CN104009467B - A kind of evaluating reliability of distribution network taking into account pre-arranged power failure impact and Forecasting Methodology - Google Patents

Abstract

The invention discloses a kind of evaluating reliability of distribution network taking into account pre-arranged power failure impact and Forecasting Methodology, consider to be caused power distribution network power-off event by element fault and power distribution network pre-arranged power failure simultaneously, according to element fault probability and pre-arranged power failure probability, adopt traversal assessment and prediction distribution network reliability index.The present invention proposes based on the maintenance Probabilistic Prediction Model of element health degree and the dilatation Probabilistic Prediction Model based on element load rate, the distribution network reliability index that pre-arranged power failure affects can not only be taken into account calculating current period afterwards, the reliability index of next cycle of operation of power distribution network can also be predicted, arrangement offer reference is run for optimizing power distribution network, overcome current reliability appraisal procedure can only power distribution network power-off event be added up, and can not run for power distribution network and arrange to provide the shortcoming instructed.The present invention is widely used in evaluating reliability of distribution network and prediction, is particularly well-suited to medium voltage distribution network reliability assessment and the prediction of 6-20kV.

Description

A kind of evaluating reliability of distribution network taking into account pre-arranged power failure impact and Forecasting Methodology

Technical field

The present invention relates to a kind of distribution network reliability index evaluation and Forecasting Methodology, be specifically related to a kind of distribution network reliability index evaluation and Forecasting Methodology considering that pre-arranged has a power failure, belong to distribution network reliability technical field.

Background technology

Power distribution network safe and reliable operation is directly connected to the normal of all trades and professions and produces and daily life, power distribution network is carried out reliability assessment and prediction, to improving distribution network reliability, improves the quality of power supply, improve power distribution network performance driving economy, optimize power distribution network and run arrangement etc. and all have and be of great significance.

Along with the development of power system, distribution network structure is more and more perfect, and user is also more and more higher to the requirement of distribution network reliability.On the other hand, power distribution network scale constantly to expand the consequence caused that causes power failure more serious；The development of electronics, computer system etc., distribution network reliability and power supply quality are proposed increasingly higher requirement by user.Incomplete statistics according to China Electricity Council, has 80%-95% to be caused by power distribution network in the power-off event of user, and the reliability direct relation of distribution system the continued power degree of power consumer.Therefore, it is necessary to the reliability of power distribution network is estimated, to ensure the safe and reliable operation of power system.

For ensureing the reliability of electric power system, region each in power distribution network and position arrangement power failure need in a planned way be processed by power department, check the running status of power equipment, and according to load variations, power equipment are adjusted, change and expand.Had a power failure by pre-arranged, it is possible to find and get rid of the potential faults that power equipment exists, promote power supply capacity and the electricity reliability of power distribution network.In the existing reliability theory assessment models for the power distribution network with planning in fortune and algorithm, mostly only considered the power failure that grid equipment fault causes, rarely have the consideration pre-arranged power failure impact on power distribution network.Lack and consider that pre-arranged has a power failure the reliability consideration model on the impact of power distribution network and method so that evaluating reliability of distribution network result differs bigger with power distribution network practical situation.

It addition, current power distribution network reliability assessment mostly is statistical property afterwards, the power-off event namely a upper cycle of operation being had occurred and that is added up, and then calculates the reliability index of this cycle power distribution network.And in order to instruct the optimization of power distribution network to run, it is necessary to the pre-arranged of arranged rational next cycle of operation of power distribution network has a power failure (maintenance of distribution element and dilatation construction), and pre-arranged will be had a power failure and predict in advance by this.

Summary of the invention

The technical problem to be solved is to provide a kind of evaluating reliability of distribution network taking into account pre-arranged power failure impact and Forecasting Methodology, the reason causing pre-arranged to have a power failure is attributed to the maintenance of power distribution network element and the big class of power distribution network element dilatation two, and puts forward the probabilistic model of prediction power distribution network element maintenance and power distribution network element dilatation respectively.The distribution network reliability taking into account pre-arranged power failure impact can not only be carried out after-action review by the present invention, the reliability index obtained more tallies with the actual situation, future can also be predicted take into account that pre-arranged has a power failure the reliability of power distribution network of impact, instruct the operation arrangement optimizing power distribution network.

The present invention solves above-mentioned technical problem by the following technical solutions:

The present invention provides a kind of evaluating reliability of distribution network taking into account pre-arranged power failure impact and Forecasting Methodology, by predicting element probability of malfunction and pre-arranged power failure probability, adopts traversal assessment and predicts the reliability of power distribution network；It is embodied as step as follows:

Step 1, inputs power distribution network data；

Step 2, set up pre-arranged power failure probabilistic model, particularly as follows: according to power distribution network element history health degree data, history overhaul data and element historic load rate data, history dilatation data, set up element maintenance probabilistic model and element dilatation probabilistic model, particularly as follows:

The expression formula of element maintenance probabilistic model is:

$P=\frac{1}{{\mathrm{tt}}_i}$

In formula,Probability, tt is overhauled for pre-arranged_iFor from i & lt pre-arranged repair time t_iTo i+1 time pre-arranged repair time t_i+1Between interval；tt_i=t_i+1-t_I,ByDraw, H (t_i+) overhaul for power distribution network element i & lt after the value-at-risk of element, H_maxFor the risk that it is maximum allowable, λ (t) is failure rate estimation,T is the time, and α is scale parameter, and β is form parameter；

The expression formula of element dilatation probabilistic model is:

P=c × [a × P₁+(1-a)×P₃]+(1-c)×[b×P₂+(1-b)×P₄]

In formula, P₁、P₂、P₃And P₄Respectively only consider the dilatation probability when load factor average of converting equipment, load factor peak value, load factor average value added, load factor peak value value added；A, b, c are the weight coefficient belonging to (0,1), and its value determines according to the practical situation of power distribution network；

Step 3, according to the element maintenance probabilistic model set up in step 2 and element dilatation probabilistic model, it was predicted that the pre-arranged power failure probability of all power distribution network elements；

Step 4, it is a node by power distribution network element equivalent consistent for fault incidence, so that power distribution network is simplified, and according to the node failure impact on all the other nodes, malfunctioning node and pre-arranged power failure node are classified, meanwhile, the probability of malfunction of each category node, pre-arranged power failure probability, frequency of power cut and power off time are calculated；

Step 5, adopts traversal that fault outage and the pre-arranged power failure of each node are enumerated calculating respectively；

Step 6, has a power failure according to each node in step 5 and enumerates the result of calculating, can calculate the reliability index of the power distribution network obtaining prediction and export.

As the further prioritization scheme of the present invention, input power distribution network data described in step 1 and include: input distribution net work structure information, line parameter circuit value, load data, security constraint, element fault and pre-arranged power failure probability, fault correction time and pre-arranged power off time；Input power distribution network element history health degree data and history overhaul data；Input power distribution network element historic load rate data and history dilatation data.

As the further prioritization scheme of the present invention, described in step 4, malfunctioning node and pre-arranged power failure node are classified, specific as follows:

For malfunctioning node, it is divided into following 4 classes: 1) A category node, do not affected by fault, it is not necessary to have a power failure；2) B category node, has a power failure 1 time after fault, and power off time is the Fault Isolation time；3) C category node, has a power failure 1 time after fault, and power off time is for turning for the time；4) D category node, has a power failure 1 time after fault, and power off time is fault correction time；

For pre-arranged power failure node, it is divided into following 4 classes: 1) A category node, identical when this category node is with fault, do not affected by fault, it is not necessary to have a power failure；2) E category node, has a power failure 1 time when pre-arranged has a power failure, and power off time is the power failure isolated operation time；3) F category node, has a power failure 1 time when pre-arranged has a power failure, and power off time is that power failure isolated operation is plus turning for the time；4) G category node, has a power failure 1 time when pre-arranged has a power failure, and power off time is pre-arranged idle time.

The present invention adopts above technical scheme compared with prior art, has following technical effect that

(1) the invention will cause the reason that pre-arranged has a power failure be attributed to element maintenance and element dilatation, propose based on the maintenance Probabilistic Prediction Model of element health degree and the dilatation Probabilistic Prediction Model based on element load rate, the distribution network reliability index that pre-arranged power failure affects can not only be taken into account calculating current period afterwards, the reliability index of next cycle of operation of power distribution network can also be predicted, run arrangement offer reference for optimizing power distribution network；

(2) pre-arranged power failure Probabilistic Prediction Model highly versatile in the present invention, can be directly embedded into current electric company Reliability Assessment Software, it is simple to popularization and application；

(3) present invention is widely used in evaluating reliability of distribution network and prediction, is particularly well-suited to medium voltage distribution network reliability assessment and the prediction of 6-20kV.

Accompanying drawing explanation

Fig. 1 is the method flow diagram of the present invention.

Fig. 2 is certain typical distribution web frame of the embodiment of the present invention.

Fig. 3 is load transfer decision flow chart.

Detailed description of the invention

Being described below in detail embodiments of the present invention, the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or has the element of same or like function from start to finish.The embodiment described below with reference to accompanying drawing is illustrative of, and is only used for explaining the present invention, and is not construed as limiting the claims.

It is understood that unless expressly stated, singulative used herein " ", " one ", " described " and " being somebody's turn to do " may also comprise plural form to those skilled in the art of the present technique.Should be further understood that, the wording " including " used in the description of the present invention refers to there is described feature, integer, step, operation, element and/or assembly, but it is not excluded that existence or adds other features one or more, integer, step, operation, element, assembly and/or their group.It should be understood that when we claim element to be " connected " or during " coupled " to another element, it can be directly connected or coupled to other elements, or can also there is intermediary element.Additionally, " connection " used herein or " coupling " can include wireless connections or couple.Wording "and/or" used herein includes one or more any cell listing item being associated and all combinations.

Those skilled in the art of the present technique it is understood that unless otherwise defined, all terms used herein (include technical term and scientific terminology) and have with the those of ordinary skill in art of the present invention be commonly understood by identical meaning.Should also be understood that in such as general dictionary, those terms of definition should be understood that have the meaning consistent with the meaning in the context of prior art, and unless defined as here, will not explain by idealization or excessively formal implication.

Below in conjunction with accompanying drawing, technical scheme is described in further detail:

The present invention designs a kind of evaluating reliability of distribution network taking into account pre-arranged power failure impact and Forecasting Methodology, by predicting element probability of malfunction and pre-arranged power failure probability, adopts traversal assessment and predicts the reliability of power distribution network, being embodied as step as follows:

Step 1, inputs power distribution network data；

Step 2, set up pre-arranged power failure probabilistic model, particularly as follows: according to power distribution network element history health degree data, history overhaul data and element historic load rate data, history dilatation data, set up element maintenance probabilistic model and element dilatation probabilistic model, particularly as follows:

The expression formula of element maintenance probabilistic model is:

$P=\frac{1}{{\mathrm{tt}}_i}$

In formula, tt_iFor overhauling the interval of i+1 time pre-arranged maintenance from i & lt pre-arranged；

The expression formula of element dilatation probabilistic model is:

P=c × [a × P₁+(1-a)×P₃]+(1-c)×[b×P₂+(1-b)×P₄]

In formula, P₁、P₂、P₃And P₄Respectively only consider the dilatation probability when load factor average of converting equipment, load factor peak value, load factor average value added, load factor peak value value added；A, b, c are the weight coefficient belonging to (0,1), and its value determines according to the practical situation of power distribution network；

Step 3, according to the element maintenance probabilistic model set up in step 2 and element dilatation probabilistic model, it was predicted that the pre-arranged power failure probability of all power distribution network elements；

Step 4, it is a node by power distribution network element equivalent consistent for fault incidence, so that power distribution network is simplified, and according to the node failure impact on all the other nodes, malfunctioning node and pre-arranged power failure node are classified, meanwhile, the probability of malfunction of each category node, pre-arranged power failure probability, frequency of power cut and power off time are calculated；

Step 5, adopts traversal that fault outage and the pre-arranged power failure of each node are enumerated calculating respectively；

Step 6, has a power failure according to each node in step 5 and enumerates the result of calculating, can calculate the reliability index of the power distribution network obtaining prediction and export.

Below in conjunction with specific embodiment, technical scheme is further elaborated, certain typical distribution web frame of the present embodiment as in figure 2 it is shown, specific implementation process as shown in Figure 1.

Step 1, inputs distribution net work structure information, line parameter circuit value, load data, security constraint, element fault and pre-arranged power failure probability, fault correction time and pre-arranged power off time.

Wherein, distribution feeder type and length data are as shown in table 1.

Table 1:

Wherein, distribution network load point data is as shown in table 2.

Table 2:

Load point	User type	Average load (MW)	Number of users
				1-3,10,11	Residential	0.535	210
12,17-19	Residential	0.450	200
				8	Small user	1.00	1
9	Small user	1.50	1
				4,5,13,14,20,21	Govt/Inst	0.566	1
6,7,15,16,22	Commercial	0.454	10
				1-3,10,11	Residential	0.535	210
12,17-19	Residential	0.450	200
				8	Small user	1.00	1
9	Small user	1.50	1
				4,5,13,14,20,21	Govt/Inst	0.566	1
6,7,15,16,22	Commercial	0.454	10

Wherein, the reliability data that power distribution network element fault is relevant is as shown in table 3.

Table 3:

Element	Average permanent fault rate (times/year)	Average time for repair of breakdowns (hour/time)
			Transformator	0.015	200
Aerial line	0.065	5

Wherein, power distribution network element pre-arranged power off time, isolation time, turn for time data as shown in table 4.

Table 4:

Wherein, load power factor cos φ=0.9 in power distribution network, line loss maximum hours of operation t=3000 hour/year, circuit model selects LGJ-240, and resistance is r=0.132 Ω/km, and reactance is x=1.136s/km, maximum allowed current I_max=613A.

Step 2, sets up pre-arranged power failure probabilistic model, including setting up element maintenance probabilistic model and element dilatation probabilistic model.Element mainly includes switch, transmission line of electricity and converting equipment.

A) element maintenance probabilistic model is set up

The use time of element and crash rate meet tub curve, and Weibull distribution can more fully embody the feature of tub curve.To a certain power distribution network element, the failure distribution function of its Weibull distribution can be expressed as

$F\left(t\right)=1-\exp \[-{\left(\frac{t}{\mathrm{\α}}\right)}^{\mathrm{\β}}\]---\left(1\right)$

And then can in the hope of its failure dense function of this equipment, Reliability Function and failure rate estimation, specific as follows:

Failure dense function is

$f\left(t\right)=\frac{{\mathrm{\βt}}^{\mathrm{\β}-1}}{{\mathrm{\α}}^{\mathrm{\β}}}\exp \[-{\left(\frac{t}{\mathrm{\α}}\right)}^{\mathrm{\β}}\]---\left(2\right)$

Reliability Function is

$R\left(t\right)=1-F\left(t\right)=\exp \[-{\left(\frac{t}{\mathrm{\α}}\right)}^{\mathrm{\β}}\]---\left(3\right)$

And failure rate estimation is

$\mathrm{\λ}\left(t\right)=\frac{f\left(t\right)}{R\left(t\right)}=\frac{{\mathrm{\βt}}^{\mathrm{\β}-1}}{{\mathrm{\α}}^{\mathrm{\β}}}---\left(4\right)$

In formula, t is the time, and α is scale parameter, and β is form parameter.

At t ∈ [t₁,t₂] the operation time in, power distribution network element at the risk function of time t is

$H\left(t\right)=H\left(t_1\right)+{\∫}_{t_1}^t\mathrm{\λ}\left(t\right)dt---\left(5\right)$

In formula, H (t₁) for element at time t₁Time value-at-risk.

Assume that the value-at-risk of element is H (t after the maintenance of power distribution network element i & lt_i+), and its maximum allowable risk is H_max.When value-at-risk reaches H_maxTime element must carry out i+1 time maintenance, then

$H_{max}=H\left(t_{i+}\right)+{\∫}_{t_{i+}}^{t_{i+1}}\mathrm{\λ}\left(t\right)dt---\left(6\right)$

Then power distribution network element is from i & lt pre-arranged repair time t_iTo i+1 time pre-arranged repair time t_i+1Between interval, can be obtained by following relation

tt_i=t_i+1-t_i(7)

Interval tt by adjacent twice pre-arranged maintenance of formula (7) gained_i, it is known that pre-arranged maintenance probability is(times/year).

Thus can obtaining, the expression formula of element maintenance probabilistic model is:

$P=\frac{1}{{\mathrm{tt}}_i}.$

B) element dilatation probabilistic model is set up

Power distribution network element needs the load factor that dilatation is primarily due to element to increase, and the four class load factors wherein having the greatest impact are: the value added Δ σ of load factor average δ, load factor peak value σ, the value added Δ δ of load factor average and load factor peak value.

When only considering the load factor average of converting equipment, dilatation probability P₁With the functional relationship of load factor average δ it is

$P_1=\left\{\begin{array}{c}0,\mathrm{\&delta;}\&le;\stackrel{\&OverBar;}{{\mathrm{\&delta;}}_{\_}}\\ {\left(\frac{\mathrm{\&delta;}-\stackrel{\&OverBar;}{{\mathrm{\&delta;}}_{\_}}}{\stackrel{\&OverBar;}{{\mathrm{\&delta;}}_{+}}-\stackrel{\&OverBar;}{{\mathrm{\&delta;}}_{-}}}\right)}^e,\stackrel{\&OverBar;}{{\mathrm{\&delta;}}_{-}}<\mathrm{\&delta;}<\stackrel{\&OverBar;}{{\mathrm{\&delta;}}_{+}}\\ 100\%,\mathrm{\&delta;}\&GreaterEqual;\stackrel{\&OverBar;}{{\mathrm{\&delta;}}_{+}}\end{array}\right.---\left(8\right)$

In formula,For the maximum load rate average of element during without dilatation,The minimum load rate average of element during for necessary dilatation.

When only considering the load factor peak value of converting equipment, dilatation probability P₂With the functional relationship of load factor peak value σ it is

$P_2=\left\{\begin{array}{c}0,\mathrm{\&sigma;}\&le;\stackrel{\&OverBar;}{{\mathrm{\&sigma;}}_{\_}}\\ {\left(\frac{\mathrm{\&sigma;}-\stackrel{\&OverBar;}{{\mathrm{\&sigma;}}_{\_}}}{\stackrel{\&OverBar;}{{\mathrm{\&sigma;}}_{+}}-\stackrel{\&OverBar;}{{\mathrm{\&sigma;}}_{-}}}\right)}^e,\stackrel{\&OverBar;}{{\mathrm{\&sigma;}}_{-}}<\mathrm{\&delta;}<\stackrel{\&OverBar;}{{\mathrm{\&sigma;}}_{+}}\\ 100\%,\mathrm{\&sigma;}\&GreaterEqual;\stackrel{\&OverBar;}{{\mathrm{\&sigma;}}_{+}}\end{array}\right.---\left(9\right)$

In formula,For the maximum load rate peak value of element during without dilatation,The minimum load rate peak value of element during for necessary dilatation.

When only considering the value added of load factor average of converting equipment, dilatation probability P₃With the functional relationship of the value added Δ δ of load factor average it is

$P_3=\frac{1}{\stackrel{\&OverBar;}{{\mathrm{\&delta;}}_{+}}-\stackrel{\&OverBar;}{{\mathrm{\&delta;}}_{-}}}{P}_1\mathrm{\&Delta;}\mathrm{\&delta;}---\left(10\right)$

When only considering the value added of load factor peak value of converting equipment, dilatation probability P₄With the functional relationship of the value added Δ σ of load factor peak value it is

$P_4=\frac{1}{\stackrel{\&OverBar;}{{\mathrm{\&delta;}}_{+}}-\stackrel{\&OverBar;}{{\mathrm{\&delta;}}_{-}}}{P}_4\mathrm{\&Delta;}\mathrm{\&delta;}---\left(11\right)$

Comprehensive four factor load factors impact, the expression formula of element dilatation probabilistic model is:

P=c × [a × P₁+(1-a)×P₃]+(1-c)×[b×P₂+(1-b)×P₄](12)

In formula, a, b, c are the weight coefficient belonging to (0,1), and value needs the practical situation according to power distribution network to determine.

Step 3, overhauls probabilistic model and element dilatation probabilistic model, it was predicted that the pre-arranged power failure probability of all power distribution network elements according to element.

In power distribution network, the pre-arranged power failure probability data of transmission line of electricity (switch on circuit also counts) is as shown in table 5.

Table 5:

In power distribution network, the pre-arranged power failure probability data of transformator is as shown in table 6.

Table 6:

Step 4, power distribution network equivalent-simplification and node-classification.

It is a node by power distribution network element equivalent consistent for fault incidence, so that power distribution network is simplified, and according to the node failure impact on all the other nodes, malfunctioning node and pre-arranged power failure node are classified, and calculates the probability of malfunction of each category node, pre-arranged power failure probability, frequency of power cut and power off time.

Following 4 classes can be divided into: 1 for distribution network failure node) A category node, do not affected by fault, it is not necessary to have a power failure；2) B category node, has a power failure 1 time after fault, and power off time is the Fault Isolation time；3) C category node, has a power failure 1 time after fault, and power off time is for turning for the time；4) D category node, has a power failure 1 time after fault, and power off time is fault correction time.

Following 4 classes can be divided into: 1 for power distribution network pre-arranged power failure node) A category node, identical when this category node is with fault, do not affected by fault, it is not necessary to have a power failure；2) E category node, has a power failure 1 time when pre-arranged has a power failure, and power off time is the power failure isolated operation time；3) F category node, has a power failure 1 time when pre-arranged has a power failure, and power off time is that power failure isolated operation is plus turning for the time；4) G category node, has a power failure 1 time when pre-arranged has a power failure, and power off time is pre-arranged idle time.

Step 5, adopts power distribution network node power failure traversal, and fault outage and pre-arranged power failure to each node enumerate calculating respectively.

Parameter includes:

A) system System average interruption frequency (SAIFI): in during adding up, the average frequency of power cut of all users (secondary/family) in system；

B) system System average interruption duration (SAIDI): in during adding up, all users run in system during adding up in the System average interruption duration (hour/user) that stands；

C) user's System average interruption frequency (CAIFI): in during adding up, in system, customer interrupted on average has a power failure lasting number of times (secondary/family)；

D) user's System average interruption duration (CAIDI): in during adding up, customer interrupted System average interruption duration in system (hour/family)；

E) system is on average powered availability (ASAI): in during adding up, and in system, customer interrupted does not have a power failure ratio meansigma methods (%) of total hourage of powering that hourage requires with user；

F) user on average lacks delivery (AENS): during statistics, average each customer interrupted is because of the electricity (kVA) of the scarce confession that has a power failure.

Calculating process considers the load transfer method based on principles such as load importance degree, distance priority and many feeder lines load balancing and power distribution network security constraint.Turn for route searching based on graph theory BFS method, as it is shown on figure 3, specific as follows:

A) node of the feeder line of non-faulting point or pre-arranged stoppage in transit point is unaffected, for A category node；

B) trouble point or pre-arranged stop transport point upstream node can by original supply terminals continue power supply, fault or pre-arranged stop transport time, they have a power failure once, and power off time is isolation time, for B category node or E category node.The turning of downstream node of failure judgement point or pre-arranged stoppage in transit point supplies probability below；

C) judge that contact node can turn the node of confession.From a certain interconnection node, scanning for by BFS, until trouble point or pre-arranged stoppage in transit point, other interconnection nodes or do not have other nodes, all nodes searched all can be turned confession by this interconnection node.All interconnection nodes are repeated this process, finds out potential turn of all interconnection nodes for set of node V_lost.Do not have searched to node be D category node or G category node；

D) the turned confession capacity S of all interconnection nodes is calculated_g, it is assumed that potential turn for set of node V_lostTotal power failure amount is S_lost, particularly as follows:

1) if S_g>S_lost, then potential turn all can be turned confession by interconnection node for all nodes in set of node.When fault or pre-arranged are stopped transport, they have a power failure once, and power off time is that isolation time adds a turn confession time, for C category node or F category node；

2) if S_g<S_lost, then potential turn for part of nodes in set of node can by interconnection node turn confession, fault or pre-arranged stop transport time, they have a power failure once, and power off time is that isolation time adds and turns for the time, for C category node or F category node；Other nodes are D category node or G category node, and their total electricity is S_g-S_lost.Therefore need to continue to judge which node can be turned confession, provide below three factors considered in judging:

I) principle " for electrical equalization ".Selecting the interconnection node that Capacity Margin is maximum, scan for by BFS, the interconnection node that the load of the node of next layer is classified as this Capacity Margin maximum is powered.Repeat this process, until the heap(ed) capacity nargin in all interconnection nodes meets the load of next node not.

Ii) total power failure amount S_g-S_lostNumber relation during with power failure family.Total power failure amount is certain, and owing to the loading of different nodes is different, when selecting different nodes to specialize in, then power failure amount is different；

When iii) selecting which load to carry out turning confession, it is considered to the importance degree of load.If taking no account of load importance degree, then total power failure amount is S_g-S_lost；If taking into account load importance, then total power failure amount is likely larger than S_g-S_lost。

Step 6, each node of power distribution network has a power failure and enumerates after calculating completes, and has a power failure according to each node and enumerates calculated result, can calculate the reliability index of the power distribution network obtaining prediction and export result.

The reliability index prediction taking into account pre-arranged power failure impact of power distribution network is as shown in table 7.

Table 7:

From the above it can be seen that use this method to be possible not only to the reliability of assessment power distribution network, moreover it is possible to the reliability that power distribution network is following is predicted.Algorithm realizes simple, and versatility is better, can be directly embedded into the existing evaluating reliability of distribution network system of electric company, it is easy to engineer applied.

The above; it is only the detailed description of the invention in the present invention; but protection scope of the present invention is not limited thereto; any people being familiar with this technology is in the technical scope that disclosed herein; it is appreciated that the conversion or replacement expected; what all should be encompassed in the present invention comprises within scope, and therefore, protection scope of the present invention should be as the criterion with the protection domain of claims.

Claims (3)

1. the evaluating reliability of distribution network taking into account pre-arranged power failure impact and Forecasting Methodology, it is characterised in that by predicting element probability of malfunction and pre-arranged power failure probability, adopt traversal assessment and predict the reliability of power distribution network；It is embodied as step as follows:

Step 1, inputs power distribution network data；

Step 2, set up pre-arranged power failure probabilistic model, particularly as follows: according to power distribution network element history health degree data, history overhaul data and element historic load rate data, history dilatation data, set up element maintenance probabilistic model and element dilatation probabilistic model, particularly as follows:

The expression formula of element maintenance probabilistic model is:

$P=\frac{1}{{\mathrm{tt}}_i}$

In formula, tt_iFor overhauling the interval of i+1 time pre-arranged maintenance from i & lt pre-arranged；

The expression formula of element dilatation probabilistic model is:

P=c × [a × P₁+(1-a)×P₃]+(1-c)×[b×P₂+(1-b)×P₄]

In formula, P₁、P₂、P₃And P₄Respectively only consider the dilatation probability when load factor average of converting equipment, load factor peak value, load factor average value added, load factor peak value value added；A, b, c are the weight coefficient belonging to (0,1), and its value determines according to the practical situation of power distribution network；

Step 3, according to the element maintenance probabilistic model set up in step 2 and element dilatation probabilistic model, it was predicted that the pre-arranged power failure probability of all power distribution network elements；

Step 4, it is a node by power distribution network element equivalent consistent for fault incidence, so that power distribution network is simplified, and according to the node failure impact on all the other nodes, malfunctioning node and pre-arranged power failure node are classified, meanwhile, the probability of malfunction of each category node, pre-arranged power failure probability, frequency of power cut and power off time are calculated；

Step 5, adopts traversal that fault outage and the pre-arranged power failure of each node are enumerated calculating respectively；

Step 6, has a power failure according to each node in step 5 and enumerates the result of calculating, can calculate the reliability index of the power distribution network obtaining prediction and export.

2. a kind of evaluating reliability of distribution network taking into account pre-arranged power failure impact according to claim 1 and Forecasting Methodology, it is characterised in that input power distribution network data described in step 1 and include:

Input distribution net work structure information, line parameter circuit value, load data, security constraint, element fault and pre-arranged power failure probability, fault correction time and pre-arranged power off time；Input power distribution network element history health degree data and history overhaul data；Input power distribution network element historic load rate data and history dilatation data.

3. a kind of evaluating reliability of distribution network taking into account pre-arranged power failure impact according to claim 1 and Forecasting Methodology, it is characterised in that described in step 4, malfunctioning node and pre-arranged power failure node are classified, specific as follows:

For malfunctioning node, it is divided into following 4 classes: 1) A category node, do not affected by fault, it is not necessary to have a power failure；2) B category node, has a power failure 1 time after fault, and power off time is the Fault Isolation time；3) C category node, has a power failure 1 time after fault, and power off time is for turning for the time；4) D category node, has a power failure 1 time after fault, and power off time is fault correction time；

For pre-arranged power failure node, it is divided into following 4 classes: 1) A category node, identical when this category node is with fault, do not affected by fault, it is not necessary to have a power failure；2) E category node, has a power failure 1 time when pre-arranged has a power failure, and power off time is the power failure isolated operation time；3) F category node, has a power failure 1 time when pre-arranged has a power failure, and power off time is that power failure isolated operation is plus turning for the time；4) G category node, has a power failure 1 time when pre-arranged has a power failure, and power off time is pre-arranged idle time.