CN104657793A - Hidden failure prediction and treatment method for overhead transmission line - Google Patents
Hidden failure prediction and treatment method for overhead transmission line Download PDFInfo
- Publication number
- CN104657793A CN104657793A CN201510102080.6A CN201510102080A CN104657793A CN 104657793 A CN104657793 A CN 104657793A CN 201510102080 A CN201510102080 A CN 201510102080A CN 104657793 A CN104657793 A CN 104657793A
- Authority
- CN
- China
- Prior art keywords
- transmission line
- unit
- probability
- branch road
- electricity
- 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.)
- Granted
Links
- 230000005540 biological transmission Effects 0.000 title claims abstract description 137
- 238000000034 method Methods 0.000 title claims abstract description 47
- 230000005611 electricity Effects 0.000 claims description 78
- 239000011159 matrix material Substances 0.000 claims description 40
- WABPQHHGFIMREM-UHFFFAOYSA-N lead(0) Chemical compound [Pb] WABPQHHGFIMREM-UHFFFAOYSA-N 0.000 claims description 35
- 230000002950 deficient Effects 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 13
- 239000012212 insulator Substances 0.000 claims description 12
- 230000001681 protective effect Effects 0.000 claims description 11
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 claims description 9
- 239000010931 gold Substances 0.000 claims description 9
- 229910052737 gold Inorganic materials 0.000 claims description 9
- 238000010606 normalization Methods 0.000 claims description 6
- 239000000203 mixture Substances 0.000 claims description 5
- 230000004069 differentiation Effects 0.000 claims description 3
- 210000003205 muscle Anatomy 0.000 claims description 2
- 238000013178 mathematical model Methods 0.000 abstract description 6
- 238000005516 engineering process Methods 0.000 abstract description 5
- 230000002457 bidirectional effect Effects 0.000 abstract 1
- 230000007797 corrosion Effects 0.000 description 30
- 238000005260 corrosion Methods 0.000 description 30
- 239000004020 conductor Substances 0.000 description 21
- HCHKCACWOHOZIP-UHFFFAOYSA-N Zinc Chemical compound [Zn] HCHKCACWOHOZIP-UHFFFAOYSA-N 0.000 description 19
- 229910052725 zinc Inorganic materials 0.000 description 19
- 239000011701 zinc Substances 0.000 description 19
- 208000037656 Respiratory Sounds Diseases 0.000 description 17
- 230000006378 damage Effects 0.000 description 15
- 239000000463 material Substances 0.000 description 13
- 230000032683 aging Effects 0.000 description 12
- 229910000831 Steel Inorganic materials 0.000 description 11
- 239000010959 steel Substances 0.000 description 11
- 230000015556 catabolic process Effects 0.000 description 9
- 238000006731 degradation reaction Methods 0.000 description 9
- 229910052751 metal Inorganic materials 0.000 description 9
- 239000002184 metal Substances 0.000 description 9
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 8
- 239000002131 composite material Substances 0.000 description 8
- 239000012535 impurity Substances 0.000 description 8
- 239000002351 wastewater Substances 0.000 description 8
- 229910000838 Al alloy Inorganic materials 0.000 description 6
- 239000004411 aluminium Substances 0.000 description 6
- 229910052782 aluminium Inorganic materials 0.000 description 6
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 6
- 230000007547 defect Effects 0.000 description 6
- 238000011156 evaluation Methods 0.000 description 6
- 125000006850 spacer group Chemical group 0.000 description 6
- 238000012360 testing method Methods 0.000 description 6
- 238000004873 anchoring Methods 0.000 description 5
- 230000006735 deficit Effects 0.000 description 5
- 239000011150 reinforced concrete Substances 0.000 description 5
- 230000000007 visual effect Effects 0.000 description 5
- 241000196324 Embryophyta Species 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- 238000006073 displacement reaction Methods 0.000 description 4
- 229910052742 iron Inorganic materials 0.000 description 4
- 150000003839 salts Chemical class 0.000 description 4
- 238000004062 sedimentation Methods 0.000 description 4
- 239000002689 soil Substances 0.000 description 4
- 241000272165 Charadriidae Species 0.000 description 3
- 229910001294 Reinforcing steel Inorganic materials 0.000 description 3
- 229910045601 alloy Inorganic materials 0.000 description 3
- 239000000956 alloy Substances 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000007747 plating Methods 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 208000027418 Wounds and injury Diseases 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000003628 erosive effect Effects 0.000 description 2
- 208000014674 injury Diseases 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 239000002243 precursor Substances 0.000 description 2
- 206010021703 Indifference Diseases 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000011109 contamination Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000002845 discoloration Methods 0.000 description 1
- 238000012854 evaluation process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000013386 optimize process Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 238000005406 washing Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Testing Of Short-Circuits, Discontinuities, Leakage, Or Incorrect Line Connections (AREA)
Abstract
The invention relates to a power system, in particular to a hidden failure prediction and treatment method for an overhead transmission line. By means of the method based on a small-world network layer and Bayesian network mixed recommendation network, the overhead transmission line is taken as the object to establish a small-world network layer and Bayesian network mixed recommendation network based mathematical model for evaluating the operation state of the overhead transmission line, hidden failures of the overhead transmission line are predicted effectively and accurately by means of high clustering performance of small-world networks and a bidirectional reasoning technology of the Bayesian network, a failure treatment method can be found out timely when a hidden failure exists, and health warning of the overhead transmission line is guaranteed reliably.
Description
Technical field
The present invention relates to a kind of electric system, be specifically related to the prediction of a kind of overhead transmission line hidden failures and disposal route.
Background technology
Electric system as produce, conveying and supply electric power bulky systems, run with social economy and daily life closely related, power outage has a far reaching influence.The consistent periodic maintenance system performed of conventional failure maintenance also exists a lot of irrationality, not only causes the unnecessary power failure of equipment or repeats to have a power failure, also result in the waste of many manpowers, financial resources, and have a strong impact on the raising of electric power facility reliability level.Repeatedly repeat to have a power failure: such as stop once to do power transformation work (circuit is got involved), stop again once to do line work (converting equipment is got involved), with once have a power failure power transformation, line facility work is all finished, Calculation of Reliability index is the same, but the former is obviously unreasonable, in fact this circuit or substation are every being subject to for a long time can not put into operation at once, in one-period, the not available time is double, therefore the security risk of electrical network also strengthens, and original combinable work only once need stop the operation of multiple labour, present needs stop the operation of multiple labour for twice, increase power transformation operation, the workload of schedule workers, increase security risk.Therefore, electric system is not broken down, and predicts and process hidden failures, and avoiding having a power failure, it is significant to repair electrical network.
At present, the prediction of electric system hidden failures and the method for process have method, artificial neural network, rough set theory etc. based on optimisation technique, and these methods go to explore hidden failures problem from different approach.Owing to presenting multiple running status in overhead transmission line operation, have larger randomness and uncertainty, most Legacy Status appraisal procedure is difficult to describe the event with polymorphism, and state estimation is only divided into normal and fault two kinds analysis; In assess effectiveness, Evaluation accuracy and assessment scale, various Traditional measurements algorithm differs greatly.For the evaluation process of transmission line of electricity running status, classic method really can only provide to property the weak link of each load point, but, generally all can not provide each element or the status of certain original paper shared by whole system reliability, and computation model is complicated, amount of calculation increases with system scale exponent function relation, the original paper causing hidden failures can not be found fast, process hidden failures in time.
Summary of the invention
For above deficiency, the invention provides the prediction of a kind of overhead transmission line hidden failures and disposal route, by the mixing recommendation network based on small-word networks network layers and Bayesian network, the present invention take overhead transmission line as object, the small-world network building transmission line of electricity operation conditions mixes recommendation network mathematical model with Bayesian network, utilize the high cluster of small-world network and the bidirection reasoning technology of Bayesian network, carry out effectively to overhead transmission line hidden failures, accurate prediction, promptly and accurately can find out fault handling method when there is hidden failures, the healthy early warning of practical guarantee transmission line of electricity.
The present invention relates to the prediction of a kind of overhead transmission line hidden failures and disposal route, overhead transmission line comprises basis and protective equipment, tower bar, lead wire and earth wire, insulator chain, gold utensil, lightning protection facility and earthing device, line protection district, people having a common goal's environment 8 unit.
Described small-word networks network layers has several small-world network models to form, small-world network model is that a class has shorter average path length, and there is the general name of the network of higher cluster coefficients, the small-world network that the present invention sets up with the unit of transmission line of electricity for node, relation of interdependence is had to be connected between unit, a Ge great community is set up according to interconnected relationship between unit, utilize module optimization method that large community is divided into clique, this clique just defines small-world network, the annexation between the unit of this small-world network reflection transmission line of electricity.
Bayesian network is also known as belief network or directed acyclic graph model, it is a kind of probability graph pattern type, it is a kind of conditional probability table set, in directed acyclic graph, each node represents a stochastic variable, can be direct observational variable or hidden variable, and the condition between oriented expression stochastic variable relies on, node unique in the corresponding directed acyclic graph of each element in conditional probability table, stores the combination condition probability of this node to its direct precursor node.Bayesian network has a very important character, and be exactly that we assert that each node is after the value of its direct precursor node is formulated, this node condition is independent of all non-immediate forerunner ANCESTORS.
The state variable that 8 unit that overhead transmission line described above is corresponding are corresponding different respectively, according to the weight of the deteriorated order of severity of the corresponding each quantity of state of 8 unit, from being gently divided into I, II, III, IV level Four successively to heavy, the weight grading standard of the deteriorated order of severity of the various quantity of states that 8 unit that overhead transmission line comprises are corresponding:
1) base unit comprises: visual condition, geological state and protection facilities three quantity of states.
I level
(1) visual condition: basis is without weathering, burn into crackle, breakage, burn, metallic member non-corroding, metal is without fracture, distortion, base matrix non-displacement, torsion, on pull out, sedimentation, basic vertical rod leaks outside, concurrent chassis, pulling plate are buried and chuck position meets designing requirement;
(2) geological condition: geological state is stablized, without caving in, coming down, wash away phenomenon;
(3) protective equipment situation: the facilities such as backfill soil is intact, bank protection, retaining wall, trench drain in order, stable.
II level
(1) visual condition: the slight weathering in basis, burn into crackle, breakage, burn, the slight corrosion of metallic member, metal foundation is without fracture, distortion, base matrix non-displacement, reverse, have slight on pull out or sedimentation, basic vertical rod leaks outside close to or reaches the buried and chuck position of designing requirement, concurrent chassis, pulling plate and substantially meets designing requirement;
(2) geological condition: geological state is stablized, without caving in, coming down, have and wash away phenomenon
(3) protective equipment situation: the facility situations such as backfill soil is intact, bank protection, retaining wall, trench drain are basicly stable, have the trend of progressively breaking.
III grade
(1) visual condition: basic weathering, burn into crackle, breakage, burn, metallic member corrosion, metal foundation is without fracture, there is local deformation, base matrix slight displacement, torsion, pull out on having or sedimentation, basic vertical rod leaks outside and exceeds the buried and chuck position of designing requirement value, concurrent chassis, pulling plate and design generation severe deviations;
(2) geological condition: geological state extremely unstable, has landslide, comes down, washes away phenomenon;
(3) protective equipment situation: the facilities such as backfill soil is imperfect, bank protection, retaining wall, trench drain are badly damaged.
IV level
(1) visual condition: the serious weathering in basis, burn into crackle, breakage, burn, metal foundation fracture, distortion, base matrix displacement, torsion, serious on pull out or sedimentation, basic vertical rod leaks outside and exceedes the buried and chuck position of designing requirement value, concurrent chassis, pulling plate and design generation gross error;
(2) geological condition: geological state is unstable, has landslide, comes down, washes away phenomenon;
(3) protective equipment situation: the facilities such as backfill soil is imperfect, bank protection, retaining wall, trench drain are badly damaged.
2) shaft tower unit evaluation comprises: tower bar tilts, tower bar cross-arm is crooked, tower bar ironware surface condition, main material adjacent node situation, reinforced concrete situation, anchoring six quantity of states;
I level
(1) tower bar inclination (comprising degree of disturbing) is less than 3 ‰;
(2) tower bar cross-arm is crooked: 110kV and be less than 5 ‰ with line, 220kV circuit is less than 3.5 ‰, 500kV is less than 2 ‰;
(3) tower bar ironware surface zinc layers is intact, nothing comes off, rustless stain, iron tower structure are intact, and main material is without distortion, phenomenon of rupture
(4) main material adjacent node flexibility is no more than 1/750, and tower material each several part is connected firmly, complete;
(5) reinforced concrete pole unprotect layer corrosion come off, reinforcing steel bars exposed;
(6) anchoring perfect, non-loosening, presented shares, disconnected stock, corrosion phenomena, guyed foundation without sinking, landslide, lack native phenomenon.
II level
(1) tower bar inclination (comprising degree of disturbing) is greater than 3 ‰, is less than 8 ‰;
(2) tower bar cross-arm is crooked: 110kV and be greater than 5 ‰ with line, is less than 8 ‰, 220kV circuit is greater than 3.5 ‰, is less than 8 ‰, 500kV is greater than 2 ‰, be less than 8 ‰;
(3) tower bar ironware surface Zinc Scaling, rustless stain, iron tower structure are intact, and main material is without distortion, phenomenon of rupture;
(4) main material adjacent node flexibility close to or arrive 1/750, each parts of tower material are complete, have a small amount of critical piece loosen;
(5) reinforced concrete pole is less than 1%, and protective seam has slight erosion, but nothing comes off, reinforcing steel bars exposed;
(6) anchoring perfect, non-loosening, presented shares, disconnected stock, have slight corrosion phenomena, guyed foundation, without landslide, has slight sinking, lacks native phenomenon.
III grade
(1) tower bar inclination (comprising degree of disturbing) is greater than 1%, is less than 1.5%;
(2) tower bar cross-arm is crooked is greater than 8 ‰, is less than 1%;
(3) tower bar ironware surface Zinc Scaling, and there is rust stain, iron tower structure is intact, and main material is without distortion, phenomenon of rupture;
(4) main material adjacent node flexibility is more than 1/750, but is less than 0.2%, and tower material parts have loses or have more non-principal component to loosen on a small quantity, but the Stability Analysis of Structures not affecting whole base steel tower in a short time has a small amount of critical piece to loosen;
(5) reinforced concrete pole is greater than 1%, is less than 1.5%, and protective seam has corrosion, slightly comes off, but no-reinforcing-bar exposes;
(6) anchoring imperfection, have loosening, present shares, the phenomenon such as disconnected stock, after bracing wire and distaff corrosion, diameter reduces and is less than 2mm, guyed foundation without landslide, have slight sink or lack native phenomenon.
IV grade
(1) tower bar inclination (comprising degree of disturbing) is greater than 1.5%;
(2) tower bar cross-arm is crooked is greater than 1%;
(3) tower bar ironware surface Zinc Scaling occur pockmark, iron tower main material has distortion, phenomenon of rupture;
(4) main material adjacent node flexibility is more than 0.2%, and tower material parts have loses or have more non-principal component to loosen in a large number, affects the Stability Analysis of Structures of whole base steel tower;
(5) reinforced concrete pole is greater than 1.5%, and protective seam has heavy corrosion, and slightly come off the phenomenons such as reinforcing steel bars exposed;
(6) anchoring imperfection, have loosening, present shares, the phenomenon such as disconnected stock, after bracing wire and distaff corrosion, diameter reduces and is greater than 2mm, and guyed foundation has landslide or extreme subsidence sink or lack native phenomenon.
3) lead wire and earth wire unit comprises: sag deviation, lead wire and earth wire distance to the ground and crossed crossing distance situation, lead wire and earth wire connector situation and lead wire and earth wire corrosion and tired situation seven quantity of states between lead wire and earth wire degree of impairment, sag of conductor and ground wire deviation, lead wire and earth wire alternate sag deviation, homophase lead wire and earth wire.
I level
(1) lead wire and earth wire degree of impairment: not damaged;
(2) sag of conductor and ground wire deviation: 35 ~ 110kV circuit is less than+5% ,-2.5%, 220kV and Above Transmission Lines is less than ± 2.5%, large cross line is less than ± and 1%;
(3) the alternate sag deviation of lead wire and earth wire: 35 ~ 110kV circuit is less than 200mm, and 220kV and Above Transmission Lines are less than 300mm, and large cross line is less than 500mm;
(4) sag deviation between homophase lead wire and earth wire: be less than 100mm without conductor spacer double bundle conductor, have other split form wires of conductor spacer 220kV to be less than 80mm, 500kV to be less than 50mm, without negative error;
(5) lead wire and earth wire distance to the ground and crossed crossing distance situation: meet DL/T741-2001 and specify;
(6) lead wire and earth wire connector situation: impulse-free robustness, bulge, crackle, burn, loosen, stock is broken in slippage, flexural deformation or exit, temperature and conductor temperature indifference, and without mistake thermo-color;
(7) lead wire and earth wire corrosion and tired situation: surface is corrosion-free, and galvanized strand wires is without Zinc Scaling or corrosion phenomena, and strength test value reaches 100%.
II level
(1) lead wire and earth wire degree of impairment: steel-cored aluminium strand, steel core aluminum alloy stranded wire stock damage cross section of breaking is no more than aluminium stock or the alloy stock total area 7%, steel strand wires, aluminium alloy stranded conductor stock damage interface of breaking is no more than the total area 7%, disconnected 1 strand of zinc steel plating twisted wire 19 strands;
(2) sag of conductor and ground wire deviation: 35 ~ 110kV circuit close to or reach+5% ,-2.5%, 220kV and Above Transmission Lines close to or reach ± 2.5%, large cross line close to or reach ± 1%;
(3) the alternate sag deviation of lead wire and earth wire: 35 ~ 110kV circuit close to or reach 200mm, 220kV and Above Transmission Lines close to or reach 300mm, large cross line close to or reach 500mm;
(4) sag deviation between homophase lead wire and earth wire: without conductor spacer double bundle conductor close to or reach 100mm, have other split form wires of conductor spacer 220kV close to or reach 80mm, 500kV close to or reach 50mm, without negative error;
(5) lead wire and earth wire distance to the ground and crossed crossing distance situation: specify substantially close with DL/T741-2001;
(6) lead wire and earth wire connector situation: have slight burr, without bulge, crackle, burn, loosen, stock is broken in slippage, flexural deformation or exit, temperature and conductor temperature and even Light Difference, and overheated light discolouration;
(7) lead wire and earth wire corrosion and tired situation: there is slight erosion on surface, and galvanized strand wires has slight Zinc Scaling or corrosion phenomena, and strength test value is greater than 100%.
III grade
(1) lead wire and earth wire degree of impairment: steel-cored aluminium strand, steel core aluminum alloy stranded wire break stock damage cross section account for aluminium stock or the alloy stock total area 7% ~ 25%, steel strand wires, aluminium alloy stranded conductor stock damage interface of breaking accounts for the total area 7% ~ 17%, disconnected 2 strands or 7 strands disconnected 1 strand of zinc steel plating twisted wire 19 strands;
(2) sag of conductor and ground wire deviation: 35 ~ 110kV circuit is greater than+5% ,-2.5%, 220kV and Above Transmission Lines is greater than ± 2.5%, large cross line is greater than ± and 1%, and temporarily do not affect equipment safety operation;
(3) the alternate sag deviation of lead wire and earth wire: 35 ~ 110kV circuit is greater than 200mm, and 220kV and Above Transmission Lines are greater than 300mm, and large cross line is greater than 500mm, cuts and wouldn't affect equipment safety operation;
(4) sag deviation between homophase lead wire and earth wire: be greater than 100mm without conductor spacer double bundle conductor, have other split form wires of conductor spacer 220kV to be greater than 80mm, 500kV to be greater than 50mm, without negative error, and wouldn't equipment safety operation to be affected;
(5) lead wire and earth wire distance to the ground and crossed crossing distance situation: exceed DL/T741-2001 and specify, and wouldn't equipment safety operation be affected;
(6) lead wire and earth wire connector situation: without bulge, crackle, loosen, stock is broken in slippage, exit, jagged and slight burns, flexural deformation close to or reach regulations stipulate, temperature is no more than 10 DEG C higher than conductor temperature, has thermo-color;
(7) lead wire and earth wire corrosion and tired situation: surface corrosion, galvanized strand wires Zinc Scaling or corrosion phenomena, strength test value reaches 80 ~ 85%.
IV level
(1) lead wire and earth wire degree of impairment: steel-cored aluminium strand, steel core aluminum alloy stranded wire stock damage cross section of breaking accounts for aluminium stock or the alloy stock total area more than 25%, steel strand wires, aluminium alloy stranded conductor stock damage interface of breaking accounts for the total area more than 17%, disconnected 3 strands or 7 strands disconnected 2 strands of zinc steel plating twisted wire 19 strands;
(2) sag of conductor and ground wire deviation: 35 ~ 110kV circuit exceedes+5% ,-2.5%, 220kV and Above Transmission Lines exceedes ± 2.5%, large cross line exceedes ± 1%, and causes the not enough or stressed significant change of shaft tower of its distance to the ground;
(3) the alternate sag deviation of lead wire and earth wire: 35 ~ 110kV circuit is more than 200mm,-2.5%, 220kV and Above Transmission Lines are more than 300mm, and large cross line more than 500mm, and causes it alternate or distance to the ground can not meet the demands or the stressed significant change of shaft tower in windage yaw situation;
(4) sag deviation between homophase lead wire and earth wire: occur negative error;
(5) lead wire and earth wire distance to the ground and crossed crossing distance situation: specify more than DL/T741-2001, cut the equipment of impact and personal safety;
(6) lead wire and earth wire connector situation: burr, bulge, crackle, burn, loosen, slippage, flexural deformation or the exit stock that breaks exceedes regulations stipulate, temperature is higher than conductor temperature 10 DEG C, and overheated serious discoloration;
(7) lead wire and earth wire corrosion and tired situation: surperficial heavy corrosion, the serious Zinc Scaling of galvanized strand wires or corrosion phenomena, strength test value is less than 80%.
4) insulating subunit evaluation comprises insulator contamination situation and insulator body situation two quantity of states.
I level
(1) insulate filthy situation: surface cleaning, and without obviously filthy, equivalent salt density meets the requirement of each gradation for surface pollution, and retting-flax wastewater meets the requirement of the retting-flax wastewater numerical value under each gradation for surface pollution;
(2) insulator body situation: outward appearance is good, without breakage, crackle, be full of cracks, aging.
II level
(1) insulate filthy situation: there is slight filth on surface, equivalent salt density meets the requirement of each gradation for surface pollution, have than this last time of test and increase and have the trend progressively worsened, retting-flax wastewater meets the requirement of the retting-flax wastewater numerical value under each gradation for surface pollution, but its validity has and weakens and have the trend progressively worsened;
(2) insulator body situation: outward appearance is good, has little damage, flawless, be full of cracks, aging.
III level
(1) insulate filthy situation: there is filth on surface, and continue to increase the weight of, equivalent salt density close to or reach the requirement of each gradation for surface pollution, have than this last time of test and increase and have the trend progressively worsened, retting-flax wastewater close to or reach the requirement of the retting-flax wastewater numerical value under each gradation for surface pollution, but its validity has and weakens and have the trend progressively worsened;
(2) insulator body situation: have micro-breakage, crackle, be full of cracks, aging.
IV level
(1) insulate filthy situation: surperficial pollution severity, and equivalent salt density does not meet the requirement of each gradation for surface pollution, and retting-flax wastewater does not meet the requirement of the retting-flax wastewater numerical value under each gradation for surface pollution;
(2) insulator body situation: breakage, crackle, be full of cracks, seriously aging.
5) gold utensil unit evaluation comprises: metallic pin situation and metal connection two quantity of states.
I level
(1) metallic pin situation: various metallic pin is complete, intact, gold utensil without distortion, corrosion, burn, loosen;
(2) metal connection: junction is flexible, and intensity reaches 100%.
II level
(1) metallic pin situation: various metallic pin is complete, intact, gold utensil without distortion, corrosion, have slight burns, loosen;
(2) metal connection: junction is flexible, and intensity is greater than 85%.
III level
(1) metallic pin situation: various metallic pin has defect, gold utensil is slight without being out of shape, having corrosion, burn, loosen;
(2) metal connection: junction is dumb, intensity is close or reach 80 ~ 85%.
IV level
(1) metallic pin situation: various metallic pin serious defect, gold utensil has distortion, corrosion, burn, loosens;
(2) metal connection: junction is dumb, and intensity is less than 80%.
6) earthing device unit evaluation comprises each parts connection and equipment situation two quantity of states.
I level
(1) each parts connection: connect good, without damaged, composite sheath surface is without aging, starved, impurity, protruding phenomenon;
(2) equipment situation: device is without damage, zinc coat non-corroding, crackle or burn.
II level
(1) each parts connection: connect good, without damaged, composite sheath surface has slightly aging, without starved, impurity, protruding phenomenon;
(2) equipment situation: device, without damage, zinc coat non-corroding, crackle or burn, has slight corrosion.
III level
(1) each parts connection: be connected with loosening, have breakage, there are aging, starved, impurity, protruding phenomenon in composite sheath surface, but defect face is no more than 5mm, and the degree of depth is not more than 1mm, and height of projection is no more than 0.8mm;
(2) equipment situation: device has damage, zinc coat corrosion, flawless or burn.
IV level
(1) each parts connection: connect and seriously loosen, damaged, there are aging, starved, impurity, protruding phenomenon in composite sheath surface, and defect face is more than 5mm, and the degree of depth is greater than 1mm, and height of projection is more than 0.8mm;
(2) equipment situation: the serious corrosion of device grievous injury, zinc coat, have crackle or burn.
7) lightning protection facility evaluation comprises each parts connection and equipment situation two quantity of states.
I level
(1) each parts connection: connect good, without damaged, composite sheath surface is without aging, starved, impurity, protruding phenomenon;
(2) equipment situation: device is without damage, zinc coat non-corroding, crackle or burn.
II level
(1) each parts connection: connect good, without damaged, composite sheath surface has slightly aging, without starved, impurity, protruding phenomenon;
(2) equipment situation: device, without damage, zinc coat non-corroding, crackle or burn, has slight corrosion.
III level
(1) each parts connection: be connected with loosening, have breakage, there are aging, starved, impurity, protruding phenomenon in composite sheath surface, but defect face is no more than 5mm, and the degree of depth is not more than 1mm, and height of projection is no more than 0.8mm;
(2) equipment situation: device has damage, zinc coat corrosion, flawless or burn.
IV level
(1) each parts connection: connect and seriously loosen, damaged, there are aging, starved, impurity, protruding phenomenon in composite sheath surface, and defect face is more than 5mm, and the degree of depth is greater than 1mm, and height of projection is more than 0.8mm;
(2) equipment situation: the serious corrosion of device grievous injury, zinc coat, have crackle or burn.
8) channel environment unit comprises: condition of road surface and line walking sidewalk situation two quantity of states.
I level
(1) condition of road surface: condition of road surface is good, without washing away, the sinking of road base, mud pit, sideslip phenomenon;
(2) line walking sidewalk situation: line walking the coast is clear, without weeds and barbed shrub.
II level
(1) condition of road surface: condition of road surface is substantially good, sinks without landslide, road base, sideslip phenomenon, have slightly wash away, mud pit, muddy;
(2) line walking sidewalk situation: line walking road is substantially unimpeded, has a small amount of weeds and barbed shrub.
III level
(1) condition of road surface: condition of road surface is severe, though to sink without landslide, road base, sideslip phenomenon, washes away, mud pit, muddy situation is serious;
(2) line walking sidewalk situation: line walking road is not smooth, has weeds and barbed shrub.
IV level
(1) condition of road surface: condition of road surface is severe, have wash away, the sinking of road base, mud pit, sideslip phenomenon, and seriously;
(2) line walking sidewalk situation: line walking road is not smooth, has a large amount of weeds and barbed shrub.The criteria for classifying of the degradation of the quantity of state that above-mentioned 8 component units comprised for overhead transmission line are corresponding, the standard of deducting point that various criterion is corresponding different.
In order to realize above object, the technical scheme that present invention employs is:
A kind of overhead transmission line hidden failures prediction and disposal route, described hidden failures comprises singlephase earth fault, two-phase short-circuit fault, double earthfault, insulator sudden strain of a muscle road, described overhead transmission line comprises multiple overhead transmission line branch road, described method is for judging whether described transmission line of electricity branch road exists hidden failures, thus to causing the unit of hidden failures to process, it is characterized in that, described prediction and disposal route comprise prediction and process two steps, and wherein prediction comprises the following steps:
1) small-word networks network layers and Bayesian network combine recommendation network model is set up;
2) according to 1) running status of unit in the combine recommendation network model determination transmission line of electricity branch road set up;
3) according to 2) the running status probability of the running status determination transmission line of electricity branch road of unit determined;
4) according to 3) whether the running status probabilistic determination transmission line of electricity branch road of transmission line of electricity branch road that draws exist hidden failures;
Described process comprises and lower step:
5) to the transmission line of electricity branch road that there is hidden failures, utilize Bayes's backward reasoning rationale above-mentioned 3) obtain the running status probability scenarios of transmission line of electricity branch road under, show that unit is in the probability of different conditions;
6) according to 5) the various unit that obtain are in the probability results of different conditions, and the probable value drawn is normalized, judges according to normalized data the formant causing hidden failures;
7) according to 6) unit of hidden failures that causes determined overhauls, changes.
Preferentially, each described overhead transmission line branch road comprises basis and protective equipment, tower bar, lead wire and earth wire, insulator chain, gold utensil, lightning protection facility and earthing device, line protection district, channel environment 8 kinds of unit, the state variable that 8 kinds of unit are corresponding different; The running status of 8 kinds of unit is divided into well, general, note, bad four kinds of states, transmission line of electricity branch road running status by good, general, note, bad four kinds of state probabilities determine.
Preferentially, above-mentioned 1) the small-word networks network layers described in is made up of several small-world networks, and small-word networks network layers is the unit connection relation of whole transmission line of electricity, and single small-world network represents the annexation of a certain transmission line of electricity tributary unit;
The establishment step of described small-world network:
1.1): the unit of composition overhead transmission line is numbered, altogether n unit, a
ij=1 represents that i unit and j unit have dependence, without dependence then a
ijbe 0, by the unit connection relation matrix A in whole transmission line of electricity
n × nrepresent, set up a large overhead transmission line interconnection network;
1.2): by unit connection relation A in whole transmission line of electricity
n × nrearrange, Rankine-Hugoniot relations is with matrix D
-1m is model;
1.3) matrix D of the unit connection relation: calculate 1.2) obtained
-1front K the maximal eigenvector v of M
1, v
2..., v
k
1.4): with file v
1, v
2..., v
krepresent, set up new unit connection relation matrix T ∈ R
n × K;
1.5): utilize K average clustering method that the ranks of above-mentioned matrix T are carried out cluster differentiation, matrix T is become K matrix representation C
1, C
2..., C
k; Described matrix is the matrix form of dependence between unit in our small-world network obtained that needs, thus sets up small-word networks network layers;
Wherein A
n × nrepresent the matrix representation method of the annexation of whole transmission line of electricity, transmission line of electricity branch road is K, and K environmentally, road conditions factor artificially determines, D
-1d in M
-1a
n × ntransposed matrix, M is and A
n × nrelated;
D
ibe the degree of i-th unit, namely i is the fillet number of the unit,
p
ij=d
id
j/ 2m, m are the fillet number summation of whole overhead transmission line, and S is the matrix of n × K, wherein S
iK=1 is that unit i belongs to V
k, otherwise be 0; V
kfor K subnet, M
ij=A
ij-P
ij.
Preferentially, the running status of the unit described 2) in transmission line of electricity branch road is that the historical data of bonding state variable and unit and the data of Real-time Collection state variable are determined.
Preferentially, the probability of the different running statuses of described overhead transmission line branch road is:
P (A
ij) be in i-th kind of state probability, P (B for jth kind unit
i| A
ij) represent that jth kind unit is in i-th kind of state to the weight coefficient of this transmission line of electricity branch road.
Preferentially, the probability of the bad running status of transmission line of electricity branch road is not 0, or the probability of attention state is greater than 0.05, or when the probability of kilter is less than 0.75, this transmission line of electricity branch road exists hidden failures;
Preferentially, when the running status probability that transmission line of electricity branch road is different is determined, it is characterized in that, the probability of the different running status of described various unit is:
Preferentially, the attention state of various unit and the probability of defective mode are normalized:
Wherein P (Q
j) and P (D
j) be respectively the attention state of unit and the normalization probability of defective mode.
Preferentially, the normalization probability of unit attention state obtained above and defective mode is sorted respectively, to the probability of attention state be greater than 0.2 and the probability of the defective mode unit that is greater than 0.1 overhaul.
The hidden failures of transmission line of electricity branch road existence effectively can be processed by said method, overhead transmission line is divided into some vertical line circuit branch roads, the two-way of Bayesian network is utilized to shift technology onto, cause-effect relationship, can accurately, effectively determine the problem place of causing hidden failures, when the limited operation of guarantee transmission line of electricity branch road, thus ensure effective operation of overhead transmission line, avoid causing unnecessary loss.
Figure of description
Fig. 1, the basic structure schematic diagram of Bayesian network that the present invention relates to;
Fig. 2, the overhead transmission line hidden failures prediction that the present invention relates to and the process flow diagram of disposal route.
Specific implementation method
The present embodiment is based on a kind of overhead transmission line hidden failures prediction and disposal route, described hidden failures comprises singlephase earth fault, expose short trouble etc., several transmission line of electricity branch roads of whole overhead transmission line route form, described transmission line of electricity branch road comprises basis and protective equipment, tower bar, lead wire and earth wire, insulator chain, gold utensil, lightning protection facility and earthing device, line protection district, channel environment 8 kinds of unit, the quantity of state that 8 kinds of unit are corresponding different respectively, the division of the degradation of quantity of state as described above, the assessment result of corresponding unit is judged according to the situation of the degradation of quantity of state, the probability of the running status of each transmission line of electricity circuit is determined respectively according to the assessment result of these 8 kinds of unit, according to the probability scenarios of the running status of transmission line of electricity branch road, judge whether to there is hidden failures, find out the formant causing hidden failures, the prediction of described a kind of overhead transmission line hidden failures and disposal route take mathematical model as foundation, it is the mathematical model of the combine recommendation network based on small-word networks network layers and Bayesian network.
First small-word networks network layers and Bayesian network combine recommendation network model is set up.Described small-world network is transmission line of electricity branch road mathematical model, built on stilts several transmission line of electricity branch roads of defeated dotted line route composition, is divided into the process that several defeated some circuit branch road is convenient to detect the detection of transmission line of electricity running status, assessment and transmission line malfunction by whole built on stilts defeated some circuit.Artificially divide overhead transmission line, will cause error, the present invention utilizes optimized treatment method to be several transmission line of electricity branch roads to whole built on stilts defeated dotted line k-path partition, the namely foundation of so-called small-world network model, and step is as follows:
1.1): the unit of composition overhead transmission line is numbered, altogether n unit, a
ij=1 represents that i unit and j unit have dependence, without dependence then a
ijbe 0, by the unit connection relation matrix A in whole transmission line of electricity
n × nrepresent, set up a large overhead transmission line interconnection network;
1.2): by unit connection relation A in whole transmission line of electricity
n × nrearrange, Rankine-Hugoniot relations is with matrix D
-1m is model;
1.3) matrix D of the unit connection relation: calculate 1.2) obtained
-1front K the maximal eigenvector v of M
1, v
2..., v
k
1.4): with file v
1, v
2..., v
krepresent, set up new unit connection relation matrix T ∈ R
n × K;
1.5): utilize K average clustering method that the ranks of above-mentioned matrix T are carried out cluster differentiation, matrix T is become K matrix representation C
1, C
2..., C
k; Described matrix is the matrix form of dependence between unit in our small-world network obtained that needs, thus sets up small-word networks network layers;
Wherein A
n × nrepresent the matrix representation method of the annexation of whole transmission line of electricity, transmission line of electricity branch road is K, and K environmentally, road conditions factor artificially determines, D
-1d in M
-1a
n × ntransposed matrix, M is and A
n × nrelated;
D
ibe the degree of i-th unit, namely i is the fillet number of the unit,
p
ij=d
id
j/ 2m, m are the fillet number summation of whole overhead transmission line, and S is the matrix of n × K, wherein S
iK=1 is that unit i belongs to V
k, otherwise be 0; V
kfor K subnet, M
ij=A
ij-P
ij.
Set up small-world network, advantageously in the operation conditions detecting transmission line of electricity, while breaking down, be convenient to fast detecting failure, guilty culprit can be found out fast, rapidly.
Bayesian network described above, the basic structure schematic diagram of Bayesian network is for shown in Fig. 1, figure interior joint represents stochastic variable, internodal arc reflects the condition dependence between stochastic variable, the all nodes pointing to certain node are referred to as the father node of this node, and in Fig. 1, the father node of C is A and B.The mathematical model of the Bayesian network involved by invention is exactly based on this, take unit as node, and the node with dependence is connected, and the Bayes principle utilized is that Bayesian formula described above calculates posterior probability formula:
The above-mentioned optimization method that utilized by whole overhead transmission line is divided into several transmission line of electricity branch roads, transmission line of electricity branch road changes into small-world network model, analyze each transmission line of electricity branch road analysis respectively and whether there is hidden failures, first analyze the running status of transmission line of electricity branch road operating unit, and the state estimation of 8 kinds of unit corresponding to overhead transmission line is relevant from corresponding different state variable.
The above-mentioned state variable degradation corresponding to 8 kinds of component units has carried out accurate division, according to the degradation of quantity of state from light to being heavily divided into level Four, is respectively I, II, III and IV grade.The basic deduction of points value of its correspondence is 2,4,8,10 points.
The performance of unit is divided into well, general, note, bad four all states, divide institute's foundation:
Z in formula
jfor the performance index of a jth unit, a
ibe the degradation of i-th quantity of state, C
ijfor a
irelative to Z
jdegree of membership, w
iit is the weight coefficient of i-th quantity of state.
Z
jbe less than or equal to 2 for kilter, be less than or equal to 4 and be greater than 2 for general state, be less than or equal to 8 and be greater than 4 for attention state, be less than or equal to 10 and be greater than 8 for bad shape body.
Z in formula
jfor the performance index of a jth unit, a
ibe the degradation of i-th quantity of state, C
ijfor a
irelative to Z
jdegree of membership, w
iit is the weight coefficient of i-th quantity of state.
Depending on the influence degree that quantity of state runs line security, from light to being heavily divided into some grades, corresponding numerical value is weighted value, is referred to as the weight of quantity of state.
Every 1 quantity of state reflection corresponding unit behavior pattern, be out referred to as degree of membership by abstract for this situation, the degree of membership sum of quantity of state corresponding in the unit of overhead transmission line composition is 1.
The determination of the weighted value of quantity of state and degree of membership, be patrol and examine in conjunction with history run, on-line checkingi, preventive experiment and machine account obtain overhead transmission line run related data be optimized process.
As follows according to the formula of the data computing mode amount weighted value detected in statistics a period of time:
W
ibe the weight of i-th quantity of state, a
ijexpression i-th and the disturbance degree of j quantity of state for corresponding unit, the value of i is relevant with the number of the quantity of state of corresponding unit, and j value is the corresponding 8 kinds of unit of 1 to 8 difference, and q is be the time period (as 1 year) added up for i-th time, v
qikfor i-th corresponding number of times at k degradation of quantity of state under the data of the q time detection, b
qimfor unit that i-th equipment under the data that detect at the q time is corresponding is in the number of times of m state-detection, the value of k is 1,2,3,4, corresponding with the degradation I, II, III and IV grade of state variable, the value of m be respectively 1,2,3,4 with unit good, general, note, bad four kinds of states are corresponding respectively.
As follows according to the data computing mode amount degree of membership formula detected in statistics a period of time:
C
iexpression is the degree of membership of the quantity of state that certain unit is corresponding, is normalized becomes C to degree of membership
imake to meet
Draw the quantity of state of unit in transmission line of electricity branch road, then the probability scenarios of the running status of this transmission line of electricity branch road is determined, the probability of the running status of transmission line of electricity branch road comprises well, general, note and defective mode probability, four kinds of probability sums are 1, and the probability of the different running statuses of described overhead transmission line branch road is:
P (A
ij) be in i-th kind of state probability, P (B for jth kind unit
i| A
ij) represent that jth kind unit is in i-th kind of state to the weight coefficient of this transmission line of electricity branch road.
P (B
i| A
ij) be that the data of the running status of unit in overhead transmission line in conjunction with historical record are relevant, in conjunction with historical data,
To p (B
i| A
ij) be normalized, become P (B
i| A
ij), meet
wherein b
qijrepresent that the lower i-th kind of unit of the data detected for the q time is in the number of times of m state-detection, the value of q is relevant to the group number of the data of extraction.
Draw the probability scenarios of the running status of transmission line of electricity branch road, analyze transmission line of electricity branch road and whether there is hidden failures problem, reference frame is the probability of the bad running status of transmission line of electricity branch road is not 0, or the probability of attention state is greater than 0.05, or the probability of kilter is when being less than 0.75, there is hidden failures in this transmission line of electricity branch road.
Process there is the transmission line of electricity branch road affecting fault, first the unit causing transmission line of electricity hidden failures is determined, when the running status probability that transmission line of electricity branch road is different is determined, utilize Bayes principle, draw the probability of the different running status of various unit, if only diagnose hidden failures to be irrational from the defective mode probability of unit, because the influence degree of unit to transmission line of electricity branch road running status is not identical, described Bayes principle is the backward reasoning technology of Bayesian network, when the probability of transmission line of electricity branch road running status is determined, draw the probability of the running status that various unit is different:
To causing the formant of hidden failures mainly to what the attention state of various unit and the probability numbers of defective mode were determined, first the attention state of various unit and the probability of defective mode are normalized:
Wherein P (Q
j) and P (D
j) be respectively the attention state of unit and the normalization probability of defective mode.
The unit attention state obtained and the normalization probability of defective mode, sort respectively to it respectively, to the probability of attention state be greater than 0.2 and the probability of the defective mode unit that is greater than 0.1 overhaul.
Effectively can process by said method the hidden failures that transmission line of electricity branch road exists, when ensureing the limited operation of transmission line of electricity branch road, thus ensure effective operation of overhead transmission line, avoid causing unnecessary loss.
Above-mentioned specific embodiment is only for this explanation the present invention, and wherein the implementation step of method can change to some extent, and every equivalents of carrying out on technical solution of the present invention basis and improvement, all should not get rid of outside the scope of protection of the invention.
Claims (9)
1. an overhead transmission line hidden failures prediction and disposal route, described hidden failures comprises singlephase earth fault, two-phase short-circuit fault, double earthfault, insulator sudden strain of a muscle road etc., described overhead transmission line comprises multiple overhead transmission line branch road, described method is for judging whether described transmission line of electricity branch road exists hidden failures, thus to causing the unit of hidden failures to process, it is characterized in that, described prediction and disposal route comprise prediction and process two steps, and wherein prediction comprises the following steps:
1) small-word networks network layers and Bayesian network combine recommendation network model is set up;
2) according to 1) running status of unit in the described recommendation joint network model determination transmission line of electricity branch road set up;
3) according to 2) the running status probability of the running status determination transmission line of electricity branch road of described transmission line of electricity unit determined;
4) according to 3) whether the running status probabilistic determination transmission line of electricity branch road of transmission line of electricity branch road that draws exist hidden failures;
Described process comprises one and lower step:
5) to the transmission line of electricity branch road that there is hidden failures, utilize Bayes's backward reasoning rationale above-mentioned 3) obtain the running status probability scenarios of transmission line of electricity branch road under, show that unit is in the probability of different conditions;
6) according to 5) the various unit that obtain are in the probability results of different conditions, and the probable value drawn is normalized, judges according to normalized data the formant causing hidden failures;
7) according to 6) unit of hidden failures that causes determined overhauls, changes.
2. method according to claim 1, each described overhead transmission line branch road comprises basis and protective equipment, tower bar, lead wire and earth wire, insulator chain, gold utensil, lightning protection facility and earthing device, line protection district, channel environment 8 kinds of unit, the state variable that 8 kinds of unit are corresponding different; The running status of 8 kinds of unit is divided into well, general, note, bad four kinds of states, transmission line of electricity branch road running status by good, general, note, bad four kinds of state probabilities determine.
3. method according to claim 1, is characterized in that:
Above-mentioned 1) the small-word networks network layers described in is made up of several small-world networks, and small-word networks network layers is the unit connection relation of whole transmission line of electricity, and single small-world network represents the annexation of a certain transmission line of electricity tributary unit;
The establishment step of described small-world network:
1.1): the unit of composition overhead transmission line is numbered, altogether n unit, a
ij=1 represents that i unit and j unit have dependence, without dependence then a
ijbe 0, by the unit connection relation matrix A in whole transmission line of electricity
n × nrepresent, set up a large overhead transmission line interconnection network;
1.2): by unit connection relation A in whole transmission line of electricity
n × nrearrange, Rankine-Hugoniot relations is with matrix D
-1m is model;
1.3) matrix D of the unit connection relation: calculate 1.2) obtained
-1front K the maximal eigenvector v of M
1, v
2..., v
k
1.4): with file v
1, v
2..., v
krepresent, set up new unit connection relation matrix T ∈ R
n × K;
1.5): utilize K average clustering method that the ranks of above-mentioned matrix T are carried out cluster differentiation, matrix T is become K matrix representation C
1, C
2..., C
k; Described matrix is the matrix form of dependence between unit in our small-world network obtained that needs, thus sets up small-word networks network layers;
Wherein A
n × nrepresent the matrix representation method of the annexation of whole transmission line of electricity, transmission line of electricity branch road is K, and K environmentally, road conditions factor artificially determines, D
-1d in M
-1a
n × ntransposed matrix, M is and A
n × nrelated;
D
ibe the degree of i-th unit, namely i is the fillet number of the unit,
p
ij=d
id
j/ 2m, m are the fillet number summation of whole overhead transmission line, and S is the matrix of n × K, wherein S
iK=1 is that unit i belongs to V
k, otherwise be 0; V
kfor K subnet, M
ij=A
ij-P
ij.
4. the running status of the unit method according to claim 1, is characterized in that, described 2) in transmission line of electricity branch road is that the historical data of bonding state variable and unit and the data of Real-time Collection state variable are determined.
5. method according to claim 1, is characterized in that, the probability of the different running statuses of described overhead transmission line branch road is:
P (A
ij) be in i-th kind of state probability, P (B for jth kind unit
i| A
ij) represent that jth kind unit is in i-th kind of state to the weight coefficient of this transmission line of electricity branch road.
6. method according to claim 1 or 5, it is characterized in that the probability of the bad running status of transmission line of electricity branch road is not 0, or the probability of attention state is greater than 0.05, or when the probability of kilter is less than 0.75, there is hidden failures in this transmission line of electricity branch road.
7. method according to claim 1, when the running status probability that transmission line of electricity branch road is different is determined, is characterized in that, the probability of the different running status of described various unit is:
8. method according to claim 7, is characterized in that, is normalized the attention state of various unit and the probability of defective mode:
Wherein P (Q
j) and P (D
j) be respectively the attention state of unit and the normalization probability of defective mode.
9. method according to claim 8, is characterized in that, sorts respectively to the normalization probability of unit attention state obtained above and defective mode, to the probability of attention state be greater than 0.2 and the probability of the defective mode unit that is greater than 0.1 overhaul.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510102080.6A CN104657793B (en) | 2015-03-09 | 2015-03-09 | Overhead transmission line hidden failures are predicted and processing method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510102080.6A CN104657793B (en) | 2015-03-09 | 2015-03-09 | Overhead transmission line hidden failures are predicted and processing method |
Publications (2)
Publication Number | Publication Date |
---|---|
CN104657793A true CN104657793A (en) | 2015-05-27 |
CN104657793B CN104657793B (en) | 2017-10-27 |
Family
ID=53248889
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201510102080.6A Expired - Fee Related CN104657793B (en) | 2015-03-09 | 2015-03-09 | Overhead transmission line hidden failures are predicted and processing method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN104657793B (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111126846A (en) * | 2019-12-24 | 2020-05-08 | 广东电网有限责任公司 | Method for evaluating differentiation state of overhead transmission line |
CN111401760A (en) * | 2020-03-23 | 2020-07-10 | 国电南瑞科技股份有限公司 | Safety and stability control device exception handling decision method and device |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20110122878A1 (en) * | 2011-01-27 | 2011-05-26 | Xiangming Li | Method of percolation networking architecture for data transmission and routing |
CN103246805A (en) * | 2013-04-23 | 2013-08-14 | 浙江大学 | Method for specifically estimating time-varying shutdown probability of overhead transmission line in windstorm weather |
CN103713237A (en) * | 2013-12-30 | 2014-04-09 | 华北电力大学 | Power system electric transmission line fault diagnosis method |
CN103793853A (en) * | 2014-01-21 | 2014-05-14 | 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 | Overhead power transmission line running state assessment method based on bidirectional Bayesian network |
-
2015
- 2015-03-09 CN CN201510102080.6A patent/CN104657793B/en not_active Expired - Fee Related
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20110122878A1 (en) * | 2011-01-27 | 2011-05-26 | Xiangming Li | Method of percolation networking architecture for data transmission and routing |
CN103246805A (en) * | 2013-04-23 | 2013-08-14 | 浙江大学 | Method for specifically estimating time-varying shutdown probability of overhead transmission line in windstorm weather |
CN103713237A (en) * | 2013-12-30 | 2014-04-09 | 华北电力大学 | Power system electric transmission line fault diagnosis method |
CN103793853A (en) * | 2014-01-21 | 2014-05-14 | 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 | Overhead power transmission line running state assessment method based on bidirectional Bayesian network |
Non-Patent Citations (2)
Title |
---|
廖苑晰 等: "应用贝叶斯网络的连锁故障模式识别", 《电力***及其自动化学报》 * |
潘一飞 等: "小世界网络下电网连锁故障预测", 《电力***及其自动化学报》 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111126846A (en) * | 2019-12-24 | 2020-05-08 | 广东电网有限责任公司 | Method for evaluating differentiation state of overhead transmission line |
CN111401760A (en) * | 2020-03-23 | 2020-07-10 | 国电南瑞科技股份有限公司 | Safety and stability control device exception handling decision method and device |
CN111401760B (en) * | 2020-03-23 | 2022-08-09 | 国电南瑞科技股份有限公司 | Safety and stability control device exception handling decision method and device |
Also Published As
Publication number | Publication date |
---|---|
CN104657793B (en) | 2017-10-27 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN104657914A (en) | Method for establishing mathematical model for evaluating operation state of overhead transmission line | |
CN103793853B (en) | Condition of Overhead Transmission Lines Based appraisal procedure based on two-way Bayesian network | |
CN103426137B (en) | A kind of drainage pipeline defect quantitative comprehensive estimation method | |
CN110535144A (en) | The intelligent distribution network toughness quantitative analysis method of the load containing polymorphic type under dusty wind weather | |
CN102708411A (en) | Method for evaluating risk of regional grid on line | |
CN109409723A (en) | A kind of overhead transmission line method for evaluating state | |
CN108389002B (en) | Multiple fault generation method based on N-1 serious fault set | |
CN103914792A (en) | Integrated risk assessment method for calculating operation uncertainty of electric transmission line | |
CN107633320A (en) | A kind of power network line importance appraisal procedure based on weather prognosis and risk assessment | |
CN103699668A (en) | Power distribution network electric equipment combination state evaluation method based on data section consistency | |
CN106485333A (en) | A kind of transmission line of electricity running status appraisal procedure | |
CN115330127A (en) | System and method for controlling operation risk of hydro-junction in real time | |
CN109359882A (en) | Transmission line of electricity trip risk appraisal procedure under a kind of typhoon disaster | |
CN112668821A (en) | Distribution line risk analysis method based on insulator fault probability of sand blown region | |
CN102590652A (en) | Electric-information-based equipment performance evaluation system and method | |
CN104657793A (en) | Hidden failure prediction and treatment method for overhead transmission line | |
Hu et al. | Early warning method for overseas natural gas pipeline accidents based on FDOOBN under severe environmental conditions | |
Oboudi et al. | Two-stage seismic resilience enhancement of electrical distribution systems | |
CN104657830B (en) | Overhead transmission line runs the appraisal procedure of data | |
CN109377108A (en) | A kind of independent micro-capacitance sensor method for evaluating reliability under natural calamity | |
CN107563641A (en) | A kind of meter and the power distribution network of disaster preference combat a natural disaster more scene differentiation planing methods | |
CN111369388B (en) | Anti-vulnerability assessment method and system for urban power grid | |
CN109472428A (en) | A kind of power distribution network operation risk assessment method based on loss expectation method | |
Lim et al. | Bridge damage prediction using deep neural network | |
CN115860478A (en) | Analysis method for possible maximum disaster of cascade hydroelectric junction group |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CF01 | Termination of patent right due to non-payment of annual fee | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20171027 |