Weak line identification method and device of novel power system and electronic equipment

Abstract

The invention discloses a weak line identification method and device of a novel power system and electronic equipment, and relates to the technical field of novel power systems, and the method comprises the steps: obtaining the historical output of new energy; constructing first probability distribution of new energy output in the novel power system based on the historical output; wherein the first probability distribution is used for fitting fluctuation characteristics of new energy output; selecting lines in the novel power system one by one as target lines; determining an evaluation index of the novel power system based on the system characteristic data and the first probability distribution of the novel power system under the condition that the target line in the novel power system exits operation; and based on each evaluation index, identifying a weak line in the novel power system. According to the scheme, weak line identification of a novel power system is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of new power systems, and in particular to a method, a device and electronic equipment for identifying weak lines in a new power system. Background Art

[0002] For a long time, power system blackouts have occurred frequently around the world, causing huge economic losses and serious social impacts. Studies have shown that catastrophic blackouts are often initially caused by one or several line failures, which are called weak lines in the power system. Therefore, the identification of weak lines in the power system is of great significance to the safe control of the power system.

[0003] In the related art, for traditional power systems that use fossil fuels as the main energy source, the system's power flow equation can be used to calculate the evaluation index of each line in the system, and then the evaluation index can be used to determine whether each line is a weak line.

[0004] However, for the new power systems that have been connected to a large number of new energy sources in recent years, their dynamic characteristics and operating modes are more complex and changeable, and the above-mentioned weak line identification method is not applicable to the new power systems. Summary of the invention

[0005] The present invention provides a novel weak line identification method, device and electronic equipment for identifying weak lines in the novel power system.

[0006] According to one aspect of the present invention, a method for identifying weak lines in a new power system is provided. The new power system includes multiple lines and at least one new energy source. The method includes:

[0007] Obtaining historical output of new energy; constructing a first probability distribution of new energy output in a new power system based on the historical output; wherein the first probability distribution is used to fit the fluctuation characteristics of new energy output;

[0008] Selecting lines in the new power system as target lines one by one; when the target lines in the new power system are out of operation, determining an evaluation index of the new power system based on system characteristic data of the new power system and the first probability distribution;

[0009] Based on various evaluation indicators, weak lines in the new power system are identified.

[0010] The technical solution of the embodiment of the present invention can fit the fluctuation characteristics of the new energy output by constructing the probability distribution of the new energy output in the new power system, and then can calculate the evaluation index of the new energy power system based on the probability distribution of the new energy output and the system characteristic data of the new energy power system, so as to identify the weak lines in the new power system by using the evaluation index. The solution provided by the embodiment of the present invention realizes the identification of the weak lines in the new power system.

[0011] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 is a flow chart of a novel weak line identification method of a power system provided according to the first embodiment of the present invention;

[0014] Figure 2 is a flow chart of another novel weak line identification method of a power system provided according to the second embodiment of the present invention;

[0015] Figure 3 is a schematic diagram of an improved IEEE-39 node system in a novel power system weak line identification simulation method provided in Embodiment 3 of the present invention;

[0016] Figure 4 is a schematic diagram of a probability distribution function curve in a novel power system weak line identification simulation method provided in Embodiment 3 of the present invention;

[0017] Figure 5 is a schematic diagram of a voltage amplitude over-limit indicator in a novel power system weak line identification simulation method provided in accordance with Embodiment 3 of the present invention;

[0018] Figure 6 It is a schematic diagram of a line power flow over-limit index in a novel power system weak line identification simulation method provided according to Embodiment 3 of the present invention;

[0019] Figure 7 is a schematic diagram of static security indicators in a novel power system weak line identification simulation method provided in accordance with Embodiment 3 of the present invention;

[0020] Figure 8 It is a schematic diagram of a static frequency stability index in a novel power system weak line identification simulation method provided in accordance with Embodiment 3 of the present invention;

[0021] Fig. 9 It is a schematic diagram of a static power angle stability index in a novel power system weak line identification simulation method provided in accordance with Embodiment 3 of the present invention;

[0022] Fig.10 is a schematic diagram of a static voltage stability index in a novel power system weak line identification simulation method provided in accordance with Embodiment 3 of the present invention;

[0023] Fig.11 is a schematic diagram of system comprehensive evaluation results in a novel power system weak line identification simulation method provided in Embodiment 3 of the present invention;

[0024] Fig.12 2 is a schematic diagram of the structure of a novel weak line identification device for a power system provided according to a fourth embodiment of the present invention;

[0025] Fig.13 It is a structural schematic diagram of an electronic device for implementing a novel weak line identification method for a power system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "target", "original", "first", "second", "third", "fourth", "fifth", "sixth", "seventh", "eighth", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1 This is a flow chart of a weak line identification method for a new power system provided according to the first embodiment of the present invention. This embodiment is applicable to the weak line identification situation in the new power system scenario. The new power system includes multiple lines and at least one new energy source. The new energy source may include wind energy, solar energy, etc. The new power system may also include direct current access. The method may be executed by a weak line identification device for the new power system, which may be implemented by software and / or hardware and specifically configured in an electronic device. Figure 1 As shown, the method includes:

[0030] Step 101, obtaining historical output of new energy; constructing a first probability distribution of the output of new energy in the new power system based on the historical output; wherein the first probability distribution is used to fit the fluctuation characteristics of the output of new energy.

[0031] In practice, taking wind energy as an example, the actual output of wind turbines can be measured at each wind turbine port using a phasor measurement unit (PMU) with a sampling interval of ten minutes. The actual output within a certain period of time is the historical output of the new energy.

[0032] The first probability distribution may be any one of the following distributions: Gaussian distribution, Weibull distribution or Beta distribution.

[0033] Step 102, selecting lines in the new power system as target lines one by one; when the target lines in the new power system are out of operation, determining evaluation indicators of the new power system based on system characteristic data of the new power system and the first probability distribution.

[0034] Among them, the system characteristic data of the new power system is used to reflect the physical characteristics of the new power system. When the new power system is determined, the system characteristic data is also determined. The system characteristic data has no volatility. For example, the system characteristic data may include the power flow equation, the upper limit of the voltage amplitude of the node, the lower limit of the voltage amplitude of the node, the voltage amplitude of the node under normal operation, and so on.

[0035] Considering that after the new energy is connected to the new power system, the new power system also has fluctuation characteristics due to the fluctuation characteristics of the new energy. The fluctuation characteristics of the new power system can be reflected in some system parameters, such as node voltage amplitude, voltage instability critical point, etc. Therefore, this scheme can use the first probability distribution to fit the fluctuation characteristics of the new energy output, and then use the first probability part and system characteristic data to determine the target system parameters. The target system parameters can be used to calculate the evaluation index of the new power system. For example, the second probability distribution of the node voltage amplitude can be determined using the power flow equation and the first probability distribution.

[0036] The evaluation index of the new power system can be used to evaluate the grid structure, operation performance or reliability of the new power system. The evaluation index can include, for example, a voltage amplitude over-limit index, a line flow over-line index, etc.

[0037] Step 103: Identify weak lines in the new power system based on various evaluation indicators.

[0038] Specifically, each target route may correspond to multiple evaluation indicators, and these multiple evaluation indicators may be combined to obtain a target evaluation score.

[0039] Then, the target evaluation scores corresponding to each target line are ranked, and based on the ranking results, the weak lines in the new power system are identified.

[0040] The technical solution provided by the embodiment of the present invention can fit the fluctuation characteristics of the new energy output by constructing the probability distribution of the new energy output in the new power system, and then can calculate the evaluation index of the new energy power system based on the probability distribution of the new energy output and the system characteristic data of the new energy power system, so as to identify the weak lines in the new power system by using the evaluation index. The solution provided by the embodiment of the present invention realizes the identification of the weak lines in the new power system.

[0041] Embodiment 2

[0042] Figure 2 FIG. 1 is a flow chart of another novel weak line identification method of a power system provided according to the second embodiment of the present invention. Figure 2 As shown, the method includes:

[0043] Step 201, obtaining the historical output of new energy; constructing a first probability distribution of the output of new energy in the new power system based on the historical output; wherein the first probability distribution is used to fit the fluctuation characteristics of the output of new energy; the first probability distribution is a Gaussian mixture model.

[0044] Specifically, the Gaussian mixture model is a superposition of multiple Gaussian distributions. The Gaussian mixture model can accurately fit the output of renewable energy to obtain a first probability distribution. Taking wind energy as an example, the formula of the probability density function corresponding to the first probability distribution is as follows. The first probability distribution can be obtained by integrating the probability density function.

[0045]

[0046] Where W is the vector of historical outputs of w wind turbines included in the new power system. f(W) is the probability density function of W, that is, the probability density function of new energy output; N m (W) is the probability density function of the mth Gaussian subcomponent; M is the number of Gaussian subcomponents; w m , μ m ,∑ m are the weight coefficient, mean vector and covariance matrix of each Gaussian sub-component, which are usually solved by the maximum expectation algorithm.

[0047] Step 202, one by one, select lines in the new power system as target lines; when the target lines in the new power system are out of operation, determine the evaluation index of the new power system based on the system characteristic data of the new power system and the first probability distribution; the evaluation index includes at least one of the following: voltage amplitude over-limit index, line flow over-limit index, static safety index, static frequency stability index, static power angle stability index, and static voltage stability index.

[0048] During implementation, multiple evaluation indicators such as voltage amplitude over-limit index, line flow over-limit index, static safety index, static frequency stability index, static power angle stability index and static voltage stability index can be used to evaluate the new power system more comprehensively and accurately.

[0049] In one implementable manner, the new power system further includes a plurality of nodes, and the system characteristic data includes a power flow equation, an upper voltage amplitude limit, and a lower voltage amplitude limit of the node; based on the system characteristic data of the new power system and the first probability distribution, determining an evaluation index of the new power system includes:

[0050] Based on the power flow equation and the first probability distribution, a second probability distribution of the node voltage amplitude is determined.

[0051] In practice, the new power system can also include DC access. The power flow equation of the new power system with new energy and DC access is as follows:

[0052]

[0053] Among them, S is the power column vector of each node, including the active power and reactive power injected by the node; Δ represents the differential; X is the node state column vector, including the voltage phase angle and amplitude of each node; d is the DC converter equation, DC network equation and DC control equation; D is the relevant parameters of the DC side, including DC voltage, DC current, control angle, converter transformer change ratio and converter power factor angle; J D is the Jacobian matrix of the AC / DC interconnected system; J is the Jacobian matrix of the traditional AC system; A, C, and F are the augmented Jacobian matrix elements corresponding to the addition of DC related variables.

[0054] Among them, the node injection power = the output of the traditional generator connected to the node + the output of new energy - the load power.

[0055] The second probability distribution of the node voltage amplitude can be calculated based on the above power flow equation and the first probability distribution of the new energy output. The formula of the second probability distribution of the node voltage amplitude is as follows:

[0056]

[0057] Where Y is the vector composed of the node voltage amplitudes of the y nodes included in the new power system; F(Y) is the second probability distribution; F m (Y) is the probability distribution function of the mth Gaussian subcomponent; M is the number of Gaussian subcomponents; w m , μ m ,∑ m are the weight coefficients, mean vectors and covariance matrices of each Gaussian subcomponent of the first probability distribution; K1 and B1 are coefficient matrices; K1μ m +B1 is the mean vector of each Gaussian subcomponent of the second probability distribution; is the covariance matrix of each Gaussian subcomponent of the second probability distribution.

[0058] The voltage-limit-crossing probability is determined based on the voltage amplitude upper limit, the voltage amplitude lower limit, and the second probability distribution.

[0059] Specifically, based on the second probability distribution, the probability sum of the probability that the node voltage amplitude is greater than the voltage amplitude upper limit and the probability that the node voltage amplitude is less than the voltage amplitude lower limit is taken as the voltage over-limit probability.

[0060] The maximum value of the node voltage amplitude corresponding to the probability value of 0 in the second probability distribution is taken as the first amplitude fluctuation extreme value; the minimum value of the node voltage amplitude corresponding to the probability value of 1 in the second probability distribution is taken as the second amplitude fluctuation extreme value.

[0061] The severity of the voltage amplitude exceeding the limit is determined based on the first amplitude fluctuation extreme value, the second amplitude fluctuation extreme value, the voltage amplitude upper limit and the voltage amplitude lower limit.

[0062] Specifically, the formula for the severity of voltage amplitude exceeding the limit is as follows:

[0063]

[0064] Among them, Sev(U i ) represents the severity of voltage amplitude exceeding the limit at the ith node; U 1i Indicates the first amplitude fluctuation extreme value of the i-th node; U 2i Indicates the second amplitude fluctuation extreme value of the i-th node; represents the lower limit of the voltage amplitude of the i-th node; Represents the upper limit of the voltage amplitude of the ith node.

[0065] Based on the voltage amplitude over-limit severity and voltage over-limit probability, the voltage amplitude over-limit index of the new power system is determined.

[0066] Specifically, the formula for the voltage amplitude over-limit indicator is as follows:

[0067]

[0068] Among them, R VV Indicates the voltage amplitude over-limit index; n1 indicates the number of system nodes; P(U i ) represents the voltage over-limit probability of the ith node; Sev(U i ) represents the severity of the voltage amplitude exceeding the limit at the ith node.

[0069] Specifically, the voltage amplitude over-limit index of the new power system can be accurately and conveniently determined through the above method.

[0070] In one possible implementation, the system characteristic data further includes a voltage amplitude of a node under normal operation; and based on the system characteristic data of the new power system and the first probability distribution, determining an evaluation index of the new power system includes:

[0071] In the process of gradually increasing the load for the new power system, the convergence of the power flow equation of the new power system is determined; the node voltage amplitude corresponding to the critical point where the power flow equation does not converge is used as the voltage instability critical point, and the second probability distribution is used as the third probability distribution corresponding to the voltage instability critical point; based on the voltage amplitude of the node under normal operation and the third probability distribution, the fourth probability distribution corresponding to the static stability reserve coefficient of the node voltage is determined.

[0072] Among them, the voltage amplitude of the node under normal operation in the new power system is a constant.

[0073] Specifically, the formula for the fourth probability distribution is as follows:

[0074] KU,i =(U 0,i -U cr,i ) / U 0,i ;

[0075] Among them, K U,i represents the fourth probability distribution; U 0,i represents the voltage amplitude of the ith node under normal operation; U cr,i Represents the third probability distribution corresponding to the voltage instability critical point of the i-th node.

[0076] According to the fourth probability distribution, the probability that the static reserve coefficient of the node voltage does not meet the preset voltage reserve requirement is determined.

[0077] Since the "Guidelines for Safety and Stability of Power Systems" stipulates that the static voltage stability reserve under the post-fault operation mode shall not be less than 8%, the preset voltage reserve requirement can be set as follows in this scheme: the static stability reserve coefficient of the node voltage is greater than or equal to 8%.

[0078] Specifically, the probability that the static reserve coefficient of the node voltage is less than 8% in the fourth probability distribution may be used as the probability that the static reserve coefficient of the node voltage does not meet the preset voltage reserve requirement.

[0079] The maximum value of the static reserve coefficient corresponding to the time when the probability value in the fourth probability distribution is 0 is taken as the extreme value of the reserve coefficient fluctuation.

[0080] The severity of static voltage instability is determined based on the reserve factor fluctuation extreme value and the preset voltage reserve requirement.

[0081] Specifically, the severity formula of static voltage instability is as follows:

[0082] Sev(K U,i )=(0.08-K U,i,min ) / 0.08;

[0083] Among them, Sev(K U,i ) represents the severity of static voltage instability of the i-th node; 0.08 is used to represent that the preset voltage reserve requirement is that the static voltage reserve coefficient of the node voltage is greater than or equal to 8%; K U,i,min Represents the extreme value of the reserve coefficient fluctuation of the i-th node.

[0084] Based on the probability that the static stability reserve coefficient of the node voltage does not meet the preset voltage reserve requirement and the severity of static voltage instability, the static voltage stability index of the new power system is determined.

[0085] Specifically, the formula for the static voltage stability index is as follows:

[0086]

[0087] Among them, R SVS Represents the static voltage stability index; P(K U,i ) represents the probability that the static reserve coefficient of the node voltage of the i-th node does not meet the preset voltage reserve requirement; Sev(K U,i ) represents the severity of static voltage instability of the ith node; n1 represents the number of system nodes.

[0088] Specifically, the above method can be used to accurately and conveniently determine the static voltage stability index of the new power system.

[0089] In one achievable manner, the system characteristic data includes a power flow equation and a transmission power limit of a line; based on the system characteristic data of the new power system and the first probability distribution, determining an evaluation index of the new power system includes:

[0090] Based on the power flow equation and the first probability distribution, a fifth probability distribution of line transmission power of the new power system is determined.

[0091] Specifically, by linearizing the line power flow equation at the preset reference operating point, a linear expression of the line transmission power and the injection power of each node can be obtained, as shown below:

[0092]

[0093] Among them, Z is the line flow column vector, including the active and reactive power of the line; Z0 represents the line flow column vector at the reference operating point; X0 represents the node state column vector at the reference operating point; X represents the node state column vector, including the voltage phase angle and amplitude of each node; Δ represents differential; S is the power injection column vector of each node, including the active power and reactive power injected by the node.

[0094] Based on the above power flow equation and the first probability distribution of the new energy output, the fifth probability distribution of the line transmission power can be solved. The formula of the fifth probability distribution is as follows:

[0095]

[0096] Wherein, H is the vector composed of the line transmission power of h lines included in the new power system; E(Y) is the fifth probability distribution; E m (H) is the probability distribution function of the mth Gaussian subcomponent; M is the number of Gaussian subcomponents; w m , μ m ,∑ m are the weight coefficients, mean vectors and covariance matrices of each Gaussian subcomponent of the first probability distribution; K2 and B2 are coefficient matrices; K2μ m +B2 is the mean vector of each Gaussian subcomponent of the fifth probability distribution; is the covariance matrix of each Gaussian subcomponent of the fifth probability distribution.

[0097] Based on the fifth probability distribution and the transmission power limit of the line, the probability of the line power flow exceeding the limit is determined.

[0098] Among them, the transmission power limit of the line refers to the maximum power that the transmission line can transmit safely and stably under certain new power system operating conditions.

[0099] Specifically, the probability corresponding to when the line transmission power in the fifth probability distribution is equal to the transmission power limit is used as the probability of the line power flow exceeding the limit.

[0100] The minimum value of the line transmission power corresponding to the probability value of 0 in the fifth probability distribution is taken as the extreme value of power fluctuation; based on the extreme value of power fluctuation and the transmission power limit of the line, the severity of the line power flow exceeding the limit is determined.

[0101] Specifically, the formula for the severity of line power flow exceeding the limit is as follows:

[0102] Sev(H i )=(P i -P i,max ) / P i,max ;

[0103] Among them, Sev(H i ) represents the severity of the line flow exceeding the limit of the ith line; P i represents the power fluctuation extreme value of the ith line; P i,max represents the transmission power limit of the i-th line.

[0104] Based on the probability and severity of line power flow exceeding the limit, the line power flow exceeding the limit index of the new power system is determined.

[0105] Specifically, the formula for the line power flow over-limit index is as follows:

[0106]

[0107] Among them, R LO Indicates the line flow over-limit index; P(H i ) represents the probability of the line power flow exceeding the limit of the i-th line; Sev(H i ) represents the severity of the line flow exceeding the limit of the ith line; n2 represents the number of remaining lines after the target line is out of operation in the new power system.

[0108] Specifically, the above method can be used to accurately and conveniently determine the line flow over-limit index of the new power system.

[0109] In one achievable manner, based on the system characteristic data of the new power system and the first probability distribution, determining an evaluation index of the new power system includes:

[0110] Under the condition that the N-1 principle is adopted to disconnect the lines except the target line in the new power system one by one without fault, the voltage amplitude over-limit index and the line power flow over-limit index of the new power system are determined based on the system characteristic data of the new power system and the first probability distribution; the sum of each voltage amplitude over-limit index and each line power flow over-limit index is used as the static safety index of the new power system.

[0111] Among them, the solution method of the voltage amplitude over-limit index and the line power flow over-limit index can be found in the above embodiment and will not be repeated here.

[0112] Specifically, the above method can be used to accurately and conveniently determine the static safety index of the new power system.

[0113] In one achievable manner, the system characteristic data includes a first linear relationship equation between a system characteristic value of the new power system and a new energy output that is pre-constructed; based on the system characteristic data of the new power system and the first probability distribution, determining an evaluation index of the new power system includes:

[0114] A sixth probability distribution of the real part of the system eigenvalue is determined based on the first linear relationship equation and the first probability distribution.

[0115] In fact, the relationship between the system characteristic value of the new power system and the output of new energy is not linear and difficult to be explicit, but considering that the static frequency stability index is used to judge the system frequency instability of the new power system under small disturbances after the target line is out of operation. Therefore, for small disturbance analysis, this scheme simplifies the relationship between the system characteristic value of the new power system and the output of new energy into a linear relationship, so as to facilitate the solution of the static frequency stability index, improve the calculation speed, and the calculation accuracy is also high after experimental verification.

[0116] Specifically, the first linear relationship equation between the system characteristic value and the new energy output can be established through the following two formulas:

[0117]

[0118] Among them, W i represents the new energy output of the i-th wind turbine; represents the sensitivity of the kth system eigenvalue to the renewable energy output of the i-th wind turbine; k represents the kth system eigenvalue; ξ k The real part of the kth system eigenvalue; ω krepresents the imaginary part of the kth system eigenvalue; w represents the total number of wind turbines included in the new power system; Δ represents the differential.

[0119] Specifically, the sixth probability distribution of the real part of the system eigenvalue can be solved according to the first linear relationship equation and the first probability distribution. The formula of the sixth probability distribution is as follows:

[0120]

[0121] Among them, λ is the system characteristic value; F(λ) is the sixth probability distribution; F m (λ) is the probability distribution function of the mth Gaussian subcomponent; M is the number of Gaussian subcomponents; w m , μ m ,Σ m are the weight coefficients, mean vectors and covariance matrices of each Gaussian subcomponent of the first probability distribution; K3 and B3 are coefficient matrices; K3μ m +B3 is the mean vector of each Gaussian subcomponent of the sixth probability distribution; is the covariance matrix of each Gaussian subcomponent of the sixth probability distribution; K represents the total number of system eigenvalues ​​in the new power system.

[0122] The probability that the real part of the system eigenvalue determined based on the sixth probability distribution is greater than 0 is used as the probability that static frequency instability occurs in the system; based on the probability of static frequency instability occurring in the system and the preset severity of static frequency instability, the static frequency stability index of the new power system is determined.

[0123] Specifically, the formula for the static frequency stability index is as follows:

[0124] R SFS =P(SFS)*Sev(SFS);

[0125] Among them, R SFS represents the static frequency stability index; P(SFS) represents the probability of static frequency instability in the system; Sev(H i ) represents the preset severity of static frequency instability. For example, when static frequency instability occurs, Sev(SFS) can be defined as 1.

[0126] Specifically, the static frequency stability index of the new power system can be accurately and conveniently determined through the above method.

[0127] In one achievable manner, the new power system further includes a generator; the system characteristic data includes a pre-constructed second linear relationship equation between a static power limit of the generator and a new energy output; and based on the system characteristic data of the new power system and the first probability distribution, determining an evaluation index of the new power system includes:

[0128] A seventh probability distribution of a static power limit of the generator is determined based on the second linear relationship equation and the first probability distribution.

[0129] The generator refers to a traditional electric power generator, such as a thermal power generator or a hydropower generator.

[0130] The static power limit of the generator refers to the maximum power that the generator can deliver to the system under the premise of static stability of the new power system.

[0131] In fact, the relationship between the static power limit of the generator and the output of new energy in the new power system is not linear and difficult to be explicit. However, considering that the static power angle stability index is used to judge the instability of the static power angle of the new power system under small disturbances after the target line is out of operation, this solution simplifies the relationship between the static power limit of the generator and the output of new energy into a linear relationship for the analysis of small disturbances, so as to facilitate the solution of the static power angle stability index, improve the calculation speed, and the calculation accuracy is also high after experimental verification.

[0132] Specifically, the second linear relationship equation between the static power limit of the generator and the output of new energy can be established through the following two formulas:

[0133]

[0134] Among them, P max,i represents the static power limit of the i-th generator; W k represents the new energy output of the k-th wind turbine; represents the sensitivity of the static power limit of the i-th generator to the new energy output of the k-th wind turbine; Δ represents the differential; w represents the total number of wind turbines included in the new power system.

[0135] Specifically, the seventh probability distribution of the static power limit of the generator can be calculated according to the second linear relationship equation and the first probability distribution. The formula of the seventh probability distribution is as follows:

[0136]

[0137] Among them, P max is the static power limit of the generator; F(P max ) is the seventh probability distribution; F m (P max ) is the probability distribution function of the mth Gaussian subcomponent; M is the number of Gaussian subcomponents; w m , μ m ,Σ m are the weight coefficients, mean vectors and covariance matrices of each Gaussian subcomponent of the first probability distribution respectively; K4 and B4 are coefficient matrices; K4μm +B4 is the mean vector of each Gaussian subcomponent of the seventh probability distribution; is the covariance matrix of each Gaussian sub-component of the seventh probability distribution; p represents the total number of generators in the new power system.

[0138] The output of the generator under normal operation is obtained; and according to the output and the seventh probability distribution, the eighth probability distribution of the static power angle stability reserve coefficient of the generator is determined.

[0139] Specifically, the formula of the static power angle stability reserve coefficient of the generator is as follows. Based on the following relationship between the static power angle stability reserve coefficient of the generator and the static power limit, the eighth probability distribution of the static power angle stability reserve coefficient of the generator can be fitted using the seventh probability distribution of the static power limit.

[0140] K P,i =(P max,i -P 0,i ) / P 0,i ;

[0141] Among them, K P,i represents the static power angle stability reserve coefficient of the i-th generator; P max,i represents the static power limit of the i-th generator; P 0,i represents the output of the i-th generator under normal operation.

[0142] Based on the eighth probability distribution and the preset power angle reserve requirement, the probability of static power angle instability is determined.

[0143] Considering that the "Guidelines for Safety and Stability of Power Systems" stipulates that under the post-accident operation mode, the static power angle stability reserve coefficient shall not be less than 10%, the preset power angle reserve requirement in this scheme can be set to a static power angle stability reserve coefficient of not less than 10%.

[0144] Specifically, the probability corresponding to the static power angle stability reserve coefficient being 0.1 in the eighth probability distribution may be used as the probability of static power angle instability.

[0145] The maximum value of the static power angle stability reserve coefficient corresponding to the probability of 0 in the eighth probability distribution is used as the target extreme value; based on the target extreme value, the severity of the static power angle instability is determined.

[0146] Specifically, the formula for the severity of static power angle instability is as follows:

[0147] Sev(K P,i )=(0.1-K P,i,min ) / 0.1;

[0148] Among them, Sev(K P,i) represents the severity of the static power angle instability of the i-th generator; K P,i,min Indicates the target extreme value of the i-th generator; 0.1 is used to indicate that the preset power angle reserve requirement is that the static power angle stability reserve coefficient is not less than 10%.

[0149] Based on the probability and severity of static power angle instability, the static power angle stability index of the new power system is determined.

[0150] Specifically, the formula for the static power angle stability index is as follows:

[0151]

[0152] Among them, R SRAS Represents the static power angle stability index; P(K P,i ) represents the probability of static power angle instability of the i-th generator; Sev(K P,i ) represents the severity of the static power angle instability of the i-th generator; n3 represents the total number of generators in the new power system.

[0153] Specifically, the above method can accurately and conveniently determine the static power angle stability index of the new power system.

[0154] Step 203, normalizing the evaluation indicators corresponding to the target line to obtain normalized evaluation indicators.

[0155] Specifically, each evaluation index may be normalized in terms of magnitude and dimension.

[0156] Step 204, obtaining the subjective weight of each evaluation index based on the hierarchical analysis method; obtaining the objective weight of each evaluation index based on the entropy weight method; and determining the target weight based on the subjective weight and the objective weight.

[0157] Specifically, the formula for target weight is as follows:

[0158] B = β1B1 + β2B2;

[0159] Among them, B represents the target weight, β1 and β2 are coefficients, B1 represents the subjective weight; B2 represents the objective weight.

[0160] In implementation, in order to minimize the deviations between the target weight and the subjective weight and the objective weight, it is necessary to establish corresponding optimization objectives to optimize the coefficients β1 and β2, as shown in the following formula:

[0161]

[0162] Among them, min means finding the minimum value; β1 and β2 are coefficients, B1 represents subjective weight; B2 represents objective weight.

[0163] According to the differential properties of the matrix, the above formula can be transformed into the following formula:

[0164]

[0165] Then, the subjective weight B1 and objective weight B2 solved in advance are substituted into the above formula to obtain β1 and β2, and then the target weight B is obtained.

[0166] Step 205, based on the target weight, perform weighted summation on the normalized evaluation indicators to obtain the target evaluation score.

[0167] Specifically, the formula for the target evaluation score is as follows:

[0168]

[0169] B=(α1,α2,α3,α4,α5,α6);

[0170] Among them, R c represents the target evaluation score; B represents the target weight, and α1, α2, α3, α4, α5, and α6 are the weights of each indicator respectively; Indicates the normalized voltage amplitude over-limit index; Represents the normalized static voltage stability index; It represents the normalized line power flow over-limit index; Represents the normalized static frequency stability index; It represents the normalized static power angle stability index; Represents the normalized static security index.

[0171] Step 206 , identifying weak lines in the new power system based on the target evaluation scores corresponding to the target lines.

[0172] In implementation, the above process can take into account the random fluctuation characteristics of new energy and the DC operation characteristics, and solve the target evaluation score corresponding to each target line of the new power system. The larger the target evaluation score, the greater the impact of the target line on the system after withdrawal, and the weaker the target line. After evaluating the weakness of each line, a reasonable power system security control strategy can be formulated and implemented in advance for some of the weakest lines in the system.

[0173] The above method can be used to conveniently determine the target evaluation score, which is conducive to the identification of weak lines.

[0174] Embodiment 3

[0175] This embodiment provides a novel power system weak line identification simulation method. Figure 3The simulation of the improved IEEE-39 node system is shown in Figure 1. Figure 3 As shown, the numbers in brackets are the line numbers. Replace the original synchronous generators of busbars 32, 34, and 38 with wind turbines. Assume that the installed capacity of each wind turbine is 650MW, 500MW, and 450MW respectively, and the total penetration rate of new energy is about 33%. Replace the original AC line L3-18 with an LCC-HVDC line, in which busbar 3 is a rectifier station and busbar 18 is an inverter station. G represents generator and WG represents wind turbine.

[0176] Disconnect each line one by one, first use the method provided in the above embodiment to calculate the voltage amplitude over-limit index and line power over-limit index of the system after each line is disconnected. Take line 1 L1-2 as an example, when it is disconnected, the probability distribution of each node voltage and each line power can be obtained by using the method provided in the above embodiment. The probability distribution of the node voltage amplitude of bus 22 and the active power of line L16-19 is respectively taken as follows: Figure 4 As shown, the Monte Carlo method (MCS) is used to calculate random AC and DC power flows to verify the accuracy of the method provided in the above embodiment (ie, the method proposed in this article).

[0177] Figure 4 In the figure, the red curve is the result obtained by the Monte Carlo method, and the blue curve is the result obtained by the method proposed in this paper. It can be seen that the results obtained by the two methods are very close. In terms of calculation time, the method proposed in this paper takes 0.43 seconds, and the Monte Carlo method takes 97 seconds, which shows that the method proposed in this paper greatly improves the calculation efficiency while meeting the calculation accuracy.

[0178] See also Figure 5 and Figure 6 On the basis of the probability distribution of node voltage and line power, the voltage amplitude over-limit index and line power flow over-limit index of the system after each line is out of operation can be calculated using the method proposed in this paper.

[0179] See also Figure 5 and Figure 6 , after some lines are out of operation, the system voltage amplitude and line power flow will exceed the limit. According to the ranking of indicators, the top 10 lines that cause voltage amplitude and line power flow to exceed the limit are selected as shown in Table 1.

[0180] Table 1 Ranking of voltage amplitude over-limit index and line power flow over-limit index

[0181]

[0182] In Table 1, the voltage amplitude over-limit indexes obtained by the withdrawal of lines L15-16, L25-26, and L9-39 are 0.1556, 0.1371, and 0.1046, respectively, ranking the top three of all lines, indicating that these lines have a high impact on the voltage safety of each node of the power grid when they are attacked and withdrawn from operation. Among them, the withdrawal of line L15-16 has the greatest impact on voltage safety, indicating that the voltage over-limit risk caused by the withdrawal of this line is the greatest. In the verification results of the line power flow over-limit index, the withdrawal of lines such as L25-26 and L20-34 is more likely to cause overload of other lines in the system, indicating that these lines are more important and weaker in the line power flow over-limit index. After some lines such as L1-2 and L16-24 are withdrawn from operation, the system voltage amplitude over-limit index and line power flow over-limit index are 0, indicating that these lines are unimportant lines in the system under these two indicators, and even if they are attacked externally or withdrawn from operation, they will not cause the risk of system voltage amplitude over-limit and line power flow over-limit.

[0183] Evaluate the static safety of the system after each line is out of operation. When each line is out of operation, the remaining lines are disconnected one by one using the N-1 principle, and then the static safety index of the system after each line is out of operation is solved, such as Figure 7 shown.

[0184] Figure 7 In the table, the decommissioning of each line has an impact on the static security of the new power system. The top 10 lines are ranked by static security index as shown in Table 2. Among them, lines L25-26, L9-39, and L8-9 are the top three lines, indicating that the decommissioning of these lines has the greatest impact on the static security of the new power system, and they are the weakest lines in the static security index.

[0185] Table 2 Ranking of static security indicators

[0186]

[0187] Evaluate the static frequency stability index of the system after each line is out of operation. The method proposed in this paper can obtain the probability distribution of the system characteristic value during the wind turbine output fluctuation period, and then obtain the static frequency stability index of the system after each line is out of operation, such as Figure 8 shown.

[0188] Depend on Figure 8 Only when line L25-26 is out of operation will the system have the risk of static frequency instability, and the other lines will not cause this risk. This shows that L25-26 is the weakest line in the system in terms of static frequency stability indicators, and it is very easy to cause the risk of static frequency instability when it is attacked from the outside.

[0189] Assess the risk of system static power angle instability after each line is out of operation. The method proposed in this paper can obtain the probability distribution of the static power limit of each generator after each line is out of operation, and then obtain the risk that the system static power angle stability reserve coefficient does not meet the operation requirements. Fig. 9 shown.

[0190] Fig. 9 The withdrawal of some lines will cause insufficient reserve of static power angle stability of the system, and there is a risk of power angle instability. The top 10 lines are shown in Table 3. Among them, lines L15-16, L2-25, L9-39 and other lines are the weakest lines in the static power angle stability index. When they are attacked, they are very likely to cause the risk of static power angle instability of the system.

[0191] Table 3 Ranking of static power angle stability indicators

[0192]

[0193] Evaluate the static voltage stability index of the system after each line is out of operation. Disconnect each line of the system one by one, use random power flow to calculate the probability distribution of the critical value of the voltage at each node, and then calculate the static voltage stability index, such as Fig.10 shown.

[0194] Depend on Fig.10 , the withdrawal of some lines makes the system static voltage stability reserve fail to meet the operation requirements, and there is a certain risk of static voltage instability. The top 10 lines with the highest risk are shown in Table 4. Among them, the withdrawal of lines such as L2-25 and L25-26 has the greatest impact on static voltage stability. When they are attacked, they are very likely to cause the risk of static voltage instability in the system.

[0195] Table 4. Risk ranking of static voltage stability

[0196]

[0197] Through the above simulation results, the impact of line withdrawal on system security and stability was evaluated, and the importance of each line under each risk assessment indicator was obtained. In order to finally obtain the importance of each line, the comprehensive weighting method is used to weight multiple indicators to form a comprehensive risk indicator to evaluate the vulnerability of each line. First, the results of each indicator are normalized, and the hierarchical analysis method is used to solve the subjective weight of each indicator B1 = [0.0859, 0.0650, 0.1508, 0.2790, 0.2097, 0.2097], and the entropy weight method is used to obtain the objective weight of each indicator B2 = [0.1823, 0.1657, 0.0430, 0.3637, 0.1603, 0.0849]. The method provided in Example 2 is used to calculate β1 = 0.2945, β2=0.7597, and then the comprehensive weight of each indicator (i.e., target weight) B=[0.1638, 0.1450, 0.0771, 0.3585, 0.1836, 0.1263] is obtained. The normalized evaluation indicators are weighted according to the target weight B, and finally the target evaluation score can be obtained, that is, the comprehensive impact of the withdrawal of each line on the system security and stability. The system risk caused by the withdrawal of each line is evaluated. Fig.11 shown.

[0198] Fig.11 In the table, the top 10 routes and their target evaluation scores are sorted according to the target evaluation scores.

[0199] Table 5 Target evaluation score ranking

[0200]

[0201] Table 5 shows that from the perspective of system safety and stability, line L25-26 is the most important in the system. When it is out of operation, it has the greatest comprehensive impact on system safety and stability. It is the most important and weakest line in the system. Compared with line L25-26, lines L9-39, L8-9, L15-16, and L2-25 are less important, but they are also relatively weak lines in the system. Once they are out of operation, they will also have a greater impact on the safety and stability of the system.

[0202] Embodiment 4

[0203] Fig.12 is a schematic diagram of a weak line identification device for a new type of power system according to a fourth embodiment of the present invention. The new type of power system includes multiple lines and at least one new energy source, such as Fig.12 As shown, the device 1200 includes:

[0204] A construction unit 1210 is used to obtain the historical output of new energy sources; based on the historical output, a first probability distribution of the output of new energy sources in the new power system is constructed; wherein the first probability distribution is used to fit the fluctuation characteristics of the output of new energy sources

[0205] The determination unit 1220 is used to select lines in the new power system as target lines one by one; when the target lines in the new power system are out of operation, the evaluation index of the new power system is determined based on the system characteristic data of the new power system and the first probability distribution.

[0206] The identification unit 1230 is used to identify weak lines in the new power system based on various evaluation indicators.

[0207] In one implementable manner, the evaluation index includes at least one of the following: a voltage amplitude over-limit index, a line flow over-limit index, a static safety index, a static frequency stability index, a static power angle stability index, and a static voltage stability index.

[0208] The novel power system further includes a plurality of nodes, and the system characteristic data includes a power flow equation, an upper voltage amplitude limit and a lower voltage amplitude limit of the node; the determination unit 1220 is specifically used for:

[0209] Determine a second probability distribution of node voltage amplitude based on the power flow equation and the first probability distribution;

[0210] Determining a voltage over-limit probability based on a voltage amplitude upper limit, a voltage amplitude lower limit, and a second probability distribution;

[0211] The maximum value of the node voltage amplitude corresponding to the probability value of 0 in the second probability distribution is taken as the first amplitude fluctuation extreme value; the minimum value of the node voltage amplitude corresponding to the probability value of 1 in the second probability distribution is taken as the second amplitude fluctuation extreme value;

[0212] Determining the severity of the voltage amplitude exceeding the limit based on the first amplitude fluctuation extreme value, the second amplitude fluctuation extreme value, the voltage amplitude upper limit, and the voltage amplitude lower limit;

[0213] Based on the voltage amplitude over-limit severity and voltage over-limit probability, the voltage amplitude over-limit index of the new power system is determined.

[0214] The system characteristic data also includes the voltage amplitude of the node under normal operation; the determination unit 1220 is specifically used for:

[0215] In the process of gradually increasing the load for the new power system, the convergence of the power flow equation of the new power system is determined; the node voltage amplitude corresponding to the critical point where the power flow equation does not converge is used as the voltage instability critical point, and the second probability distribution is used as the third probability distribution corresponding to the voltage instability critical point;

[0216] Determine a fourth probability distribution corresponding to a static reserve coefficient of the node voltage according to the voltage amplitude of the node under normal operation and the third probability distribution;

[0217] Determining, according to a fourth probability distribution, a probability that a static reserve coefficient of a node voltage does not meet a preset voltage reserve requirement;

[0218] The maximum value of the static reserve coefficient corresponding to the probability value of 0 in the fourth probability distribution is taken as the extreme value of the reserve coefficient fluctuation;

[0219] Determine the severity of static voltage instability based on the reserve factor fluctuation extreme value and the preset voltage reserve requirement;

[0220] Based on the probability that the static stability reserve coefficient of the node voltage does not meet the preset voltage reserve requirement and the severity of static voltage instability, the static voltage stability index of the new power system is determined.

[0221] The system characteristic data includes the power flow equation and the transmission power limit of the line; the determination unit 1220 is specifically used for:

[0222] Determine a fifth probability distribution of line transmission power of the new power system based on the power flow equation and the first probability distribution;

[0223] Determining a probability of the line power flow exceeding the limit based on the fifth probability distribution and the transmission power limit of the line;

[0224] The minimum value of the line transmission power corresponding to the time when the probability value in the fifth probability distribution is 0 is taken as the extreme value of the power fluctuation;

[0225] Determine the severity of line power flow exceeding the limit based on the power fluctuation extreme value and the transmission power limit of the line;

[0226] Based on the probability and severity of line power flow exceeding the limit, the line power flow exceeding the limit index of the new power system is determined.

[0227] The weak line identification device of the new power system provided in the embodiment of the present invention can execute the weak line identification method of the new power system provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the weak line identification method of the new power system.

[0228] Embodiment 5

[0229] Fig.13 FIG. 2 shows a schematic diagram of the structure of an electronic device that can be used to implement an embodiment of the present invention. Fig.13 As shown, the electronic device provided in this embodiment includes:

[0230] Memory 1301;

[0231] Processor 1302; and

[0232] Computer programs;

[0233] The computer program is stored in the memory 1301 and is configured to be executed by the processor 1302 to implement any of the above-mentioned weak line identification methods for the new power system.

[0234] This embodiment also provides a computer-readable storage medium having a computer program stored thereon.

[0235] The computer program is executed by the processor to implement any of the above-mentioned weak line identification methods for the novel power system.

[0236] This embodiment also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements any of the above-mentioned weak line identification methods of the new power system.

[0237] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0238] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A novel weak line identification method for a power system, characterized in that: The novel power system includes a plurality of lines and at least one new energy source, and the method includes: Acquire the historical output of new energy; construct a first probability distribution of the output of new energy in the new power system based on the historical output; wherein the first probability distribution is used to fit the fluctuation characteristics of the output of new energy; Selecting lines in the new power system as target lines one by one; when the target lines in the new power system are out of operation, determining an evaluation index of the new power system based on system characteristic data of the new power system and the first probability distribution; Based on the evaluation indicators, weak lines in the new power system are identified.

2. The method according to claim 1, characterized in that The evaluation index includes at least one of the following: a voltage amplitude over-limit index, a line flow over-limit index, a static safety index, a static frequency stability index, a static power angle stability index, and a static voltage stability index.

3. The method according to claim 2, characterized in that The novel power system further comprises a plurality of nodes, and the system characteristic data comprises a power flow equation, an upper voltage amplitude limit and a lower voltage amplitude limit of the nodes; The step of determining an evaluation index of the new power system based on the system characteristic data of the new power system and the first probability distribution includes: Determining a second probability distribution of node voltage amplitudes based on the power flow equation and the first probability distribution; Determining a voltage over-limit probability based on the voltage amplitude upper limit, the voltage amplitude lower limit, and the second probability distribution; The maximum value of the node voltage amplitude corresponding to the probability value of 0 in the second probability distribution is used as the first amplitude fluctuation extreme value; the minimum value of the node voltage amplitude corresponding to the probability value of 1 in the second probability distribution is used as the second amplitude fluctuation extreme value; Determining a voltage amplitude limit-crossing severity based on the first amplitude fluctuation extreme value, the second amplitude fluctuation extreme value, the voltage amplitude upper limit, and the voltage amplitude lower limit; Based on the voltage amplitude exceeding limit severity and the voltage exceeding limit probability, a voltage amplitude exceeding limit index of the novel power system is determined.

4. The method according to claim 3, characterized in that The system characteristic data also includes the voltage amplitude of the node under normal operation; the determining of the evaluation index of the new power system based on the system characteristic data of the new power system and the first probability distribution includes: In the process of gradually increasing the load for the new power system, determining the convergence of the power flow equation of the new power system; taking the node voltage amplitude corresponding to the critical point where the power flow equation does not converge as the voltage instability critical point, and taking the second probability distribution as the third probability distribution corresponding to the voltage instability critical point; Determine a fourth probability distribution corresponding to a static reserve coefficient of the node voltage according to the voltage amplitude of the node under normal operation and the third probability distribution; Determining, according to the fourth probability distribution, the probability that the static reserve coefficient of the node voltage does not meet the preset voltage reserve requirement; The maximum value of the static reserve coefficient corresponding to the probability value of 0 in the fourth probability distribution is used as the extreme value of the reserve coefficient fluctuation; Determining the severity of static voltage instability based on the reserve coefficient fluctuation extreme value and the preset voltage reserve requirement; Based on the probability that the static voltage reserve coefficient of the node voltage does not meet the preset voltage reserve requirement and the severity of static voltage instability, a static voltage stability index of the novel power system is determined.

5. The method according to claim 2, characterized in that: The system characteristic data includes power flow equation and transmission power limit of the line; The step of determining an evaluation index of the new power system based on the system characteristic data of the new power system and the first probability distribution includes: Determining a fifth probability distribution of line transmission power of the novel power system based on the power flow equation and the first probability distribution; Determining a probability of the line power flow exceeding the limit based on the fifth probability distribution and the transmission power limit of the line; The minimum value of the line transmission power corresponding to the time when the probability value in the fifth probability distribution is 0 is used as the power fluctuation extreme value; Determine the severity of line power flow exceeding the limit based on the power fluctuation extreme value and the transmission power limit of the line; Based on the probability of line power flow exceeding the limit and the severity of line power flow exceeding the limit, a line power flow exceeding the limit index of the novel power system is determined.

6. The method according to claim 2, characterized in that The step of determining an evaluation index of the new power system based on the system characteristic data of the new power system and the first probability distribution includes: Under the condition that the lines other than the target line in the novel power system are disconnected one by one without faults by adopting the N-1 principle, determining the voltage amplitude over-limit index and the line power flow over-limit index of the novel power system based on the system characteristic data of the novel power system and the first probability distribution; The sum of each voltage amplitude over-limit index and each line power flow over-limit index is used as the static safety index of the new power system.

7. The method according to claim 2, characterized in that: The system characteristic data includes a pre-constructed first linear relationship equation between a system characteristic value of the new power system and a new energy output; Determining the evaluation index of the new power system based on the system characteristic data of the new power system and the first probability distribution includes: Determine a sixth probability distribution of the real part of the system eigenvalue according to the first linear relationship equation and the first probability distribution; The probability that the real part of the system eigenvalue determined based on the sixth probability distribution is greater than 0 is used as the probability that static frequency instability occurs in the system; Based on the probability of static frequency instability occurring in the system and the preset severity of static frequency instability, a static frequency stability index of the novel power system is determined.

8. The method according to claim 2, characterized in that: The novel power system further comprises a generator; the system characteristic data comprises a pre-constructed second linear relationship equation between the static power limit of the generator and the output of the new energy; The step of determining an evaluation index of the new power system based on the system characteristic data of the new power system and the first probability distribution includes: Determining a seventh probability distribution of a static power limit of the generator according to the second linear relationship equation and the first probability distribution; Obtaining the output of the generator under normal operation; and determining the eighth probability distribution of the static power angle stability reserve coefficient of the generator according to the output and the seventh probability distribution; Determining a probability of static power angle instability based on an eighth probability distribution and a preset power angle reserve requirement; The maximum value of the static power angle stability reserve coefficient corresponding to the probability of 0 in the eighth probability distribution is used as the target extreme value; based on the target extreme value, the severity of the static power angle instability is determined; Based on the probability of static power angle instability and the severity of static power angle instability, a static power angle stability index of the novel power system is determined.

9. The method according to claim 1, characterized in that: The first probability distribution is a Gaussian mixture model.

10. The method according to claim 1, characterized in that The step of identifying weak lines in the novel power system based on the evaluation indicators includes: Normalizing each evaluation index corresponding to the target line to obtain a normalized evaluation index; The subjective weight of each evaluation index is obtained based on the hierarchical analysis method; the objective weight of each evaluation index is obtained based on the entropy weight method; Determining a target weight based on the subjective weight and the objective weight; Based on the target weight, each normalized evaluation index is weighted and summed to obtain a target evaluation score; Based on the target evaluation score corresponding to each target line, the weak lines in the new power system are identified.

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