1=(0.179,0.107,0.230,0.172,0.110,0.202)
According to the fuzzy matrix for assessment in (1), obtaining its objective weight is:
E 1=(0.058,0.618,0.129,0.049,0.062,0.084)
According to the comprehensive weight calculated be:
W 1=(0.135,0.102,0.187,0.127,0.083,0.158)
The comprehensive weight that in like manner can obtain daily tour class quantity of state is:
W 2=(0.235,0.143,0.162,0.210,0.250)
(3) to on-line monitoring class quantity of state, its Result of Fuzzy Comprehensive Evaluation is calculated:
B 1=W 1οR 1=(0.146,0.446,0.203,0)
In like manner can obtain daily tour class evaluation result:
B 2=(0.414,0.263,0.323,0)
(4) to detection data, its state estimation matrix is calculated:
Wherein w 1, w 2represent the comprehensive weight of on-line monitoring class and daily tour class quantity of state, its numerical value is 0.7 and 0.3.
The state estimation matrix that in like manner can arrive basic data is:
B basis=(0.140,0.320,0.420,0.120)
(5) to these parts of wire, its state estimation matrix and form factor f1 is calculated:
Wherein w basis, w detectrepresent the comprehensive weight of basic data and detection data respectively, its numerical value is respectively 0.7 and 0.3.
Form factor can be obtained: f1=2.185 by formula.
(6) according to (1) ~ (5) to the evaluation of Lead status, in like manner can the state estimation matrix of all parts and each parts relative to the comprehensive weight of transmission facility entirety, as shown in table 3.
The corresponding comprehensive weight of each parts of table 3 and state estimation matrix
Component names Weight Well Note Generally Seriously
Shaft tower 0.125 0.175 0.341 0.384 0.100
Gold utensil 0.225 0.231 0.353 0.414 0.003
Insulator 0.275 0.16 0.620 0.280 0.003
Lead wire and earth wire 0.225 0.199 0.369 0.293 0.036
Basis 0.05 0.370 0.620 0.010 0.001
Affiliated facility 0.025 0.830 0.170 0.000 0.000
Channel environment 0.025 0.500 0.500 0.000 0.000
Earthing device 0.05 0.150 0.500 0.350 0.000
(7) according to table 3, can obtain the Result of Fuzzy Comprehensive Evaluation of transmission facility entirety, its state estimation matrix is:
B overall=W overallο R overall=(0.221,0.448,0.302,0.022)
By formula, the form factor f=2.152 of transmission facility entirety.
According to the form factor of wire and the form factor of transmission facility entirety, illustrate that the state of wire and transmission facility is just developed from " generally " toward " attention ", this represent represent circuit had part important state amount close to or only slight beyond standard value, should operation be monitored, and need to arrange maintenance as early as possible.
The actual conditions of this section of transmission line of electricity are: the heavy snow weather being in winter at that time, and on transmission line of electricity, ice covering thickness is close to design load, and because the sag affecting wire of icing departs from normal value, wire exists abnormal vibrations; Maintenance record had been carried out before showing this section lead about splicing fitting and the maintenance of repairing wire strand breakage.Comprehensive above actual conditions, can judge the quantity of state slight degradation of this section of transmission line of electricity, running status integral working is not good enough, should keep a close eye on its follow-up state development, arranges maintenance as early as possible.This is consistent with the conclusion that this paper appraisal procedure draws.
If only consider subjective weight, and do not use comprehensive weight, the fuzzy evaluation result obtaining transmission facility entirety is:
B ' overall=(0.321,0.407,0.261,0.020)
Form factor f '=1.979 can be obtained by formula.This shows that the state of transmission facility is just developed from " well " toward " generally ", is not inconsistent with actual conditions.By contrast, more can objectively respond than normal power method the impact that some parameter drift-out normal value of transmission facility brings to integrality with comprehensive weight, its assessment result can closer to actual motion state.
Although the present invention is described by specific embodiment, it will be appreciated by those skilled in the art that, without departing from the present invention, various conversion can also be carried out and be equal to substituting to the present invention.In addition, for particular condition or application, various amendment can be made to the present invention, and not depart from the scope of the present invention.Therefore, the present invention is not limited to disclosed specific embodiment, and should comprise the whole embodiments fallen within the scope of the claims in the present invention.

Claims (3)

1. a transmission facility state evaluating method, is characterized in that, it comprises the following steps:
Step S1, merge multi-source heterogeneous information, the Model Establishment of multi-layer framework is adopted to play the state estimation parameter system of transmission facility, described multi-source heterogeneous information at least comprises equipment on-line monitoring and O&M service information, operation of power networks information and environmental state information, described state estimation parameter system comprise be made from multiple components transmission facility layer, for enumerate each unit status assessment corresponding to the state-detection layer of quantity of state and the sensor layer for enumerating various kinds of sensors parameter, described sensor parameters is the original vol of described quantity of state;
Step S2, determine the state grade assessed, and obtain fuzzy matrix for assessment according to the quantity of state limit value in design document and comment directive/guide;
Step S3, employing information Entropy Method determine the objective weight of each quantity of state;
Step S4, employing analytical hierarchy process determine the subjective weight of each quantity of state, and described subjective weight are combined with objective weight, calculate the comprehensive weight of each quantity of state;
Step S5, according to the fuzzy matrix for assessment in step S2 and the comprehensive weight in step S4, calculate the state estimation matrix of each parts and the state estimation matrix of transmission facility entirety;
Quantity of state in described step S1 comprises detection data and basic data;
Described step S2 comprises:
Step S2.1, the measured value of each quantity of state to be normalized, to describe the relative inferiority degree that each quantity of state departs from normal operating conditions;
Step S2.2, to transmission facility state demarcation be good, general, note, serious 4 state grades, determine the membership function of each quantity of state facing to 4 kinds of state grades respectively by trigonometric sum half is trapezoidal;
Step S2.3, the measured value of each quantity of state is normalized rear substitution membership function, what calculate that each quantity of state corresponds to 4 kinds of state grades is subordinate to angle value, and obtain fuzzy matrix for assessment, described fuzzy matrix for assessment is:
R = R 1 R 2 ... R n = r 11 r 12 r 13 r 14 r 21 r 22 r 23 r 24 ... ... ... ... r n 1 r n 2 r n 3 r n 4 - - - ( 1 )
Wherein, R is the fuzzy matrix for assessment of each parts, R ifor the fuzzy matrix for assessment of i-th quantity of state in the fuzzy matrix for assessment R of each parts, r ijrepresent each quantity of state u ito comment v jmembership; 0≤r ij≤ 1, j=4;
Described normalized comprises:
For large quantity of state, the larger state of its numerical value is more excellent, and its computing formula is:
f(x)=(x-a)/(b-a)(2)
For minimal type quantity of state, the less state of its numerical value is more excellent, and its computing formula is:
f(x)=(b-x)/(b-a)(3)
Wherein, f (x) is the relative inferiority degree of i-th quantity of state, and x is the measured value of i-th quantity of state; A is the ratings of i-th quantity of state, and b is the demand value of i-th quantity of state;
Described step S3 comprises the following steps:
Step S3.1, calculate each quantity of state u ientropy H i:
H i = - k &Sigma; j = 1 4 r i j lnr i j - - - ( 4 )
Wherein, k=ln4, r ijmeet and work as r ijwhen=0, H i=0;
Step S3.2, calculate each quantity of state u icoefficient of variation g i:
g i=1-H i(5)
Step S3.3, calculate each quantity of state u iobjective weight e i:
e i = g i &Sigma; i = 1 n g i - - - ( 6 ) ;
Described step S4 comprises the following steps:
Step S4.1, obtain judgment matrix P according to expertise;
Step S4.2, consistency check is carried out to judgment matrix P:
CR=CI/RI(7)
Wherein, CR is the random Consistency Ratio of judgment matrix P, and CI is the general coincident indicator of judgment matrix, and the computing method of described CI are:
C I = 1 n - 1 ( &lambda; m a x - n ) - - - ( 8 )
Wherein, RI is called the general coincident indicator of judgment matrix P, along with the exponent number of judgment matrix P gets fixed numbers, and λ maxfor the Maximum characteristic root of judgment matrix P;
As CR<0.1, namely think that judgment matrix P has satisfied consistance, the rationality that flexible strategy are distributed is described; Otherwise need to adjust judgment matrix, until by consistency check;
Step S4.3, when judgment matrix P by inspection after, obtain the proper vector C={c corresponding to Maximum characteristic root of judgment matrix P 1, c 2..., c n, required proper vector C is each quantity of state importance ranking, c iit is the subjective weighted value of i-th quantity of state;
Step S4.4, subjective weight and objective weight to be combined, calculate comprehensive weight:
w i = c i e i &part; &Sigma;c i e i &part; - - - ( 9 )
Wherein, w ibe the comprehensive weight of i-th quantity of state, for becoming weight coefficient;
Described parts are 9, are respectively basis, shaft tower, wire, ground wire, insulator, gold utensil, earthing device, affiliated facility, channel environment;
Described step S5 comprises the following steps:
Step S5.1, acquisition calculate the state estimation matrix of all parts:
Wherein, B represents the state estimation matrix of a certain parts, w={w 1, w 2..., w nrepresent the comprehensive weight of these parts, w ifor the comprehensive weight of i-th quantity of state in these parts, R = r 11 r 12 r 13 r 14 r 21 r 22 r 23 r 24 ... ... ... ... r n 1 r n 2 r n 3 r n 4 Represent the fuzzy evaluation matrix of these parts, b 1, b 2, b 3, b 4represent respectively these parts be under the jurisdiction of well, general, note, serious degree of membership;
Step S5.2, acquisition calculate the state estimation matrix of transmission facility entirety:
Wherein, W parts={ W 1, W 2..., W 9in W 1-W 9be respectively the comprehensive weight of 9 parts, the comprehensive weight computing formula of each parts is formula (9), in B 1-B 9be respectively the state estimation matrix of 9 parts, the computing formula of the state estimation matrix of each parts is formula (10), B overall={ d 1, d 2, d 3, d 4, wherein, d 1-d 4be respectively that transmission facility entirety belongs in good condition, general, notes, serious state estimation matrix;
Step S5.3, give score value 1,2,3,4 respectively to state grade, then average to the fuzzy set theory of 4 kinds of states according to evaluation result, draw the value of form factor:
f = &Sigma; j = 1 4 d j k h / &Sigma; j = 1 4 d j k - - - ( 12 )
Wherein f is form factor, and h is the score value of 4 state grades, and k is undetermined coefficient.
2. transmission facility state evaluating method according to claim 1, is characterized in that, described in
3. transmission facility state evaluating method according to claim 1, is characterized in that, described k=1.
CN201410157794.2A 2014-04-18 2014-04-18 A kind of transmission facility state evaluating method CN103971171B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201410157794.2A CN103971171B (en) 2014-04-18 2014-04-18 A kind of transmission facility state evaluating method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201410157794.2A CN103971171B (en) 2014-04-18 2014-04-18 A kind of transmission facility state evaluating method

Publications (2)

Publication Number Publication Date
CN103971171A CN103971171A (en) 2014-08-06
CN103971171B true CN103971171B (en) 2016-03-23

Family

ID=51240636

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201410157794.2A CN103971171B (en) 2014-04-18 2014-04-18 A kind of transmission facility state evaluating method

Country Status (1)

Country Link
CN (1) CN103971171B (en)

Families Citing this family (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104700321B (en) * 2015-03-16 2018-03-13 国家电网公司 A kind of power transmission and transformation equipment state operation trend analysis method
CN104952000A (en) * 2015-07-01 2015-09-30 华侨大学 Wind turbine operating state fuzzy synthetic evaluation method based on Markov chain
CN105069693A (en) * 2015-07-24 2015-11-18 东北农业大学 Water area health evaluation method
CN105719094A (en) * 2016-01-27 2016-06-29 刘冰 State evaluation method of power transmission equipment
CN106780111A (en) * 2016-11-24 2017-05-31 广州供电局有限公司 Power transmission cable synthetical condition assessment method and system
CN106529830A (en) * 2016-12-01 2017-03-22 贵州电网有限责任公司电力科学研究院 Multi-dimensional evaluation-based power transmission line risk evaluation system and evaluation method thereof
CN106713322B (en) * 2016-12-14 2019-12-13 北京邮电大学 Fuzzy measurement method for network equipment information security assessment
CN106708786A (en) * 2016-12-25 2017-05-24 杭州博烁晟斐智能科技有限公司 Method and system for calculating problem severity of iron tower based on sensor detection
CN106651731B (en) * 2016-12-25 2020-10-09 杭州博烁晟斐智能科技有限公司 Communication tower to-be-solved problem set generation method and system based on historical data
CN108170645A (en) * 2018-01-12 2018-06-15 国网安徽省电力有限公司池州供电公司 Alternating current gapless metal oxide arrester condition judgement method based on fuzzy matrix
CN108362263B (en) * 2018-02-10 2020-06-12 杭州后博科技有限公司 Inclination risk assessment method and system for multi-section iron tower
CN108805412A (en) * 2018-05-18 2018-11-13 广东电网有限责任公司 Arrester evaluating apparatus based on big data analysis and method
CN108982989A (en) * 2018-05-28 2018-12-11 国网内蒙古东部电力有限公司检修分公司 Extra-high voltage DC grounding electrode operating status assessment system based on multi-characteristicquantity quantity information
CN109064074A (en) * 2018-09-26 2018-12-21 广东电网有限责任公司 Arrester method for diagnosing status, system and equipment
CN109696901A (en) * 2018-11-30 2019-04-30 红云红河烟草(集团)有限责任公司 A kind of evaluation of cigarette packet equipment running status and prediction technique
CN109711687A (en) * 2018-12-17 2019-05-03 国网湖南省电力有限公司 A kind of insulator state fuzzy evaluation method based on improved entropy method

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6167525A (en) * 1997-02-26 2000-12-26 Pirelli Cavi E Sistemi S.P.A. Method and system for analysis of electric power transmission link status
CN103337043A (en) * 2013-06-27 2013-10-02 广东电网公司电力调度控制中心 Pre-warning method and system for running state of electric power communication equipment
CN103400310A (en) * 2013-08-08 2013-11-20 华北电力大学(保定) Method for evaluating power distribution network electrical equipment state based on historical data trend prediction

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6167525A (en) * 1997-02-26 2000-12-26 Pirelli Cavi E Sistemi S.P.A. Method and system for analysis of electric power transmission link status
CN103337043A (en) * 2013-06-27 2013-10-02 广东电网公司电力调度控制中心 Pre-warning method and system for running state of electric power communication equipment
CN103400310A (en) * 2013-08-08 2013-11-20 华北电力大学(保定) Method for evaluating power distribution network electrical equipment state based on historical data trend prediction

Also Published As

Publication number Publication date
CN103971171A (en) 2014-08-06

Similar Documents

Publication Publication Date Title
Igba et al. Analysing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes
Yu et al. Big data analytics in power distribution systems
CN102621421B (en) Transformer state evaluation method based on correlation analysis and variable weight coefficients
CN102721479B (en) Online monitoring method for temperature rise of outdoor electrical device
CN102663412B (en) Power equipment current-carrying fault trend prediction method based on least squares support vector machine
CN101614602B (en) Method and device for monitoring power transmission line
CN104700321B (en) A kind of power transmission and transformation equipment state operation trend analysis method
CN105975735B (en) A kind of modeling method for power equipment health state evaluation
AU2002302818B2 (en) Method of monitoring a high voltage grid power system
EP2868921B1 (en) Wind turbine and method for evaluating health state of blade thereof
EP2461024B1 (en) System, device, and method for estimating the power output of wind turbines
US10393788B2 (en) Apparatus and method for diagnosing state of power cable and measuring remaining life thereof using VLF TD measurement data
CN102289731B (en) Method for maintaining state of power transmission equipment based on system risk
CN102496069B (en) Cable multimode safe operation evaluation method based on fuzzy analytic hierarchy process (FAHP)
CN103033359B (en) A kind of main transmission in wind generating set method for diagnosing faults of multiple features Multilateral Comprehensive Judge
CN106125714A (en) Failure Rate Forecasting Method in conjunction with BP neutral net Yu two parameters of Weibull
US8154297B2 (en) System and method for predictive maintenance of a battery assembly using temporal signal processing
CN103810328B (en) Transformer maintenance decision method based on hybrid model
CN102590651B (en) Measured lightning data-based transmission line failure probability evaluation method
CN103296685B (en) SVC (static var compensator) compensation strategy optimizing method
CN102779230B (en) State analysis and maintenance decision judging method of power transformer system
CN105512474B (en) A kind of method for detecting abnormality of Transformer&#39;s Condition Monitoring data
CN107870306A (en) A kind of lithium battery charge state prediction algorithm based under deep neural network
CN104915747A (en) Electricity generation performance evaluation method of generator set and equipment thereof
CN101162247B (en) Sub-health running status recognition method of electrical device

Legal Events

Date Code Title Description
PB01 Publication
C06 Publication
SE01 Entry into force of request for substantive examination
C10 Entry into substantive examination
GR01 Patent grant
C14 Grant of patent or utility model