CN103825272A - Reliability determination method for power distribution network with distributed wind power based on analytical method - Google Patents

Abstract

The invention provides a reliability determination method for a power distribution network with distributed wind power based on an analytical method. The method comprises the following steps: step I, building a distributed power reliability model; step II, building a wind power output model; step III, building a state transition probability model of a wind turbine generator; step IV, determining the time of every operating state in an output model of the wind turbine generator to obtain the transition probability between the states of the wind turbine generator; step V, determining the fault rate of the wind turbine generator; and step VI, modeling a wind power farm into a conventional unit in a plurality of derated states and performing reliability calculation. The method is suitable for analyzing the reliability of a feeder line radiation type power distribution system and a power distribution system with distributed wind power supply, and the method not only solves the modeling problem of a power supply point in the reliability calculation after a distributed wind turbine generator is connected to the power distribution network, but also solves the problem that the analytical method cannot be used for calculation during reliability analysis of the power distribution network with distributed power.

Description

A kind of distribution network reliability containing disperseing wind-powered electricity generation based on analytic method is determined method

Technical field

The present invention relates to the method in a kind of reliability evaluation field, specifically relate to a kind of distribution network reliability containing disperseing wind-powered electricity generation based on analytic method and determine method.

Background technology

The research of distribution Power System Reliability starts from the phase at the beginning of the eighties in last century, conventional distribution Power System Reliability is fault mode consequences analysis method (FMEA), the method is utilized element (circuit, circuit breaker, isolating switch, transformer) reliability data, set up fault mode consequence table, analyze each event of failure and consequence thereof, then comprehensively form reliability index.

In recent years, increasing distributed power source access power distribution network.Along with a large amount of distributed power source access power distribution networks, make the analysis and calculation model of its reliability that basic change occur.The feeder line of what traditional reliability assessment was considered is power distribution network is all powered by single power supply point, and power distribution network is typically a radiant type supply power mode.On any feeder line, breaking down, will cause the load after feeder line all to have a power failure, there is large-area power failure in system.

Along with distributed power source access power distribution network makes its structure that variation occur, power distribution network becomes a network that spreads all over power supply and user's interconnection from a radioactive network, there is in essence variation in the model of fail-safe analysis and method, fail-safe analysis is also by the complexity becoming, and therefore researching and analysing after the common distributed power source access such as wind-powered electricity generation power distribution network is the task of top priority on the impact of reliability.But the Reliability Index of quantitative analysis distribution formula power supply access power distribution network also rarely has finding.

In traditional evaluating reliability of distribution network, power distribution network is typical radiant type supply power mode, and feeder line is by breaking down on any feeder line of single power supply point power supply, will cause the load after feeder line all to be stopped transport.But, along with distributed power source access power distribution network, its electric power-feeding structure changes, power distribution network becomes one from a radioactive network and spreads all over power supply and the interconnected network of user, exerting oneself of power supply is uncertain, direction of tide also becomes two-way direction from single direction, and therefore, the method that fail-safe analysis is calculated and model all need to occur larger variation.

In prior art, the method for quantitative evaluating system reliability is mainly divided three classes: parsing, simulation and the method for utilizing fuzzy mathematics to analyze.Simulation and utilize fuzzy mathematics to be used for qualitative analysis, computational speed is fast but not accurate enough.The consequences analysis method based on fault mode in analytic method is the main method that current distribution network reliability is analyzed, although analysis result accurately exists and is only applicable to radiation distribution, and amount of calculation slow shortcoming of computational speed when large.

Summary of the invention

For overcoming above-mentioned the deficiencies in the prior art, the invention provides a kind of distribution network reliability containing disperseing wind-powered electricity generation based on analytic method and determine method, be suitable for analyzing feeder line radiant type distribution system and the reliability containing the distribution system of disperseing wind-powered electricity generation to access, solve on the one hand after distributing wind-powered electricity generation unit access power distribution network the modeling problem of power supply point in Calculation of Reliability, solve on the one hand the problem that cannot use analytic calculation containing the analysis of distributed power source distribution network reliability.

Realizing the solution that above-mentioned purpose adopts is:

The distribution network reliability containing disperseing wind-powered electricity generation based on analytic method is determined a method, and its improvements are: said method comprising the steps of:

I, set up distributed power source reliability model;

II, set up the wind-powered electricity generation model of exerting oneself;

III, set up the state transition probability model of wind-powered electricity generation unit;

IV, determine each running status time in described wind-driven generator output model, obtain the rate of transform between wind-powered electricity generation set state;

V, determine the failure rate of described wind-driven generator;

VI, wind energy turbine set is modeled as to the conventional unit of many derates state, carries out Calculation of Reliability.

Further, in described step I, described distributed power source reliability model is the reliability model with non-rated output running status generator;

The non-rated output running status of described distributed power source reliability model comprises volume operation, derate running status and shut down condition in full.

Further, described Step II comprises: in the time that wind speed changes between incision and rated wind speed, and the one-tenth non-linear relation of the power output of wind-driven generator, the output power model of wind-driven generator is determined as shown in the formula (1):

$P=\left\{\begin{array}{cc}0& 0\≤V_t\≤V_{\mathrm{ci}}\\ (A+B\×V_t+C\×{V_t}^2)P_r& V_{\mathrm{ci}}\≤V_t\≤V_r\\ P_r& V_r\≤V_t\≤V_{\mathrm{co}}\\ 0& V_t\≥V_{\mathrm{co}}\end{array}\right.---\left(1\right)$

In formula, P _rfor the rated power of wind-driven generator; Parameter A, B and C are respectively V _ciand V _rfunction, determine as shown in the formula (2), (3), (4):

$A=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[V_{\mathrm{ci}}(V_{\mathrm{ci}}+V_r)-{4V}_{\mathrm{ci}}{V}_r{\left(\frac{V_{\mathrm{ci}}+V_r}{{2V}_r}\right)}^3]---\left(2\right)$

$B=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[4(V_{\mathrm{ci}}+V_r){\left(\frac{V_{\mathrm{ci}}+V_r}{{2V}_r}\right)}^3-({3V}_{\mathrm{ci}}+V_r)]---\left(3\right)$

$C=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[2-4{\left(\frac{V_{\mathrm{ci}}+V_r}{{2V}_r}\right)}^3]---\left(4\right)$

In formula, V _cifor incision wind speed; V _rfor rated wind speed; V _tfor current time wind speed; V _cofor cut-out wind speed.

Further, in described Step II I, exert oneself according to the wind-powered electricity generation that described II is definite, determine the state transition model of described wind-driven generator.

Further, described state transition model comprises variation power rating, firm power state and the zero power phase of wind-driven generator output, determines the probability of variation power rating, firm power state and the zero power phase of wind-powered electricity generation output according to the forced outage rate FOR of the power output of wind-driven generator and wind-driven generator as shown in the formula (5):

$\left\{\begin{array}{c}{P}_{U1}=(1-\mathrm{FOR})P(V_{\mathrm{ci}}\&le;V_t\&le;V_r)\\ P_{U2}=(1-\mathrm{FOR})P(V_r\&le;V_t\&le;V_{\mathrm{co}})\\ P_{U3}=(1-\mathrm{FOR})P(V_t<V_{\mathrm{ci}},V_t>V_{\mathrm{co}})\end{array}\right.---\left(5\right)$

In formula, P _u1, P _u2, P _u3be respectively the probability of described variation power, firm power and zero energy; V _cifor incision wind speed; V _rfor rated wind speed; V _tfor current time wind speed; V _cofor cut-out wind speed.

Further, described step IV comprises: according to the power output of the described wind-driven generator in described Step II, to the wind-powered electricity generation segmentation of exerting oneself, to described wind-driven generator, power rating, firm power state and zero power phase are operating carries out data acquisition changing, and sets up Weibull-Markov model;

Obtain the power output of random described wind-driven generator of unit interval, the output area of wind-driven generator, by the fixing scope of exerting oneself demarcation interval, is added up in different interval levels wind power generation in a year;

Obtain the rate of transform between wind-powered electricity generation set state.

Further, the preparation method of the rate of transform between described wind-powered electricity generation set state comprises:

S401, suppose, in each state duration obeys index distribution, to determine the rate of transform between state i and state j as shown in the formula (6):

λ _ij＝N _ij/t _i （6）

In formula, λ _ijrepresent the rate of transform, N _ijrepresent the transfer number from state i to state j, t _iexpression state i occupied time during whole;

S402, for improving computational efficiency, gathers limited state by the power stage of wind energy turbine set; Wind energy turbine set is regarded the conventional unit with many derates state as, forms the capacity stoppage in transit probability tables of whole system;

S403, utilize the concept of Capacity Margin to determine the reliability of described power distribution network.

Further, in described step V, use homogeneous Poisson processes, the failure rate of wind-driven generator all parts is calculated, obtain the failure rate of blower fan.

Further, in described step VI, by wind speed is carried out to cluster, utilize Weibull-Markov chain model to determine the rate of transform between duration and the state of various wind speed states;

Obtain wind-powered electricity generation and export the rate of transform between each shape probability of state and state, determine the random transition probability matrix of each state;

Wind energy turbine set is modeled as to the conventional unit of many derates state, obtains stoppage in transit capacity probability tables according to described random transition probability matrix, carry out Calculation of Reliability.

Compared with prior art, the present invention has following beneficial effect:

1, the thinking that after distributed power source access that method provided by the invention is clear and definite, distribution network reliability is analyzed, according to the characteristic difference of distributed power source power output, by distributed power source be divided into conventional substation, etc. capacity generator and the three kinds of reliability models with multiple non-rated output running statuses study; And then determined emphasis by distributed power source with the third mode modeling, and adopt the analytic method that traditional accuracy is high to carry out the definite thinking of reliability.

2, method provided by the invention has accurately provided the wind-powered electricity generation model of exerting oneself, and has set up the functional relation stage by stage that wind speed and blower fan are exerted oneself, and has provided wind speed profile function; Next determined the state transition model of wind-driven generator, i.e. the probabilistic model of each state, provides the probability calculation formula of each state; Determine each running status time in the wind power generation output model of most critical by setting up a Weibull-Markov model; Finally, provided the distribution network reliability computation model containing dispersion wind-powered electricity generation based on analytic method.

3, method provided by the invention has proposed a kind of wind energy turbine set reliability model based on analytic method, this model is exerted oneself under the condition of randomness taking into full account blower fan, wind energy turbine set is modeled as to a conventional unit that is similar to many derates state, the power stage of wind energy turbine set is gathered to limited state, and then can form the capacity stoppage in transit probability tables of whole system, utilize the concept of Capacity Margin (active volume and system loading poor) to evaluate the reliability of electricity generation system, solve after distributed power source access, the problem that system reliability cannot be evaluated, and further improve computational efficiency.

4, the failure rate of blower fan provided by the invention is used Weibull distribution to calculate by the failure rate to blower fan all parts, even different from normal power supplies failure rate method, normal power supplies failure rate is failure rate or follows exponential distribution; And failure rate of the present invention adopts Weibull distribution, be suitable for describing the unit interval (or space) quantity in chance event, the failure rate difference under different wind conditions, has fully demonstrated the stochastic behaviour that blower fan is exerted oneself.

5, method provided by the invention is suitable for analyzing feeder line radiant type distribution system and the reliability containing the distribution system of disperseing wind-powered electricity generation to access, and is convenient to programming and realizes, and strong operability, has stronger actual application value.

6, because blower fan is exerted oneself and is had fluctuation and uncertainty, the power supply using power supply point as rated output in conventional system Calculation of Reliability, its failure rate is also for the method for failure rate cannot be applied in during the electric network reliability that contains distributed power source calculates; Method provided by the invention has taken into full account the randomness that blower fan is exerted oneself, the probability of exerting oneself of blower fan is to provide according to wind speed, failure rate also can change along with the difference of wind speed, when Calculation of Reliability, more will consider the different running statuses of blower fan, therefore method of the present invention can adopt conventional analytic method to carry out Calculation of Reliability to the electric power system containing distributed power source.

Accompanying drawing explanation

Fig. 1 is the graph of relation of wind speed and power output;

Fig. 2 is fan operation state model figure;

Fig. 3 is that the distribution network reliability containing disperseing wind-powered electricity generation based on analytic method is determined method flow diagram.

Embodiment

Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in further detail.

As shown in Figure 3, Fig. 3 is that Fig. 3 is based on disperseing the distribution network reliability of wind-powered electricity generation to determine method flow diagram containing of analytic method; The distribution network reliability containing disperseing wind-powered electricity generation based on analytic method provided by the invention determines that method comprises the following steps:

Step 1, set up distributed power source reliability model;

Step 2, set up the wind-powered electricity generation model of exerting oneself;

Step 3, set up the state transition probability model of wind-powered electricity generation unit;

Step 4, determine each running status time in wind power generation output model, obtain the rate of transform between wind-powered electricity generation set state;

Step 5, determine the failure rate of blower fan;

Step 6, wind energy turbine set is modeled as to the conventional unit of many derates state, carries out Calculation of Reliability.

Distributed power source Reliability modeling in step 1

Distributed power source is divided into fuel cell and miniature gas turbine, wind power generation, solar energy power generating etc.The common feature of various distributed power sources is exert oneself intermittent and uncertain, according to the characteristic difference of distributed power source power output, distributed power source can be divided into three kinds of reliability models.

1. distributed power source is regarded as to common transformer station

This model is considered distributed power source as power distribution station, suppose that power output is unrestricted.In this case, evaluating reliability of distribution network can adopt traditional analytic method or simulation to calculate the reliability index of radial distribution.Because the capacity of distributed power source does not limit, therefore needn't carry out the division of isolated island, in the time that certain section of feeder line breaks down, by switching manipulation, faulty section to be isolated, all the other non-faulty components that are connected with distributed power source can be powered again by distributed power source.This model can well improve Distribution Network Reliability, but due to too idealized, actual application value is little.

2. distributed power source is regarded as to general generating set

This model is that distributed power source equivalence is become to a generator with rated capacity.Because its Capacitance reserve is constant, needn't consider its exporting change at simulation time section internal power.But because its power output is restricted, must take measures, distribution is carried out to isolated island division, to guarantee the equilibrium of supply and demand of electric power in isolated island, stable.This model can only improve the reliability level of isolated island internal loading point, and does not contribute for the reliability index of the outer load of isolated island.

3. distributed power source is regarded as with electromechanical source

This model is mainly considered, take wind energy and solar energy as the distributed power source of power, to be equivalent to a generator that has multiple running statuses,, except rated output, also has multiple non-rated output running statuses.This model has operation in full, derate running status and shut down condition in full.

Method of the present invention can fully demonstrate the stochastic behaviour that distributed power source is exerted oneself, and the modeling method of distributed power source adopts the third scheme, and distributed power source is regarded as with electromechanical source.

In step 2, set up the wind-powered electricity generation model of exerting oneself

The state of the exerting oneself key of determining distributing blower fan is to set up wind speed model accurately.

In the time that wind speed reaches incision wind speed, wind-driven generator starts to send electric energy; In the time that wind speed meets or exceeds rated wind speed, wind-driven generator Operation at full power; When wind speed continues to strengthen, until arrive when cut-out wind speed, consider security of operation, wind-driven generator is by out of service, and power output is 0.The relation curve of the power output of wind speed and blower fan as shown in Figure 1.

In the time that wind speed changes between incision and rated wind speed, linear rule is not followed in the variation of wind driven generator output power, but becomes non-linear relation, and the functional relation between the two is also that the output power model of wind-driven generator can be expressed as:

$P=\left\{\begin{array}{cc}0& 0\&le;V_t\&le;V_{\mathrm{ci}}\\ (A+B\&times;V_t+C\&times;{V_t}^2)P_r& V_{\mathrm{ci}}\&le;V_t\&le;V_r\\ P_r& V_r\&le;V_t\&le;V_{\mathrm{co}}\\ 0& V_t\&GreaterEqual;V_{\mathrm{co}}\end{array}\right.$

Wherein, the rated power that Pr is wind-driven generator.Parameter A, B and C are respectively V _ciand V _rfunction, A, B, C are as follows respectively:

$A=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[V_{\mathrm{ci}}(V_{\mathrm{ci}}+V_r)-{4V}_{\mathrm{ci}}{V}_r{\left(\frac{V_{\mathrm{ci}}+V_r}{{2V}_r}\right)}^3]$

$B=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[4(V_{\mathrm{ci}}+V_r){\left(\frac{V_{\mathrm{ci}}+V_r}{{2V}_r}\right)}^3-({3V}_{\mathrm{ci}}+V_r)]$

$C=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[2-4{\left(\frac{V_{\mathrm{ci}}+V_r}{{2V}_r}\right)}^3]$

In above formula, V _cifor incision wind speed; V _rfor rated wind speed; V _tfor current time wind speed; V _cofor cut-out wind speed.

In step 3, set up the state transition probability model of wind-powered electricity generation unit

It is the functional relation of wind speed according to wind driven generator output power, the state model of wind-driven generator can be expressed as three kinds of states shown in Fig. 2, be tri-kinds of states of U1, U2 and U3, represent respectively three kinds of the power, firm power of wind-driven generator exporting change and zero energys, μ is the time, λ is failure rate, considers the forced outage rate FOR of wind-driven generator simultaneously, and the probability calculation formula of each state is:

$\left\{\begin{array}{c}{P}_{U1}=(1-\mathrm{FOR})P(V_{\mathrm{ci}}\&le;V_t\&le;V_r)\\ P_{U2}=(1-\mathrm{FOR})P(V_r\&le;V_t\&le;V_{\mathrm{co}})\\ P_{U3}=(1-\mathrm{FOR})P(V_t<V_{\mathrm{ci}},V_t>V_{\mathrm{co}})\end{array}\right.$

In above formula, V _cifor incision wind speed; V _rfor rated wind speed; V _tfor current time wind speed; V _cofor cut-out wind speed.

In specific implementation process, consider the multiple output situation of wind-powered electricity generation, under above-mentioned three kinds of states, carry out more detailed division.

In step 4, determine each running status time in wind power generation output model, obtain the rate of transform between wind-powered electricity generation set state

Obtain the power output of wind-driven generator according to step 2, by by the power output segmentation of actual wind-driven generator, and the data of each running status time in the output model of wind-driven generator are gathered, obtain the running time of each state, set up accurately the model of exerting oneself at random of blower fan.

Exert oneself and meet Weibull distribution due to blower fan, therefore in the present embodiment, use Weibull probability to solve output state probability, solve reliability thereby set up a Weibull-Markov model.

The air speed data of Weibull distribution is to calculate wind power generation output by wind power equation, and the data processing function of Weibull distribution has been verified to wind power generation output also meets Weibull distribution.

According to wind power generation output statistics: divide several intervals by the output area of a blower fan by the fixing scope of exerting oneself, wind power generation in a year is added up in different interval levels.

Analytic method is generally based on Markov Chain method.Wind speed is carried out to cluster, calculate duration and the rate of transform of various wind speed states by Weibull-Markov chain model.Use Markov Chain method to obtain the random transition probability matrix of probability, frequency, duration and the various states of output of wind electric field of wind speed.

Integrity problem is usually directed to spatially discrete and continuous system in time, be spatially present in one of some discrete and discernible states, and until there is certain transfer in the state that they are present in system continuously, this shifts their discrete strips of frame to another state, and in this state, they exist again continuously until generation is shifted in another time.

Suppose that, in each state duration obeys index distribution, the rate of transform between state i and state j can be expressed as so:

λ _ij＝N _ij/t _i

In formula, λ _ijthe rate of transform between expression state, N _ijrepresent the transfer number from state i to state j, t _iexpression state i occupied time during whole.

In the present embodiment, incision wind speed, rated wind speed, the cut-out wind speed of setting blower fan are respectively 4m/s, 15m/s, 25m/s, suppose the forced outage rate FOR(Forced Outage Rate of blower fan) be 4%.

0,0.25,0.50,0.75,1.00,1.25,1.50,1.75,2.00MW in the situation that not considering unit fault, exerting oneself of the blower fan of 2MW is gathered into 9 states:.Thereby the transfer matrix of 9 states that can calculate according to above formula obtains the capacity stoppage in transit probability tables of blower fan.

In step 5, calculate the failure rate of blower fan

The failure rate of blower fan obtains by using homogeneous Poisson processes to calculate to the failure rate of blower fan all parts, and Poisson distribution is suitable for describing the unit interval (or space) quantity in chance event.For example: (as: be t) Poisson process cumulative number running time after failure to a counting process N (t).So, meet Poisson distribution by the fault cumulative number of time t:

N(t)＝ρt ^β

Density function is:

$\mathrm{\&lambda;}\left(t\right)=\frac{\mathrm{dN}\left(t\right)}{\mathrm{dt}}\mathrm{\&rho;\&beta;}{t}^{\mathrm{\&beta;}-1}$

Wherein, β is form parameter, and ρ is scale parameter.By curve, known:

If 1. β <1, failure rate reduces along with the increase of time;

If 2. β=1, failure rate is constant;

If 3. β >1, failure rate increases along with the increase of time.

Conventionally, in the present embodiment, can obtain by statistics the failure rate of wind power generation, adopt Poisson distribution to obtain the probability of malfunction of wind power generation.If setting failure rate is annual 2.2 times, also can obtain the Poisson process of probability of malfunction, calculate the probability of K fault in a year.

$P(x=k)=\frac{e^{-\mathrm{\&lambda;}}{\mathrm{\&lambda;}}^k}{k!}{|}_{\mathrm{\&lambda;}=2.2}$

Wherein, k is 1 year internal fault number of times.

In above method, be the probability that obtains K fault in a year by probability of malfunction, and failure rate obtain by statistics.

In step 6, wind energy turbine set is modeled as to the conventional unit of many derates state, carries out Calculation of Reliability

Obtain the rate of transform that above-mentioned wind-powered electricity generation is exerted oneself between state probability, each state, determine the random transition probability matrix of each state.Wind energy turbine set is modeled as to the conventional unit of many derates state, this model is exerted oneself under the condition of randomness taking into full account blower fan, and wind energy turbine set is modeled as to a conventional unit that is similar to many derates state, and then formation system stoppage in transit capacity probability tables.

In order to improve computational efficiency, the power stage of wind energy turbine set is gathered to limited state.Wind energy turbine set just can be regarded the conventional unit with many derates state as, and then can form the capacity stoppage in transit probability tables of whole system.

Utilize the concept of Capacity Margin (active volume and system loading poor) to evaluate the reliability of electricity generation system, therefore, negative Capacity Margin represents that system loading exceedes the state of active volume.

Finally should be noted that: above embodiment is only for illustrating the application's technical scheme but not restriction to its protection range; although the application is had been described in detail with reference to above-described embodiment; those of ordinary skill in the field are to be understood that: those skilled in the art still can carry out all changes, revise or be equal to replacement to the embodiment of application after reading the application; but these change, revise or be equal to replacement, within the claim protection range all awaiting the reply in application.

Claims (9)

1. the distribution network reliability containing disperseing wind-powered electricity generation based on analytic method is determined a method, it is characterized in that: said method comprising the steps of:

I, set up distributed power source reliability model;

II, set up the wind-powered electricity generation model of exerting oneself;

III, set up the state transition probability model of wind-powered electricity generation unit;

IV, determine each running status time in described wind-driven generator output model, obtain the rate of transform between wind-powered electricity generation set state;

V, determine the failure rate of described wind-driven generator;

VI, wind energy turbine set is modeled as to the conventional unit of many derates state, carries out Calculation of Reliability.

2. distribution network reliability as claimed in claim 1 is determined method, it is characterized in that: in described step I, described distributed power source reliability model is the reliability model with non-rated output running status generator;

The non-rated output running status of described distributed power source reliability model comprises volume operation, derate running status and shut down condition in full.

3. distribution network reliability as claimed in claim 1 is determined method, it is characterized in that: described Step II comprises: in the time that wind speed changes between incision and rated wind speed, the one-tenth non-linear relation of the power output of wind-driven generator, the output power model of wind-driven generator is determined as shown in the formula (1):

$P=\left\{\begin{array}{cc}0& 0\&le;V_t\&le;V_{\mathrm{ci}}\\ (A+B\&times;V_t+C\&times;{V_t}^2)P_r& V_{\mathrm{ci}}\&le;V_t\&le;V_r\\ P_r& V_r\&le;V_t\&le;V_{\mathrm{co}}\\ 0& V_t\&GreaterEqual;V_{\mathrm{co}}\end{array}\right.---\left(1\right)$

In formula, P _rfor the rated power of wind-driven generator; Parameter A, B and C are respectively V _ciand V _rfunction, determine as shown in the formula (2), (3), (4):

$A=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[V_{\mathrm{ci}}(V_{\mathrm{ci}}+V_r)-{4V}_{\mathrm{ci}}{V}_r{\left(\frac{V_{\mathrm{ci}}+V_r}{{2V}_r}\right)}^3]---\left(2\right)$

$B=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[4(V_{\mathrm{ci}}+V_r){\left(\frac{V_{\mathrm{ci}}+V_r}{{2V}_r}\right)}^3-({3V}_{\mathrm{ci}}+V_r)]---\left(3\right)$

$C=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[2-4{\left(\frac{V_{\mathrm{ci}}+V_r}{{2V}_r}\right)}^3]---\left(4\right)$

In formula, V _cifor incision wind speed; V _rfor rated wind speed; V _tfor current time wind speed; V _cofor cut-out wind speed.

4. distribution network reliability as claimed in claim 1 is determined method, it is characterized in that: in described Step II I, exert oneself according to the wind-powered electricity generation that described Step II is definite, determine the state transition model of described wind-driven generator.

5. distribution network reliability as claimed in claim 4 is determined method, it is characterized in that: described state transition model comprises variation power rating, firm power state and the zero power phase of wind-driven generator output, determine the probability of variation power rating, firm power state and the zero power phase of wind-powered electricity generation output according to the forced outage rate FOR of the power output of wind-driven generator and wind-driven generator as shown in the formula (5):

$\left\{\begin{array}{c}{P}_{U1}=(1-\mathrm{FOR})P(V_{\mathrm{ci}}\&le;V_t\&le;V_r)\\ P_{U2}=(1-\mathrm{FOR})P(V_r\&le;V_t\&le;V_{\mathrm{co}})\\ P_{U3}=(1-\mathrm{FOR})P(V_t<V_{\mathrm{ci}},V_t>V_{\mathrm{co}})\end{array}\right.---\left(5\right)$

In formula, P _u1, P _u2, P _u3be respectively the probability of described variation power, firm power and zero energy; V _cifor incision wind speed; V _rfor rated wind speed; V _tfor current time wind speed; V _cofor cut-out wind speed.

6. distribution network reliability as claimed in claim 1 is determined method, it is characterized in that: described step IV comprises: according to the power output of the described wind-driven generator in described Step II, to the wind-powered electricity generation segmentation of exerting oneself, to described wind-driven generator, power rating, firm power state and zero power phase are operating carries out data acquisition changing, and sets up Weibull-Markov model;

Obtain the power output of random described wind-driven generator of unit interval, the output area of wind-driven generator, by the fixing scope of exerting oneself demarcation interval, is added up in different interval levels wind power generation in a year;

Obtain the rate of transform between wind-powered electricity generation set state.

7. distribution network reliability as claimed in claim 6 is determined method, it is characterized in that: the preparation method of the rate of transform between described wind-powered electricity generation set state comprises::

S401, suppose, in each state duration obeys index distribution, to determine the rate of transform between state i and state j as shown in the formula (6):

λ _ij＝N _ij/t _i （6）

In formula, λ _ijrepresent the rate of transform, N _ijrepresent the transfer number from state i to state j, t _iexpression state i occupied time during whole;

S402, for improving computational efficiency, gathers limited state by the power stage of wind energy turbine set; Wind energy turbine set is regarded the conventional unit with many derates state as, forms the capacity stoppage in transit probability tables of whole system;

S403, utilize the concept of Capacity Margin to determine the reliability of described power distribution network.

8. distribution network reliability as claimed in claim 1 is determined method, it is characterized in that: in described step V, use homogeneous Poisson processes, the failure rate of wind-driven generator all parts is calculated, obtain the failure rate of blower fan.

9. distribution network reliability as claimed in claim 1 is determined method, it is characterized in that: in described step VI, by wind speed is carried out to cluster, utilize Weibull-Markov chain model to determine the rate of transform between duration and the state of various wind speed states;

Obtain wind-powered electricity generation and export the rate of transform between each shape probability of state and state, determine the random transition probability matrix of each state;

Wind energy turbine set is modeled as to the conventional unit of many derates state, obtains stoppage in transit capacity probability tables according to described random transition probability matrix, carry out Calculation of Reliability.

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