CN107169261A - A kind of power transmission and transformation system health degree appraisal procedure for considering seasonal conditions change - Google Patents
A kind of power transmission and transformation system health degree appraisal procedure for considering seasonal conditions change Download PDFInfo
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Abstract
The present invention relates to Power System Analysis field, more particularly to a kind of power transmission and transformation system health degree appraisal procedure for considering seasonal conditions change.Methods described comprises the following steps：(S1) according to the difference of operation of power networks state constraint condition, system mode is divided into healthy (H), inferior health (M), dangerous (R) three kinds of states；(S2) the seasonal statistics of line failure rate；(S3) consider characteristic of the fault rate with seasonal variations, derive the emulation mode of power transmission and transformation element periodic intensity inhomogeous Poisson process running status；The emulation mode adds seasonal fault rate Fei Shiqi emulation in the overall calculation procedures of sequential Monte Carlo, and simulation result is the probability of each running status of system under well being theoretical frames.It is of the invention to be changed according to season alternation, the operation health degree of power transmission and transformation system is assessed, assessment result more truly can reflect under season acute variation, the health degree level of power transmission and transformation system, so as to improve the benefit of planning investment.
Description
Technical field
The present invention relates to Power System Analysis field, more particularly to a kind of power transmission and transformation system for considering seasonal conditions change
Health degree appraisal procedure.
Background technology
The initial stage nineties, the Well-being analysis methods (WBAM) proposed by scholars such as Billinton, in RAM frame
DSC is embedded under frame, system devoid of risk state is further made a distinction using certainty evaluation criterion, its analysis result energy
Understanding habit of the operational management personnel to power network is enough more nearly, so as to compensate for a certain extent to power grid risk level
Be appreciated and understood by.At present, WBAM is in hair transmission of electricity composite electric system and considers that the integrated power system of new energy infiltration is reliable
Property analysis in have relevant report.Simultaneously to solve the problem of simulation time is long, proposed based on non-sequential Monte Carlo
A kind of one-step forward methods are used for calculating the reliability index of frequency dependence, greatly improve calculating speed,
Asking for for Well-being indexs is further simplify on the basis of this, calculating speed is improved.
WBAM more than by the fault rate of element in the simulation process of system reliability, being often taken as constant, such as year
Average value is this to assume that in the case where element is influenceed less by running environment be acceptable, but for exposed to family
For outer equipment, such as overhead line, its fault rate and geographical environment, weather conditions are closely related, such as snowstorm, strong wind, thunder
Electricity, vegetation growth, birds and beasts invasion and attack and load growth etc..Such as ignore the effect of severe weather conditions, will be to system reliability
Analysis produce larger deviation.
At this stage to the evaluation of system safe condition, mainly based on deterministic N-1 safety criterions (DSC), it has
Convenient deployment is implemented, visual result, the advantage readily appreciated, but because the screening of failure is completely dependent on operations staff and expert
Experience, does not account for the random behavior of system running environment and equipment in itself, thus can not substantially effectively reaction system wind
Dangerous situation.Well-being analysis methods (WBAM), Risk theory is embedded under RAM framework, to system devoid of risk
State is further made a distinction using certainty evaluation criterion, and its analysis result can be more nearly operational management personnel to electricity
The understanding habit of net, so as to compensate for being appreciated and understood by the general level of the health of line system to a certain extent.In tradition
WBAM analysis in, often by the element of power transmission and transformation system, such as transformer, overhead line fault rate is taken as constant, such as annual
Value.It is this to assume that in the case where element is influenceed less by running environment be acceptable, but for exposing out of doors
For equipment, such as overhead line, its fault rate and geographical environment, weather conditions are closely related, such as snowstorm, strong wind, thunder and lightning, plant
Grown, birds and beasts invasion and attack and load growth etc., at present, still lack and consider that the health degree under season alternation change condition is commented
Estimate method.
The content of the invention
The problem of in background technology, it is good for the invention provides a kind of power transmission and transformation system for considering seasonal conditions change
Kang Du appraisal procedures.
To achieve these goals, the present invention proposes following technical scheme：
A kind of power transmission and transformation system health degree appraisal procedure for considering seasonal conditions change, methods described comprises the following steps：
(S1) system mode is divided：WBAM meets the difference of constraints according to operation of power networks state, and system mode is drawn
It is divided into healthy (H), inferior health (M), dangerous (R) three kinds of states；
(S2) the seasonal statistics of line failure rate：Power network event is obtained from Outage Management Systems (OMS) or similar system
Hinder related statistical information, count equipment failure rate information；
(S3) consider that the power transmission and transformation system health degree of seasonal conditions change is assessed：Consider spy of the fault rate with seasonal variations
Property, derive the emulation mode of power transmission and transformation element periodic intensity inhomogeous Poisson process running status；The emulation mode is sequential
Seasonal fault rate Fei Shiqi emulation is added in Monte Carlo totality calculation procedures, simulation result is well-being theoretical
The probability of each running status of system under framework.
Further, in the step (S2), when the equipment failure rate information includes fault element information, failure
Between information and fault reason information.
Further, in the step (S2), if historical data base has N line fault information, Sij, i=
[1 ..., m], j=[1 ... N] is i time spans to be counted in jth year, and unit is hour, nij、RijRespectively jth year
Same class failure cause induces in i-th of time span line fault number of times sum and repair time sum, unit is small
When, then：
The fault rate of i-th of time span circuit can be expressed as：
(1) equal sign both sides are multiplied by simultaneouslyObtain：
Then λiWith average annual fault rate λcRelation be：
Further, in the step (S2), if considering, time span is taken as the equal four seasons, and ignores element and repair
During the influence of multiple time：
Wherein,《Represent a kind of " newline "；
The fault rate in each season and the relation (6) of average annual fault rate can be obtained by then into (4) (5) being brought into (3)：
Further, comprise the following steps again in the step (S3)：
(S3-1) system is initialized, network parameter, machine unit characteristic, load curve, the reliability model of element are seasonal
Fault rate, repair rate；
(S3-2) emulation initial time Tsys=0；
(S3-3) all elements are randomly generated with state panorama arrival time according to the emulation mode of inhomogeous Poisson process
T, and T at the time of taking the minimum value min (T) to be changed as next system modesys=Tsys+min(T)；
(S3-4) system mode is analyzed；State analysis includes Load flow calculation and correction is controlled, negative if there is cutting
Lotus, then into step (S3-6)；
(S3-5) forecast failure collection scanning is carried out to system mode and correction is controlled, if at least one forecast failure
Constraints can not be met, then is determined as M state；Conversely, being then H states；
(S3-6) judge whether simulation time reaches 1 year, if be unsatisfactory for, be back to (S3-3)；
Otherwise, judge whether related reliability index meets the condition of convergence or reach default simulation times, if condition
Meet, then export health degree index；Otherwise, it is back to (S3-2).
Further, the derivation power transmission and transformation element periodic intensity inhomogeous Poisson process operation described in step (S3-3)
The process of the emulation mode of state is：
If η (T) is Poisson process intensity function, then the mean failure rate number of times in circuit [0, T] can be expressed as：
Wherein, T represents the panorama arrival time of element inhomogeous Poisson process；
Then the number of stoppages in [0, T], is designated as N (T), obeys Poisson distributions：
Then [0, T] is interval interior, and element fault probability is：
Therefore the conditional probability that failure-free operation T element breaks down in [T, T+X], X ＞ 0 is：
Wherein, X represents the malfunctioning internal transfer time of element inhomogeous Poisson process；
Then (T, T+X] desired value of interval element fault number of times can be expressed as：
Wherein, λ (ε) represents fault rate of the equipment at the ε moment；
If fault rate meets a point time span statistical condition (1), i.e., it is changed constantly by the cycle of year, another T=0 bands
Enter formula (10) then：
Wherein, ki(X)=λiX,Each auxiliary parameter computational methods be：
gi=λi·si, g0=0, hij=λi·sj, hi,0=0, s0=0, yj=8760j, j=0 ..., N；
Combinatorial formula (7) (10) (11), can be obtainedAnd
Wherein, auxiliary parameter computational methods be, τ=T+X,
Further, using T=0 as starting simulation time produce periodic intensity it is non-when neat fault time sequence method such as
Under：
Step one：Randomly generate p independent random number pi~U (0,1), i=1 ..., p, failure is calculated by (13)
Rate is the HPP malfunctioning internals transfer time HX of unit value (λ=1)j：
HXi=-log (ui) (29)
Step 2：HPP failure panorama arrival time is calculated by (14)：
Step 3：Another T=HTiBring (12) into and calculate NHPP failure panoramas arrival time NTi：
NTi=Λ-1(HTi) (31)
Step 4：(15) are brought into (16) and obtain NHPP malfunctioning internals transfer time NXi：
NXi=NTi-NTi-1 (32)
Beneficial effects of the present invention are：
The present invention can change relative to prior art according to season alternation, assess the operation health of power transmission and transformation system
Spend, assessment result more truly can reflect under season acute variation, the health degree level of power transmission and transformation system, so as to improve rule
Draw the benefit of investment.
Brief description of the drawings
System mode under Fig. 1 being to determine property criterions and under Well-being frameworks divides schematic diagram.
Fig. 2 is WBAM frame diagrams.
Fig. 3 is seasonal variety fault rate schematic diagram.
Fig. 4 is two state element states transfer schematic diagram.
Average annual and according to (6) the conditionary periodics of Fig. 5 are fluctuated under two kinds of fault rates, fault time first time T1With-log
(u), the relation curve difference schematic diagram between 0 ＜ u ＜=1.
Fig. 6 is the system reliability simulation process figure for considering seasonal variations.
Embodiment
With reference to the accompanying drawings and detailed description, specific embodiments of the present invention are made with detailed elaboration.These tools
Body embodiment is not used for limiting the scope of the present invention or implementation principle only for narration, protection scope of the present invention still with
Claim is defined, including made on this basis obvious changes or variations etc..
System mode is divided：
System mode is divided into healthy (H), inferior health by WBAM according to the difference of operation of power networks state constraint condition
(M), dangerous (R) three kinds of states, as shown in Figure 1 to the relation between the division of system mode with DSC.Therefore the health degree of system
The reliability index that three kinds of states can be entered with system is weighed.In Fig. 1, normal condition, generation in H states correspondence DSC
Every operating index (busbar voltage, branch road, generator power, system of the table system operation under current and forecast failure collection
Frequency) in normal range (NR), M state is similar to the state of alert in original DSC, i.e. the current operating index of system is normal,
It is envisioned that at least in the presence of a failure some (a little) index of system can be caused out-of-limit in fault set, but can again it be adjusted by unit
The method such as degree or voltage correction is restored to H states.R states then correspond to urgent, collapse conditions, and the state must be by cutting
Load just can make the indices of system return to normal level.
WBAM analysis methods：
WBAM analysis methods are one kind extensions to conventional reliability analysis method, and Fig. 2 gives WBAM can with traditional
The relation analyzed by property.WBAM will add tri- states of Well-being for any one system mode filtered out
Index more new procedures, specific method is：If the out-of-limit state of failure, then be directly determined as R；If system mode is normal
Or can return to normal devoid of risk state through overcorrection, then need to judge the shape by the scanning of forecast failure collection
State belongs to H or M；It can be seen that, the most obvious features of WBAM are exactly to add the state reliability point to devoid of risk state
Analysis, the analysis describes the margin of safety of devoid of risk state by deterministic forecast failure collection to a certain extent.Fault set
N-1 criterions or system unit maximum capacity typically can be chosen according to being actually needed.
The seasonal statistics of line failure rate：
The related statistical information of electric network fault, such as fault element, fault time, failure cause etc. can be easily from stopping
Obtained in electric management system (OMS) or similar system." Outage Management Systems " are not unified and standard concept at present, some
The collected fault message in operation scene, relies more heavily on artificial upload, without single system.If historical data base has N
Line fault information, Sij, i=[1 ..., m], j=[1 ... N] is (small for i time spans to be counted in jth year
When), nij、RijLine fault number of times sum that respectively same class failure cause induces in jth year i-th of time span and
Repair time sum (hour).For simplicity, the calculating process of following line failure rate, is not included in line length, in reality
In the statistic processes of border, it is only necessary to the fault rate divided by line length occurred in (1)~(6).
The fault rate of i-th of time span circuit can be expressed as：
(1) equal sign both sides are multiplied by simultaneouslyObtain：
Then λiWith average annual fault rate λcRelation be
If considering, time span is taken as the equal four seasons, and when ignoring the influence of element repair time：
Rij＜ ＜ sij, i=1 ..., 4, j=1 ..., N (37)
Wherein,《Represent a kind of " newline ".
Consider that the power transmission and transformation system health degree of seasonal conditions change is assessed：
Overhead transmission line meets two state operating conditions, and the state transfer of two state operating elements includes running (B=1)
To failure (B=0) or from failure to resuming operation.The relation of each state transfer time is as shown in Figure 3 and Figure 4.Wherein, X is
State interior shifting time, T is state panorama arrival time.Influenceed to have periodically by weather in view of overhead line, if assuming
Using minimum reparation after failure, if it is possible to which it is Poisson process further to prove the failure of element, repair process, then circuit
Failure-repair process is the superposition of two separate periodic intensity inhomogeous Poisson process.
Below by taking failure process as an example, the emulation mode of periodic intensity inhomogeous Poisson process is derived.If η (T) is Poisson
Procedural strength function, then the mean failure rate number of times in circuit [0, T] can be expressed as：
Wherein, T represents the panorama arrival time of element inhomogeous Poisson process；
Then the number of stoppages in [0, T], is designated as N (T), obeys Poisson distributions：
Then [0, T] is interval interior, and element fault probability is：
Therefore the conditional probability that failure-free operation T element breaks down in [T, T+X], X ＞ 0 is
Wherein, X represents the malfunctioning internal transfer time of element inhomogeous Poisson process；
It can be seen that, the malfunctioning internal transfer time X of element inhomogeous Poisson process is relevant with its panorama arrival time T.
(T, T+X] desired value of interval element fault number of times can be expressed as：
Wherein, λ (ε) represents fault rate of the equipment at the ε moment；
If fault rate meets a point time span statistical condition (1), i.e., it is to be changed constantly in the cycle with year (8760h), separately
T=0 brings (10) into then：
Wherein, ki(X)=λiX,And the computational methods of each auxiliary parameter
For：gi=λi·si, g0=0, hij=λi·sj, hi,0=0, s0=0, yj=8760j, j=0 ..., N；
Combine (7) (10) (11), can obtainAnd
Wherein, auxiliary parameter computational methods be, τ=T+X,
In the emulation of element state, when can combine (9) (12) interior shifting of element is randomly generated by inverse transformation
Between.Fig. 5 gives average annual and according to (6) conditionary periodics and fluctuated under two kinds of fault rates, fault time first time T1With-log
(u), the relation curve difference between 0 ＜ u ＜=1.As can be seen that for identical-log (u), when NHPP state is shifted
Between it is shorter than HPP, it is meant that in same time interval, the NHPP number of stoppages is more than HPP.
It is as follows using T=0 as the method for starting simulation time generation periodic intensity neat fault time sequence when non-：
Step one：Randomly generate p independent random number pi~U (0,1), i=1 ..., p, failure is calculated by (13)
Rate is the HPP malfunctioning internals transfer time HX of unit value (λ=1)j：
HXi=-log (ui) (45)
Step 2：HPP failure panorama arrival time is calculated by (14)：
Step 3：Another T=HTiBring (12) into and calculate NHPP failure panoramas arrival time NTi：
NTi=Λ-1(HTi) (47)
Step 4：(15) are brought into (16) and obtain NHPP malfunctioning internals transfer time NXi：
NXi=NTi-NTi-1 (48)
Step 1~step 4 is equally applicable to meet the repair process simulation of Poisson process.If panorama state is reached
Between be defined to 1 year, then malfunction needs satisfaction simultaneously with repairing state number p, q
NX'jFor it is non-when neat repair process the reparation interior shifting time.
In view of characteristic of the fault rate with seasonal variations, the sequential Monte Carlo under well-being theoretical frames
Neat simulation process when the seasonal fault rate of addition is non-in overall calculation procedure, as indicated with 6：
The first step：Initialization system, network parameter, machine unit characteristic, load curve, the reliability model of element is seasonal
Fault rate, repair rate.
Second step：Emulate initial time Tsys=0.
3rd step：When emulation mode according to inhomogeous Poisson process randomly generates the arrival of state panorama to all elements
Between T, and T at the time of taking the minimum value min (T) to be changed as next system modesys=Tsys+min(T)。
4th step：System mode is analyzed.State analysis includes Load flow calculation and correction is controlled, if there is cutting
Load, then into the 6th step.
5th step：Forecast failure collection scanning and correction control are carried out to system mode, if at least one forecast failure
Constraints can not be met, then is determined as M state；Conversely, being then H states.Update related system health degree index.
6th step：Judge whether simulation time reaches 1 year, if be unsatisfactory for, be back to the 3rd step；Otherwise, judge related
Whether reliability index meets the condition of convergence or reaches default simulation times, if condition is met, and output health degree refers to
Mark.Otherwise, it is back to second step.
Claims (7)
1. a kind of power transmission and transformation system health degree appraisal procedure for considering seasonal conditions change, it is characterised in that methods described includes
Following steps：
(S1) system mode is divided：WBAM meets the difference of constraints according to operation of power networks state, and system mode is divided into
Healthy (H), inferior health (M), dangerous (R) three kinds of states；
(S2) the seasonal statistics of line failure rate：Electric network fault phase is obtained from Outage Management Systems (OMS) or similar system
The statistical information of pass, counts equipment failure rate information；
(S3) consider that the power transmission and transformation system health degree of seasonal conditions change is assessed：Consider that fault rate, with the characteristic of seasonal variations, is pushed away
Lead the emulation mode of power transmission and transformation element periodic intensity inhomogeous Poisson process running status；The emulation mode is in sequential Monte
Seasonal fault rate Fei Shiqi emulation is added in Carlo totality calculation procedures, simulation result is under well-being theoretical frame
The probability of each running status of system.
2. a kind of power transmission and transformation system health degree appraisal procedure for considering seasonal conditions change according to claim 1, it is special
Levy and be：
In the step (S2), it is former that the equipment failure rate information includes fault element information, fault time information and failure
Because of information.
3. a kind of power transmission and transformation system health degree appraisal procedure for considering seasonal conditions change according to claim 1, it is special
Levy and be：
In the step (S2), if historical data base has N line fault information, Sij, i=[1 ..., m], j=
[1 ... N] it is i time spans to be counted in jth year, unit is hour, nij、RijRespectively jth year i-th when span
Same class failure cause induces in degree line fault number of times sum and repair time sum, unit is hour, then：
The fault rate of i-th of time span circuit can be expressed as：
(1) equal sign both sides are multiplied by simultaneouslyObtain：
Then λiWith average annual fault rate λcRelation be：
。
4. a kind of power transmission and transformation system health degree appraisal procedure for considering seasonal conditions change according to claim 3, it is special
Levy and be：
In the step (S2), if considering, time span is taken as the equal four seasons, and when ignoring the influence of element repair time：
Wherein, ＜ ＜ are represented a kind of " newline "；
The fault rate in each season and the relation (6) of average annual fault rate can be obtained by then into (4) (5) being brought into (3)：
。
5. a kind of power transmission and transformation system health degree appraisal procedure for considering seasonal conditions change according to claim 1, it is special
Levy and be：
Comprise the following steps again in the step (S3)：
(S3-1) system, network parameter, machine unit characteristic, load curve, the reliability model of element, seasonal failure are initialized
Rate, repair rate；
(S3-2) emulation initial time Tsys=0；
(S3-3) all elements are randomly generated with state panorama arrival time T according to the emulation mode of inhomogeous Poisson process, and
T at the time of taking the minimum value min (T) to be changed as next system modesys=Tsys+min(T)；
(S3-4) system mode is analyzed；State analysis includes Load flow calculation and correction is controlled, if there is cutting load, then
Into step (S3-6)；
(S3-5) forecast failure collection scanning is carried out to system mode and correction is controlled, if at least one forecast failure can not expire
Sufficient constraints, then be determined as M state；Conversely, being then H states；
(S3-6) judge whether simulation time reaches 1 year, if be unsatisfactory for, be back to (S3-3)；
Otherwise, judge whether related reliability index meets the condition of convergence or reach default simulation times, if condition is met,
Then export health degree index；Otherwise, it is back to (S3-2).
6. a kind of power transmission and transformation system health degree appraisal procedure for considering seasonal conditions change according to claim 5, it is special
Levy and be：
The emulation mode of derivation power transmission and transformation element periodic intensity inhomogeous Poisson process running status described in step (S3-3)
Process be：
If η (T) is Poisson process intensity function, then the mean failure rate number of times in circuit [0, T] can be expressed as：
Wherein, T represents the panorama arrival time of element inhomogeous Poisson process；
Then the number of stoppages in [0, T], is designated as N (T), obeys Poisson distributions：
Then [0, T] is interval interior, and element fault probability is：
Therefore the conditional probability that failure-free operation T element breaks down in [T, T+X], X ＞ 0 is：
Wherein, X represents the malfunctioning internal transfer time of element inhomogeous Poisson process；
Then (T, T+X] desired value of interval element fault number of times can be expressed as：
Wherein, λ (ε) represents fault rate of the equipment at the ε moment；
If fault rate meets a point time span statistical condition (1), i.e., it is changed constantly by the cycle of year, another T=0 brings formula into
(10) then：
Wherein, ki(X)=λiX,Each auxiliary parameter computational methods be：gi=
λi·si, g0=0, hij=λi·sj, hi,0=0, s0=0, yj=8760j, j=0 ..., N；
Combinatorial formula (7) (10) (11), can be obtainedAnd
Wherein, auxiliary parameter computational methods be, τ=T+X,
7. a kind of power transmission and transformation system health degree appraisal procedure for considering seasonal conditions change according to claim 6, it is special
Levy and be：
It is as follows using T=0 as the method for starting simulation time generation periodic intensity neat fault time sequence when non-：
Step one：Randomly generate p independent random number pi~U (0,1), i=1 ..., p, it is list to calculate fault rate by (13)
The HPP malfunctioning internals transfer time HX of place value (λ=1)j：
HXi=-log (ui) (13)
Step 2：HPP failure panorama arrival time is calculated by (14)：
Step 3：Another T=HTiBring (12) into and calculate NHPP failure panoramas arrival time NTi：
NTi=Λ-1(HTi) (15)
Step 4：(15) are brought into (16) and obtain NHPP malfunctioning internals transfer time NXi：
NXi=NTi-NTi-1(16)。
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107784431A (en) * | 2017-09-15 | 2018-03-09 | 北京天元创新科技有限公司 | Technologies of Patrolling Line of Communication task processing method and device |
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2017
- 2017-03-31 CN CN201710204805.1A patent/CN107169261A/en active Pending
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| Title |
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| 栗文义等: "基于well-being模型的风力发电系统可靠性评估", 《华东电力》 * |
| 段东立等: "基于时变故障率与服务恢复时间模型的配电系统可靠性评估", 《中国电机工程学报》 * |
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| CN107784431A (en) * | 2017-09-15 | 2018-03-09 | 北京天元创新科技有限公司 | Technologies of Patrolling Line of Communication task processing method and device |
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