CN104009467A - Meter and power distribution network reliability assessment and prediction method for pre-arranging power outage influence - Google Patents

Abstract

The invention discloses a meter and a power distribution network reliability assessment and prediction method for pre-arranging power outage influence. Power distribution network power outage events caused by element faults and power outage pre-arranged by the power distribution network are considered, and the traversing method is adopted to assess and predict the power distribution network reliability index according to the element fault probability and pre-arranged power outage probability. A maintenance probability prediction model based on the element health degree and a dilatation probability prediction model based on element load probability are provided, not only can a current period meter and the reliability index of the power distribution network reliability index influenced by pre-arranged power outage be calculated after the event, but also the reliability index of the next operation period of the power distribution network can be predicted, the reference is provided for optimizing operation arrangement of the power distribution network, and the defects that a current reliability assessment method only can perform statistics on the power outage event of the power distribution network rather than providing guiding for operation arrangement of the power distribution network are overcome. The meter and the method are widely applied to reliability assessment and prediction of the power distribution network, and particularly suitable for reliability assessment and prediction of the power distribution network with the medium voltage being 6 kV-20 kV.

Description

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

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 pre-distribution network reliability index evaluation and Forecasting Methodology that arranges power failure of considering, belong to distribution network reliability technical field.

Background technology

Power distribution network safe and reliable operation is directly connected to normal production and the daily life of all trades and professions, power distribution network is carried out to reliability assessment and prediction, to improving distribution network reliability, improve the quality of power supply, improve power distribution network performance driving economy, optimize power distribution network operation and all tools such as arrange to be of great significance.

Along with the development of electric power system, power distribution network rack is more and more perfect, and user is also more and more higher to the requirement of distribution network reliability.On the other hand, the cause power failure consequence that causes of the continuous expansion of power distribution network scale is more serious; The development of electronics, computer system etc., user has proposed more and more higher requirement to distribution network reliability and power supply quality.According to the incomplete statistics of China Electricity Council, in user's power-off event, there is 80%-95% to be caused by power distribution network, 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 to assess, to guarantee the safe and reliable operation of electric power system.

For guaranteeing the reliability of electric power system, power department need in a planned way be processed each region in power distribution network and position arrangement power failure, checks the running status of power equipment, and according to load variations, power equipment is adjusted, changed and increases.By pre-arrangement, have a power failure, can find and get rid of the potential faults that power equipment exists, promote power supply capacity and the electric reliability of power distribution network.Existing, in fortune and the reliability theory assessment models and algorithm of power distribution network planning, mostly only considered the power failure that grid equipment fault causes, rarely had and consider to arrange power failure on the impact of power distribution network in advance.Lack and consider pre-reliability consideration model and the method for power failure on the impact of power distribution network that arrange, make evaluating reliability of distribution network result and power distribution network actual conditions differ larger.

In addition, current evaluating reliability of distribution network mostly is statistical property afterwards, and the power-off event a upper cycle of operation having been occurred is added up, and then calculates the reliability index of this cycle power distribution network.And in order to instruct the optimization operation of power distribution network, needing the pre-arrangement of next cycle of operation of arranged rational power distribution network to have a power failure (maintenance of distribution element and dilatation construction), this will have a power failure and carry out prior forecast pre-arrangement.

Summary of the invention

Technical problem to be solved by this invention is to provide a kind of evaluating reliability of distribution network and Forecasting Methodology that pre-arrangement has a power failure and affects of taking into account, to cause that the reason that pre-arrangement has a power failure is summed up as the maintenance of power distribution network element and the large class of power distribution network element dilatation two, and put forward respectively to predict the probabilistic model of the maintenance of power distribution network element and the dilatation of power distribution network element.The present invention can not only carry out after-action review to taking into account the distribution network reliability of pre-arrangement power failure impact, the reliability index obtaining more tallies with the actual situation, can also predict future take into account the have a power failure reliability of power distribution network of impact of pre-arrangement, instruct the operation arrangement of optimizing power distribution network.

The present invention is for solving the problems of the technologies described above by the following technical solutions:

The invention provides a kind of evaluating reliability of distribution network and Forecasting Methodology that pre-arrangement has a power failure and affects of taking into account, by prediction element probability of malfunction and the pre-power failure probability that arranges, the reliability of employing traversal Evaluation and Prediction power distribution network; Concrete implementation step is as follows:

Step 1, input power distribution network data; Described input power distribution network data comprise: input distribution net work structure information, line parameter circuit value, load data, security constraint, element fault and arrange in advance power failure probability, fault correction time and arrange in advance interruption duration; The historical health degree data of input power distribution network element and historical overhaul data; The historical load factor data of input power distribution network element and historical dilatation data;

Step 2, sets up the pre-power failure probabilistic model that arranges, and is specially: according to the historical health degree data of power distribution network element, historical overhaul data and the historical load factor data of element, historical dilatation data, set up element maintenance probabilistic model and element dilatation probabilistic model;

The expression formula of described element maintenance probabilistic model is:

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

In formula, tt _ifor overhauling the time interval that arranges in advance maintenance for the i+1 time from the i time pre-arrangement;

The expression formula of described element dilatation probabilistic model is:

P＝c×[a×P ₁+(1-a)×P ₃]+(1-c)×[b×P ₂+(1-b)×P ₄]

In formula, P ₁, P ₂, P ₃and P ₄while being respectively the load factor average of only considering converting equipment, load factor peak value, load factor average added value, load factor peak value added value, the dilatation probability of power distribution network element; A, b, c are the weight coefficient that belongs to (0,1), and its value decides according to the actual conditions of power distribution network;

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

Step 4, the consistent power distribution network element of fault incidence is equivalent to a node, so that power distribution network is simplified, and the impact on all the other nodes according to node failure, malfunctioning node and the pre-power failure node that arranges are classified, meanwhile, calculate the probability of malfunction of each category node, arrange power failure probability, frequency of power cut and interruption duration in advance;

Step 5, adopt power distribution network node power failure traversal, the fault outage of each node and pre-arrangement power failure are enumerated respectively to calculating, in computational process, consider load transfer method and power distribution network security constraint based on principles such as load importance degree, distance priority and many feeder lines load balancing;

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

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

For malfunctioning node, be divided into following 4 classes: 1) category-A node, be not subject to fault effects, do not need to have a power failure; 2) category-B node, has a power failure after fault 1 time, and interruption duration is the Fault Isolation time; 3) C category node, has a power failure after fault 1 time, and interruption duration is for turning for the time; 4) D category node, has a power failure after fault 1 time, and interruption duration is fault correction time;

For pre-arrangement power failure node, be divided into following 4 classes: 1) category-A node, this category node is identical during with fault, is not subject to fault effects, does not need to have a power failure; 2) E category node, pre-arrangement has a power failure 1 time while having a power failure, and interruption duration is the power failure isolated operation time; 3) F category node, pre-arrangement has a power failure 1 time while having a power failure, and interruption duration is that power failure isolated operation adds the time of confession that turns; 4) G category node, pre-arrangement has a power failure 1 time while having a power failure, and interruption duration is to arrange idle time in advance.

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

(1) the invention will cause the pre-reason having a power failure that arranges to be summed up as element maintenance and element dilatation, maintenance Probabilistic Prediction Model based on element health degree and the dilatation Probabilistic Prediction Model based on element load rate have been proposed, not only can take into account the have a power failure distribution network reliability index of impact of pre-arrangement calculating current period afterwards, can also predict the reliability index of next cycle of operation of power distribution network, for optimizing power distribution network operation, arrange to provide reference;

(2) the pre-power failure Probabilistic Prediction Model highly versatile that arranges in the present invention, can directly embed current electric company Reliability Assessment Software, easy to utilize;

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

Accompanying drawing explanation

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

Fig. 2 is certain typical distribution net work structure of the embodiment of the present invention.

Fig. 3 is load transfer decision flow chart.

Embodiment

Describe embodiments of the present invention below in detail, the example of described execution mode is shown in the drawings, and wherein same or similar label represents same or similar element or has the element of identical or similar functions from start to finish.Below by the execution mode being described with reference to the drawings, be exemplary, only for explaining the present invention, and can not be interpreted as limitation of the present invention.

Those skilled in the art of the present technique are understandable that, unless specially statement, singulative used herein " ", " one ", " described " and " being somebody's turn to do " also can comprise plural form.Should be further understood that, the wording of using in specification of the present invention " comprises " and refers to and have described feature, integer, step, operation, element and/or assembly, but do not get rid of, do not exist or adds one or more other features, integer, step, operation, element, assembly and/or their group.Should be appreciated that, when we claim element to be " connected " or " coupling " when another element, it can be directly connected or coupled to other elements, or also can have intermediary element.In addition, " connection " used herein or " coupling " can comprise wireless connections or couple.Wording "and/or" used herein comprises arbitrary unit of listing item and all combinations that one or more is associated.

Those skilled in the art of the present technique are understandable that, unless otherwise defined, all terms used herein (comprising technical term and scientific terminology) have with the present invention under the identical meaning of the general understanding of those of ordinary skill in field.Should also be understood that such as those terms that define in general dictionary and should be understood to have the consistent meaning of meaning in the context with prior art, unless and definition as here, can not explain by idealized or too formal implication.

Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

The present invention designs a kind of evaluating reliability of distribution network and Forecasting Methodology that pre-arrangement has a power failure and affects of taking into account, by prediction element probability of malfunction and the pre-power failure probability that arranges, and the reliability of employing traversal Evaluation and Prediction power distribution network, concrete implementation step is as follows:

Step 1, input power distribution network data;

Step 2, sets up the pre-power failure probabilistic model that arranges, and is specially: according to the historical health degree data of power distribution network element, historical overhaul data and the historical load factor data of element, historical dilatation data, set up element maintenance probabilistic model and element dilatation probabilistic model;

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

Step 4, the consistent power distribution network element of fault incidence is equivalent to a node, so that power distribution network is simplified, and the impact on all the other nodes according to node failure, malfunctioning node and the pre-power failure node that arranges are classified, meanwhile, calculate the probability of malfunction of each category node, arrange power failure probability, frequency of power cut and interruption duration in advance;

Step 5, adopts traversal to enumerate respectively calculating to the fault outage of each node and pre-arrangement power failure;

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

Below in conjunction with specific embodiment, technical scheme of the present invention is further elaborated, as shown in Figure 2, specific implementation process as shown in Figure 1 for certain typical distribution net work structure of the present embodiment.

Step 1, input distribution net work structure information, line parameter circuit value, load data, security constraint, element fault arranges power failure probability, fault correction time and arranges in advance interruption duration with pre-.

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

Table 1:

Wherein, power distribution network load point data are 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)
			Transformer	0.015	200
Overhead wire	0.065	5

Wherein, power distribution network element arranges in advance interruption duration, isolation time, turns as shown in table 4 for time data.

Table 4:

Wherein, load power factor in power distribution network the maximum hours of operation of line loss t=3000 hour/year, circuit model is selected LGJ-240, and resistance is r=0.132 Ω/km, and reactance is x=1.136s/km, maximum permissible current I _max=613A.

Step 2, sets up the pre-power failure probabilistic model that arranges, and comprises and sets up element maintenance probabilistic model and element dilatation probabilistic model.Element mainly comprises switch, transmission line and converting equipment.

A) set up element maintenance probabilistic model

Service time and the failure rate of element meet bath-tub curve, and Weibull distribution can more intactly embody the feature of bath-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 be in the hope of its failure dense function, Reliability Function and the failure rate function of this equipment, 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 function 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 ∈ [t ₁, t ₂] running time in, power distribution network element at the risk function of time t is

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

In formula, H (t ₁) be that element is at time t ₁time value-at-risk.

The value-at-risk of supposing the i time rear element of maintenance of power distribution network element is H (t _i+), and its maximum risk allowing is H _max.When value-at-risk reaches H _maxtime element must carry out i+1 time maintenance, so

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

Power distribution network element is from the i time pre-repair time t that arranges _ito the i+1 time pre-repair time t that arranges _i+1between the time interval, can be obtained by following relation

tt _i＝t _i+1-t _i (7)

By adjacent twice pre-time interval tti that arranges maintenance of formula (7) gained, known pre-arrangement maintenance probability is (times/year).

Can obtain thus, the expression formula of element maintenance probabilistic model is:

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

B) set up element dilatation probabilistic model

Power distribution network element needs dilatation to be mainly because the load factor of element increases, and the four class load factors that wherein have the greatest impact are: the added value Δ δ of load factor average δ, load factor peak value σ, load factor average and the added value Δ σ of load factor peak value.

While only considering the load factor average of converting equipment, dilatation possibility P ₁with the functional relation of load factor average δ be

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

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

While only considering the load factor peak value of converting equipment, dilatation possibility P ₂with the functional relation of load factor peak value σ be

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

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

While only considering the added value of load factor average of converting equipment, dilatation possibility P ₃with the functional relation of the added value Δ δ of load factor average be

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

While only considering the added value of load factor peak value of converting equipment, dilatation possibility P ₄with the functional relation of the added value Δ σ of load factor peak value be

$P_4=\frac{1}{\stackrel{\&OverBar;}{{\mathrm{\&sigma;}}_{+}}-\stackrel{\&OverBar;}{{\mathrm{\&sigma;}}_{-}}}{P}_1\mathrm{\&Delta;\&sigma;}---\left(11\right)$

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

P＝c×[a×P ₁+(1-a)×P ₃]+(1-c)×[b×P ₂+(1-b)×P ₄] (12)

In formula, a, b, c are the weight coefficient that belongs to (0,1), and value need to decide according to the actual conditions of power distribution network.

Step 3, according to element maintenance probabilistic model and element dilatation probabilistic model, predicts the pre-arrangement power failure probability of all power distribution network elements.

The pre-arrangement power failure probability data of transmission line in power distribution network (switch on circuit also counts) is as shown in table 5.

Table 5:

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

Table 6:

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

The consistent power distribution network element of fault incidence is equivalent to a node, so that power distribution network is simplified, and the impact on all the other nodes according to node failure, malfunctioning node and the pre-power failure node that arranges are classified, and calculate the probability of malfunction of each category node, arrange power failure probability, frequency of power cut and interruption duration in advance.

For distribution network failure node, can be divided into following 4 classes: 1) category-A node, be not subject to fault effects, do not need to have a power failure; 2) category-B node, has a power failure after fault 1 time, and interruption duration is the Fault Isolation time; 3) C category node, has a power failure after fault 1 time, and interruption duration is for turning for the time; 4) D category node, has a power failure after fault 1 time, and interruption duration is fault correction time.

For the power distribution network node that arranges in advance to have a power failure, can be divided into following 4 classes: 1) category-A node, this category node is identical during with fault, is not subject to fault effects, does not need to have a power failure; 2) E category node, pre-arrangement has a power failure 1 time while having a power failure, and interruption duration is the power failure isolated operation time; 3) F category node, pre-arrangement has a power failure 1 time while having a power failure, and interruption duration is that power failure isolated operation adds the time of confession that turns; 4) G category node, pre-arrangement has a power failure 1 time while having a power failure, and interruption duration is to arrange idle time in advance.

Step 5, adopts power distribution network node power failure traversal, and the fault outage of each node and pre-arrangement power failure are enumerated respectively to calculating.

Parameter comprises:

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

B) system System average interruption duration, Suo Xie SAID (SAIDI): in during adding up, the System average interruption duration, Suo Xie SAID that all users that move in system stand in during adding up (hour/user);

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

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

E) the system availability (ASAI) of on average powering: in during adding up, the customer interrupted ratio mean value (%) of total power supply hourage that hourage and user require that do not have a power failure in system;

F) user on average lacks amount of power supply (AENS): during statistics, on average each customer interrupted lacks the electric weight (kVA) of confession because having a power failure.

In computational process, consider load transfer method and power distribution network security constraint based on principles such as load importance degree, distance priority and many feeder lines load balancing.Turn and supply route searching based on graph theory BFS method, as shown in Figure 3, specific as follows:

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

B) fault point or the pre-upstream node of stoppage in transit point that arranges can continue power supply by original supply terminals, and when fault or pre-arrangement are stopped transport, they have a power failure once, and interruption duration is isolation time, is category-B node or E category node.The downstream node of failure judgement point or pre-arrangement stoppage in transit point turns for possibility below;

C) judgement contact node can turn the node of confession.From a certain interconnection node, by BFS, search for, until fault point or arrange in advance stoppage in transit point, other interconnection nodes or there is no other nodes, all nodes that search all can turn confession by this interconnection node.All interconnection nodes are repeated to this process, find out the potential of all interconnection nodes and turn for set of node V _lost.Do not have searched to node be D category node or G category node;

D) calculate the turned confession capacity S of all interconnection nodes _g, suppose potential turning for set of node V _losttotal power failure amount is S _lost, be specially:

1) if S _g>S _lost, potential turning for all nodes in set of node all can be turned confession by interconnection node.Fault or pre-while arranging to stop transport, they have a power failure once, and interruption duration is that isolation time adds and turns the confession time, is C category node or F category node;

2) if S _g<S _lost, potential turning for part of nodes in set of node can be turned confession by interconnection node, fault or pre-while arranging to stop transport, and they have a power failure once, and interruption duration is that isolation time adds and turns the time of confession, is C category node or F category node; Other nodes are for being D category node or G category node, and their total electric weight is S _g-S _lost.Therefore need to continue which node of judgement and can be turned confession, the factor of considering in three judgements is provided below:

I) " for electrical equalization " principle.Select the interconnection node of Capacity Margin maximum, by BFS, search for, the interconnection node that the load of the node of lower one deck is classified as to 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 _lostduring with power failure family, count relation.Total power failure amount is certain, and because the load of different nodes is different, amount difference has a power failure while selecting different nodes to specialize in;

Iii) select which load to turn while supplying, consider the importance degree of load.If take no account of load importance degree, total power failure amount is S _g-S _lost; If take into account load importance, total power failure amount may be greater than S _g-S _lost.

Step 6, each node power failure of power distribution network has a power failure and enumerates the result calculating according to each node after enumerating and having calculated, and can calculate reliability index the Output rusults of the power distribution network of prediction.

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

Table 7:

From the above results, use this method not only can assess the reliability of power distribution network, can also predict the reliability in power distribution network future.Algorithm is realized simple, and versatility is better, can directly embed the existing evaluating reliability of distribution network system of electric company, is easy to engineering application.

The above; it is only the embodiment in the present invention; but protection scope of the present invention is not limited to this; any people who is familiar with this technology is in the disclosed technical scope of the present invention; can understand conversion or the replacement expected; all should be encompassed in of the present invention comprise scope within, therefore, protection scope of the present invention should be as the criterion with the protection range of claims.

Claims (5)

1. take into account evaluating reliability of distribution network and a Forecasting Methodology that pre-arrangement has a power failure and affects, it is characterized in that, by prediction element probability of malfunction and the pre-power failure probability that arranges, the reliability of employing traversal Evaluation and Prediction power distribution network; Concrete implementation step is as follows:

Step 1, input power distribution network data;

Step 2, sets up the pre-power failure probabilistic model that arranges, and is specially: according to the historical health degree data of power distribution network element, historical overhaul data and the historical load factor data of element, historical dilatation data, set up element maintenance probabilistic model and element dilatation probabilistic model;

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

Step 4, the consistent power distribution network element of fault incidence is equivalent to a node, so that power distribution network is simplified, and the impact on all the other nodes according to node failure, malfunctioning node and the pre-power failure node that arranges are classified, meanwhile, calculate the probability of malfunction of each category node, arrange power failure probability, frequency of power cut and interruption duration in advance;

Step 5, adopts traversal to enumerate respectively calculating to the fault outage of each node and pre-arrangement power failure;

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

2. a kind of evaluating reliability of distribution network and Forecasting Methodology that pre-arrangement has a power failure and affects of taking into account according to claim 1, is characterized in that, inputs power distribution network data and comprise described in step 1:

Input distribution net work structure information, line parameter circuit value, load data, security constraint, element fault and arrange in advance power failure probability, fault correction time and arrange in advance interruption duration; The historical health degree data of input power distribution network element and historical overhaul data; The historical load factor data of input power distribution network element and historical dilatation data.

3. a kind of evaluating reliability of distribution network and Forecasting Methodology that pre-arrangement has a power failure and affects of taking into account according to claim 1, is characterized in that, the expression formula of the maintenance of element described in step 2 probabilistic model is:

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

In formula, tt _ifor overhauling the time interval that arranges in advance maintenance for the i+1 time from the i time pre-arrangement.

4. a kind of evaluating reliability of distribution network and Forecasting Methodology that pre-arrangement has a power failure and affects of taking into account according to claim 1, is characterized in that, the expression formula of the dilatation of element described in step 2 probabilistic model is:

P＝c×[a×P ₁+(1-a)×P ₃]+(1-c)×[b×P ₂+(1-b)×P ₄]

In formula, P ₁, P ₂, P ₃and P ₄while being respectively the load factor average of only considering converting equipment, load factor peak value, load factor average added value, load factor peak value added value, the dilatation probability of power distribution network element; A, b, c are the weight coefficient that belongs to (0,1), and its value decides according to the actual conditions of power distribution network.

5. a kind of evaluating reliability of distribution network and Forecasting Methodology that pre-arrangement has a power failure and affects of taking into account according to claim 1, is characterized in that, described in step 4, malfunctioning node and the pre-power failure node that arranges is classified, specific as follows:

For malfunctioning node, be divided into following 4 classes: 1) category-A node, be not subject to fault effects, do not need to have a power failure; 2) category-B node, has a power failure after fault 1 time, and interruption duration is the Fault Isolation time; 3) C category node, has a power failure after fault 1 time, and interruption duration is for turning for the time; 4) D category node, has a power failure after fault 1 time, and interruption duration is fault correction time;

For pre-arrangement power failure node, be divided into following 4 classes: 1) category-A node, this category node is identical during with fault, is not subject to fault effects, does not need to have a power failure; 2) E category node, pre-arrangement has a power failure 1 time while having a power failure, and interruption duration is the power failure isolated operation time; 3) F category node, pre-arrangement has a power failure 1 time while having a power failure, and interruption duration is that power failure isolated operation adds the time of confession that turns; 4) G category node, pre-arrangement has a power failure 1 time while having a power failure, and interruption duration is to arrange idle time in advance.