CN103592575A - Self-adaptation weighting data fusion fault distance measurement method based on multi-sensor system - Google Patents

Self-adaptation weighting data fusion fault distance measurement method based on multi-sensor system Download PDF

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Publication number
CN103592575A
CN103592575A CN201310597624.1A CN201310597624A CN103592575A CN 103592575 A CN103592575 A CN 103592575A CN 201310597624 A CN201310597624 A CN 201310597624A CN 103592575 A CN103592575 A CN 103592575A
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fault
distance
distance measurement
fault distance
finding method
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Inventor
高厚磊
陈学伟
邹贵彬
刘炳旭
刘洪正
王振河
冯迎春
韩志骏
李超
袁森
杨晓滨
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State Grid Corp of China SGCC
Maintenance Branch of State Grid Shandong Electric Power Co Ltd
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State Grid Corp of China SGCC
Maintenance Branch of State Grid Shandong Electric Power Co Ltd
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/22—Flexible AC transmission systems [FACTS] or power factor or reactive power compensating or correcting units

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Abstract

The invention discloses a self-adaptation weighting data fusion fault distance measurement method based on a multi-sensor system. The method is used for meeting requirements for accurate distance measurement when a high-voltage transmission line breaks down. According to the concept of the method, multi-sensor data resources of different time and spaces are made full use of, and observation data of multiple sensors are obtained by means of a computer according to a time sequence so as to obtain accurate and reliable distance measurement results. The method includes the steps of firstly a fault position and a fault style are initially estimated by means of a double-end synchronous fault distance measurement method according to PMU data, then the fault position and the fault style are initially estimated in a double-end non-synchronous fault distance measurement method and a single-end distance measurement method according to fault recording data, thirdly, the fault position and the fault style are initially estimated according to a traveling wave fault measurement result, fourthly, by means of existing prior knowledge, mean square errors on different faults occasions are obtained in different methods, weight coefficients of the mean square errors are obtained respectively, and ultimately, weighting fusion distance measurement is carried out through the self-adaptation weighting fusion distance measurement method.

Description

A kind of adaptive weighting data fusion fault distance-finding method based on multisensor syste
Technical field
The present invention is a kind of adaptive weighting data fusion fault distance-finding method based on multisensor syste, belongs to field of relay protection in power.
Background technology
Along with the complexity day by day of electric system scale, particularly putting into operation of UHV (ultra-high voltage) and UHV transmission line, also more and more higher to the requirement of localization of fault.Can ultra-high-tension power transmission line accurately locate is one of key measure guaranteeing technically power system security, stable and economical operation.Localization of fault can reduce line walking workman's workload accurately, reduces economic loss, has huge economic benefit and social benefit; In addition, high-precision real-time online Fault Locating Method for improve system stability, guarantee that system safety operation has great importance.
At present, the distance-finding method of ultra-high-tension power transmission line has a variety of.Due to the existence of fault resistance, there is larger range error in method of single end distance measurement, in order to reduce its impact, introduces peer-to-peer system impedance, and range finding is subject to again the impact of peer-to-peer system impedance variation like this, and this problem is not resolved for a long time.Although two ends telemetry does not exist the errors of principles, although can effectively overcome the problem that single end distance measurement method exists, synchronization range finding method is stronger to gps clock dependence, invests larger; There is pseudo-root discrimination in asynchronous distance-finding method, needs further improvement.Although travelling wave ranging method is not subject to the impact of system operation mode, transition resistance, line distribution capacitance in principle, there is higher distance accuracy, but being become by velocity of wave variation and parameter frequently, its distance accuracy affects greatly, while especially breaking down when voltage phase angle zero passage or approaching zero, can cause and find range unsuccessfully, and also have the impalpable problem of near region reflection wave.
Owing to affecting the factor major part of safe operation of power system, be all that uncertain, single fault distance-finding method has its intrinsic defect, therefore these single fault distance-finding methods organically need to be combined to meet the requirement of precision ranging.And how single fault distance-finding method is organically combined, it is the biggest problem facing now.
Summary of the invention
In order to overcome above-mentioned the deficiencies in the prior art, the invention provides a kind of adaptive weighting data fusion fault distance-finding method based on multisensor syste.It is based on information fusion technology, the fully collaborative multi-sensor data resource of utilizing different time and space, adopt computer technology by time series, to obtain the observation data of multisensor, under certain criterion, analyze, comprehensively, domination and using, obtaining more accurately, fault localization result reliably.
To achieve these goals, the present invention adopts following technical proposals:
An adaptive weighting data fusion fault distance-finding method for multisensor syste, the performing step of the method is as follows:
Step 1): pass through the synchronous fault distance-finding method of both-end abort situation and fault type according to a preliminary estimate according to PMU data;
Step 2): according to fault recorder data, pass through the asynchronous fault distance-finding method of both-end, method of single end distance measurement abort situation and fault type according to a preliminary estimate;
Step 3): according to both-end type travelling wave ranging result is abort situation and type according to a preliminary estimate;
Step 4): utilize existing priori to ask for the square error of each method in different faults situation, ask for respectively its weights coefficient;
Step 5): be weighted and merge range finding by adaptive weighted fusion distance-finding method.
In described step 1), based on PMU both-end synchrodata distance-finding method ultimate principle, be described below:
Accompanying drawing 1 is faulty line schematic diagram, while normally moving, according to equation for transmission line by
Figure 934715DEST_PATH_IMAGE004
voltage, the electric current phasor of end represent
Figure 652135DEST_PATH_IMAGE006
the electric current and voltage of end, can row formula (1) and formula (2).In formula, ,
Figure 315513DEST_PATH_IMAGE010
represent respectively
Figure 75659DEST_PATH_IMAGE006
the positive-sequence component of voltage, electric current before end fault;
Figure 342692DEST_PATH_IMAGE012
,
Figure 479275DEST_PATH_IMAGE014
represent
Figure 522055DEST_PATH_IMAGE004
the positive-sequence component of voltage, electric current before end fault;
Figure 453102DEST_PATH_IMAGE016
for circuit propagation coefficient,
Figure 145115DEST_PATH_IMAGE018
for line characteristic impedance,
Figure 819810DEST_PATH_IMAGE020
for total track length.
（1）
（2）
By formula (1), formula (2) simultaneous, can solve circuit propagation coefficient
Figure 450883DEST_PATH_IMAGE016
and line characteristic impedance
Figure 663690DEST_PATH_IMAGE018
;
after point breaks down, can row equation for transmission line be:
Figure 580010DEST_PATH_IMAGE028
（3）
Figure 308932DEST_PATH_IMAGE030
（4）
Because short dot voltage phasor is same physical quantities are, simultaneous formula (3) and formula (4) solve to obtain fault distance
Figure 325429DEST_PATH_IMAGE032
.
Described step 2), in, the both-end non-synchronous data distance-finding method ultimate principle based on fault oscillograph is described below:
Accompanying drawing 1 is faulty line schematic diagram, when circuit normally moves, and can be by
Figure 823407DEST_PATH_IMAGE004
voltage, the electric current phasor of end represent
Figure 31272DEST_PATH_IMAGE006
voltage, the electric current phasor of end, consider that both end voltage current data is asynchronous, might as well establish asynchronous angle and be
Figure 981910DEST_PATH_IMAGE034
, and take into account the impact of line parameter circuit value error, there is transmission line of electricity equation:
Figure 864416DEST_PATH_IMAGE036
（5）
Figure 154583DEST_PATH_IMAGE038
（6）
In formula (5), formula (6),
Figure 97131DEST_PATH_IMAGE008
,
Figure 472749DEST_PATH_IMAGE010
,
Figure DEST_PATH_IMAGE039
, while being respectively steady-state operation
Figure 303618DEST_PATH_IMAGE006
,
Figure 859146DEST_PATH_IMAGE004
the positive-sequence component of terminal voltage, electric current,
Figure 722059DEST_PATH_IMAGE016
,
Figure 884050DEST_PATH_IMAGE018
,
Figure 945547DEST_PATH_IMAGE020
for given circuit propagation coefficient, characteristic impedance and total track length, above parameter is known quantity; Unknown quantity is error reduction coefficient
Figure DEST_PATH_IMAGE041
with asynchronous angle
Figure 167581DEST_PATH_IMAGE042
.
By formula (5), formula (6) simultaneous, can solve error reduction coefficient
Figure 16326DEST_PATH_IMAGE041
with asynchronous angle
Figure 44325DEST_PATH_IMAGE034
;
Figure 163591DEST_PATH_IMAGE026
after point breaks down, if with circuit
Figure 353264DEST_PATH_IMAGE006
,
Figure 190770DEST_PATH_IMAGE004
the voltage of end, electric current, as boundary condition, can be released respectively the line fault point representing with both end voltage current data
Figure 756880DEST_PATH_IMAGE026
the voltage equation at place:
Figure 996232DEST_PATH_IMAGE044
（7）
Figure 356806DEST_PATH_IMAGE046
（8）
In formula,
Figure 180143DEST_PATH_IMAGE048
,
Figure 549945DEST_PATH_IMAGE050
represent respectively by
Figure 378223DEST_PATH_IMAGE006
,
Figure 175278DEST_PATH_IMAGE004
terminal voltage electric current positive-sequence component calculate fault point voltage positive-sequence component, therefore have
Figure 721797DEST_PATH_IMAGE052
, simultaneous formula (7), formula (8) can be tried to achieve fault distance .
In described step 3), both-end type travelling wave ranging method ultimate principle is described below:
Accompanying drawing 2 is both-end
Figure 843654DEST_PATH_IMAGE002
type travelling wave ranging principle schematic, it is the mistiming of utilizing fault initial row ripple to arrive circuit to calculate fault distance, computing formula is:
Figure DEST_PATH_IMAGE055
（9）
In formula,
Figure DEST_PATH_IMAGE057
with
Figure DEST_PATH_IMAGE059
represent that respectively initial row ripple arrives faulty line both sides transformer station
Figure 185511DEST_PATH_IMAGE006
,
Figure 281643DEST_PATH_IMAGE004
precise time,
Figure DEST_PATH_IMAGE061
for the row velocity of wave propagation on faulty line,
Figure DEST_PATH_IMAGE063
for circuit
Figure DEST_PATH_IMAGE065
physical length.
In described step 4), in weighted data fusion method, weights coefficient acquiring method is as follows:
Accompanying drawing 3 is weighted data fusion method schematic diagram, and the core concept of method is: the measured value of each sensor is
Figure DEST_PATH_IMAGE067
, the corresponding optimal weighted factor of each sensor is
Figure DEST_PATH_IMAGE069
, when the overall mean square error of measured value hour, the estimated value after fusion
Figure DEST_PATH_IMAGE071
reach optimum.
For convenient, analyze, two sensors of take are example, and same constant is measured, and measured value is
(10)
Wherein
Figure DEST_PATH_IMAGE075
the stochastic error existing during for measurement, and establish
Figure DEST_PATH_IMAGE077
, for constant actual value to be measured, two sensors measured value
Figure DEST_PATH_IMAGE081
,
Figure DEST_PATH_IMAGE083
separate.
Suppose
Figure 112022DEST_PATH_IMAGE084
estimated value
Figure 977210DEST_PATH_IMAGE086
with observed reading linear, and for
Figure 323112DEST_PATH_IMAGE032
without inclined to one side estimation, have:
Figure 308386DEST_PATH_IMAGE090
(11)
Wherein,
Figure 555827DEST_PATH_IMAGE092
weights for each measurement value sensor.
If evaluated error is , square error be:
Figure 457421DEST_PATH_IMAGE098
(12)
Because
Figure 938081DEST_PATH_IMAGE086
for without inclined to one side estimation, so have:
Figure 523838DEST_PATH_IMAGE100
(13)
Again
Figure 155808DEST_PATH_IMAGE102
, :
Figure 790368DEST_PATH_IMAGE106
(14)
Formula (14) substitution formula (12) arrangement can be obtained:
Figure 185578DEST_PATH_IMAGE108
(15)
Due to
Figure 672054DEST_PATH_IMAGE110
separate, have
Figure 760095DEST_PATH_IMAGE112
, again
Figure 27129DEST_PATH_IMAGE114
,
Figure 662247DEST_PATH_IMAGE116
:
Figure 65547DEST_PATH_IMAGE118
(16)
For making
Figure 262173DEST_PATH_IMAGE120
minimum, should make
Figure 16502DEST_PATH_IMAGE122
, solve optimum weights and be:
(17)
Optimal estimation value is:
Figure 214582DEST_PATH_IMAGE126
(18)
Promote this conclusion to the situation of a plurality of sensors, the variance of establishing multisensor is respectively
Figure 582110DEST_PATH_IMAGE128
, the measured value of each sensor is respectively
Figure 558156DEST_PATH_IMAGE130
, be mutually independent.True value
Figure DEST_PATH_IMAGE131
estimated value be , and be that the weighting factor of each sensor is respectively without partially estimating
Figure DEST_PATH_IMAGE133
, according to multivariate function extreme value theory, can obtain square error a hour corresponding weighting factor be:
Figure DEST_PATH_IMAGE135
(19)
In described step 5), adaptive weighted fusion fault distance-finding method range finding formula is as follows:
Figure DEST_PATH_IMAGE137
(20)
Wherein number for sensor.
Compared with prior art, the invention has the beneficial effects as follows: the new method that the present invention proposes a kind of adaptive weighting data fusion fault localization based on multisensor syste, method takes full advantage of the redundant information that multisensor provides, application weighted data fusion method is asked for fault distance, solved the inherent shortcoming that single distance-finding method exists, met the accurate range finding requirement of ultra-high-tension power transmission line when breaking down, improved the stability of system, robustness is stronger.
Accompanying drawing explanation
Fig. 1 is faulty line schematic diagram;
Fig. 2 is both-end
Figure 59774DEST_PATH_IMAGE002
type travelling wave ranging principle schematic;
Fig. 3 is weighted data fusion method schematic diagram;
Fig. 4 is the adaptive weighting data fusion fault distance-finding method process flow diagram based on multisensor syste;
Fig. 5 is that weighted data merges fault distance-finding method realistic model;
Fig. 6 is each distance-finding method range finding result contrast broken line graph.
Embodiment
Below in conjunction with accompanying drawing, the present invention is further described.
An adaptive weighting data fusion fault distance-finding method for multisensor syste, as shown in Figure 4, implementation step is as follows for concrete method flow:
Step 1): pass through the synchronous fault distance-finding method of both-end abort situation and fault type according to a preliminary estimate according to PMU data;
Step 2): according to fault recorder data, pass through the asynchronous fault distance-finding method of both-end, method of single end distance measurement abort situation and fault type according to a preliminary estimate;
Step 3): according to both-end
Figure 394940DEST_PATH_IMAGE002
type travelling wave ranging result is abort situation and type according to a preliminary estimate;
Step 4): utilize existing priori to ask for the square error of each method in different faults situation, ask for respectively its weights coefficient;
Step 5): be weighted and merge range finding by adaptive weighted fusion distance-finding method.
In described step 1), based on PMU both-end synchrodata distance-finding method ultimate principle, be described below:
Accompanying drawing 1 is faulty line schematic diagram, while normally moving, according to equation for transmission line by voltage, the electric current phasor of end represent
Figure 140359DEST_PATH_IMAGE006
the electric current and voltage of end, can row formula (21) and formula (22).In formula,
Figure 74555DEST_PATH_IMAGE008
, represent respectively
Figure 734523DEST_PATH_IMAGE006
the positive-sequence component of voltage, electric current before end fault;
Figure 617029DEST_PATH_IMAGE012
,
Figure 907196DEST_PATH_IMAGE014
represent
Figure 849744DEST_PATH_IMAGE004
the positive-sequence component of voltage, electric current before end fault;
Figure 225362DEST_PATH_IMAGE016
for circuit propagation coefficient,
Figure 645979DEST_PATH_IMAGE018
for line characteristic impedance,
Figure 118548DEST_PATH_IMAGE020
for total track length.
Figure DEST_PATH_IMAGE140
（21）
Figure 402637DEST_PATH_IMAGE024
（22）
By formula (21), formula (22) simultaneous, can solve circuit propagation coefficient
Figure 265551DEST_PATH_IMAGE016
and line characteristic impedance
Figure 489859DEST_PATH_IMAGE018
;
Figure 754618DEST_PATH_IMAGE026
after point breaks down, can row equation for transmission line be:
Figure DEST_PATH_IMAGE141
（23）
Figure 38969DEST_PATH_IMAGE030
（24）
Because short dot voltage phasor is same physical quantities are, simultaneous formula (23) and formula (24) solve to obtain fault distance
Figure 389179DEST_PATH_IMAGE032
.
Described step 2), in, the both-end non-synchronous data distance-finding method ultimate principle based on fault oscillograph is described below:
Accompanying drawing 1 is faulty line schematic diagram, when circuit normally moves, and can be by
Figure 151598DEST_PATH_IMAGE004
voltage, the electric current phasor of end represent
Figure 769399DEST_PATH_IMAGE006
voltage, the electric current phasor of end, consider that both end voltage current data is asynchronous, might as well establish asynchronous angle and be
Figure 224651DEST_PATH_IMAGE034
, and take into account the impact of line parameter circuit value error, there is transmission line of electricity equation:
Figure 62157DEST_PATH_IMAGE036
（25）
Figure 628268DEST_PATH_IMAGE038
（26）
In formula (25), formula (26),
Figure DEST_PATH_IMAGE142
,
Figure 602040DEST_PATH_IMAGE010
,
Figure DEST_PATH_IMAGE143
,
Figure 619673DEST_PATH_IMAGE014
while being respectively steady-state operation ,
Figure 111014DEST_PATH_IMAGE004
the positive-sequence component of terminal voltage, electric current,
Figure 204872DEST_PATH_IMAGE016
,
Figure 1927DEST_PATH_IMAGE018
,
Figure 548446DEST_PATH_IMAGE020
for given circuit propagation coefficient, characteristic impedance and total track length, above parameter is known quantity; Unknown quantity is error reduction coefficient
Figure 721938DEST_PATH_IMAGE041
with asynchronous angle
Figure 670303DEST_PATH_IMAGE042
.
By formula (25), formula (26) simultaneous, can solve error reduction coefficient
Figure 638259DEST_PATH_IMAGE041
with asynchronous angle
Figure 170609DEST_PATH_IMAGE034
;
Figure 616634DEST_PATH_IMAGE026
after point breaks down, if with circuit
Figure 685084DEST_PATH_IMAGE006
,
Figure 823941DEST_PATH_IMAGE004
the voltage of end, electric current, as boundary condition, can be released respectively the line fault point representing with both end voltage current data
Figure 345052DEST_PATH_IMAGE026
the voltage equation at place:
（27）
Figure 532451DEST_PATH_IMAGE046
（28）
In formula,
Figure 517725DEST_PATH_IMAGE048
,
Figure 263702DEST_PATH_IMAGE050
represent respectively by
Figure 334426DEST_PATH_IMAGE006
,
Figure 325516DEST_PATH_IMAGE004
terminal voltage electric current positive-sequence component calculate fault point voltage positive-sequence component, therefore have
Figure 165296DEST_PATH_IMAGE052
, simultaneous formula (27), formula (28) can be tried to achieve fault distance
Figure 645955DEST_PATH_IMAGE053
.
In described step 3), both-end
Figure 876080DEST_PATH_IMAGE002
type travelling wave ranging method ultimate principle is described below:
Accompanying drawing 2 is both-end
Figure 733177DEST_PATH_IMAGE002
type travelling wave ranging principle schematic, it is the mistiming of utilizing fault initial row ripple to arrive circuit to calculate fault distance, computing formula is:
Figure 365147DEST_PATH_IMAGE055
(29)
In formula,
Figure 16708DEST_PATH_IMAGE057
with
Figure 498243DEST_PATH_IMAGE059
represent that respectively initial row ripple arrives faulty line both sides transformer station
Figure 893452DEST_PATH_IMAGE006
,
Figure 379928DEST_PATH_IMAGE004
precise time,
Figure 467970DEST_PATH_IMAGE061
for the row velocity of wave propagation on faulty line,
Figure 735003DEST_PATH_IMAGE063
for circuit physical length.
In described step 4), in weighted data fusion method, weights coefficient acquiring method is as follows:
Accompanying drawing 3 is weighted data fusion method schematic diagram, and the core concept of method is: the measured value of each sensor is
Figure DEST_PATH_IMAGE145
, the corresponding optimal weighted factor of each sensor is , when the overall mean square error of measured value hour, the estimated value after fusion
Figure DEST_PATH_IMAGE147
reach optimum.
The weights coefficient formulas of each sensor that meets this condition is as follows:
Figure DEST_PATH_IMAGE148
(30)
Bringing formula (30) into the adaptive weighted fusion fault distance-finding method of following formula (31) range finding formula, can to try to achieve fault distance as follows:
(31)
Wherein
Figure 851273DEST_PATH_IMAGE139
number for sensor.
Emulation experiment checking based on PSCAD/EMTDC:
The realistic model that accompanying drawing 5 is the adaptive weighting data fusion fault distance-finding method based on multisensor syste that adopts PSCAD and build.Wherein, in order to make emulated data closer to reality, adopt the line parameter circuit value of certain actual 500kV, wire type is 4 * LGJ400/35, and analog line length 300km adopts distributed parameter model; Emulation T.T. 0.3s, simulated failure initial time 0.2s, trouble duration 0.06s.
In simulation process, the faults such as simulation single-phase earthing, phase fault, two phase ground short circuit and three-phase shortcircuit different faults apart from time situation, getting transition resistance is 100 Ω, according to different faults type, fault zone, choose at random fault distance and apply each distance-finding method and find range, its range finding result is as shown in table 1.
Range finding relative error computing formula:
Figure DEST_PATH_IMAGE150
(32)
Table 1 weighted data fusion method distance accuracy checking and with the contrast of each single distance-finding method
Figure DEST_PATH_IMAGE152
As can be seen from Table 1, the range error that adopts adaptive weighting data fusion distance-finding method obviously will be lower than other each single distance-finding method, adaptive weighting data fusion method is fully worked in coordination with the redundant information of having utilized multisensor, has improved precision and the reliability of fault localization.
Accompanying drawing 6 is each distance-finding method range finding result contrast broken line graph, and as seen from Figure 6, under the condition of various fault types and fault distance, the range error of weighted data fusion method will be starkly lower than the range error of other single distance-finding methods.
The invention provides a kind of adaptive weighting data fusion fault distance-finding method based on multisensor syste, the redundant information of utilizing multisensor to provide can be fully provided, realize the accurate localization of fault of ultra-high-tension power transmission line, improve the reliability of ultra-high-tension power transmission line localization of fault.The above, be only the specific embodiment of the present invention, but protection scope of the present invention is not limited to this, and those skilled in the art do not need the various modifications that can make through creative work or are out of shape still within protection scope of the present invention.Protection scope of the present invention is as the criterion with the protection domain that claims were limited.

Claims (6)

1. the adaptive weighting data fusion fault distance-finding method based on multisensor syste, is characterized in that, comprises the following steps:
Step 1): pass through the synchronous fault distance-finding method of both-end abort situation and fault type according to a preliminary estimate according to PMU data;
Step 2): according to fault recorder data, pass through the asynchronous fault distance-finding method of both-end, method of single end distance measurement abort situation and fault type according to a preliminary estimate;
Step 3): according to both-end
Figure 2013105976241100001DEST_PATH_IMAGE002
type travelling wave ranging result is abort situation and type according to a preliminary estimate;
Step 4): utilize existing priori to ask for the square error of each method in different faults situation, ask for respectively its weights coefficient;
Step 5): be weighted and merge range finding by adaptive weighted fusion distance-finding method.
2. the adaptive weighting data fusion fault distance-finding method based on multisensor syste according to claim 1, it is characterized in that in described step 1), the synchronous fault distance-finding method of the both-end based on PMU data is as follows: first, the propagation coefficient of the voltage while moving by fault presteady state, electric current phasor data correction circuit
Figure DEST_PATH_IMAGE004
and line characteristic impedance
Figure DEST_PATH_IMAGE006
; Then by revised propagation coefficient and line characteristic impedance
Figure DEST_PATH_IMAGE006A
be brought in the equation for transmission line after fault, solve and obtain fault distance
Figure DEST_PATH_IMAGE008
.
3. the adaptive weighting data fusion fault distance-finding method based on multisensor syste according to claim 1, it is characterized in that described step 2) in the asynchronous fault distance-finding method of both-end based on fault recorder data as follows: first, the voltage while moving by fault presteady state, electric current phasor data are calculated and are tried to achieve error reduction coefficient
Figure DEST_PATH_IMAGE010
angle asynchronous with two ends
Figure DEST_PATH_IMAGE012
; Then by error reduction coefficient
Figure DEST_PATH_IMAGE010A
with asynchronous angle
Figure DEST_PATH_IMAGE012A
be brought in the equation for transmission line after fault, solve and obtain fault distance
Figure DEST_PATH_IMAGE008A
.
4. the adaptive weighting data fusion fault distance-finding method based on multisensor syste according to claim 1, is characterized in that both-end in described step 3)
Figure DEST_PATH_IMAGE002A
type travelling wave ranging method is as follows: both-end
Figure DEST_PATH_IMAGE002AA
type travelling wave ranging is the mistiming of utilizing fault initial row ripple to arrive circuit two ends to calculate fault distance, and computing formula is:
Figure DEST_PATH_IMAGE014
in formula,
Figure DEST_PATH_IMAGE016
with
Figure DEST_PATH_IMAGE018
represent that respectively initial row ripple arrives faulty line both sides transformer station
Figure DEST_PATH_IMAGE020
, precise time,
Figure DEST_PATH_IMAGE024
for the row velocity of wave propagation on faulty line,
Figure DEST_PATH_IMAGE026
for circuit
Figure DEST_PATH_IMAGE028
physical length.
5. the adaptive weighting data fusion fault distance-finding method based on multisensor syste according to claim 1, the computing method that it is characterized in that each weights coefficient in described step 4) are as follows: method core concept is for to make under the condition of overall mean square error minimum, according to the resulting measured value of each sensor
Figure DEST_PATH_IMAGE030
find the corresponding optimal weighted factor of each sensor
Figure DEST_PATH_IMAGE032
,
Make the estimated value after merging reach optimum,
A square error hour corresponding weighting factor is
Figure DEST_PATH_IMAGE036
, wherein
Figure DEST_PATH_IMAGE038
for the variance of multisensor,
Figure DEST_PATH_IMAGE040
for each measurement value sensor, be mutually independent.
6. the adaptive weighting data fusion fault distance-finding method based on multisensor syste according to claim 1, is characterized in that the computing formula of the fault distance that in described step 5), weighted data fusion distance-finding method is tried to achieve is , wherein number for sensor.
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