CN114493365A - Method for evaluating cascading failure vulnerability of power system including wind power plant - Google Patents

Method for evaluating cascading failure vulnerability of power system including wind power plant Download PDF

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CN114493365A
CN114493365A CN202210198717.6A CN202210198717A CN114493365A CN 114493365 A CN114493365 A CN 114493365A CN 202210198717 A CN202210198717 A CN 202210198717A CN 114493365 A CN114493365 A CN 114493365A
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line
total
cascading failure
vulnerability
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边晓燕
黄阮明
王晓晖
费斐
李灏恩
吴恩琦
宋天立
戚宇辰
顾嘉凤
欧鉴贤
杨云轶
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Shanghai Electric Power University
State Grid Shanghai Electric Power Co Ltd
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State Grid Shanghai Electric Power Co Ltd
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Abstract

The invention relates to an evaluation method for cascading failure vulnerability of a power system including a wind power plant, which comprises the following steps of: 1) constructing an optimal power flow model based on a line overload model; 2) under the power grid cascading failure, the line shutdown routing is carried out by combining a thermal stability model and a random model of the power transmission line, and the total trip occupation ratio of the line is obtained; 3) performing cascading failure simulation based on the mixed optimal power flow model and the random model, constructing a power grid vulnerability index considering the wind power uncertainty level and the penetration rate, and evaluating the influence of the power grid vulnerability through the total percentage of line tripping and the total load shedding percentage under the cascading failure. Compared with the prior art, the method effectively solves the problems of uncertainty and permeability of the power grid caused by popularization of renewable energy sources such as wind power generation and the like, quickly and accurately evaluates the vulnerability of the power grid to different uncertainty levels and penetration rates during cascading failures, and is helpful for technicians and decision-makers to more efficiently solve related practical problems.

Description

Method for evaluating cascading failure vulnerability of power system including wind power plant
Technical Field
The invention relates to the technical field of cascading failure vulnerability assessment of a power system, in particular to a cascading failure vulnerability assessment method of a power system including a wind power plant.
Background
While promoting economic development, the global industrial revolution also uses a large amount of limited and environmentally unfriendly fossil energy, which leads to rapid consumption of energy and large carbon emission, and the threat is becoming serious, so that renewable energy sources such as wind energy and solar energy are receiving wide attention due to their environmental protection properties and contribution to saving fossil fuel resources. Meanwhile, the ratio of new energy power generation such as wind power generation in a power system has gradually increased, but the evaluation of cascading failure of the power system is also more challenging.
Large-scale blackouts caused by cascading failures, which originate from strong interdependencies within the grid, weaken the power system when they occur, and may lead to system instability and large-scale blackouts, represent a significant economic and social cost each year. The enormous economic and social impact of such events has prompted people to study the vulnerability of the grid to cascading failures and to find more efficient evaluation methods. In power systems, the penetration of new energy generation can bring the grid closer to its operating limits and introduce a large amount of uncertainty, which comes from the randomness of the new energy, thus changing the dynamic performance of the grid, which is first of all the increase of cascading failures related to wind farms. Therefore, it is becoming more and more important to evaluate the impact of different wind power uncertainty levels and penetration rates under cascading failures on grid vulnerability under cascading failures.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide an evaluation method for cascading failure vulnerability of a power system of a wind power plant.
The purpose of the invention can be realized by the following technical scheme:
a method for evaluating cascading failure vulnerability of a power system of a wind power plant comprises the following steps:
1) constructing an optimal power flow model based on a line overload model;
2) under the power grid cascading failure, a line shutdown path finding is carried out by combining a power transmission line thermal stability model and a random model, and the total trip occupation ratio of the line is obtained;
3) performing cascading failure simulation based on a mixed optimal power flow model and a random model, constructing a power grid vulnerability index considering wind power uncertainty level and penetration rate, identifying the most vulnerable line in the power grid under different wind power uncertainty levels and penetration rates, and evaluating the influence of the power grid vulnerability through the total line tripping percentage mu and the total load shedding percentage eta under cascading failure.
In the step 1), the total cost is taken as an optimization target, the initial scheduling output of the conventional generator is determined according to the load and the wind power prediction information, and an optimal power flow model is established through an online island detection algorithm and an automatic power balance algorithm, so that the method comprises the following steps:
Figure BDA0003528281440000021
Figure BDA0003528281440000022
Figure BDA0003528281440000023
Figure BDA0003528281440000024
wherein the content of the first and second substances,
Figure BDA0003528281440000025
as a polynomial cost function of the generator i,
Figure BDA0003528281440000026
is the output power of generator i, ngIn order to be the total number of the generators,
Figure BDA0003528281440000027
is the maximum value of the output power of the generator i, Pl jIs the demanded power of load j, nlFor the number of loads, G is the generator set, L is the load set, FijFor the power flow between the node where generator i is located and the node where load j is located,
Figure BDA0003528281440000028
for the tidal current capacity between the node where generator i is located and the node where load j is located,
Figure BDA0003528281440000029
Figure BDA00035282814400000210
presentation generation and consumptionBalancing the total power of (a).
In step 1), during a cascading failure, all islands generated due to line tripping are identified according to an online island detection algorithm, and an automatic power balance algorithm is adopted to reduce the total load to the minimum so as to limit the maximum capacity of a generator, maintain the power balance of each island, and change the electrical frequency of a system based on the power balance, then:
Figure BDA00035282814400000211
wherein, PGenFor total emitted power, PLoadFor total power consumption, H is total inertia, f is the electrical frequency of the system, and t is time.
In the step 2), the uncertainty of wind and load in the power flow process is simulated through the overload probability of the line, the line with the minimum overload distance is obtained, and the line is tripped in the case of cascading failure, so that the total tripping percentage of the line is obtained.
The uncertainty modeling specifically comprises the following steps:
21) adopting a sliding window method as an m multiplied by m matrix, and calculating a covariance matrix C of each time stepF(t, τ) there are:
Figure BDA0003528281440000031
wherein the content of the first and second substances,
Figure BDA00035282814400000314
for uncertain items in the first line load flow signal at the time t, matrix elements
Figure BDA0003528281440000032
22) The uncertainty of wind and load in the process of line overload probability simulation load flow is introduced into a stochastic model, and the load flow F of the first line is assumed that a power input function P (t) meets Gaussian distributionl(t) satisfies the Gaussian distribution, lineProbability of road overload ρl(t) calculated by the Q function, then there are:
Figure BDA0003528281440000033
Figure BDA0003528281440000034
wherein, alFor the standard overload distance of the l line, the expression of the Q function is
Figure BDA0003528281440000035
Figure BDA0003528281440000036
Figure BDA0003528281440000037
For the tidal current capacity of the l-th line,
Figure BDA0003528281440000038
respectively is the power flow mean value and the variance of the first line;
23) according to the standard overload distance and overload probability rho of each linelCalculating a load flow F at the time tlAverage overload time in (t)
Figure BDA0003528281440000039
Then there are:
Figure BDA00035282814400000310
wherein, BWlThe equivalent bandwidth of the flow process of the first line is obtained;
24) when average overload time
Figure BDA00035282814400000311
Greater than the trip time ttrWhen it is, the step returns to step 21), when the average overload time is
Figure BDA00035282814400000312
Is less than or equal to the tripping time ttrAnd then, tripping off the overload circuit after the tripping time, detecting a new island formed by the tripping circuit by adopting an island detection algorithm, balancing the generated energy and the load of each new island, and reducing the load loss to the maximum extent to obtain the total tripping percentage mu and the total load shedding percentage eta of the circuit.
In the step 24), the tripping time t of the overload line is obtained according to the line thermal stability modeltrThen, there are:
Figure BDA00035282814400000313
wherein F is the overload line current, FopFor the initial operating trend, FmaxAs a line flow threshold, TthAre thermal time constants related to the type of wire and environmental parameters, specifically wind speed and ambient temperature.
In the step 3), acquiring a vulnerability index psi of the wind power uncertainty level to the power grid under the cascading failure according to the total trip percentage mu and the total load shedding percentage eta of the line1Then, there are:
Figure BDA0003528281440000041
wherein, WuncIs a weight parameter of the wind power uncertainty level in the system under cascading failure.
In the step 3), the wind power uncertainty level represents the relative error of wind power prediction, and the uncertainty level of the wind power generator is dependent on factors
Figure BDA0003528281440000042
Is increased by the amount of the additive, wherein,
Figure BDA0003528281440000043
as a new uncertainty index for wind power，
Figure BDA0003528281440000044
For the initial uncertainty index of wind power, as gamma increases, the total trip proportion of a line during cascading failure increases, so that more islands and greater load shedding are formed, namely, as the uncertainty level of wind power increases, the vulnerability of the system is continuously increased.
In the step 3), acquiring a vulnerability index psi of wind power penetration rate to the power grid under cascading failure according to the total trip percentage mu and the total load shedding percentage eta of the line2Then, there are:
Figure BDA0003528281440000045
wherein, WpenThe weight parameter of the wind power penetration rate in the system under the cascading failure is shown.
In the step 3), the wind power penetration rate is defined as:
Figure BDA0003528281440000046
wherein the content of the first and second substances,
Figure BDA0003528281440000047
in order to be the total capacity of the wind turbine,
Figure BDA0003528281440000048
for the total capacity of all generators, as alpha is increased, cascading failures are also upgraded, so that the total duty ratio of line tripping is increased, more islands are generated, and larger load shedding is caused, namely, when the wind power penetration rate is increased, the vulnerability of the system is increased.
Compared with the prior art, the invention has the following advantages:
the invention provides a more efficient evaluation method for the vulnerability evaluation of a power grid with cascading failures of a power system of a wind power plant, and the spreading time of the cascading failures is very short, and the uncertainty and permeability of the wind power also bring difficulty to the vulnerability evaluation, therefore, the invention firstly takes the total cost as an optimization target, combines a line overload model, an online island detection algorithm and an automatic power balance algorithm, establishes an optimal power flow model based on direct current power flow, greatly improves the power flow calculation time under the condition of ensuring the high fidelity of the system, then combines a thermal stability model of a power transmission line with a random model, simulates the uncertainty of the wind power and the load in the power flow process through the line overload probability, reduces the randomness in the actual process, provides reliable parameters for the calculation of evaluation indexes, and finally is based on an optimal power flow and random model mixing method, the vulnerability index under the cascading failure of the power grid is established, the wind power uncertainty level and the penetration rate under the cascading failure are calculated, the problem of evaluation of the vulnerability of the power grid under different wind power uncertainty levels and penetration rates is solved, and the evaluation efficiency is greatly improved.
Drawings
Fig. 1 is a flow chart of a power balancing algorithm for newly forming islands in a power grid.
Fig. 2 is a general flow chart of a cascading failure simulation program and vulnerability assessment of a power system based on an optimal power flow & stochastic model hybrid.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Examples
The invention provides an evaluation method for cascading failure vulnerability of a power system including a wind power plant, which comprises the following three steps:
step 1, providing an Optimal Power Flow (OPF) modeling method based on a line overload model, determining initial scheduling output of a conventional generator according to load and wind power prediction information by taking total cost as an optimization target, and then establishing an optimal power flow model based on direct current power flow through an online island detection algorithm and an automatic power balance algorithm;
step 2, providing a line outage path-finding method combining a thermal stability model and a random model of the power transmission line under the power grid cascading failure, wherein the method provides a line overload probability to simulate the uncertainty of wind and load in a tidal current process, calculates a line with the minimum overload distance, trips the line in the cascading failure, and finally provides the total tripping percentage of the line for the step 3;
and 3, establishing an evaluation method for the wind power uncertainty level and penetration rate to the power grid vulnerability under the power grid cascading failure, establishing a power grid vulnerability index considering the wind power uncertainty level and the penetration rate based on the optimal power flow and random model mixing method in the step 1 and the step 2, identifying the most vulnerable line in the power grid under different wind power uncertainty levels and penetration rates, and evaluating the influence of the power grid vulnerability through the total line tripping percentage and the total load shedding percentage under the cascading failure.
In step 1, determining the steady-state operation condition of the power grid requires solving a complete power flow equation, so that a large number of solutions exist in the evolution process of cascading faults, repeated solving of the complete nonlinear power flow equation becomes very difficult to calculate, the response time of the cascading faults is short, and the evaluation benefit is also influenced by too long calculation time. Because the evaluation method only concerns the influence of the uncertainty and the penetration rate of the load and the wind power on the vulnerability of the power grid, a complete nonlinear network equation is not necessarily required, a line overload model can be established, direct current load flow calculation is carried out, and the total cost of the system is taken as an optimization target to establish an optimal load flow function:
Figure BDA0003528281440000061
Figure BDA0003528281440000062
Figure BDA0003528281440000063
Figure BDA0003528281440000064
wherein the content of the first and second substances,
Figure BDA0003528281440000065
as a polynomial cost function of the generator i,
Figure BDA0003528281440000066
is the output power of generator i, ngIn order to be the total number of the generators,
Figure BDA0003528281440000067
is the maximum value of the output power of the generator i, Pl jIs the demanded power of load j, nlFor the number of loads, G is the generator set, L is the load set, FijFor the power flow between the node where generator i is located and the node where load j is located,
Figure BDA0003528281440000068
for the tidal current capacity between the node where generator i is located and the node where load j is located,
Figure BDA0003528281440000069
Figure BDA00035282814400000610
representing the total power balance of production and consumption.
During a cascading failure, all islands generated due to line tripping are identified based on an online island detection algorithm. Reducing the total load to the minimum through an automatic power balance algorithm to limit the maximum capacity of the generator, maintaining the power balance of each island, and changing the electrical frequency of the system based on the power balance, wherein the specific formula is as follows:
Figure BDA00035282814400000611
in the formula, PGenFor total emitted power, PLoadFor total power consumption, H is total inertia, f is the electrical frequency of the system, and t is time.
Usually the frequency in the power system is considered as a global parameter and is not affected by a small part. However, when a part of the network becomes islanded, inertia and load balancing depend only on the generator and load within the islanded, where load shedding may be required. In the power balancing algorithm for any island, the total load and the total generation capacity are compared to each other and if the total demand exceeds the maximum available generation, some de-loading is required to maintain power balance. Likewise, if the total demand is less than the current power generation, one or more gensets should reduce their power generation. A flow chart of the automatic power balancing algorithm is shown in fig. 1. Let t be t in the grid0There are k independent islands if t ═ t0A line trip at + Δ t results in the formation of a new island, it is necessary to run an automatic power balancing algorithm on the newly formed island and its separate parent island. Thus, the automatic power balancing algorithm will balance the power generation and load of the two clusters for the next load flow calculation.
In step 2, for modeling of system uncertainty, a covariance matrix C at each time step is first calculated as an m × m matrix using a sliding window methodF(t,τ)：
Figure BDA0003528281440000071
In the formula (I), the compound is shown in the specification,
Figure BDA0003528281440000072
is an uncertain item in the first line tide signal at the time t, and a matrix element
Figure BDA0003528281440000073
The line overload probability is introduced into a stochastic model to simulate the uncertainty of wind and load in the power flow process, and the power input function P (t) is assumed to meet the Gaussian distribution, so that the power flow F of the first linel(t) also satisfies the Gaussian distribution, line overload probability
Figure BDA0003528281440000074
Can be calculated using the Q function as shown below:
Figure BDA0003528281440000075
in the formula (I), the compound is shown in the specification,
Figure BDA0003528281440000076
is the standard overload distance of the l-th line, and the Q function is
Figure BDA0003528281440000077
Figure BDA0003528281440000078
Figure BDA0003528281440000079
For the tidal current capacity of the l-th line,
Figure BDA00035282814400000710
respectively, the power flow mean value and the variance of the ith line.
Using the standard overload distance (a) of each linel) And overload probability (p)l) Calculating a load flow F at the time tlThe average overload time in (t) is shown as follows:
Figure BDA00035282814400000711
wherein, BWlIs the equivalent bandwidth of the flow process of the l-th line, which can be calculated using the Spectral Power Density (SPD) of the flow process.
In the line thermal stability model, the trip time of the thermal overload relay is determined according to the maximum allowable current flowing in the conductor, and thermal instability is not caused. Generally, overload protection for high voltage transmission lines has a time dependent trip characteristic, which is determined using a dynamic thermal balance between thermal gain and loss in the conductor. The trip time of the thermal relay is determined by the maximum point temperature and, in order to analyze the dc current flow more effectively, taking into account the initial operating current, the current is replaced by a current flow measured in units, assuming that the voltage in the entire network remains V1.0 p.u., so the trip time is given by:
Figure BDA00035282814400000712
wherein F is overload line current (p.u.), and FopFor the initial operating trend (p.u.), FmaxAs a line flow threshold, TthIs a thermal time constant associated with the type of conductor and environmental parameters such as wind speed and ambient temperature.
The optimal power flow & stochastic model hybrid method cascading failure simulation and the general flow chart for the vulnerability assessment of the power system are shown in fig. 2.
In step 3, establishing a grid vulnerability index considering wind power uncertainty level and penetration rate, identifying the most vulnerable line in the grid under different wind power uncertainty levels and penetration rates, and evaluating the influence of the grid vulnerability through the total line tripping percentage and the total load shedding percentage under cascading failure, wherein the specific evaluation method is as follows:
(1) wind power uncertainty level under cascading failures
The wind power uncertainty level represents the relative error of wind power prediction, and the uncertainty level of a wind power generator is dependent on factors
Figure BDA0003528281440000081
Is increased by an increase in which
Figure BDA0003528281440000082
Is a new uncertainty index of the wind power,
Figure BDA0003528281440000083
is an initial uncertainty index of wind power and belongs towThe uncertainty index is determined by the randomness, the volatility and the intermittence of the output active power in the wind power cascading failure stage.
Based on optimal power flow&Performing cascading failure simulation on a system model established by a random model mixing method to obtain a total tripping proportion mu and a total load shedding percentage eta of a line, and regarding a vulnerability index psi of a wind power uncertainty level under cascading failure to a power grid1As follows:
Figure BDA0003528281440000084
in the formula, WuncIs a weight parameter of the wind power uncertainty level in the system under cascading failure.
As γ increases, the line trip total occupancy increases during cascading failures, creating more islands and greater load shedding, i.e., as the wind uncertainty level increases, the vulnerability of the system also increases.
(2) Penetration rate of wind power
The wind power penetration rate is defined as
Figure BDA0003528281440000085
Wherein
Figure BDA0003528281440000086
In order to be the total capacity of the wind turbine,
Figure BDA0003528281440000087
is the total capacity of all generators.
Based on optimal power flow&Performing cascading failure simulation on a system model established by a random model mixing method to obtain a total tripping proportion mu and a total load shedding percentage eta of a line, and regarding a vulnerability index psi of wind power penetration rate under cascading failure to a power grid2As follows:
Figure BDA0003528281440000088
in the formula, WpenThe weight parameter of the wind power penetration rate in the system under the cascading failure is shown.
As alpha increases, cascading failures will also escalate, causing the line trip total duty to climb, creating more islands and causing greater load shedding, i.e., as wind penetration increases, the vulnerability of the system will also increase.

Claims (10)

1. A method for evaluating cascading failure vulnerability of a power system of a wind power plant is characterized by comprising the following steps:
1) constructing an optimal power flow model based on a line overload model;
2) under the power grid cascading failure, a line shutdown path finding is carried out by combining a power transmission line thermal stability model and a random model, and the total trip occupation ratio of the line is obtained;
3) performing cascading failure simulation based on a mixed optimal power flow model and a random model, constructing a power grid vulnerability index considering wind power uncertainty level and penetration rate, identifying the most vulnerable line in the power grid under different wind power uncertainty levels and penetration rates, and evaluating the influence of the power grid vulnerability through the total line tripping percentage mu and the total load shedding percentage eta under cascading failure.
2. The method for evaluating the cascading failure vulnerability of the power system comprising the wind power plant according to claim 1, characterized in that in the step 1), the total cost is taken as an optimization target, the initial dispatching output of the conventional generator is determined according to the load and the wind power prediction information, and an optimal power flow model is established through an online island detection algorithm and an automatic power balance algorithm, and then:
Figure FDA0003528281430000011
Figure FDA0003528281430000012
Figure FDA0003528281430000013
Figure FDA0003528281430000014
wherein the content of the first and second substances,
Figure FDA0003528281430000015
as a polynomial cost function of the generator i,
Figure FDA0003528281430000016
is the output power of generator i, ngIn order to be the total number of the generators,
Figure FDA0003528281430000017
is the maximum value of the output power of the generator i,
Figure FDA0003528281430000018
is the demanded power of load j, nlFor the number of loads, G is the generator set, L is the load set, FijFor the power flow between the node at which generator i is located and the node at which load j is located,
Figure FDA0003528281430000019
for the power flow capacity between the node at which generator i is located and the node at which load j is located,
Figure FDA00035282814300000110
Figure FDA00035282814300000111
representing the total power balance of production and consumption.
3. The method for evaluating cascading failure vulnerability of power system including wind farm according to claim 1, characterized in that in step 1), during cascading failure, all islands generated due to line tripping are identified according to online island detection algorithm, and total load is reduced to minimum by using automatic power balance algorithm to limit maximum capacity of generator, maintain power balance of each island, and change electrical frequency of system based on power balance, then:
Figure FDA0003528281430000021
wherein, PGenFor total emitted power, PLoadFor total power consumption, H is total inertia, f is the electrical frequency of the system, and t is time.
4. The method for evaluating the cascading failure vulnerability of the power system with the wind farm according to claim 1, characterized in that in the step 2), the line with the minimum overload distance is obtained by simulating the uncertainty of wind power and load in the power flow process through the line overload probability, and the line is tripped in the cascading failure, so that the total tripping proportion of the supply line is obtained.
5. The method for evaluating cascading failure vulnerability of wind farm-containing power system according to claim 4, wherein the uncertainty modeling specifically comprises the following steps:
21) adopting a sliding window method as an m multiplied by m matrix, and calculating a covariance matrix C of each time stepF(t, τ) there are:
Figure FDA0003528281430000022
wherein the content of the first and second substances,
Figure FDA0003528281430000023
for uncertain items in the first line load flow signal at the time t, matrix elements
Figure FDA0003528281430000024
22) The uncertainty of wind and load in the process of line overload probability simulation load flow is introduced into a stochastic model, and the load flow F of the first line is assumed that a power input function P (t) meets Gaussian distributionl(t) satisfying Gaussian distribution, line overload probability ρl(t) calculated by the Q function, then there are:
Figure FDA0003528281430000025
Figure FDA0003528281430000026
wherein, alFor the standard overload distance of the l line, the expression of the Q function is
Figure FDA0003528281430000027
Figure FDA0003528281430000028
Figure FDA0003528281430000029
For the tidal current capacity of the l-th line,
Figure FDA00035282814300000210
respectively is the power flow mean value and the variance of the first line;
23) according to the standard overload distance and overload probability rho of each linelCalculating a load flow F at the time tlAverage overload time in (t)
Figure FDA00035282814300000211
Then there are:
Figure FDA00035282814300000212
wherein, BWlThe equivalent bandwidth of the flow process of the first line is obtained;
24) when average overload time
Figure FDA00035282814300000213
Greater than the trip time ttrWhen it is, the step returns to step 21), when the average overload time is
Figure FDA0003528281430000031
Is less than or equal to the tripping time ttrAnd then, tripping off the overload circuit after the tripping time, detecting a new island formed by the tripping circuit by adopting an island detection algorithm, balancing the generated energy and the load of each new island, and reducing the load loss to the maximum extent to obtain the total tripping percentage mu and the total load shedding percentage eta of the circuit.
6. The method for evaluating cascading failure vulnerability of power system of wind power plant according to claim 5, wherein in step 24), the tripping time t of the overload line is obtained according to the line thermal stability modeltrThen, there are:
Figure FDA0003528281430000032
wherein F is the overload line current, FopFor the initial operating trend, FmaxAs a line flow threshold, TthAre thermal time constants related to the type of wire and environmental parameters, specifically wind speed and ambient temperature.
7. The method for evaluating cascading failure vulnerability of power system including wind farm according to claim 5, characterized in that in step 3), wind power uncertainty level under cascading failure is obtained according to total line trip ratio μ and total load shedding percentage η to obtain grid vulnerability index ψ1Then, there are:
Figure FDA0003528281430000033
wherein, WuncIs a weight parameter of the wind power uncertainty level in the system under cascading failure.
8. The method for evaluating cascading failure vulnerability of power system of wind power plant according to claim 7, wherein in step 3), wind power uncertainty level represents relative error of wind power prediction, and uncertainty level of wind power generator is dependent on factors
Figure FDA0003528281430000034
Is increased by the amount of the additive, wherein,
Figure FDA0003528281430000035
is a new uncertainty index of the wind power,
Figure FDA0003528281430000036
for the initial uncertainty index of wind power, as gamma increases, the total trip proportion of a line during cascading failure increases, so that more islands and greater load shedding are formed, namely, as the uncertainty level of wind power increases, the vulnerability of the system is continuously increased.
9. The method for evaluating cascading failure vulnerability of power system including wind farm according to claim 7, characterized in that in step 3), wind power penetration rate to grid vulnerability index psi under cascading failure is obtained according to total line trip ratio μ and total load shedding percentage η2Then, there are:
Figure FDA0003528281430000037
wherein, WpenThe weight parameter of the wind power penetration rate in the system under the cascading failure is shown.
10. The method for evaluating cascading failure vulnerability of wind power system according to claim 9, wherein in step 3), the wind power penetration rate is defined as:
Figure FDA0003528281430000038
wherein the content of the first and second substances,
Figure FDA0003528281430000039
in order to be the total capacity of the wind turbine,
Figure FDA00035282814300000310
for the total capacity of all generators, as alpha is increased, cascading failures are also upgraded, so that the total duty ratio of line tripping is increased, more islands are generated, and larger load shedding is caused, namely, when the wind power penetration rate is increased, the vulnerability of the system is increased.
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