CN104376504A - Power distribution system probability reliability assessing method based on analytical method - Google Patents

Abstract

The invention discloses a power distribution system probability reliability assessing method based on an analytical method. The method comprises the steps that a given power distribution network is partitioned into a fault area, an isolation area, a gapless island area and an influence-free area are classified, a fault mode impact analysis sheet base is established, and parameters are initialized; a stimulation clock is initialized, a random number is generated, the minimum non-failure operation time is obtained according to failure rate parameters of all element state models, the fault isolation time and the load band transferring time are worked out, and the stimulation clock is pushed; the fault mode impact analysis sheet base is inquired, classifications of all cells are determined, whether an island is formed or not is judged, and different methods are adopted for processing the island area and the non-island area; the state sample of an energy storage device is established according to the energy storage device electrical charge state probability distribution obtained through the probability reliability calculating method; distribution probabilities of single-time fault indexes of load points are overlapped, and the probability reliability indexes of the loads and the system are calculated. The simulation speed is increased while certain calculation precision is ensured, and the power grid situation is comprehensively reflected.

Description

A kind of distribution system probabilistic reliability appraisal procedure based on analytical method

Technical field

The present invention relates to a kind of distribution system probabilistic reliability appraisal procedure.Particularly relate to a kind of distribution system probabilistic reliability appraisal procedure based on analytical method be applicable to containing distributed power source.

Background technology

In face of energy science and technology innovation and the opportunity to develop of energy system transition, modern society energy security is realized and is progressively improved, distribution Power System Reliability and then become power consumer focal point; Simultaneously, promote the marginal cost relation between distribution Power System Reliability and investment, the direct economic benefit affecting electric power enterprise and society, in addition, the exert oneself impact of undulatory property and energy storage device operation characteristic of the renewable distributed power source such as blower fan, photovoltaic more exacerbates the complex nature of the problem.Therefore, overall scientific ground assessment distribution Power System Reliability has important practical significance.

After distributed power source (DG) accesses power distribution network, electrical network becomes the network that the point of generators and loads more than is connected, and deep change all will occur for the structure of distribution system and the method for operation.Therefore, in reliability evaluation, how considering the impact of this new system architecture and the method for operation, and how to consider that distributed power source self is exerted oneself the impact of undulatory property, is the research focus in current power Reliability evaluation field.

Can find by analyzing, traditional analytical approach considers the randomness that fault generation, fault isolation reparation and load power fluctuate, consider that DG exerts oneself the impact of randomness on distribution network reliability simultaneously, adopting the reliability of the expectation value characterization system of reliability index, describing undulatory property and the uncertainty that fully can not reflect active power distribution network only by expecting; Simultaneously, existing method is when calculating power supply reliability after fault, in order to embody randomness diversity, adopt the method process to DG and the sampling of load sequential, balancing the load is carried out by the data from the sample survey of discontinuity surface time same, multiple sampling is tried to achieve reliability and is expected, such computational accuracy will affect by the sampling interval.In present stage situation, how analyze containing the randomness of distributed power source and undulatory property, set up suitable probabilistic reliability evaluation system, to evaluating reliability of distribution network important.

In traditional reliability evaluation, according to the feature of power distribution network " closed loop design, open loop operation ", during normal operation, electrical network is only powered to load point by single power supply.When in system during element failure, cause power failure because line powering interrupts in the load point of fault feeder section, and whether be positioned at the needs of the load point after fault feeder section sufficient by determining whether existence contact or getting in touch with margin capacity, and then draw and can restore electricity.But DG accesses after distribution system, network then becomes the structure that the point of generators and loads more than is connected, and the basic characteristic of power distribution network there occurs change, and this brings many new impacts and problem to the reliability assessment process of distribution system.

The most appreciable impact of distributed power source is not only that it will cause the method for operation generation profound change of distribution system, but the uncertainty of DG itself also has material impact to power distribution network.Also to comprise the outage model of DG in fail-safe analysis, consider that DG fault is on the impact of system.As source element, the outage model of DG is more complicated than two state models, so the access of DG can make state of electric distribution network scale significantly increase, complexity and the calculated amount of fail-safe analysis increase thereupon.

In addition, in order to reduce the negative effect of distributed power source to electrical network, be linked in power distribution network by different classes of distributed power source, energy storage device, load and corresponding control device, be the most effective means playing distributed electrical source efficiency viewed from Perspective of Energy angle.Like this, to the reliability assessment of microgrid, be actually the extension of the reliability evaluation problem containing distributed power source, same needs pays attention to.So just more need new disposal route and analysis means.

Therefore, build the reliability estimation method considering probability problem based on analytical method, be practical problems urgently to be resolved hurrily, there is good using value and construction value.

Summary of the invention

Technical matters to be solved by this invention is, provides a kind of in order to calculate the distribution system probabilistic reliability appraisal procedure based on analytical method with user-dependent distribution class probabilistic reliability index.

The technical solution adopted in the present invention is: a kind of distribution system probabilistic reliability appraisal procedure based on analytical method, comprises the steps:

1) distribution net work structure is inputted, include each component information, line length, each load position and each load peak, isolating switch and disconnector position, power distribution network communication relationship, multidomain treat-ment is carried out to each several part in power distribution network, sets up Failure Mode Effective Analysis table storehouse; Setting simulation step length is 1h, and total simulated time N, N are positive integer; Set up the time series data sequence of blower fan output power, photovoltaic power generation system output power and each load power, each energy storage device original state of initialization is 100% state-of-charge, the reliability index probability description matrix W of initialization power off time and scarce delivery _kbe 1;

2) initialization simulated clock simulation clock is 0, the random random number produced between m 0-1, according to the crash rate parameter lambda in each element state model try to achieve m non-failure operation time TTF, use TTF _irepresent the non-failure operation time of i-th element; Find out minimum TTF non-failure operation time _i, a random number is produced to i-th element, tries to achieve fault correction time TTR according to i-th element repair rate parameter μ _i; Meanwhile, produce fault isolation time ST and fault isolation and load and turn band time SRT, and simulated clock simulation clock is advanced to TTF non-failure operation time of i-th element _i;

3) inquiry Failure Mode Effective Analysis table storehouse, determines classification belonging to each community, judges whether community forms isolated island, adopts the computing method of isolated island probabilistic reliability calculating power off time and load to lack the probability distribution of delivery to the community forming isolated island; For the community not forming isolated island, the time series data sequence determination power off time of load power and load is utilized to lack the probability distribution of delivery.

4) according to the energy storage device state-of-charge probability distribution that the computing method of isolated island probabilistic reliability obtain, set up the state sampling of energy storage device when power distribution network normally runs, return step 2) until reach total simulated time N of setting;

5) the probability distribution probability of the reliability index of each load point single failure in distribution net work structure is superposed, calculate the probabilistic reliability index of each load point and system.

Step 1) described in multidomain treat-ment is carried out to each several part in power distribution network, setting up Failure Mode Effective Analysis table storehouse is, the first a certain section failure of establishing electrical network, and the power distribution network after fault is divided into following 6 regions:

(1) faulty section refers to feeder line district, fault element place, all load power failure in described faulty section, and power off time is the repair time of fault element;

(2) upstream isolated area is positioned at the upstream in fault feeder district, the feeder line district be connected by non-isolating switch with feeder line district, and upstream isolated area has a power failure after a failure, restores electricity after fault isolation, and power off time is the fault isolation time;

(3) refer to be connected by isolating switch with faulty section or upstream isolated area without the zone of influence, and the feeder line district be connected with active distribution system primary power, without the load point in impact not by the impact of element fault, therefore do not have a power failure;

(4) seamless isolated island district, upstream is referred to and to be connected by isolating switch with faulty section or upstream isolated area, and comprises the feeder line district of distributed power source; Initiatively the formation of isolated island depends on the timely action of seamless isolated island district, upstream outlet breaker, the islet operation time is the fault isolation time, whether load point has a power failure and power off time is determined by power balance situation in island, and after fault isolation, upstream is seamless, and isolated island district accesses distribution system again;

(5) isolation isolated island district in downstream refers to and is positioned at downstream, faulty section, the feeder line district be connected by non-isolating switch with faulty section, before fault isolation, the all load power failure in isolation isolated island district, described downstream, until the isolated island on a large scale formed with other downstream areas is formed, the islet operation time is the repair time of fault element and the difference of fault isolation time;

(6) seamless isolated island district, downstream refers to and is positioned at downstream, faulty section, the feeder line district that isolated island district is connected by isolating switch is isolated with faulty section or downstream, downstream is seamless, and isolated island district can enter isolated island mode immediately runs when fault occurs, the islet operation time is the fault isolation time, on the basis of subregion, travel through each component information in power distribution network one by one, construct other region formational situations of power distribution network under corresponding specific fault element, set up Failure Mode Effective Analysis table storehouse.

Step 1) described in foundation to generate blower fan output power sequence be adopt Weibull distribution to describe by approximate for wind speed v, consider that most of the time wind speed is between incision wind speed and wind rating, then blower fan output power P _wprobability density function following formula be expressed as:

$f\left(P_w\right)=\frac{K}{\mathrm{bC}}{\left(\frac{P_w-a}{\mathrm{bC}}\right)}^{K-1}{e}^{-{\left(\frac{P_w-a}{\mathrm{bC}}\right)}^K}---\left(13\right)$

In formula: K and C is shape and the scale parameter of wind speed Weibull distribution respectively; Coefficient a=P _rv _ci/ (v _ci-v _r), b=P _r/ (v _r-v _ci), P _rthe output rating of blower fan, v _ci, v _rincision wind speed and the wind rating of blower fan respectively.

Step 1) described in set up photovoltaic power generation system output power sequence be adopt following manner:

According to the output power P of photovoltaic generating system _sand the relation between intensity of illumination I, photovoltaic battery array area S and photoelectric transformation efficiency η, i.e. photovoltaic power generation system output power P _s=IS η, if intensity of illumination I obeys beta distribution in certain hour section, the probability density obtaining photovoltaic generating system is:

$f\left(P_s\right)=\frac{\mathrm{\Γ}(\mathrm{\α}+\mathrm{\β})}{P_{s,\max }\mathrm{\Γ}\left(\mathrm{\α}\right)\mathrm{\Γ}\left(\mathrm{\β}\right)}{\left(\frac{P_s}{P_{s,\max }}\right)}^{\mathrm{\α}-1}{(1-\frac{P_s}{P_{s,\max }})}^{\mathrm{\β}-1}---\left(15\right)$

In formula: P _{s, max}for the peak power output of photovoltaic array; α, β are the form parameter of intensity of illumination beta distribution.

Step 1) described in set up each load power sequence be adopt following manner:

According to the superposition of different time dimension typical load curve, obtain the time series data sequence L of load power _t=L _p× P _w× P _d× P _h(t), L _pby the annual maximal value of research load point, P _wfor corresponding with t hour year-all load curves in value, P _dfor corresponding with t hour week-daily load curve in value, P _h(t) be corresponding with t hour-time load curve in value.

Step 3) described in the computing method of isolated island probabilistic reliability, comprise the steps:

(1) determine the isolated island community formed, read distribution net work structure and correlation parameter in isolated island community;

(2) according to the power sequence of blower fan output power, photovoltaic system output power and load point, the probability Distribution Model of stochastic variable in isolated island Formation period is set up; According to the charge and discharge cycles of energy storage before fault, determine that the isolated island moment forms the probability distribution of energy storage charge state;

(3) the Ge Jie center square of each stochastic variable is asked for by the Monte Carlo methods of sampling;

(4) in each stochastic variable, choose 3 estimation points, calculate position and the weight of estimation point, form rank, n × 3 estimation point group matrix and weight coefficient matrix;

(5) reliability index under each estimation point group value is calculated one by one;

(6) the valuation matrix of each load reliability index and energy storage charge state is formed, each rank moment of the orign of calculated load reliability index and energy storage charge state;

(7) calculate the Ge Jie center square of target stochastic variable, use Gram-Charlier series expansion, the reliability index of each load point and the probability distribution of energy storage charge state at the end of obtaining islet operation.

Step 4) described in the state sampling of energy storage device comprise two parts: one is the sampling of the running status of energy storage device, and two is sampling of the state-of-charge of energy storage device; Wherein, the methods of sampling of energy storage device running status is that the various power combination that battery pack connection in series-parallel is combined into are sorted, put in order after normalized in [0,1] interval, and then produce one [0,1] interval random number, determining energy storage device running status, the sampling of state-of-charge, is by producing a random number, according to the distribution situation of different energy storage device state-of-charge, random number is converted into the value of corresponding state-of-charge.

Step 5) described in each load point of calculating and the probabilistic reliability of system refer to that calibration method is: after the reliability index probability distribution after obtaining each load point primary fault, if X and Y represents the reliability index probability distribution in same load point twice different faults situation, the probability density function of Z=X+Y is so had to be written as

$f_Z\left(z\right)={\∫}_{-\∞}^{+\∞}{f}_X\left(x\right)f_Y(z-x)\mathrm{dx}={\∫}_{-\∞}^{+\∞}{f}_X(z-y)f_Y\left(x\right)\mathrm{dy},$

Namely the probabilistic reliability index of each load point and system is obtained according to the method for convolution algorithm.

A kind of distribution system probabilistic reliability appraisal procedure based on analytical method of the present invention, have studied the Failure Mode Effective Analysis process of the rear distribution system of distributed power source access, by classifying to network system, and adopt the power off time in point estimations analysis isolated island and scarce delivery.Under the prerequisite of higher level's power supply capacity abundance, point estimations sampling is carried out to the state of blower fan, photovoltaic, battery pack distributed power supply, randomness and the undulatory property of distributed power source can be reflected, and while guaranteeing certain calculation accuracy, improve analog rate, comprehensively reflect grid condition.

Accompanying drawing explanation

Fig. 1 is two state models of non-source element;

Fig. 2 is three condition element sampling schematic diagram;

Fig. 3 is cycle discharge and recharge strategy storage battery charge state situation of change;

Fig. 4 is the IEEE-RBTS distribution network systems of transformation;

Fig. 5 is active power distribution network probabilistic reliability estimation flow figure;

Fig. 6 is active power distribution network probabilistic reliability estimation flow figure isolated island evaluation part;

Fig. 7 a is No. 13 load point APOD probability density function figure in Fig. 4;

Fig. 7 b is No. 13 load point AENS probability density function figure in Fig. 4.

Embodiment

Below in conjunction with embodiment and accompanying drawing, a kind of distribution system probabilistic reliability appraisal procedure based on analytical method of the present invention is described in detail.

A kind of distribution system probabilistic reliability appraisal procedure based on analytical method of the present invention, for calculating and user-dependent distribution class probabilistic reliability index.And combine the concept in feeder line district, have studied the Failure Mode Effective Analysis process of the rear distribution system of distributed power source access, network system is classified, and adopt the power balance in didactic load summate method maintenance isolated island.Under the prerequisite of higher level's power supply capacity abundance, point estimations sampling is carried out to the state of blower fan, photovoltaic, battery pack distributed power supply, analog rate can be improved while guaranteeing certain calculation accuracy.

Establish the fault effects sorted table of system, after have found the feeder line district included by every class fault zone, the power-off condition of system internal loading point just can be analyzed according to the difference of fault zone belonging to each feeder line district, load point place respectively.For faulty section, without the load point in the zone of influence and upstream isolated area three class region, its power-off condition can directly be determined: in faulty section, the power off time of load point is fault correction time; Do not have a power failure without the load point in the zone of influence; In the isolated area of upstream, the power off time of load point is the fault isolation time.And for seamless isolated island district, upstream, isolation isolated island district, downstream and seamless isolated island district, downstream, its power-off condition depends on the power balance in isolated island, cannot directly determine.If the gross capability of distributed power source is greater than total load in isolated island, the load point so in island will not have a power failure; And if the undercapacity of distributed power source in isolated island, just need to carry out load summate.

Adopt point estimations to process distributed power source, utilize cumulative distribution function and probability density function can completely to describe the probabilistic statistical characteristics of stochastic variable simultaneously.But in some practical problems, not easily determine or do not need to determine the definite probability distribution (PDF or CDF) of stochastic variable, only can make analysis with its some numerical characteristic.The basic thought of point estimations in known continuous random variable, selects discrete estimation point according to certain principle, the estimation point of multiple random variables forms estimation point group, the estimation point group utilizing these discrete estimates the numerical characteristic of output variable to be asked, as expectation, variance and each rank square etc.

By function y=h (x) at μ _xnear carry out Taylor expansion

$h\left(x\right)=h\left({\mathrm{\μ}}_x\right)+\underset{i=1}{\overset{\∞}{\mathrm{\Σ}}}\frac{1}{i!}{h}^{\left(i\right)}\left({\mathrm{\μ}}_x\right){(x-{\mathrm{\μ}}_x)}^i---\left(1\right)$

The expectation of note y is μ _y, can be obtained by above formula:

${\mathrm{\μ}}_y=E\left(h\left(x\right)\right)={\∫}_{-\∞}^{+\∞}h\left(x\right)f\left(x\right)\mathrm{dx}=h\left({\mathrm{\μ}}_x\right)+\underset{i=1}{\overset{\∞}{\mathrm{\Σ}}}\frac{1}{i!}{h}^{\left(i\right)}\left({\mathrm{\μ}}_x\right){\mathrm{\β}}_{x,i}---\left(2\right)$

Further can in the hope of the concrete weight of point estimations selected point.Provable equally, the high-order moment of the orign of y also can calculate by similar approach, and formula is as follows

${\mathrm{\α}}_{y,k}=\underset{j=1}{\overset{r}{\mathrm{\Σ}}}{p}_{j}h{\left(x_j\right)}^k,k=\mathrm{1,2},...,2r-1---\left(3\right)$

Further genralrlization in the multivariate function, the computing formula of each rank moment of the orign

${\mathrm{\α}}_{y,k}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\underset{j=1}{\overset{r}{\mathrm{\Σ}}}{p}_{i,j}\×{\left[h({\mathrm{\μ}}_{x,1},{\mathrm{\μ}}_{x,2},...,{\mathrm{\μ}}_{x,i-1},x_{i,j},{\mathrm{\μ}}_{x,i+1},...,{\mathrm{\μ}}_{x,n})\right]}^k,k=\mathrm{1,2},...,2r-1---\left(4\right)$

On this basis, each stochastic variable is processed.Load point Calculation of Reliability represents by following nonlinear function:

y＝h(x)＝h(x ₁,x ₂,…,x _n) (5)

Wherein y is that m ties up target stochastic variable, y _krepresent POD or ENS of each load point, or the SOC of isolated island finish time energy storage device; X is n dimension input stochastic variable, x _krepresent that the wind speed of certain node, light intensity, isolated island form moment energy storage charge state or payload.For there being k ₁individual load, k ₂fans, k ₃group photovoltaic and k ₄the isolated island of group energy storage device, has

$\left\{\begin{array}{c}m=2\×k_1+k_4\\ n=k_1+k_2+k_3+k_4\end{array}\right.---\left(6\right)$

If x _kexpectation, standard deviation, v rank moment of the orign and center square be respectively μ _k, σ _k, α _k,vand β _k,v.These input stochastic variable numerical characters are by asking for the probability model of DG and load and energy storage device model sampling.

At each stochastic variable x _k(k=1,2 ..., choose 3 estimation points n), be denoted as x _{k, 1}, x _{k, 2}and x _{k, 3}, expression formula is

x _k,i＝μ _k+ξ _k,iσ _k,i＝1,2,3 (7)

In formula, ξ is measured in position _k,iexpression formula be

$\left\{\begin{array}{c}{\mathrm{\ξ}}_{k,1}=\frac{{\mathrm{\λ}}_{k,3}}{2}+\sqrt{{\mathrm{\λ}}_{k,4}-\frac{{3\mathrm{\λ}}_{k,3}^2}{4}}\\ {\mathrm{\ξ}}_{k,2}=0\\ {\mathrm{\ξ}}_{k,3}=\frac{{\mathrm{\λ}}_{k,3}}{2}-\sqrt{{\mathrm{\λ}}_{k,4}-\frac{{3\mathrm{\λ}}_{k,3}^2}{4}}\end{array}\right.---\left(8\right)$

Wherein λ _{k, 3}and λ _{k, 4}be respectively stochastic variable x _kthe coefficient of skewness and coefficient of kurtosis, can by σ _kand β _k,vcalculate.

Can be seen by formula (8), 3 estimation points that each stochastic variable is got are respectively in mean variable value and left and right neighborhood thereof two points determining distance, and these 3 specified points can as the estimation to stochastic variable probability distribution situations such as wind speed, light intensity, loads.

N dimension input stochastic variable has n × 3 estimation point.Expected to be combined into estimation point group with its dependent variable respectively by the estimation point of each stochastic variable, can form rank, n × 3 estimation point group matrix X, matrix element is

X _k,i＝[μ ₁,μ ₂,...,μ _k-1,x _k,i,μ _k+1,...,μ _n],i＝1,2,3 (9)

Meanwhile, with each estimation point (group) to there being certain weight coefficient, weight coefficient represents the probability size being taken as this point in stochastic variable probability distribution, can measure ξ by the position of each estimation point _k,icalculate.Corresponding estimation point group matrix X, generate rank, n × 3 weight coefficient matrix P, matrix element is

$\left\{\begin{array}{c}{p}_{k,1}=\frac{1}{{\mathrm{\ξ}}_{k,1}({\mathrm{\ξ}}_{k,1}-{\mathrm{\ξ}}_{k,3})}\\ p_{k,2}=\frac{1}{n}-\frac{1}{{\mathrm{\λ}}_{k,4}-{\mathrm{\λ}}_{k,3}^2}\\ p_{k,3}=-\frac{1}{{\mathrm{\ξ}}_{k,3}({\mathrm{\ξ}}_{k,1}-{\mathrm{\ξ}}_{k,3})}\end{array}\right.---\left(10\right)$

After obtaining estimation point group, can think that in during isolated island, each stochastic variable is taken as determined value by this estimation point group, exert oneself according to blower fan photovoltaic, payload and energy storage charge status, in conjunction with load summate strategy, carry out balancing the load, and then calculate the load point reliability under this determined value.So just calculate to the probabilistic reliability of stochastic variable the determination Calculation of Reliability be converted into multiple estimation point group.

Use y _lrepresent POD or ENS of certain load point, h _lrepresent the Calculation of Reliability process of balancing the load and this load point, then certain estimation point group X _k,iload point reliability valuation under value is y _l=h _l(X _k,i), each element in estimation point group matrix X is calculated one by one, forms rank, n × 3 valuation matrix.Then y _lv rank moment of the orign can be expressed as

${\mathrm{\α}}_{k,v}=E^v\left(y_l\right)=\underset{k=1}{\overset{n}{\mathrm{\Σ}}}\underset{i=1}{\overset{3}{\mathrm{\Σ}}}[p_{k,i}\×h_l{\left(X_{k,i}\right)}^v]---\left(11\right)$

Utilize Gram-Charlier expansion progression can be calculated probability density function and the cumulative distribution function of reliability index or energy storage SOC by each rank moment of the orign.Y _lstandardized random variable probability density function be

The coefficient C of progression is launched in formula _iabout β _k,vpolynomial expression, can by formula (3-38).Right carry out corresponding anti-standardized transformation, the probability density function f (y of reliability index or energy storage SOC can be obtained _l), complete this isolated island probabilistic reliability and calculate.

As shown in Figure 5, a kind of distribution system probabilistic reliability appraisal procedure based on analytical method of the present invention, specifically comprises the steps:

1) distribution net work structure is inputted, include each component information, line length, each load position and each load peak, isolating switch and disconnector position, power distribution network communication relationship, multidomain treat-ment is carried out to each several part in power distribution network, sets up Failure Mode Effective Analysis (FMEA) and show storehouse; Setting simulation step length is 1h, and total simulated time N, N are positive integer; Set up the time series data sequence of blower fan output power, photovoltaic power generation system output power and each load power, each energy storage device original state of initialization is 100% state-of-charge, the reliability index probability description matrix W of initialization power off time and scarce delivery _kbe 1; Wherein,

Described carries out multidomain treat-ment to each several part in power distribution network, and set up Failure Mode Effective Analysis (FMEA) and show storehouse and be, the first a certain section failure of establishing electrical network, the power distribution network after fault can be divided into following 6 regions:

(1) faulty section refers to feeder line district, fault element place, all load power failure in described faulty section, and power off time is the repair time of fault element;

(2) upstream isolated area is positioned at the upstream in fault feeder district, the feeder line district be connected by non-isolating switch with feeder line district, and upstream isolated area has a power failure after a failure, restores electricity after fault isolation, and power off time is the fault isolation time;

(3) refer to be connected by isolating switch with faulty section or upstream isolated area without the zone of influence, and the feeder line district be connected with active distribution system primary power (bus), without the load point in impact not by the impact of element fault, therefore do not have a power failure;

(4) seamless isolated island district, upstream is referred to and to be connected by isolating switch with faulty section or upstream isolated area, and comprises the feeder line district of distributed power source; Initiatively the formation of isolated island depends on the timely action of seamless isolated island district, upstream outlet breaker, the islet operation time is the fault isolation time, whether load point has a power failure and power off time is determined by power balance situation in island, and after fault isolation, upstream is seamless, and isolated island district can access distribution system again;

(5) isolation isolated island district in downstream refers to and is positioned at downstream, faulty section, the feeder line district be connected by non-isolating switch with faulty section, before fault isolation, the all load power failure in isolation isolated island district, described downstream, until the isolated island on a large scale formed with other downstream areas is formed, the islet operation time is the repair time of fault element and the difference of fault isolation time;

(6) seamless isolated island district, downstream refers to and is positioned at downstream, faulty section, the feeder line district that isolated island district is connected by isolating switch is isolated with faulty section or downstream, downstream is seamless, and isolated island district can enter isolated island mode immediately runs when fault occurs, the islet operation time is the fault isolation time, on the basis of subregion, travel through each component information in power distribution network one by one, construct other region formational situations of power distribution network under corresponding specific fault element, set up Failure Mode Effective Analysis table storehouse.

Existing research thinks that wind speed has statistical property, and be generally positive skewness distribution, more for the function or curve describing wind speed profile, wherein Weibull and normal distribution are widely adopted.It is adopt following manner that described foundation generates blower fan output power sequence: adopt Weibull distribution to describe by approximate for wind speed v, considers that most of the time wind speed is between incision wind speed and wind rating, then blower fan output power P _wprobability density function can be similar to and be expressed as with following formula:

$f\left(P_w\right)=\frac{K}{\mathrm{bC}}{\left(\frac{P_w-a}{\mathrm{bC}}\right)}^{K-1}{e}^{-{\left(\frac{P_w-a}{\mathrm{bC}}\right)}^K}---\left(13\right)$

In formula: K and C is shape and the scale parameter of wind speed Weibull distribution respectively; Coefficient a=P _rv _ci/ (v _ci-v _r), b=P _r/ (v _r-v _ci), P _rthe output rating of blower fan, v _ci, v _rincision wind speed and the wind rating of blower fan respectively.

Described photovoltaic power generation system output power sequence of setting up is: according to the output power P of photovoltaic generating system _sand the relation between intensity of illumination I, photovoltaic battery array area S and photoelectric transformation efficiency η, i.e. photovoltaic power generation system output power P _s=IS η, if intensity of illumination I is approximate in certain hour section obey beta distribution, the probability density obtaining photovoltaic generating system is:

$f\left(P_s\right)=\frac{\mathrm{\Γ}(\mathrm{\α}+\mathrm{\β})}{P_{s,\max }\mathrm{\Γ}\left(\mathrm{\α}\right)\mathrm{\Γ}\left(\mathrm{\β}\right)}{\left(\frac{P_s}{P_{s,\max }}\right)}^{\mathrm{\α}-1}{(1-\frac{P_s}{P_{s,\max }})}^{\mathrm{\β}-1}---\left(15\right)$

In formula: P _{s, max}for the peak power output of photovoltaic array; α, β are the form parameter of intensity of illumination beta distribution.

Described each load power sequence of setting up adopts following manner: according to the superposition of different time dimension typical load curve, obtain the time series data sequence L of load power _t=L _p× P _w× P _d× P _h(t), L _pby the annual maximal value of research load point, P _wfor corresponding with t hour year-all load curves in value, P _dfor corresponding with t hour week-daily load curve in value, P _h(t) be corresponding with t hour-time load curve in value.

2) initialization simulated clock simulation clock is 0, the random random number produced between m 0-1, according to the crash rate parameter lambda in each element state model try to achieve m non-failure operation time TTF, use TTF _irepresent the non-failure operation time of i-th element; Find out minimum TTF non-failure operation time _i, a random number is produced to i-th element, tries to achieve fault correction time TTR according to i-th element repair rate parameter μ _i; Meanwhile, produce fault isolation time ST and fault isolation and load and turn band time SRT, and simulated clock simulation clock is advanced to TTF non-failure operation time of i-th element _i;

3) inquire about Failure Mode Effective Analysis (FMEA) and show storehouse, determine classification belonging to each community, judge whether community forms isolated island, adopt the computing method of isolated island probabilistic reliability calculating power off time and load to lack the probability distribution of delivery to the community forming isolated island; For the community not forming isolated island, the time series data sequence determination power off time of load power and load is utilized to lack the probability distribution of delivery.

The computing method of described isolated island probabilistic reliability, flow process as shown in Figure 6, comprises the steps:

(1) determine the isolated island community formed, read distribution net work structure and correlation parameter in isolated island community;

(2) according to the power sequence of blower fan output power, photovoltaic system output power and load point, the probability Distribution Model of stochastic variable in isolated island Formation period is set up; According to the charge and discharge cycles of energy storage before fault, determine that the isolated island moment forms the probability distribution of energy storage charge state;

(3) the Ge Jie center square of each stochastic variable is asked for by the Monte Carlo methods of sampling;

(4) in each stochastic variable, choose 3 estimation points, calculate position and the weight of estimation point, form rank, n × 3 estimation point group matrix and weight coefficient matrix;

(5) reliability index under each estimation point group value is calculated one by one;

(6) the valuation matrix of each load reliability index and energy storage charge state is formed, each rank moment of the orign of calculated load reliability index and energy storage charge state;

(7) calculate the Ge Jie center square of target stochastic variable, use Gram-Charlier series expansion, the reliability index of each load point and the probability distribution of energy storage charge state at the end of obtaining islet operation.

4) according to the energy storage device state-of-charge probability distribution that the computing method of isolated island probabilistic reliability obtain, set up the state sampling of energy storage device when power distribution network normally runs, return step 2) until reach total simulated time N of setting;

The state sampling of described energy storage device comprises two parts: one is the sampling of the running status of energy storage device, and two is sampling of the state-of-charge of energy storage device; Because the energy storage device in each distributed power source is also formed by multiple accumulator connection in series-parallel, wherein, the methods of sampling of energy storage device running status is that the various power combination that battery pack connection in series-parallel is combined into are sorted, put in order after normalized in [0,1] interval, and then produce one [0,1] interval random number, determine energy storage device running status, as shown in Figure 2, derate running status represents the fault of part accumulator.The sampling of state-of-charge, being by producing a random number, according to the distribution situation of different energy storage device state-of-charge, random number being converted into the value of corresponding state-of-charge.

5) the probability distribution probability of the reliability index of each load point single failure in distribution net work structure is superposed, calculate the probabilistic reliability index of each load point and system.

The probabilistic reliability of each load point of described calculating and system refers to that calibration method is: after the reliability index probability distribution after obtaining each load point primary fault, if X and Y represents the reliability index probability distribution in same load point twice different faults situation, the probability density function of Z=X+Y is so had to can be written as

$f_Z\left(z\right)={\∫}_{-\∞}^{+\∞}{f}_X\left(x\right)f_Y(z-x)\mathrm{dx}={\∫}_{-\∞}^{+\∞}{f}_X(z-y)f_Y\left(x\right)\mathrm{dy},$

Namely the probabilistic reliability index of each load point and system is obtained according to the method for convolution algorithm.

Of the present invention a kind of based in the distribution system probabilistic reliability appraisal procedure of analytical method, probabilistic reliability calculates using stochastic variables such as wind speed, illumination, load and energy storage SOC as initial conditions, using energy storage SOC at the end of the reliability index of isolated island load and isolated island etc. as target variable, be stochastic variable nonlinear conversion processes more than.Based on the probabilistic reliability computing method of point estimation, estimation point is chosen to input stochastic variable, forms estimation point group and corresponding valuation weight matrix; The probabilistic reliability index that progression obtains isolated island internal loading is launched by Gram-Charlier.

Putting before this, the heuristic load summate strategy in isolated island can formulated:

1) first suppose that all loads all can supply, the total delivery of each load point during calculating isolated island;

2) if DG exerts oneself and cannot meet existing workload demand with current energy storage maximum output sum, then cut down the load that in current supply load, during isolated island, total delivery is minimum, be designated as T by being cut down the time that moment to isolated island terminates by reduction plans _cut, and enter step 3), otherwise enter step 4);

3) again carry out power balance calculating, repeat step (2), until meet formula (5-9);

4) change according to energy storage SOC, constantly carry out judging in step (2), until isolated island terminates.

According to heuristic load summate model, in during can isolated island being calculated along with battery discharging successively by the load cut down and cut down the moment, thus calculate POD and ENS of each load point.

MATLAB is the business mathematics software of U.S. MathWorks Company, is one and can be used for algorithm development, data visualization, the advanced techniques computational language of data analysis and numerical evaluation and interactive environment.The present invention, based on MATLAB, achieves the Distribution Network Reliability assessment models containing distributed power source access, the present invention is applied wherein, and carried out testing authentication to effect based on the IEEE RBTS power distribution network standard example shown in Fig. 4.

Analyze using the multiple-limb feeder line in the IEEE RBTS Bus6 of transformation as example.Improved system as shown in Figure 4, comprises 1 section of bus, 30 feeder line section, 26 nodes, 23 distribution transformings, 23 load point (LP1 to LP23), 5 distributed power sources, some isolating switchs and disconnectores, without fuse.Each distributed power source comprises that same model blower fan is some, 1 photovoltaic array and 1 battery pack, and design parameter is as follows.

1) blower fan: separate unit blower fan rated power 335kW; Incision wind speed 2.5m/s; Wind rating 12.5m/s; Cut-out wind speed 25m/s; Fitting parameter A, B, C are respectively-39.58,6.37,2.02; Mean wind speed is 19.56m/s, and wind speed profile standard deviation is 10.06m/s.

2) photovoltaic array: parameter R _cand G _stdbe respectively 0.15kW/m ²and 1kW/m ².

3) accumulator: every block battery rating 3000Ah, rated voltage 2V (6kWh); Parameter c=0.317, α=1, k=1.22, η _c=η _d=0.927, I _max=610A.

4) element failure rate: feeder fault rate is 0.065 times/year × km, distribution transforming failure rate is 0.015 times/year, and switch fault rate is 0.006 times/year, and mean repair time is 5h, obeys index distribution.Fault isolation and load turn the band time and get steady state value 1h.As shown in Table 1 and Table 2, the number of users of each load point is 1 family for each feeder line section length and each load point peak value.Separate unit fan trouble state probability P _d=7.3%; Photovoltaic array is identical with the state model parameter of battery pack, malfunction probability P _d=3.2%, derate state probability P _e=5%.

Table 1 feeder line section length

Length (km)	Feeder line section sequence number
		0.6	7,13
0.75	9,27
		0.8	21
0.9	4,10
		1.6	3,5,8,15,20,28
2.5	2,6,18,23,26
		2.8	1,12,16,22,25,30
3.2	11,17,19,24,29
		3.5	14

Table 2 load data

Load point sequence number	Load peak (kW)	Load point sequence number	Load peak (kW)
				1,6	360.1	7,23	796.2
2	380.6	8,11,14,19	337.6
				3,13,17	653.4	9,21	737.4
4,18	686.4	10,12,16,22	340.9
				5	434.7	15,20	501.8

Result of calculation shows, and for the load point forming isolated island community, itself APOD and AENS all shows certain undulatory property, such as, choose certain load point, and the probability density function image of itself APOD and AENS as shown in Figure 7; For the load point that cannot form isolated island community, its APOD is determined value, the probability distribution situation of AENS and Fig. 7 similar.Can see, the probability distribution of the reliability index of single load point is similar to Normal Distribution.Because the electric estimation technique chooses 3 estimation points to every n-dimensional random variable n, each stochastic variable 5 rank and following center square is only taken into account in calculating, therefore cannot simulate the distribution of reliability index true probability completely when using Gram-Charlier series expansion, having occurred that local probability is the situation of negative value.

Claims (8)

1., based on a distribution system probabilistic reliability appraisal procedure for analytical method, it is characterized in that, comprise the steps:

1) distribution net work structure is inputted, include each component information, line length, each load position and each load peak, isolating switch and disconnector position, power distribution network communication relationship, multidomain treat-ment is carried out to each several part in power distribution network, sets up Failure Mode Effective Analysis table storehouse; Setting simulation step length is 1h, and total simulated time N, N are positive integer; Set up the time series data sequence of blower fan output power, photovoltaic power generation system output power and each load power, each energy storage device original state of initialization is 100% state-of-charge, the reliability index probability description matrix W of initialization power off time and scarce delivery _kbe 1;

2) initialization simulated clock simulation clock is 0, the random random number produced between m 0-1, according to the crash rate parameter lambda in each element state model try to achieve m non-failure operation time TTF, use TTF _irepresent the non-failure operation time of i-th element; Find out minimum TTF non-failure operation time _i, a random number is produced to i-th element, tries to achieve fault correction time TTR according to i-th element repair rate parameter μ _i; Meanwhile, produce fault isolation time ST and fault isolation and load and turn band time SRT, and simulated clock simulation clock is advanced to TTF non-failure operation time of i-th element _i;

3) inquiry Failure Mode Effective Analysis table storehouse, determines classification belonging to each community, judges whether community forms isolated island, adopts the computing method of isolated island probabilistic reliability calculating power off time and load to lack the probability distribution of delivery to the community forming isolated island; For the community not forming isolated island, the time series data sequence determination power off time of load power and load is utilized to lack the probability distribution of delivery.

4) according to the energy storage device state-of-charge probability distribution that the computing method of isolated island probabilistic reliability obtain, set up the state sampling of energy storage device when power distribution network normally runs, return step 2) until reach total simulated time N of setting;

5) the probability distribution probability of the reliability index of each load point single failure in distribution net work structure is superposed, calculate the probabilistic reliability index of each load point and system.

2. a kind of distribution system probabilistic reliability appraisal procedure based on analytical method according to claim 1, it is characterized in that, step 1) described in multidomain treat-ment is carried out to each several part in power distribution network, setting up Failure Mode Effective Analysis table storehouse is, first a certain section failure of establishing electrical network, the power distribution network after fault is divided into following 6 regions:

(1) faulty section refers to feeder line district, fault element place, all load power failure in described faulty section, and power off time is the repair time of fault element;

(2) upstream isolated area is positioned at the upstream in fault feeder district, the feeder line district be connected by non-isolating switch with feeder line district, and upstream isolated area has a power failure after a failure, restores electricity after fault isolation, and power off time is the fault isolation time;

(3) refer to be connected by isolating switch with faulty section or upstream isolated area without the zone of influence, and the feeder line district be connected with active distribution system primary power, without the load point in impact not by the impact of element fault, therefore do not have a power failure;

(4) seamless isolated island district, upstream is referred to and to be connected by isolating switch with faulty section or upstream isolated area, and comprises the feeder line district of distributed power source; Initiatively the formation of isolated island depends on the timely action of seamless isolated island district, upstream outlet breaker, the islet operation time is the fault isolation time, whether load point has a power failure and power off time is determined by power balance situation in island, and after fault isolation, upstream is seamless, and isolated island district accesses distribution system again;

(5) isolation isolated island district in downstream refers to and is positioned at downstream, faulty section, the feeder line district be connected by non-isolating switch with faulty section, before fault isolation, the all load power failure in isolation isolated island district, described downstream, until the isolated island on a large scale formed with other downstream areas is formed, the islet operation time is the repair time of fault element and the difference of fault isolation time;

(6) seamless isolated island district, downstream refers to and is positioned at downstream, faulty section, the feeder line district that isolated island district is connected by isolating switch is isolated with faulty section or downstream, downstream is seamless, and isolated island district can enter isolated island mode immediately runs when fault occurs, the islet operation time is the fault isolation time, on the basis of subregion, travel through each component information in power distribution network one by one, construct other region formational situations of power distribution network under corresponding specific fault element, set up Failure Mode Effective Analysis table storehouse.

3. a kind of distribution system probabilistic reliability appraisal procedure based on analytical method according to claim 1, it is characterized in that, step 1) described in foundation to generate blower fan output power sequence be adopt Weibull distribution to describe by approximate for wind speed v, consider that most of the time wind speed is between incision wind speed and wind rating, then blower fan output power P _wprobability density function following formula be expressed as:

$f\left(P_w\right)=\frac{K}{\mathrm{bC}}{\left(\frac{P_w-a}{\mathrm{bC}}\right)}^{K-1}{e}^{-{\left(\frac{P_w-a}{\mathrm{bC}}\right)}^K}---\left(13\right)$

In formula: K and C is shape and the scale parameter of wind speed Weibull distribution respectively; Coefficient a=P _rv _ci/ (v _ci-v _r), b=P _r/ (v _r-v _ci), P _rthe output rating of blower fan, v _ci, v _rincision wind speed and the wind rating of blower fan respectively.

4. a kind of distribution system probabilistic reliability appraisal procedure based on analytical method according to claim 1, is characterized in that, step 1) described in set up photovoltaic power generation system output power sequence be adopt following manner:

According to the output power P of photovoltaic generating system _sand the relation between intensity of illumination I, photovoltaic battery array area S and photoelectric transformation efficiency η, i.e. photovoltaic power generation system output power P _s=IS η, if intensity of illumination I obeys beta distribution in certain hour section, the probability density obtaining photovoltaic generating system is:

$f\left(P_s\right)=\frac{\mathrm{\Γ}(\mathrm{\α}+\mathrm{\β})}{P_{s,\max }\mathrm{\Γ}\left(\mathrm{\α}\right)\mathrm{\Γ}\left(\mathrm{\β}\right)}{\left(\frac{P_s}{P_{s,\max }}\right)}^{\mathrm{\α}-1}{(1-\frac{P_s}{P_{s,\max }})}^{\mathrm{\β}-1}---\left(15\right)$

In formula: P _{s, max}for the peak power output of photovoltaic array; α, β are the form parameter of intensity of illumination beta distribution.

5. a kind of distribution system probabilistic reliability appraisal procedure based on analytical method according to claim 1, is characterized in that, step 1) described in set up each load power sequence be adopt following manner:

According to the superposition of different time dimension typical load curve, obtain the time series data sequence L of load power _t=L _p× P _w× P _d× P _h(t), L _pby the annual maximal value of research load point, P _wfor corresponding with t hour year-all load curves in value, P _dfor corresponding with t hour week-daily load curve in value, P _h(t) be corresponding with t hour-time load curve in value.

6. a kind of distribution system probabilistic reliability appraisal procedure based on analytical method according to claim 1, is characterized in that, step 3) described in the computing method of isolated island probabilistic reliability, comprise the steps:

(1) determine the isolated island community formed, read distribution net work structure and correlation parameter in isolated island community;

(2) according to the power sequence of blower fan output power, photovoltaic system output power and load point, the probability Distribution Model of stochastic variable in isolated island Formation period is set up; According to the charge and discharge cycles of energy storage before fault, determine that the isolated island moment forms the probability distribution of energy storage charge state;

(3) the Ge Jie center square of each stochastic variable is asked for by the Monte Carlo methods of sampling;

(4) in each stochastic variable, choose 3 estimation points, calculate position and the weight of estimation point, form rank, n × 3 estimation point group matrix and weight coefficient matrix;

(5) reliability index under each estimation point group value is calculated one by one;

(6) the valuation matrix of each load reliability index and energy storage charge state is formed, each rank moment of the orign of calculated load reliability index and energy storage charge state;

(7) calculate the Ge Jie center square of target stochastic variable, use Gram-Charlier series expansion, the reliability index of each load point and the probability distribution of energy storage charge state at the end of obtaining islet operation.

7. a kind of distribution system probabilistic reliability appraisal procedure based on analytical method according to claim 1, it is characterized in that, step 4) described in the state sampling of energy storage device comprise two parts: one is the sampling of the running status of energy storage device, and two is sampling of the state-of-charge of energy storage device; Wherein, the methods of sampling of energy storage device running status is that the various power combination that battery pack connection in series-parallel is combined into are sorted, put in order after normalized in [0,1] interval, and then produce one [0,1] interval random number, determining energy storage device running status, the sampling of state-of-charge, is by producing a random number, according to the distribution situation of different energy storage device state-of-charge, random number is converted into the value of corresponding state-of-charge.

8. a kind of distribution system probabilistic reliability appraisal procedure based on analytical method according to claim 1, it is characterized in that, step 5) described in each load point of calculating and the probabilistic reliability of system refer to that calibration method is: after the reliability index probability distribution after obtaining each load point primary fault, if X and Y represents the reliability index probability distribution in same load point twice different faults situation, the probability density function of Z=X+Y is so had to be written as

$f_Z\left(z\right)={\∫}_{-\∞}^{+\∞}{f}_X\left(x\right)f_Y(z-x)\mathrm{dx}={\∫}_{-\∞}^{+\∞}{f}_X(z-y)f_Y\left(x\right)\mathrm{dy},$

Namely the probabilistic reliability index of each load point and system is obtained according to the method for convolution algorithm.