CN105760979A - Power system transient risk evaluation method taking natural disasters into consideration - Google Patents

Abstract

The invention provides a power system transient risk evaluation method taking natural disasters into consideration. The method comprises the following steps: determining element damage probabilities when the natural disasters are considered; determining preliminary fault sets, and classifying the preliminary fault sets; and carrying out transient risk evaluation on a power system. According to the invention, the disasters in a region are classified, multiple disaster faults in the region can be comprehensively considered, the element probabilities can be more accurate, the severity of random combination faults can be rapidly calculated, important faults are prevented from being neglected due to a restriction of a calculation amount, and the method can be applied to an actual power grid and facilitates solution of actual problems.

Description

A kind of electrical power system transient methods of risk assessment considering natural disaster

Technical field

The present invention relates to a kind of appraisal procedure, be specifically related to a kind of electrical power system transient methods of risk assessment considering natural disaster.

Background technology

Although natural disaster is small probability event, but it is very big to the harm of power system, as easy as rolling off a log causes large area blackout.Electric Power Network Planning is the premise of safe operation of power system, and system is caused substantial spoilage to need to consider the impact of natural disaster in the process of Electric Power Network Planning by defence natural disaster.At present, electrical power system transient risk assessment not yet proceeds to analyze software with actual electric network and combines, and utilizes its analysis result to instruct the stage of Electric Power Network Planning.

In the process of Electric Power Network Planning, it is considered to disaster factors as much as possible, Electric Power Network Planning result just can be made more accurate, thus avoiding the disaster that following electrical network can suffer to damage.Owing to planning electrical network may suffer from different types of disaster within project period, how various disasters being considered as a whole is one of problem to be solved at present.It addition, most of risk assessment adopt non-sequential Monte Carlo simulation method, the comparison of [0,1] upper equally distributed random number and probability of damage is namely utilized to determine the distress condition of element；And the risk assessment of actual electric network adopts analytic method mostly, but due to transmission line of electricity huge number, it is relatively time-consuming to add stability Calculation ratio again, N-1 or the N-2 fault of general computing electric power line；Under disaster scenarios it, it is possible to substantial amounts of component wear can be produced, it is therefore necessary to analytic method is improved reform of nature disaster.

Summary of the invention

In order to overcome above-mentioned the deficiencies in the prior art, the present invention provides a kind of electrical power system transient methods of risk assessment considering natural disaster, describes, by the failure probability of power system and scarce delivery index, the electrical power system transient risk considering natural disaster.

In order to realize foregoing invention purpose, the present invention adopts the following technical scheme that:

The present invention provides a kind of electrical power system transient methods of risk assessment considering natural disaster, said method comprising the steps of:

Step 1: determine the component wear probability considering natural disaster；

Step 2: determine preliminary failure collection, and it is classified；

Step 3: power system is carried out transient state risk assessment.

Described step 1 specifically includes following steps:

Step 1-1: natural disaster is divided into I class natural disaster and II class natural disaster；

Step 1-2: determine the component wear probability considering I class natural disaster and II class natural disaster respectively.

In described step 1-1, I class natural disaster includes typhoon, icing and earthquake；

II class natural disaster includes heavy rain, thunder and lightning and mountain fire；

The probability of happening of I class natural disaster less than the probability of happening of II class natural disaster, I class natural disaster on the impact of power system more than the impact on power system of the II class natural disaster.

Described step 1-2 comprises the following steps:

Step 1-2-1: the component wear probability considering I class natural disaster by analyzing the danger of I class natural disaster to determine；

Step 1-2-2: determine the component wear probability considering II class natural disaster, specifically include:

The state of element includes normal condition, heavy rain state, Lightning State and mountain fire state, and under heavy rain state, Lightning State and mountain fire state, the intact probability of element uses P respectively₂、P₃And P₄Represent, and have:

$P_2==\frac{{\mathrm{\μ}}_r}{{\mathrm{\μ}}_r+{\mathrm{\λ}}_r}---\left(1\right)$

$P_3=\frac{{\mathrm{\μ}}_f}{{\mathrm{\μ}}_f+{\mathrm{\λ}}_f}---\left(2\right)$

$P_4=\frac{{\mathrm{\μ}}_l}{{\mathrm{\μ}}_l+{\mathrm{\λ}}_l}---\left(3\right)$

Wherein, λ_rAnd μ_rForward fault rate and the repair rate of heavy rain state, λ to from normal condition for element_lAnd μ_lForward fault rate and the repair rate of Lightning State, λ to from normal condition for element_fAnd μ_fForward fault rate and the repair rate of mountain fire state to from normal condition for element；

Consider that the component wear probability U of II class natural disaster represents have:

$U=1-P_2\×P_3\×P_4=\frac{A+C}{A+B+C}---\left(4\right)$

Wherein, intermediate quantity A=λ_fμ_rμ_l+μ_fλ_rμ_l+μ_fμ_rλ_l, intermediate quantity B=μ_fμ_rμ_l, intermediate quantity C=λ_fμ_rλ_l+μ_fλ_rλ_l+λ_fμ_rλ_l+λ_fλ_rλ_l。

Described step 2 specifically includes following steps:

Step 2-1: read in flow data and the geographical wiring diagram of power system；

Step 2-2: choose electric pressure and line range, the fault of the required exponent number of combination, form preliminary failure collection；

Step 2-3: optional fault i, the circuit in open failure i, the weight index PI of fault i from preliminary failure collection_iIt is expressed as:

${\mathrm{PI}}_i=\underset{j=1}{\overset{L}{\Σ}}{w}_{sj}{\left(\frac{S_j}{S_j^{\max }}\right)}^{2m_j}---\left(5\right)$

Wherein, w_sjRepresent the weight factor of circuit j, S_jRepresent the apparent energy of circuit j,Represent the apparent energy ultimate value of circuit j, m_jThe integral indices of expression circuit j, j=1,2 ..., L, L represents circuit sum；

The risk indicator RI of fault i_iIt is expressed as:

${\mathrm{RI}}_i=\underset{j=1}{\overset{L}{\Σ}}P\left(X_i\right)\·w_{sj}{\left(\frac{S_j}{S_j^{\max }}\right)}^{2m_j}---\left(6\right)$

Wherein, P (X_i) represent fault i probability of happening；

Step 2-4: calculate the out of order weight index of institute and risk indicator in preliminary failure collection；

Step 2-5: preliminary failure collection is divided into persistent fault set and variable failure collection；

Persistent fault set includes single loop line three-phase permanent fault, double-circuit lines on the same pole three fault forever, bus-bar fault, DC power transmission line one pole fault, the bipolar fault of DC power transmission line and transformer fault；

The risk indicator of fault in variable failure collection is ranked up according to order from big to small, and according to similarity and the degree of association, fault is sorted out.

In described step 3, the failure probability of power system is more little, it was shown that power system unstability risk is more little；The failure probability P of power system^sRepresent, have:

$P^s=P_{dI}\×P_{dI}^S\left(X\right)+P_{Nd}\×P_{Nd}^S\left(X\right)---\left(7\right)$

Wherein, P_NdRepresent the probability of happening of power system, P in day-to-day operation situation_dIRepresent the probability of happening of power system in II class natural disaster situation,Represent the state probability of power system in day-to-day operation situation,Represent the state probability of power system in II class natural disaster situation, and have:

$P_{Nd}^S\left(X\right)=\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K}{\Π}}P\left(X_{km}\right)\×F_m---\left(8\right)$

$P_{dI}^S\left(X\right)=\underset{n=1}{\overset{N}{\Σ}}\underset{k=1}{\overset{K}{\Π}}P\left(X_{kn}\right)\×F_n---\left(9\right)$

Wherein, M is the fault sum in day-to-day operation situation, m=1,2 ..., M；N is the fault sum in II class natural disaster situation, n=1,2 ..., N；F_mRepresent the power system test function that in day-to-day operation situation, m-th fault is corresponding, during power system unstability, F_m=1, electric power system stability timing, F_m=0；F_nRepresent the power system test function that in II class natural disaster situation, the n-th fault is corresponding, during power system unstability, F_n=1, electric power system stability timing, F_n=0；P(X_km) for element k probability under m-th fault in day-to-day operation situation, P (X_kn) for element k probability under the n-th fault in II class natural disaster situation, and have:

Wherein, μ_kmAnd λ_kmRepresent the repair rate under m-th fault of element k in day-to-day operation situation and fault rate, μ respectively_knAnd λ_knRepresent the repair rate under the n-th fault of element k in II class natural disaster situation and fault rate respectively；

It is more little that the expectation of power system lacks delivery, it was shown that it is more little that load risk is lost in power system；The expectation of power system lacks delivery EENS and represents have:

$\begin{array}{c}EENS={\mathrm{EENS}}_D+{\mathrm{EENS}}_{ND}\\ =P_{dI}\×\underset{n=1}{\overset{N}{\Σ}}\underset{k=1}{\overset{K}{\Π}}P\left(X_{kn}\right)\×C\left(X_n\right)\×T_n+P_{Nd}\×\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K}{\Π}}P\left(X_{km}\right)\×C\left(X_m\right)\×T_m\end{array}---\left(12\right)$

Wherein, EENS_DRepresent that in II class natural disaster situation, the expectation of power system lacks delivery, EENS_NDRepresent that in day-to-day operation situation, the expectation of power system lacks delivery；C(X_m) for power system cutting load amount corresponding to m-th fault in day-to-day operation situation, T_mFor the POWER SYSTEM STATE persistent period that m-th fault in day-to-day operation situation is corresponding；C(X_n) for power system cutting load amount corresponding to the n-th fault in II class natural disaster situation, T_nFor the POWER SYSTEM STATE persistent period that the n-th fault in II class natural disaster situation is corresponding.

Compared with prior art, the beneficial effects of the present invention is:

1) disaster in region is classified by the present invention, it is possible to consider the multiple disaster fault in region, it is possible to make element probability more accurate；

2) present invention can quickly calculate the order of severity of random combine fault；

3) present invention proposes the choosing method of preliminary failure collection, it is to avoid because calculating quantitative limitation, omit important fault；

4) present invention can make the result that system risk is assessed more accurate；

5) present invention can be applied to actual electric network, contributes to solving practical problems.

Accompanying drawing explanation

Fig. 1 is the electrical power system transient methods of risk assessment flow chart considering natural disaster in the embodiment of the present invention；

Fig. 2 is Sichuan Electric Power Network geographical configuration figure in the embodiment of the present invention.

Detailed description of the invention

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

The present invention provides a kind of electrical power system transient methods of risk assessment considering natural disaster, and (such as Fig. 1) said method comprising the steps of:

Step 1: determine the component wear probability considering natural disaster；

Step 2: determine preliminary failure collection, and it is classified；

Step 3: power system is carried out transient state risk assessment.

Described step 1 specifically includes following steps:

Step 1-1: natural disaster is divided into I class natural disaster and II class natural disaster；

Step 1-2: determine the component wear probability considering I class natural disaster and II class natural disaster respectively.

In described step 1-1, I class natural disaster includes typhoon, icing and earthquake；

II class natural disaster includes heavy rain, thunder and lightning and mountain fire；

The probability of happening of I class natural disaster less than the probability of happening of II class natural disaster, I class natural disaster on the impact of power system more than the impact on power system of the II class natural disaster.

Described step 1-2 comprises the following steps:

Step 1-2-1: the component wear probability considering I class natural disaster by analyzing the danger of I class natural disaster to determine；

Step 1-2-2: determine the component wear probability considering II class natural disaster, specifically include:

The state of element includes normal condition, heavy rain state, Lightning State and mountain fire state, and under heavy rain state, Lightning State and mountain fire state, the intact probability of element uses P respectively₂、P₃And P₄Represent, and have:

$P_2==\frac{{\mathrm{\μ}}_r}{{\mathrm{\μ}}_r+{\mathrm{\λ}}_r}---\left(1\right)$

$P_3=\frac{{\mathrm{\μ}}_f}{{\mathrm{\μ}}_f+{\mathrm{\λ}}_f}---\left(2\right)$

$P_4=\frac{{\mathrm{\μ}}_l}{{\mathrm{\μ}}_l+{\mathrm{\λ}}_l}---\left(3\right)$

Wherein, λ_rAnd μ_rForward fault rate and the repair rate of heavy rain state, λ to from normal condition for element_lAnd μ_lForward fault rate and the repair rate of Lightning State, λ to from normal condition for element_fAnd μ_fForward fault rate and the repair rate of mountain fire state to from normal condition for element；

Consider that the component wear probability U of II class natural disaster represents have:

$U=1-P_2\×P_3\×P_4=\frac{A+C}{A+B+C}---\left(4\right)$

Wherein, intermediate quantity A=λ_fμ_rμ_l+μ_fλ_rμ_l+μ_fμ_rλ_l, intermediate quantity B=μ_fμ_rμ_l, intermediate quantity C=λ_fμ_rλ_l+μ_fλ_rλ_l+λ_fμ_rλ_l+λ_fλ_rλ_l。

Described step 2 specifically includes following steps:

Step 2-1: read in flow data and the geographical wiring diagram of power system；

Step 2-2: choose electric pressure and line range, the fault of the required exponent number of combination, form preliminary failure collection；

Step 2-3: optional fault i, the circuit in open failure i, the weight index PI of fault i from preliminary failure collection_iIt is expressed as:

${\mathrm{PI}}_i=\underset{j=1}{\overset{L}{\Σ}}{w}_{sj}{\left(\frac{S_j}{S_j^{\max }}\right)}^{2m_j}---\left(5\right)$

Wherein, w_sjRepresent the weight factor of circuit j, S_jRepresent the apparent energy of circuit j,Represent the apparent energy ultimate value of circuit j, m_jThe integral indices of expression circuit j, j=1,2 ..., L, L represents circuit sum；

The risk indicator RI of fault i_iIt is expressed as:

${\mathrm{RI}}_i=\underset{j=1}{\overset{L}{\Σ}}P\left(X_i\right)\·w_{sj}{\left(\frac{S_j}{S_j^{\max }}\right)}^{2m_j}---\left(6\right)$

Wherein, P (X_i) represent fault i probability of happening；

Step 2-4: calculate the out of order weight index of institute and risk indicator in preliminary failure collection；

Step 2-5: preliminary failure collection is divided into persistent fault set and variable failure collection；

Persistent fault set includes single loop line three-phase permanent fault, double-circuit lines on the same pole three fault forever, bus-bar fault, DC power transmission line one pole fault, the bipolar fault of DC power transmission line and transformer fault；

The risk indicator of fault in variable failure collection is ranked up according to order from big to small, and according to similarity and the degree of association, fault is sorted out.

In described step 3, the failure probability of power system is more little, it was shown that power system unstability risk is more little；The failure probability P of power system^sRepresent, have:

$P^s=P_{dI}\×P_{dI}^S\left(X\right)+P_{Nd}\×P_{Nd}^S\left(X\right)---\left(7\right)$

Wherein, P_NdRepresent the probability of happening of power system, P in day-to-day operation situation_dIRepresent the probability of happening of power system in II class natural disaster situation,Represent the state probability of power system in day-to-day operation situation,Represent the state probability of power system in II class natural disaster situation, and have:

$P_{Nd}^S\left(X\right)=\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K}{\Π}}P\left(X_{km}\right)\×F_m---\left(8\right)$

$P_{dI}^S\left(X\right)=\underset{n=1}{\overset{N}{\Σ}}\underset{k=1}{\overset{K}{\Π}}P\left(X_{kn}\right)\×F_n---\left(9\right)$

Wherein, M is the fault sum in day-to-day operation situation, m=1,2 ..., M；N is the fault sum in II class natural disaster situation, n=1,2 ..., N；F_mRepresent the power system test function that in day-to-day operation situation, m-th fault is corresponding, during power system unstability, F_m=1, electric power system stability timing, F_m=0；F_nRepresent the power system test function that in II class natural disaster situation, the n-th fault is corresponding, during power system unstability, F_n=1, electric power system stability timing, F_n=0；P(X_km) for element k probability under m-th fault in day-to-day operation situation, P (X_kn) for element k probability under the n-th fault in II class natural disaster situation, and have:

Wherein, μ_kmAnd λ_kmRepresent the repair rate under m-th fault of element k in day-to-day operation situation and fault rate, μ respectively_knAnd λ_knRepresent the repair rate under the n-th fault of element k in II class natural disaster situation and fault rate respectively；

It is more little that the expectation of power system lacks delivery, it was shown that it is more little that load risk is lost in power system；The expectation of power system lacks delivery EENS and represents have:

$\begin{array}{c}EENS={\mathrm{EENS}}_D+{\mathrm{EENS}}_{ND}\\ =P_{dI}\×\underset{n=1}{\overset{N}{\Σ}}\underset{k=1}{\overset{K}{\Π}}P\left(X_{kn}\right)\×C\left(X_n\right)\×T_n+P_{Nd}\×\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K}{\Π}}P\left(X_{km}\right)\×C\left(X_m\right)\×T_m\end{array}---\left(12\right)$

Wherein, EENS_DRepresent that in II class natural disaster situation, the expectation of power system lacks delivery, EENS_NDRepresent that in day-to-day operation situation, the expectation of power system lacks delivery；C(X_m) for power system cutting load amount corresponding to m-th fault in day-to-day operation situation, T_mFor the POWER SYSTEM STATE persistent period that m-th fault in day-to-day operation situation is corresponding；C(X_n) for power system cutting load amount corresponding to the n-th fault in II class natural disaster situation, T_nFor the POWER SYSTEM STATE persistent period that the n-th fault in II class natural disaster situation is corresponding.

Ending for the end of the year 2014, Sichuan Electric Power Network has 500 kv substation 44,220,110 kv substations 207 and 740 respectively；The whole province 110 kilovolts and above transmission line of electricity 2423, length 55791 kilometers of transmitting electricity.Its geographical configuration is as shown in Figure 2.

The number of Sichuan Electric Power Network second order random fault is 6109, the number of three rank random faults is 222365, for second order fault, calculate the PI value of each fault, all of second order fault can be divided into 6 set altogether, whether reasonable in order to weigh the check of PI value and failure collection classification, adopt the computing system safety under gathering circuit three phase short circuit fault situation shown in 1-6 respectively of PSD-BPA program, second order fault PI value and safety check result are as shown in table 1:

Table 1

As can be seen from Table 1: the PI value of each rank fault all can find out a scope, and the consequence that system is caused by the fault within the scope of this is more similar；The PI of most of faults broadly falls into this scope, and small part fault belongs to the consequence that system is caused than more serious fault, and this partial fault belongs to the catastrophe failure to find just.

Owing to lacking the electrical equipment statistical law of II class natural disaster, in order to prove institute's extracting method herein, temporarily substitute studying more I class natural disaster data.Sichuan is earthquake severely afflicated area, therefore according to the probability of happening that 50 Annual exceeding probabilities are 10% estimation earthquake disaster, according to the probability of happening meeting estimation circuit ice damage for 30 years.According to earthquake intensity figure, the PGA estimating asbestos transformer station is 0.2, red scape, Yaan, Kowloon transformer station PGA value be 0.15, the PGA value of all the other transformer stations is and is not more than 0.1, when being 20mm according to rainfall, the failure probability that estimation circuit is possible.Owing to length is limit, the fault filtering out probability of malfunction bigger is as shown in table 2.

Table 2

From table 2 it can be seen that individually consider a kind of risk, underestimating the probability of damage of element, thus underestimating the risk that system is likely to face, adopting comprehensive probability of damage model, its result is relatively accurate.

When calculating random fault to quadravalence fault, in conjunction with BPA-PSD stability Calculation program, system is carried out the risk assessment under day-to-day operation and disaster scenarios it；With after the earthquake, repair time is 20 days, computing system probability of damage, and its assessment result is as shown in table 3:

Table 3

As can be seen from the table, carried herein that to consider that the comprehensive scene failure probability of disaster compares the scene generally only taking into account N-2 fault big close to 1 order of magnitude, be left out consequent malfunction and can underestimate the risk of system；When the earthquake based on seismic rehionalization map norm for civil defense occurs, although System failure probability is only small, but once occur, the loading of its loss is very big, is necessary to consider the impact of natural disaster hence for programme.

Finally should be noted that: above example is only in order to illustrate that technical scheme is not intended to limit; the specific embodiment of the present invention still can be modified or equivalent replacement by those of ordinary skill in the field with reference to above-described embodiment; these are without departing from any amendment of spirit and scope of the invention or equivalent replace, within the claims of the present invention all awaited the reply in application.

Claims (6)

1. the electrical power system transient methods of risk assessment considering natural disaster, it is characterised in that: said method comprising the steps of:

Step 1: determine the component wear probability considering natural disaster；

Step 2: determine preliminary failure collection, and it is classified；

Step 3: power system is carried out transient state risk assessment.

2. the electrical power system transient methods of risk assessment of consideration natural disaster according to claim 1, it is characterised in that: described step 1 specifically includes following steps:

Step 1-1: natural disaster is divided into I class natural disaster and II class natural disaster；

Step 1-2: determine the component wear probability considering I class natural disaster and II class natural disaster respectively.

3. the electrical power system transient methods of risk assessment of consideration natural disaster according to claim 2, it is characterised in that: in described step 1-1, I class natural disaster includes typhoon, icing and earthquake；

II class natural disaster includes heavy rain, thunder and lightning and mountain fire；

The probability of happening of I class natural disaster less than the probability of happening of II class natural disaster, I class natural disaster on the impact of power system more than the impact on power system of the II class natural disaster.

4. the electrical power system transient methods of risk assessment of consideration natural disaster according to claim 2, it is characterised in that: described step 1-2 comprises the following steps:

Step 1-2-1: the component wear probability considering I class natural disaster by analyzing the danger of I class natural disaster to determine；

Step 1-2-2: determine the component wear probability considering II class natural disaster, specifically include:

The state of element includes normal condition, heavy rain state, Lightning State and mountain fire state, and under heavy rain state, Lightning State and mountain fire state, the intact probability of element uses P respectively₂、P₃And P₄Represent, and have:

$P_2==\frac{{\mathrm{\μ}}_r}{{\mathrm{\μ}}_r+{\mathrm{\λ}}_r}---\left(1\right)$

$P_3=\frac{{\mathrm{\μ}}_f}{{\mathrm{\μ}}_f+{\mathrm{\λ}}_f}---\left(2\right)$

$P_4=\frac{{\mathrm{\μ}}_l}{{\mathrm{\μ}}_l+{\mathrm{\λ}}_l}---\left(3\right)$

Wherein, λ_rAnd μ_rForward fault rate and the repair rate of heavy rain state, λ to from normal condition for element_lAnd μ_lForward fault rate and the repair rate of Lightning State, λ to from normal condition for element_fAnd μ_fForward fault rate and the repair rate of mountain fire state to from normal condition for element；

Consider that the component wear probability U of II class natural disaster represents have:

$U=1-P_2\×P_3\×P_4=\frac{A+C}{A+B+C}---\left(4\right)$

Wherein, intermediate quantity A=λ_fμ_rμ_l+μ_fλ_rμ_l+μ_fμ_rλ_l, intermediate quantity B=μ_fμ_rμ_l, intermediate quantity C=λ_fμ_rλ_l+μ_fλ_rλ_l+λ_fμ_rλ_l+λ_fλ_rλ_l。

5. the electrical power system transient methods of risk assessment of consideration natural disaster according to claim 1, it is characterised in that: described step 2 specifically includes following steps:

Step 2-1: read in flow data and the geographical wiring diagram of power system；

Step 2-2: choose electric pressure and line range, the fault of the required exponent number of combination, form preliminary failure collection；

Step 2-3: optional fault i, the circuit in open failure i, the weight index PI of fault i from preliminary failure collection_iIt is expressed as:

${\mathrm{PI}}_i=\underset{j=1}{\overset{L}{\Σ}}{w}_{sj}{\left(\frac{S_j}{S_j^{\max }}\right)}^{2m_j}---\left(5\right)$

Wherein, w_sjRepresent the weight factor of circuit j, S_jRepresent the apparent energy of circuit j,Represent the apparent energy ultimate value of circuit j, m_jThe integral indices of expression circuit j, j=1,2 ..., L, L represents circuit sum；

The risk indicator RI of fault i_iIt is expressed as:

${\mathrm{RI}}_i=\underset{j=1}{\overset{L}{\Σ}}P\left(X_i\right)\·w_{sj}{\left(\frac{S_j}{S_j^{\max }}\right)}^{2m_j}---\left(6\right)$

Wherein, P (X_i) represent fault i probability of happening；

Step 2-4: calculate the out of order weight index of institute and risk indicator in preliminary failure collection；

Step 2-5: preliminary failure collection is divided into persistent fault set and variable failure collection；

Persistent fault set includes single loop line three-phase permanent fault, double-circuit lines on the same pole three fault forever, bus-bar fault, DC power transmission line one pole fault, the bipolar fault of DC power transmission line and transformer fault；

The risk indicator of fault in variable failure collection is ranked up according to order from big to small, and according to similarity and the degree of association, fault is sorted out.

6. the electrical power system transient methods of risk assessment of consideration natural disaster according to claim 1, it is characterised in that: in described step 3, the failure probability of power system is more little, it was shown that power system unstability risk is more little；The failure probability P of power system^sRepresent, have:

$P^s=P_{dI}\×P_{dI}^S\left(X\right)+P_{Nd}\×P_{Nd}^S\left(X\right)---\left(7\right)$

Wherein, P_NdRepresent the probability of happening of power system, P in day-to-day operation situation_dIRepresent the probability of happening of power system in II class natural disaster situation,Represent the state probability of power system in day-to-day operation situation,Represent the state probability of power system in II class natural disaster situation, and have:

$P_{Nd}^S\left(X\right)=\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K}{\Π}}P\left(X_{km}\right)\×F_m---\left(8\right)$

$P_{dI}^S\left(X\right)=\underset{n=1}{\overset{N}{\Σ}}\underset{k=1}{\overset{K}{\Π}}P\left(X_{kn}\right)\×F_n---\left(9\right)$

Wherein, M is the fault sum in day-to-day operation situation, m=1,2 ..., M；N is the fault sum in II class natural disaster situation, n=1,2 ..., N；F_mRepresent the power system test function that in day-to-day operation situation, m-th fault is corresponding, during power system unstability, F_m=1, electric power system stability timing, F_m=0；F_nRepresent the power system test function that in II class natural disaster situation, the n-th fault is corresponding, during power system unstability, F_n=1, electric power system stability timing, F_n=0；P(X_km) for element k probability under m-th fault in day-to-day operation situation, P (X_kn) for element k probability under the n-th fault in II class natural disaster situation, and have:

Wherein, μ_kmAnd λ_kmRepresent the repair rate under m-th fault of element k in day-to-day operation situation and fault rate, μ respectively_knAnd λ_knRepresent the repair rate under the n-th fault of element k in II class natural disaster situation and fault rate respectively；

It is more little that the expectation of power system lacks delivery, it was shown that it is more little that load risk is lost in power system；The expectation of power system lacks delivery EENS and represents have:

$\begin{array}{c}EENS={\mathrm{EENS}}_D+{\mathrm{EENS}}_{ND}\\ =P_{dI}\×\underset{n=1}{\overset{N}{\Σ}}\underset{k=1}{\overset{K}{\Π}}P\left(X_{kn}\right)\×C\left(X_n\right)\×T_n+P_{Nd}\×\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K}{\Π}}P\left(X_{km}\right)\×C\left(X_m\right)\×T_m\end{array}---\left(12\right)$

Wherein, EENS_DRepresent that in II class natural disaster situation, the expectation of power system lacks delivery, EENS_NDRepresent that in day-to-day operation situation, the expectation of power system lacks delivery；C(X_m) for power system cutting load amount corresponding to m-th fault in day-to-day operation situation, T_mFor the POWER SYSTEM STATE persistent period that m-th fault in day-to-day operation situation is corresponding；C(X_n) for power system cutting load amount corresponding to the n-th fault in II class natural disaster situation, T_nFor the POWER SYSTEM STATE persistent period that the n-th fault in II class natural disaster situation is corresponding.