Novel electric power system weak branch identification method, system and device and storage medium

Abstract

The invention discloses a novel electric power system weak branch identification method, system and device and a storage medium, and the method comprises the steps: constructing a novel electric power system weak branch risk evaluation index system composed of risk evaluation indexes from the perspective of system safety and stability; new energy volatility and direct current operation characteristics are considered, and a calculation method of each risk assessment index in a novel power system weak branch risk assessment index system is determined, so that risks caused by outage of different lines to the system are quantified; and weighting all the risk assessment indexes to obtain a comprehensive risk index integrating the safety and the stability of the system, thereby identifying the weak branch in the novel power system. According to the method, the fluctuation of the new energy and the DC operation characteristics are considered, and weak branch identification can be carried out on the new energy large-scale access and AC-DC interconnection novel power system.

Description

Technical Field

[0001] The present invention relates to the identification technology of weak branches in power systems, and particularly to a new method, system, device and storage medium for identifying weak branches in power systems. Background Art

[0002] In recent years, large-scale power outages in power systems have occurred frequently worldwide, causing huge economic losses and serious social impacts. Research shows that catastrophic large-scale power outages are usually triggered by the failure of one or several components. These components can be called weak components, including transmission lines, transformers, etc. At the same time, the access of wind power, DC transmission, etc. to the power system makes the dynamic characteristics and operation modes of the power system more complex, and its safe and stable operation faces severe challenges. Therefore, identifying weak components in a new power system with new energy and DC access is of great significance for maintaining the safe operation of the system and preventing large-scale power outages in the system.

[0003] Regarding the identification problem of weak components in power systems, existing scholars have conducted a large number of studies. The research fields mainly include two types of methods. The first type of research is based on complex network theory, which uses the concepts of degree and betweenness and combines electrical characteristics to identify weak components in power systems; the second type of research method is based on the operating state of the power system, aiming to define several indicators reflecting the operating state of the system. Due to the need to jointly consider the randomness of new energy output and the operating characteristics of DC transmission, there are relatively few studies on the identification of weak components in a new power system with new energy and DC access. In addition, the research method based on the operating state of the power system mainly uses power flow calculation to identify vulnerable components in the system, focuses on the steady-state operating state of the system, and rarely considers the dynamic characteristics of the system, making it difficult to comprehensively reflect the impact of vulnerable components on the power system. Summary of the Invention

[0004] Object of the Invention: The object of the present invention is to provide a new method, system, device and storage medium for identifying weak branches in a power system. This solution takes into account the volatility of new energy and the operating characteristics of DC, and can identify weak branches in a new power system with large-scale access of new energy and AC-DC interconnection.

[0005] Technical Solution: A new method for identifying weak branches in a power system according to the present invention includes:

[0006] Constructing a new risk evaluation index system for weak branches in a power system, which consists of a system security risk assessment index and a system stability risk assessment index; wherein, the risk assessment index includes a node voltage violation index, a line overload index, a static security index, a static frequency stability index, a static power angle stability index, and a static voltage stability index;

[0007] Taking into account the volatility of new energy and the DC operation characteristics, determine the calculation algorithms for various risk assessment indicators in the risk assessment index system of weak branches in the new power system, so as to quantify the risks caused by different line outages to the system;

[0008] Assign weights to the calculated values of all risk assessment indicators and sum them up weighted to obtain the calculated value of the comprehensive risk index that comprehensively reflects the system security and stability. Identify the weak branches in the new power system according to the magnitude of the calculated value of the comprehensive risk index.

[0009] Furthermore, the calculation methods of the node voltage violation index and the line overload index are as follows:

[0010] Define the severity of voltage violation and the severity of line overload:

[0011]

[0012] In the formula, Sev(U i ) is the severity of voltage violation at node i; Sev(L ij ) is the severity of line overload of line ij; are the upper and lower limits of the voltage amplitude at node i; U i is the most severe value of the voltage amplitude at node i during the fluctuation of new energy output; P ij,max is the transmission power limit value of line ij; P ij is the maximum value that the transmission power of line ij may appear with the fluctuation of new energy output; The node voltage violation index and the line overload index both take the most severe situation when defining the severity;

[0013] Obtain the risk R VV of system voltage violation caused by the outage of a line in the new power system LO and the risk R

[0014]

[0015] of line overload respectively as follows: i ) is the probability of voltage violation; P(L i ) is the probability of line overload.

[0016] Furthermore, the calculation method of the static security index is as follows:

[0017] The risk R SSA of the system losing static security after the outage of a line in the new power system is as follows:

[0018] R SSA = R VV + R LO .

[0019] Furthermore, the calculation method of the static frequency stability index is as follows:

[0020] Assume that there are a total of K eigenvalues in the new power system under high proportion of new energy and AC-DC interconnection. The k-th eigenvalue is λ k = ξ k + jω k , linearize the functional relationship between the eigenvalues of the new power system and the output power of the fan at the reference operating point, and the following linear relationship between the two can be established:

[0021]

[0022] In the formula, W i represents the output power of the i-th fan; w represents the number of fans; Δλ k represents the small change in the k-th eigenvalue; ΔW i represents the small change in the output power of the i-th fan; represents the sensitivity of the k-th eigenvalue of the system to the output power of the i-th fan, which can be solved by numerical methods:

[0023]

[0024] Establish the linear relationship between the system eigenvalues and the fan output power through the above two formulas; the probability distribution of the fan output power has been modeled by GMM, which is weighted by multiple Gaussian distributions with a mean of μ m , variance of Σ m . Since the Gaussian distribution still follows the Gaussian distribution after linear transformation, each Gaussian sub-component of the system eigenvalue probability distribution should follow a Gaussian distribution with a mean of K2μ m + B2 and a variance of K2Σ m K2 T , where K2 and B2 are the coefficient matrices of the linear expressions obtained through the above calculation formulas. Thus, the cumulative distribution function expression of the m-th Gaussian sub-component is as follows:

[0025]

[0026] Then, through the total probability formula, the probability distribution function F(λ) expression of the eigenvalues λ of the new power system can be finally obtained as follows:

[0027]

[0028] Among them, M is the number of Gaussian sub-components; ω m is the weight coefficient of each Gaussian sub-component;

[0029] When the real part of the eigenvalue is greater than 0, the system has static frequency instability. Thus, the probability P(SFS) of the system experiencing static frequency instability can be obtained from the probability distribution function expression of the eigenvalues of the new power system. When static frequency instability occurs, its severity Sev(SFS) is defined as 1. Furthermore, after a line in the new power system is taken out of service, the static frequency instability risk R SFS can be defined as:

[0030] R SFS = P(SFS) × Sev(SFS)

[0031] Furthermore, the calculation method of the static power angle stability index is as follows:

[0032] After a line in the new power system is taken out of service, the static power angle instability risk R of the new power system SRAS is:

[0033]

[0034] where n3 is the number of generators; P(K P,i ) is the probability that the static power angle stability reserve coefficient of the i-th generator is less than 10%; Ser(K P,i ) is the severity of the static power angle instability of the i-th generator;

[0035] K P,i =(P max,i -P 0,i ) / P 0,i

[0036] Sev(K P,i )=(0.1 - K P,i,min ) / 0.1

[0037] where P max,i is the static stability power limit of the i-th generator; P 0,i is the output power of the i-th generator under normal operation; K P,i is the static power angle stability reserve coefficient of the i-th generator; K P,i,min is the minimum value that the static stability limit power of generator i may reach during the power fluctuation of the fan.

[0038] Furthermore, the calculation method of the static voltage stability index is as follows:

[0039] Define the static stability reserve coefficient of the node voltage as:

[0040] K U,i =(U 0,i -U cr,i ) / U cr,i

[0041] where K U,i is the static voltage stability reserve coefficient of the i-th node; U 0,i is the voltage magnitude of the i-th node under normal operation; U cr,i is the voltage instability critical point of the i-th node;

[0042] The probability distribution of the voltage instability critical points of each node is obtained by using the stochastic AC-DC power flow calculation method;

[0043] Based on the probability distribution of the voltage instability critical points of each node and the static voltage stability reserve coefficient K U,i of the i-th node, the probability P(K U,i ) that the static voltage stability reserve coefficient of the i-th node in the new power system does not meet the operation requirements is solved;

[0044] Define the severity function Ser(K U,i ) of the static voltage instability of the i-th node:

[0045] Sev(K U,i ) = (0.08 - K U,i,min ) / 0.08

[0046] where K U,i,min is the minimum value that the static voltage stability reserve coefficient may reach during the fluctuation of new energy output; thus, the static voltage instability risk R SVS caused by the outage of a line in the new power system can be defined as:

[0047]

[0048] Furthermore, the calculated values of all risk assessment indicators are weighted and summed to obtain the calculated value of the comprehensive risk indicator that comprehensively reflects the system security and stability. According to the magnitude of the calculated value of the comprehensive risk indicator, the weak branches in the new power system are identified, including:

[0049] Normalize the calculation results of each risk assessment indicator and perform weighted summation to obtain the comprehensive risk indicator R c that comprehensively reflects the system security and stability:

[0050]

[0051] where α1 to α6 are the weights of each risk assessment indicator; is the normalized node voltage violation indicator; is the normalized line overload indicator; is the normalized static security indicator; is the normalized static frequency stability indicator; is the normalized static power angle stability index; is the normalized static voltage stability index; among them, the comprehensive weighting method combining subjective weighting and objective weighting is used to calculate the weights α1~α6 of each risk assessment index, and the calculation method is as follows:

[0052] Based on the analytic hierarchy process, the subjective weight C1 of each risk assessment index is obtained, and based on the entropy weight method, the objective weight C2 of each risk assessment index is obtained; the subjective weight C1 and the objective weight C2 are linearly combined to obtain the weight C of the comprehensive weighting method = β1C1 + β2C2, where β1 and β2 are the comprehensive weight combination coefficients.

[0053] Based on the same inventive concept, a new type of weak branch identification system for a power system of the present invention includes:

[0054] A risk evaluation index system establishment module for constructing a new type of power system weak branch risk evaluation index system composed of system security risk assessment indexes and system stability risk assessment indexes; among them, the risk assessment indexes include node voltage violation indexes, line overload indexes, static security indexes, static frequency stability indexes, static power angle stability indexes, and static voltage stability indexes;

[0055] A risk assessment index calculation module for taking into account the volatility of new energy and the operation characteristics of DC to determine the calculation algorithms of various risk assessment indexes in the new type of power system weak branch risk evaluation index system, so as to quantify the risks caused by different line outages to the system;

[0056] A comprehensive risk index calculation module for weighting and summing the calculated values of all risk assessment indexes to obtain the calculated value of the comprehensive risk index of the comprehensive system security and stability, and identifying the weak branches in the new type of power system according to the size of the calculated value of the comprehensive risk index.

[0057] Furthermore, the risk evaluation index system establishment module includes a voltage violation line overload index calculation unit, and the voltage violation line overload index calculation unit is used to calculate according to the following method:

[0058] Define the voltage violation severity and the line overload severity:

[0059]

[0060] In the formula, Sev(U i ) is the voltage violation severity of node i; Sev(L ij ) is the line overload severity of line ij; are the upper and lower limits of the voltage amplitude of node i; U i is the most severe value of the voltage amplitude of node i during the new energy output fluctuation period; P ij,maxis the transmission power limit value of line ij; P ij is the maximum value that the transmission power of line ij may reach with the fluctuation of new energy output; the node voltage violation index and the line overload index both take the most severe case when defining the severity;

[0061] Obtain the system voltage violation risk R caused by the outage of a line in the new power system VV and the line overload risk R LO which are respectively:

[0062]

[0063] In the formula, n1 is the number of nodes in the new power system; n2 is the number of remaining lines after the outage of a line; P(U i ) is the probability of voltage violation; P(L i ) is the probability of line overload.

[0064] Furthermore, the risk assessment index system establishment module includes a static security index calculation unit, and the static security index calculation unit is used to calculate according to the following method:

[0065] The risk R of the new power system losing static security after the outage of a line SSA , is as follows:

[0066] R SSA = R VV + R LO .

[0067] Furthermore, the risk assessment index system establishment module includes a static frequency stability index calculation unit, and the static frequency stability index calculation unit is used to calculate according to the following method:

[0068] Assume that there are a total of K eigenvalues in the new power system with high proportion of new energy and AC / DC interconnection, and the kth eigenvalue is λ k = ξ k + jω k , linearize the functional relationship between the eigenvalues of the new power system and the output power of the wind turbines at the reference operating point, and the following linear relationship can be established between the two:

[0069]

[0070] In the formula, W i represents the output power of the ith wind turbine; w represents the number of wind turbines; Δλ k represents the small change in the kth eigenvalue; ΔW i represents the small change in the output power of the ith wind turbine; Denote the sensitivity of the \(k\)-th eigenvalue of the system to the output power of the \(i\)-th wind turbine, which can be solved numerically as follows:

[0071]

[0072] Establish a linear relationship between the system eigenvalues and the output power of the wind turbines through the above two formulas; the probability distribution of the output power of the wind turbines has been modeled by GMM, which is composed of multiple Gaussian distributions with mean \(\mu\) m , variance \(\Sigma\) m weighted together. Since the Gaussian distribution still follows a Gaussian distribution after linear transformation, each Gaussian sub-component of the probability distribution of the system eigenvalues should follow a Gaussian distribution with mean \(K_2\mu\) m + \(B_2\), variance \(K_2\Sigma\) m \(K_2\) T , where \(K_2\) and \(B_2\) are the coefficient matrices of the linear expressions obtained through the above calculation formulas respectively. Thus, the cumulative distribution function expression of the \(m\)-th Gaussian sub-component is as follows:

[0073]

[0074] Then, through the total probability formula, the probability distribution function \(F(\lambda)\) expression of the eigenvalues \(\lambda\) of the new power system can be finally obtained as follows:

[0075]

[0076] where \(M\) is the number of Gaussian sub-components; \(\omega\) m is the weight coefficient of each Gaussian sub-component;

[0077] When the real part of the eigenvalue is greater than 0, the system is statically frequency unstable. Thus, the probability \(P(SFS)\) of the system experiencing static frequency instability can be obtained through the probability distribution function expression of the eigenvalues of the new power system; when static frequency instability occurs, define its severity \(Sev(SFS)\) as 1, and further, the static frequency instability risk \(R\) SFS of the new power system after a line is taken out of service can be defined as:

[0078] \(R\) SFS = \(P(SFS)\times Sev(SFS)\).

[0079] Furthermore, the risk assessment index system establishment module includes a static power angle stability index calculation unit, and the static power angle stability index calculation unit is used to calculate in the following manner:

[0080] When a line in the new power system is taken out of service, the static power angle instability risk \(R\) SRAS of the new power system is:

[0081]

[0082] In the formula, n3 is the number of generators; P(K P,i ) is the probability that the static power angle stability reserve coefficient of the i-th generator is lower than 10%; Ser(K P,i ) is the severity of the static power angle instability of the i-th generator;

[0083] K P,i =(P max,i -P 0,i ) / P 0,i

[0084] Sev(K P,i )=(0.1 - K P,i,min ) / 0.1

[0085] In the formula, P max,i is the static stability power limit of the i-th generator; P 0,i is the output power of the i-th generator under normal operation; K P,i is the static power angle stability reserve coefficient of the i-th generator; K P,i,min is the minimum value that the static stability limit power of generator i may reach during the wind power fluctuation period.

[0086] Furthermore, the risk assessment index system establishment module includes a static voltage stability index calculation unit, and the static voltage stability index calculation unit is used to calculate according to the following method:

[0087] Define the static stability reserve coefficient of the node voltage as:

[0088] K U,i =(U 0,i -U cr,i ) / U cr,i

[0089] In the formula, K U,i is the static voltage stability reserve coefficient of the i-th node; U 0,i is the voltage amplitude of the i-th node under normal operation; U cr,i is the voltage instability critical point of the i-th node;

[0090] Use the stochastic AC-DC power flow calculation method to obtain the probability distribution of the voltage instability critical points of each node;

[0091] Through the probability distribution of the voltage instability critical points of each node and the static voltage stability reserve coefficient K U,i of the i-th node, solve the probability P(K U,i ) that the static voltage stability reserve coefficient of the i-th node in the new power system does not meet the operation requirements;

[0092] Define the severity function Ser(K U,i ) for the static voltage instability of the i-th node:

[0093] Sev(K U,i ) = (0.08 - K U,i,min ) / 0.08

[0094] where K U,i,min is the minimum value that the static voltage stability reserve coefficient may reach during the fluctuation of new energy output; thus, the static voltage instability risk R SVS caused by the outage of a line in the new power system can be defined as:

[0095]

[0096] Furthermore, the comprehensive risk index calculation module is used to assign weights to the calculated values of all risk assessment indicators and perform weighted summation to obtain the calculated value of the comprehensive risk index for the comprehensive system security and stability. According to the magnitude of the calculated value of the comprehensive risk index, the weak branches in the new power system are identified, including:

[0097] Normalize the calculation results of each risk assessment indicator and perform weighted summation to obtain the comprehensive risk index R c for the comprehensive system security and stability:

[0098]

[0099] where α1 to α6 are the weights of each risk assessment indicator; is the normalized node voltage violation index; is the normalized line overload index; is the normalized static security index; is the normalized static frequency stability index; is the normalized static power angle stability index; is the normalized static voltage stability index; among them, the weights α1 to α6 of each risk assessment indicator are calculated by using the comprehensive weighting method that combines subjective weighting and objective weighting, and the calculation method is as follows:

[0100] Obtain the subjective weight C1 of each risk assessment indicator based on the analytic hierarchy process, and obtain the objective weight C2 of each risk assessment indicator based on the entropy weight method; linearly combine the subjective weight C1 and the objective weight C2 to obtain the weight C = β1C1 + β2C2 of the comprehensive weighting method, where β1 and β2 are the comprehensive weight combination coefficients.

[0101] Based on the same inventive concept, a new type of weak branch identification device for a power system according to the present invention includes a processor and a memory. Computer instructions are stored in the memory, and the processor is configured to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the above-mentioned new type of weak branch identification method for a power system.

[0102] Based on the same inventive concept, a computer-readable storage medium according to the present invention stores a computer program thereon. When the program is executed by a processor, the steps of the above-mentioned new type of weak branch identification method for a power system are implemented.

[0103] Advantageous effects: Compared with the prior art, the remarkable technical effects of the present invention are as follows:

[0104] For a new type of power system with a high proportion of new energy access and AC-DC interconnection, from the perspectives of system safety and stability, a risk assessment index system for weak branches of the new type of power system is established, thereby taking into account the system dynamic characteristics. Then, a stochastic AC-DC power flow calculation method based on the analytical method is proposed, in which the Gaussian mixture model (GMM) is used to characterize the probability model of new energy output, thereby taking into account the volatility of new energy and the DC operation characteristics. On this basis, calculation methods for various risk assessment indexes in the index system are further given. Finally, a comprehensive weight method based on game theory is proposed. By weighting each risk assessment index, a comprehensive risk assessment index is obtained to more accurately identify the weak branches in the new type of power system, thereby providing a decision-making basis for dispatching and operation personnel to formulate control strategies and emergency plans and avoiding the occurrence of large-scale power outages. Description of the Drawings

[0105] Figure 1 is a schematic flow chart of a new type of weak branch identification method for a power system disclosed in an embodiment of the present invention;

[0106] Figure 2 is a schematic structural diagram of a risk assessment index system for weak branches of a new type of power system disclosed in an embodiment of the present invention;

[0107] Figure 3 is a schematic structural diagram of an improved IEEE-39 bus system disclosed in an embodiment of the present invention;

[0108] Figure 4 is a schematic diagram of a probability distribution function curve disclosed in an embodiment of the present invention;

[0109] Figure 5 is a schematic diagram of the voltage violation risk after each line is taken out of operation disclosed in an embodiment of the present invention;

[0110] Figure 6It is a schematic diagram of the system line overload risk after each line in the embodiment of the present invention is taken out of service;

[0111] Figure 7 It is a schematic diagram of the system static security after each line in the embodiment of the present invention is taken out of service

[0112] Figure 8 It is a schematic diagram of the system static frequency stability after each line in the embodiment of the present invention is taken out of service.

[0113] Figure 9 It is a schematic diagram of the system static power angle stability after each line in the embodiment of the present invention is taken out of service.

[0114] Figure 10 It is a schematic diagram of the system static voltage stability after each line in the embodiment of the present invention is taken out of service.

[0115] Figure 11 It is a schematic diagram of the comprehensive evaluation result of the system after each line in the embodiment of the present invention is taken out of service;

[0116] Figure 12 It is a schematic diagram of the structure of a new type of weak branch identification system for a power system in the embodiment of the present invention;

[0117] Figure 13 It is a schematic diagram of the structure of a new type of weak branch identification device for a power system in the embodiment of the present invention. Detailed implementation manners

[0118] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art will understand that the purposes and advantages that can be achieved by the present invention are not limited to the specific beneficial effects described above, and it will be more clearly understood from the following detailed description that the above and other purposes that the present invention can achieve.

[0119] Those of ordinary skill in the art should understand that the various exemplary components, systems, and methods described in combination with the embodiments disclosed in the present invention can be implemented in hardware, software, or a combination of both. Specifically, whether to implement in hardware or software depends on the specific application and design and tree conditions of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0120] As used in this invention, the term "embodiment" means that the specific features, structures, or characteristics described in connection with an embodiment can be included in at least one embodiment of the invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0121] Embodiment 1

[0122] Please refer to Figure 1 , Figure 1 which is a schematic flow chart of a new method for identifying weak branches in a power system disclosed in an embodiment of the present invention. Among them, Figure 1 the described new method for identifying weak branches in a power system is applied to a power system, such as for identifying weak branches in a power system, etc., which is not limited in the embodiments of the present invention. As Figure 1 shown, the new method for identifying weak branches in a power system may include the following operations:

[0123] S1. Construct a new risk evaluation index system for weak branches in a power system, which consists of system security risk assessment indicators and system stability risk assessment indicators; among them, the risk assessment indicators include node voltage violation indicators, line overload indicators, static security indicators, static frequency stability indicators, static power angle stability indicators, and static voltage stability indicators.

[0124] For a new power system with a high proportion of new energy and AC-DC interconnection, six risk indicators are proposed from the perspectives of system operation safety and stability to evaluate the risk levels of each line from different angles, as Figure 2 shown. Among them, the security indicators include: node voltage violation, line overload, and static security indicators, which are mainly used to evaluate whether the voltages of each node in the system and the transmission power of the lines are operating within the safe constraint range after the outage of different lines; the stability indicators include: static frequency stability, static power angle stability, and static voltage stability indicators, which mainly focus on the static stability of the system, that is, to evaluate the stable operation ability of the system under small disturbances after the outage of different lines.

[0125] S2. Considering the volatility of new energy and the operating characteristics of DC, determine the calculation algorithms for each risk assessment indicator in the new risk evaluation index system for weak branches in a power system to quantify the risks caused by the outage of different lines. The specific steps are as follows:

[0126] S2.1. Calculate the node voltage violation indicator and the line overload indicator, and the calculation methods are as follows:

[0127] In a new power system with a high proportion of new energy access and AC-DC interconnection, the node voltage over-limit and line overload risks caused by different line outages can be obtained through power flow calculation. The power flow equation of the AC-DC interconnection system is as follows:

[0128]

[0129] Where: S is the column vector of injection power at each node, including the active and reactive power injected at the node; X is the column vector of node states, including the voltage phase angle and amplitude of each node; d is the DC converter equation, DC network equation, and DC control equation; are the relevant parameters on the DC side, specifically including DC voltage, DC current, control angle, converter transformer turns ratio, and converter power factor angle; J D is the Jacobian matrix of the AC-DC interconnection system; J is the Jacobian matrix of the traditional AC system, and A, C, and F are the augmented Jacobian matrix elements corresponding to the increased DC-related variables.

[0130] To account for the volatility of new energy, a stochastic AC-DC power flow calculation method based on the analytical method is proposed, and a probability model of new energy output is established using the Gaussian mixture model (GMM). GMM can accurately model the probability distribution of random variables through the superposition of multiple Gaussian distributions:

[0131]

[0132] Where: W is the vector composed of the output powers of w wind turbines; f(W) is the probability density function of W; N m (W) is the probability density function of the m-th Gaussian sub-component; M is the number of Gaussian sub-components; ω m , μ m , Σ m are the weight coefficient, mean vector, and covariance matrix of each Gaussian sub-component, respectively, and usually the expectation-maximization algorithm is used to solve them.

[0133] By linearizing the line power flow equation at the base operating point, a linear expression between the line transmission power and the injection power at each node can be obtained:

[0134]

[0135] Where: Z is the line power flow column vector, including the active and reactive power of the line; the subscript 0 represents the base operating point. Equations (1) and (4) are the linear expressions between the node state variables, line transmission power variables, and node injection power, respectively. For the convenience of the following description, the two equations are uniformly simplified into a general linear expression form:

[0136] Y = K1S + B1 (5)

[0137] Wherein: The elements in Y represent node state variables or branch power flow variables, including y variables; K1 and B1 represent the corresponding coefficient matrices. The output of the wind turbines in S has been modeled by GMM, which is weighted by multiple Gaussian distributions with a mean of μ m , and a variance of Σ m . Considering that a random variable obeying a Gaussian distribution still obeys a Gaussian distribution after a linear transformation, therefore, from Equation (5), each Gaussian sub-component of the variable Y obeys a Gaussian distribution with a mean of K1μ m + B1 and a variance of K1Σ m K1 T . Then the expression of the cumulative distribution function of each Gaussian sub-component of Y is:

[0138]

[0139] By the total probability formula (7), the cumulative probability distribution functions of multiple sub-components are weighted according to the weights calculated by Equation (3), and finally the probability distribution function of the random variables in Y can be obtained as shown in Equation (8).

[0140]

[0141] Wherein: P(Y) is the probability that the state variable corresponding to the m-th sub-component occurs. For a new power system with a high proportion of new energy and AC-DC interconnection, the outage of a certain line may cause the voltage of some nodes to exceed the limit or some lines to be overloaded. To quantify these risks, first, the probability distribution of the wind turbine output can be obtained by using GMM modeling, and then the probability distribution functions of the state variables of each node and the branch power flow variables of the system after the outage of this line can be quickly solved through the above process. By setting the critical values of voltage over-limit and line overload, the probabilities of voltage over-limit of each node and line overload after the outage of a certain line can be obtained through Equation (8). Next, the voltage over-limit severity and line overload severity are defined:

[0142]

[0143] Sev(L ij ) = (P ij - P ij,max ) / P ij,max (10)

[0144] Wherein, Sev(U i ) is the voltage over-limit severity of node i; Sev(L ij ) is the line overload severity of line ij; are the upper and lower limits of the voltage amplitude of node i; U i is the most severe value of the voltage amplitude of node i during the fluctuation of new energy output, which can be obtained through stochastic power flow calculation; P ij,max is the transmission power limit value of line ij; Pij is the maximum value that the transmission power of line ij may reach with the fluctuation of new energy output, which can also be obtained by stochastic power flow calculation. When defining the severity, the node voltage violation index and the line overload index both take the most severe case. Thus, the risk of system voltage violation R caused by the outage of a line in the new power system can be obtained VV and the line overload risk R LO are respectively as follows:

[0145]

[0146]

[0147] In the formula, n1 is the number of nodes in the new power system; n2 is the number of remaining lines after the outage of a line; P(U i ) is the probability of voltage violation; P(L i ) is the probability of line overload.

[0148] S2.2. Calculate the static security index, and the calculation method is as follows:

[0149] Power system static security analysis means applying the N-1 principle to disconnect each line one by one without fault and check whether other components will have line overload and voltage violation conditions, so as to test whether the grid structure strength and operation mode meet the requirements of safe operation. When a line in the new power system is out of service, the N-1 principle will be used to disconnect the remaining lines one by one without fault, and the system voltage violation risk and line overload risk will be solved respectively using formulas (11) and (12). Finally, the two risk results will be added together as the risk R of the system losing static security after the line outage SSA , as shown in formula (13):

[0150] R SSA =R VV +R LO (13)

[0151] S2.3. Calculate the static frequency stability index, and the calculation method is as follows:

[0152] Based on the eigenvalue analysis method, a calculation method of static frequency stability eigenvalues under the access of new energy and DC based on the analytical method is proposed to solve the risk of frequency instability of the system under small disturbances after each line is taken out of service. Assume that there are a total of K eigenvalues in the new power system with high proportion of new energy and AC-DC interconnection, and the kth eigenvalue is λ k =ξ k +jω k . At the reference operating point, linearize the functional relationship between the eigenvalues of the new power system and the output power of the fan, and the following linear relationship between the two can be established:

[0153]

[0154] Wherein, W i represents the output power of the i-th wind turbine; w represents the number of wind turbines; Δλ k represents the small change in the k-th eigenvalue; ΔW i represents the small change in the output power of the i-th wind turbine; is the sensitivity of the k-th eigenvalue of the system to the output power of the i-th wind turbine, which can be solved by numerical methods:

[0155]

[0156] By equations (14) and (15), a linear relationship between the system eigenvalue and the output power of the wind turbine is established. The probability distribution of the output power of the wind turbine has been modeled by GMM, which is weighted by multiple Gaussian distributions with a mean of μ m , and a variance of Σ m . Since the Gaussian distribution still follows the Gaussian distribution after linear transformation, each Gaussian sub-component of the probability distribution of the system eigenvalue should follow a Gaussian distribution with a mean of K2μ m +B2 and a variance of K2Σ m K2 T . Among them, referring to equation (5), K2 and B2 are the coefficient matrices of the linear expression obtained by the calculation formula of equation (15) . Thus, the cumulative distribution function expression of the m-th Gaussian sub-component is shown in equation (16)

[0157]

[0158] Then, through the total probability formula (7), the probability distribution function F(λ) expression of the eigenvalue λ of the new power system can finally be obtained as shown in equation (17):

[0159]

[0160] Wherein, M is the number of Gaussian sub-components; ω m is the weight coefficient of each Gaussian sub-component.

[0161] When the real part of the eigenvalue is greater than 0, the system has static frequency instability. Thus, the probability P(SFS) of the system having static frequency instability can be obtained through equation (17). When static frequency instability occurs, define its severity Sev(SFS) as 1. Furthermore, the static frequency instability risk R SFS of the system after a certain line is taken out of operation can be defined as:

[0162] R SFS =P(SFS)×Sev(SFS) (18)

[0163] S2.4. Calculate the static power angle stability index, and the calculation method is as follows:

[0164] There is also a non-explicit functional relationship between the static stability power limit of any generator and the output power of the fan. Since it is a small disturbance analysis, the above functional relationship can be linearized at the reference operating point to obtain a linear expression of the static stability power limit of any generator and the output power of a certain fan:

[0165]

[0166] In the formula: P max,i is the static stability power limit of the i-th generator; W k is the output power of the k-th fan; is the sensitivity of the static stability power limit of the i-th generator to the output power of the k-th fan. Similarly, the sensitivity can be calculated by numerical methods:

[0167]

[0168] Through equations (19)-(20), the linear relationship between the static stability power limit of the generator and the output power of the fan is established. The probability distribution of the output power of the fan has been modeled by GMM. After the linear transformation of equation (19), each Gaussian sub-component of the probability distribution of the static stability power limit of the generator should follow a Gaussian distribution with a mean of K3μ m +B3 and a variance of K3Σ m K3 T where K3 and B3 are the coefficient matrices of the linear expression obtained through equation (20). Then, using the total probability formula (7), the final probability distribution of the static stability power limit of each generator can be obtained as shown in equation (21).

[0169]

[0170] The "Guide for Power System Security and Stability" (hereinafter referred to as the "Guide") stipulates that in the post-accident operation mode, the static power angle stability reserve coefficient shall not be less than 10%, and its calculation formula is:

[0171] K P,i =(P max,i -P 0,i ) / P 0,i (22)

[0172] In the formula, P max,i is the static stability power limit of the i-th generator; P 0,i is the output power of the i-th generator under normal operation; K P,iis the static power angle stability reserve coefficient of the i-th generator. The minimum value of the static stability power limit that meets the requirements of the "Guidelines" can be obtained through Equation (22). Then, through the probability distribution function of the static stability limit power, Equation (21), the probability P(K P,i ) that the static power angle stability reserve coefficient of the i-th generator is less than 10% can be obtained. Next, define the severity Ser(K P,i ) of the static power angle instability of the i-th generator:

[0173] Sev(K P,i ) = (0.1 - K P,i,min ) / 0.1 (23)

[0174] In the formula: K P,i,min is the minimum value that the static stability limit power of generator i may reach during the wind power fluctuation period, that is, the most severe situation is taken as the severity of the static power angle instability. Thus, when a certain line in the new power system is out of service, the static power angle instability risk R SRAS of the new power system is:

[0175]

[0176] In the formula, n3 is the number of generators.

[0177] S2.5. Calculate the static voltage stability index. The calculation method is as follows:

[0178] The "Guidelines" point out that the method of gradually increasing the load is used to solve the voltage instability critical point in the static voltage stability calculation and analysis, so as to estimate the voltage stability margin of the current operating point. The static stability reserve coefficient of the node voltage is defined as:

[0179] K U,i = (U 0,i - U cr,i ) / U cr,i (25)

[0180] In the formula: K U,i is the static voltage stability reserve coefficient of the i-th node; U 0,i is the voltage amplitude of the i-th node under normal operation; U cr,i is the voltage instability critical point of the i-th node.

[0181] For a new power system with a high proportion of new energy and AC / DC interconnected, when a certain line is out of service, the load of each node is gradually increased, and the probability distribution of the voltage instability critical point of each node can be obtained through the stochastic AC / DC power flow calculation method in formulas (6)-(8). The "Guidelines" stipulate that the static voltage stability reserve under the post-fault operation mode shall not be less than 8%. Through the probability distribution of the voltage instability critical point of each node and formula (25), the probability P(K U,i ) that the static voltage stability reserve coefficient of the i-th node in the new power system does not meet the operation requirements can be solved. Then, the severity function Ser(K U,i ) of the static voltage instability of the i-th node is defined as:

[0182] Sev(K U,i )=(0.08 - K U,i,min ) / 0.08 (26)

[0183] In the formula: K U,i,min is the minimum value that the static voltage stability reserve coefficient may reach during the fluctuation of new energy output, that is, the most severe situation deviating from the safety constraint is used as the severity of this index. Thus, the static voltage instability risk R SVS caused by the outage of a certain line in the new power system can be defined as:

[0184]

[0185] S3. Assign weights to the calculated values of all risk assessment indicators and perform weighted summation to obtain the calculated value of the comprehensive risk indicator that comprehensively reflects the system safety and stability. Identify the weak branches in the new power system according to the magnitude of the calculated value of the comprehensive risk indicator.

[0186] After a certain line is out of service, by calculating the above-defined 6 indicators respectively, the importance of this line in the system can be evaluated from multiple perspectives. On this basis, the comprehensive weighting method that combines subjective weighting and objective weighting is used to weight the 6 proposed indicators, so as to comprehensively evaluate the weakness of each line.

[0187] First, the calculation results of the 6 risk assessment indicators need to be unified in terms of order of magnitude and dimension, and then weighted summation is performed to obtain the comprehensive risk indicator R c of the system that comprehensively reflects the system safety and stability:

[0188]

[0189] In the formula, α1~α6 are the weights of each risk assessment indicator; is the normalized node voltage over-limit indicator; is the normalized line overload indicator; is the normalized static security indicator; is the normalized static frequency stability index; is the normalized static power angle stability index; is the normalized static voltage stability index; among them, the comprehensive weighting method combining subjective weighting and objective weighting is used to calculate the weights α1~α6 of each risk assessment index, and the calculation method is as follows:

[0190] 1) Obtain the subjective weight C1 of each risk assessment index based on the analytic hierarchy process, and obtain the objective weight C2 of each risk assessment index based on the entropy weight method. The subjective weight C1 and the objective weight C2 obtained by the two methods are linearly combined to obtain the weight C of the comprehensive weighting method, C = β1C1 + β2C2, where β1 and β2 are the comprehensive weight combination coefficients.

[0191] 2) To minimize the respective deviations between the comprehensive weight and each basic weight, it is necessary to establish a corresponding optimization objective to optimize the coefficients β1 and β2:

[0192]

[0193] 3) According to the differential properties of the matrix, Equation (29) can be transformed into:

[0194]

[0195] 4) Substitute the previously solved weights C1 and C2 into Equation (30) respectively, and the comprehensive weights β1 and β2 can be obtained. Then, through the linear combination in step S1, the comprehensive weight C based on the comprehensive weighting method can be obtained.

[0196] When a certain line is out of service, the higher the comprehensive risk assessment index, the greater the system risk caused by the outage of this line, the more important this line is in the system, and the more vulnerable it is.

[0197] Next, a specific embodiment is used to verify the novel power system weak branch identification method of the present invention.

[0198] The improved IEEE-39 bus system with wind turbines and DC access is used as a simulation example to verify the applicability of the method proposed in the verification scheme. The structure of the improved IEEE-39 bus system with wind turbines and DC access is as Figure 3 shown, and the numbers in parentheses are the line numbers of each line. Replace the original synchronous generators of buses 32, 34, and 38 with wind turbines. Assume that the installed capacities of each wind turbine are 650MW, 500MW, and 450MW respectively, and the total new energy penetration rate is about 33%. Replace the original AC line L6 with an LCC-HVDC line, where bus 3 is the rectifier station and bus 18 is the inverter station.

[0199] Disconnect each line one by one. First, use the proposed random AC-DC power flow calculation method based on the analytical method to calculate the system voltage violation and line overload risks after each line is disconnected. Taking line L1 as an example, after it is disconnected, the probability distributions of the node voltages and line powers at each node can be obtained by using the proposed random AC-DC power flow calculation method. The probability distributions of the node voltage amplitude of bus 22 and the active power of line L22 are taken respectively as Figure 3 shown. At the same time, use MCS to calculate the random AC-DC power flow to verify the accuracy of the proposed random power flow calculation method based on the analytical method.

[0200] Figure 4 In the (a) figure of Figure 4 represents the probability density of the node voltage; Figure 4 In, the red curve is the result obtained by MCS, and the blue curve is the result obtained by the method proposed in the present invention. It can be seen that the results obtained by the two methods are extremely close. In terms of calculation time, the method proposed in the present invention takes 0.43 seconds, and MCS takes 97 seconds, indicating that the method proposed in the present invention greatly improves the calculation efficiency while meeting the calculation accuracy.

[0201] Based on the obtained probability distributions of the node voltages and line powers, the risks of system voltage violation and line overload after each line is out of service can be obtained respectively through equations (9) to (12), as shown in Figure 5 and Figure 6 shown. After some lines are out of service, it will cause the risks of system voltage violation and line overload. Sorting by the level of risk, the top 3 lines that cause voltage violation risks and their risk values are line L20 (0.1556), L31 (0.1371), L15 (0.1046), indicating that when these lines are attacked and taken out of operation, they have a relatively high impact on the voltage security of each node in the power grid. The top 3 lines that cause line overload risks and their risk values are line L31 (5.2697), L41 (2.7717), L42 (2.4761), indicating that in the line overload risk index, these lines are more important and weaker. After some lines such as L1 and L25 are out of service, the risks of system voltage violation and line overload are 0, indicating that under these two indicators, these lines are unimportant lines in the system and will not cause the risks of system voltage violation and line overload even if they are attacked externally or taken out of operation.

[0202] Evaluate the static security of the system after each line is out of service. When each line is out of service, use the N-1 principle to disconnect the remaining lines one by one, and solve the static security risk of the system after each line is out of service through equation (13) as shown in Figure 7As shown in the figure. The outage of each line has an impact on the static security of the power grid. The top three lines and their risk values are L31 (7.9996), L15 (6.0973), and L14 (5.2484), which are the top three lines. This indicates that the withdrawal of these lines from operation has the greatest impact on the static security of the power system and they are the weakest lines in the static security index.

[0203] Evaluate the static frequency instability risk of the system after the outage of each line. The probability distribution of the system eigenvalue during the fluctuation of the fan output can be obtained respectively through equations (14)-(17). Then, the static frequency instability risk of the system after the outage of each line can be obtained by using equation (18), as Figure 8 shown. Only when line L31 is out of operation will there be a static frequency instability risk in the system, and the other lines will not cause this kind of risk. This shows that L31 is the weakest line in the static frequency stability risk index, and it is very likely to cause the static frequency instability risk of the system when it is attacked by external forces.

[0204] Evaluate the risk of static power angle instability of the system after the outage of each line. Through equations (19)-(21), the probability distribution of the static stability limit power of each generator after the outage of each line can be obtained. Then, the risk that the static power angle stability reserve coefficient of the system does not meet the operation requirements can be obtained through equations (22)-(24), as Figure 9 shown. The outage of some lines will cause insufficient static power angle stability reserve in the system, and there is a risk of power angle instability. The top three lines and their risk values are L20 (0.8680), L4 (0.8336), and L15 (0.6304). When they are attacked, it is very likely to cause the risk of static power angle instability of the system.

[0205] Evaluate the static voltage instability risk of the system after the outage of each line. Disconnect each line of the system one by one, and use the stochastic power flow to calculate the probability distribution of the voltage critical value of each node. Then, calculate the static voltage instability risk through equations (25)-(27), as Figure 10 shown. The outage of some lines makes the static voltage stability reserve of the system not meet the operation requirements, and there is a certain risk of static voltage instability. The top three lines and their risk values are L4 (22.22), L31 (21.71), and L15 (18.92). When they are attacked, it is very easy to cause the risk of static voltage instability of the system.

[0206] Through the above simulation results, the impact of line withdrawal on system security and stability was evaluated, and the importance of each line under various risk assessment indicators was obtained. To finally obtain the importance of each line, the comprehensive weighting method was used to weight multiple indicators to form a comprehensive risk indicator to evaluate the vulnerability of each line. First, the results of each indicator were normalized. The subjective weights B1 = [0.0859, 0.0650, 0.1508, 0.2790, 0.2097, 0.2097] of each indicator were solved using the analytic hierarchy process, and the objective weights B2 = [0.1823, 0.1657, 0.0430, 0.3637, 0.1603, 0.0849] of each indicator were obtained using the entropy weight method. Through Equation (30), β1 = 0.2945 and β2 = 0.7597 were obtained, and then the comprehensive weights B = [0.1638, 0.1450, 0.0771, 0.3585, 0.1836, 0.1263] of each indicator were obtained. The normalized risk assessment indicator results were weighted according to the comprehensive weight B, and finally the comprehensive impact of each line withdrawal on system security and stability could be obtained, thereby evaluating the system risks caused by the withdrawal of each line as Figure 11 shown.

[0207] Figure 11 Among them, sorted by the level of risk, the top 3 lines and their risk values are L31 (0.8469), L15 (0.4361), and L14 (0.4066). From the perspective of system security and stability, the line L31 in this system has the highest importance. When it withdraws from operation, the comprehensive impact on system security and stability is the greatest, and it is the most important and weakest line in this system. Compared with line L31, the importance of lines such as L15 and L14 is secondary, but they are also relatively weak lines in the system. Once they withdraw from operation, they will also have a greater impact on the security and stability of the system.

[0208] The novel power system weak branch identification method and system of the present invention aim to provide decision-making ideas for the preventive control of unpredictable extreme events. First, from the perspective of system security and stability, a novel power system weak branch risk assessment index system composed of 6 risk assessment indicators was constructed; secondly, calculation methods for 6 risk assessment indicators considering the volatility of new energy and the operation characteristics of direct current were defined, thereby quantifying the risks caused by different line outages to the system; then the comprehensive weighting method was used to weight the above 6 risk assessment indicators to obtain a comprehensive risk indicator that comprehensively reflects system security and stability, thereby locating the weak branches of the novel power system; finally, the correctness and effectiveness of the proposed weak branch identification method and system were verified in an improved IEEE-39 bus simulation system containing wind farms and DC lines.

[0209] Example 2

[0210] Please refer to Figure 12 , Figure 12 which is a schematic structural diagram of a new-type weak branch identification system for a power system disclosed in an embodiment of the present invention. This system can realize the identification of weak branches in the power system and specifically includes:

[0211] A risk assessment index system establishment module, which is used to construct a new-type weak branch risk assessment index system for the power system composed of risk assessment indexes from the perspectives of system security and stability; wherein, the risk assessment indexes include node voltage violation indexes, line overload indexes, static security indexes, static frequency stability indexes, static power angle stability indexes, and static voltage stability indexes;

[0212] A risk assessment index calculation module, which is used to determine the calculation methods of various risk assessment indexes in the new-type weak branch risk assessment index system of the power system by taking into account the volatility of new energy and the operation characteristics of direct current, so as to quantify the risks caused by different line outages to the system;

[0213] A comprehensive risk index calculation module, which is used to weight all risk assessment indexes to obtain a comprehensive risk index that comprehensively reflects system security and stability, so as to identify weak branches in the new-type power system.

[0214] In an optional implementation manner, the method for identifying weak branches in the new-type power system includes: a) constructing a new-type weak branch risk assessment index system for the power system composed of risk assessment indexes from the perspectives of system security and stability; b) determining the calculation methods of various risk assessment indexes in the new-type weak branch risk assessment index system of the power system by taking into account the volatility of new energy and the operation characteristics of direct current, and quantifying the risks caused by different line outages to the system; c) weighting all risk assessment indexes to obtain a comprehensive risk index that comprehensively reflects system security and stability, so as to identify weak branches in the new-type power system.

[0215] Among them, the risk assessment index system establishment module includes a voltage violation and line overload index calculation unit, a static security index calculation unit, a static power angle stability index calculation unit, a static voltage stability index calculation unit, and a static frequency stability index calculation unit.

[0216] In this embodiment, the voltage violation and line overload index calculation unit is used to calculate according to the following method:

[0217] Define the severity of voltage violation and the severity of line overload:

[0218]

[0219] In the formula, Sev(U i ) is the severity of node i voltage violation; Sev(Lij ) is the severity of line ij overload; are the upper and lower limits of the voltage amplitude of node i; U i is the most severe value of the voltage amplitude of node i during the fluctuation of new energy output; P ij,max is the transmission power limit value of line ij; P ij is the maximum value that the transmission power of line ij may appear with the fluctuation of new energy output; The node voltage violation index and the line overload index both take the most severe situation when defining the severity;

[0220] Obtain the risk R of system voltage violation caused by the outage of a line in the new power system VV and the line overload risk R LO are respectively:

[0221]

[0222] In the formula, n1 is the number of nodes in the new power system; n2 is the number of remaining lines after the outage of a line; P(U i ) is the probability of voltage violation; P(L i ) is the probability of line overload.

[0223] In this embodiment, the static security index calculation unit is used to calculate according to the following method:

[0224] The risk R of the new power system losing static security after the outage of a line SSA , as follows:

[0225] R SSA = R VV + R LO .

[0226] In this embodiment, the static frequency stability index calculation unit is used to calculate according to the following method:

[0227] Assume that there are K eigenvalues in the new power system with high proportion of new energy and AC-DC interconnection, and the kth eigenvalue is λ k = ξ k + jω k , linearize the functional relationship between the eigenvalues of the new power system and the output power of the fan at the reference operating point, and the following linear relationship between the two can be established:

[0228]

[0229] In the formula, W i represents the output power of the ith fan; w represents the number of fans; Δλ k represents the small change amount of the kth eigenvalue; ΔW iIt represents the small change in the output power of the i-th wind turbine; It represents the sensitivity of the k-th eigenvalue of the system to the output power of the i-th wind turbine, which can be solved numerically:

[0230]

[0231] A linear relationship between the system eigenvalue and the wind turbine output power is established through the above two formulas; the probability distribution of the wind turbine output power has been modeled by GMM, which is weighted by multiple Gaussian distributions with a mean of μ m , and a variance of Σ m . Since the Gaussian distribution still follows a Gaussian distribution after a linear transformation, each Gaussian sub-component of the system eigenvalue probability distribution should follow a Gaussian distribution with a mean of K2μ m + B2 and a variance of K2Σ m K2 T , where K2 and B2 are the coefficient matrices of the linear expressions obtained through the above calculation formulas. Thus, the cumulative distribution function expression of the m-th Gaussian sub-component is as follows:

[0232]

[0233] Then, through the total probability formula, the probability distribution function F(λ) expression of the eigenvalue λ of the new power system can be finally obtained as follows:

[0234]

[0235] where M is the number of Gaussian sub-components; ω m is the weight coefficient of each Gaussian sub-component;

[0236] When the real part of the eigenvalue is greater than 0, the system is statically frequency unstable. Thus, the probability P(SFS) of the system experiencing static frequency instability can be obtained from the probability distribution function expression of the eigenvalue of the new power system; when static frequency instability occurs, its severity Sev(SFS) is defined as 1. Furthermore, the static frequency instability risk R SFS of the new power system after a line is taken out of service can be defined as:

[0237] R SFS = P(SFS) × Sev(SFS).

[0238] In this embodiment, the static power angle stability index calculation unit is used to calculate according to the following method:

[0239] When a line in the new power system is taken out of service, the static power angle instability risk R SRAS of the new power system is:

[0240]

[0241] Wherein, n3 is the number of generators; P(K P,i ) is the probability that the static power angle stability reserve coefficient of the i-th generator is lower than 10%; Ser(K P,i ) is the severity of the static power angle instability of the i-th generator;

[0242] K P,i =(P max,i -P 0,i ) / P 0,i

[0243] Sev(K P,i )=(0.1 - K P,i,min ) / 0.1

[0244] Wherein, P max,i is the static stability power limit of the i-th generator; P 0,i is the output power of the i-th generator under normal operation; K P,i is the static power angle stability reserve coefficient of the i-th generator; K P,i,min is the minimum value that the static stability limit power of generator i may reach during the wind power fluctuation period.

[0245] In this embodiment, the static voltage stability index calculation unit is used to calculate according to the following method:

[0246] Define the static stability reserve coefficient of the node voltage as:

[0247] K U,i =(U 0,i -U cr,i ) / U cr,i

[0248] Wherein, K U,i is the static voltage stability reserve coefficient of the i-th node; U 0,i is the voltage amplitude of the i-th node under normal operation; U cr,i is the voltage instability critical point of the i-th node;

[0249] Use the stochastic AC-DC power flow calculation method to find the probability distribution of the voltage instability critical points of each node;

[0250] Through the probability distribution of the voltage instability critical points of each node and the static voltage stability reserve coefficient K U,i of the i-th node, solve the probability P(K U,i ) that the static voltage stability reserve coefficient of the i-th node in the new power system does not meet the operation requirements;

[0251] Define the severity function Ser(K U,i ) of the static voltage instability of the i-th node:

[0252] Sev(K U,i ) = (0.08 - K U,i,min ) / 0.08

[0253] where K U,i,min is the minimum value that the static voltage stability reserve coefficient may reach during the fluctuation of new energy output; thus, the static voltage instability risk R SVS caused by the outage of a line in the new power system can be defined as:

[0254]

[0255] In this embodiment, the comprehensive risk index calculation module is used to assign weights to the calculated values of all risk assessment indicators and perform weighted summation to obtain the calculated value of the comprehensive risk index of the comprehensive system security and stability. According to the magnitude of the calculated value of the comprehensive risk index, the weak branches in the new power system are identified, which specifically includes the following contents:

[0256] Normalize the calculation results of each risk assessment indicator and perform weighted summation to obtain the comprehensive risk index R c of the comprehensive system security and stability:

[0257]

[0258] where α1 to α6 are the weights of each risk assessment indicator; is the normalized node voltage violation index; is the normalized line overload index; is the normalized static security index; is the normalized static frequency stability index; is the normalized static power angle stability index; is the normalized static voltage stability index; among them, the weights α1 to α6 of each risk assessment indicator are calculated by using the comprehensive weighting method that combines subjective weighting and objective weighting, and the calculation method is as follows:

[0259] Obtain the subjective weight C1 of each risk assessment indicator based on the analytic hierarchy process, and obtain the objective weight C2 of each risk assessment indicator based on the entropy weight method; linearly combine the subjective weight C1 and the objective weight C2 to obtain the weight C = β1C1 + β2C2 of the comprehensive weighting method, where β1 and β2 are the comprehensive weight combination coefficients.

[0260] Example 3

[0261] Please refer to Figure 13 , Figure 13It is a schematic structural diagram of a new type of weak branch identification device for a power system disclosed in an embodiment of the present invention. Among them, Figure 13 The described device can be applied to a power system, such as for weak branch identification in a power system, and the embodiments of the present invention do not make limitations.

[0262] Such as Figure 13 As shown, the device may include a processor and a memory. Computer instructions are stored in the memory, and the processor is configured to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the method described in the above embodiments and can achieve the same technical effects as the above method.

[0263] The memory may include a computer system readable medium in the form of a volatile memory, such as a random access memory (RAM) and / or a cache memory. The device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the memory may be used to read and write a non-removable, non-volatile magnetic medium (commonly referred to as a "hard disk drive"). A program / utility having a set (at least one) of program modules may be stored in the memory, such as the operating system, one or more application programs, other program modules, and program data. The implementation of a network environment may be included in each or some combination of these examples. The program modules generally execute the functions and / or methods in the embodiments described in the present invention.

[0264] The processor executes various functional applications and data processing by running the programs stored in the memory, such as implementing the method provided in Embodiment 1 of the present invention.

[0265] Embodiment 4

[0266] Embodiment 4 of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the steps of the method described in the above embodiments and can achieve the same technical effects as the above method.

[0267] The computer storage medium of the embodiments of the present invention may adopt any combination of one or more computer-readable media. The computer-readable media may be computer-readable signal media or computer-readable storage media. The computer-readable storage media may be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, the computer-readable storage media may be any tangible medium that contains or stores a program, and this program may be used by or in combination with an instruction execution system, device, or component.

[0268] The computer-readable signal media may include data signals propagated in a baseband or as part of a carrier wave, which carry computer-readable program codes. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal media may also be any computer-readable media other than the computer-readable storage media, and this computer-readable media may send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or component.

[0269] The program codes contained on the computer-readable media may be transmitted by any appropriate media, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0270] The computer program codes for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program codes may be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0271] Of course, for the storage medium containing computer-executable instructions provided by the embodiments of the present invention, the computer-executable instructions are not limited to the above method operations, and can also execute relevant operations in the methods provided by any embodiments of the present invention.

[0272] The specific embodiments described above have further elaborated on the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A novel method for identifying weak branches in a power system, characterized in that: Including: Construct a new-type power system weak branch risk evaluation index system composed of system security risk assessment indexes and system stability risk assessment indexes; among them, the risk assessment indexes include node voltage over-limit indexes, line overload indexes, static security indexes, static frequency stability indexes, static power angle stability indexes, and static voltage stability indexes; Taking into account the volatility of new energy and the operation characteristics of direct current, determine the calculation algorithms of various risk assessment indexes in the new-type power system weak branch risk evaluation index system to quantify the risks caused by different line outages to the system; Assign weights to the calculated values of all risk assessment indexes and perform weighted summation to obtain the calculated value of the comprehensive risk index that synthesizes system security and stability. Identify the weak branches in the new-type power system according to the magnitude of the calculated value of the comprehensive risk index.

2. The novel weak branch identification method for a power system according to claim 1, characterized in that The calculation methods of the node voltage over-limit index and the line overload index are as follows: Define the voltage over-limit severity and the line overload severity: Sev(L i ) = (P ij - P ij,max ) / P ij,max Where Sev(U i ) is the severity of voltage violation at node i; Sev(L ij ) is the severity of overload of line ij; are the upper and lower limits of the voltage amplitude at node i; U i is the most severe value of the voltage amplitude at node i during the fluctuation of new energy output; P ij,max is the transmission power limit value of line ij; P ij is the maximum value that the transmission power of line ij may reach with the fluctuation of new energy output; Both the node voltage over-limit index and the line overload index take the most severe case when defining the severity; Obtain the risk \(R\) of system voltage violation caused by the outage of a line in the new power system VV and the risk \(R\) of line overload LO They are respectively as follows: Where, n1 is the number of nodes in the new power system; n2 is the number of remaining lines after one line is out of service; P(U i ) is the probability of voltage violation; P(L i ) is the probability of line overload.

3. The novel method for identifying weak branches in a power system according to claim 2, characterized in that: The calculation method of the static security index is as follows: Risk R of the system losing static security after a line outage in a new power system SSA , is as follows: R SSA = R VV + R LO 。 4. The novel method for identifying weak branches in a power system according to claim 1, wherein The calculation method of the static frequency stability index is as follows: Assume that the new power system with high proportion of new energy and AC / DC interconnection has K eigenvalues, where the kth eigenvalue is λ k =ξ k +jω k , linearizing the functional relationship between the new power system characteristic value and the wind turbine output power at the benchmark operating point, the linear relationship between the two can be established as follows: Where W i represents the output power of the i-th fan; w represents the number of fans; Δλ k Indicates the small change of the kth eigenvalue; ΔW i Indicates the slight change in the output power of the i-th wind turbine; The sensitivity of the kth eigenvalue of the system to the output power of the i-th wind turbine can be solved numerically: The linear relationship between the system eigenvalue and the wind turbine output power is established by the above two formulas; the probability distribution of the wind turbine output power has been modeled by GMM, which consists of multiple m , the variance is Σ m Since the Gaussian distribution still obeys the Gaussian distribution after linear transformation, each Gaussian subcomponent of the system eigenvalue probability distribution should obey the mean K2μ m +B2, with variance K2Σ m K2 T Gaussian distribution, where K2 and B2 are respectively obtained through the above The coefficient matrix of the linear expression is obtained by the calculation formula. Therefore, the cumulative distribution function expression of the m-th Gaussian sub-component is as follows: Then, through the total probability formula, the probability distribution function F(λ) expression of the eigenvalue λ of the new-type power system can be finally obtained as follows: where M is the number of Gaussian sub-components; ω m is the weight coefficient of each Gaussian sub-component; When the real part of the eigenvalue is greater than 0, the system static frequency is unstable. Therefore, the probability P(SFS) of the system static frequency instability can be obtained through the probability distribution function expression of the eigenvalue of the new power system. When static frequency instability occurs, its severity Sev(SFS) is defined as 1, and the risk R of system static frequency instability after a line in the new power system is out of operation is obtained. SFS It can be defined as: R SFS =P(SFS)×Sev(SFS)。 5. The novel method for identifying weak branches in a power system according to claim 1, characterized in that, The calculation method of the static power angle stability index is as follows: When a line in the new power system is out of operation, the static power angle instability risk R SRAS for: Where n3 is the number of generators; P(K P,i ) is the probability that the static power angle stability reserve coefficient of the i-th generator is less than 10%; Ser(K P,i ) is the severity of the static power angle instability of the i-th generator; K P,i =(P max,i -P 0,i ) / P 0,i Sev(K P,i ) = (0.1 - K P,i,min ) / 0.1 Where, P max,i is the static stability power limit of the i-th generator; P 0,i is the output power of the i-th generator under normal operation; K P,i is the static power angle stability reserve coefficient of the i-th generator; K P,i,min is the minimum value that the static stability limit power of generator i may reach during the wind turbine power fluctuation.

6. The novel method for identifying weak branches in a power system according to claim 1, characterized in that: The calculation method of the static voltage stability index is as follows: Define the static stability reserve coefficient of the node voltage as: K U,i = (U 0,i - U cr,i ) / U cr,i where K U,i is the static voltage stability reserve coefficient of the i-th node; U 0,i is the voltage amplitude of the ith node under normal operation; U cr,i is the critical point of voltage instability at the i-th node; Use the stochastic AC-DC power flow calculation method to obtain the probability distribution of the voltage instability critical points of each node; The probability distribution of the critical point of voltage instability at each node and the static voltage stability reserve coefficient K of the i-th node are used to calculate the voltage stability of the node. U,i , solve the probability P(K U,i ); Define the severity function of static voltage instability of the i-th node Ser(K U,i ): Sev(K U,i ) = (0.08 - K U,i,min ) / 0.08 where K U,i,min is the minimum value that the static voltage stability reserve coefficient may reach during the new energy output fluctuation period; Therefore, the static voltage instability risk R caused by the outage of a line in the new power system SVS can be defined as:

7. The novel method for identifying weak branches in a power system according to claim 1, characterized in that: Assign weights to the calculated values of all risk assessment indexes and perform weighted summation to obtain the calculated value of the comprehensive risk index that synthesizes system security and stability. Identify the weak branches in the new-type power system according to the magnitude of the calculated value of the comprehensive risk index, including: Normalize the calculation results of each risk assessment indicator and sum them up with weights to obtain the comprehensive risk indicator R that comprehensively reflects the system's security and stability c : where α1 to α6 are the weights of each risk assessment index; is the normalized node voltage violation index; is the normalized line overload index; is the normalized static security index; is the normalized static frequency stability index; is the normalized static power angle stability index; is the normalized static voltage stability index. Among them, the weights α1 to α6 of each risk assessment index are calculated by using a comprehensive weighting method that combines subjective weighting and objective weighting, and the calculation method is as follows: Obtain the subjective weight C1 of each risk assessment index based on the analytic hierarchy process, and obtain the objective weight C2 of each risk assessment index based on the entropy weight method; linearly combine the subjective weight C1 and the objective weight C2 to obtain the weight C of the comprehensive weighting method = β1C1 + β2C2, where β1 and β2 are the comprehensive weight combination coefficients.

8. A novel weak branch identification system for a power system, characterized in that, Including: A risk evaluation index system establishment module for constructing a new-type power system weak branch risk evaluation index system composed of system security risk assessment indexes and system stability risk assessment indexes; among them, the risk assessment indexes include node voltage over-limit indexes, line overload indexes, static security indexes, static frequency stability indexes, static power angle stability indexes, and static voltage stability indexes; A risk assessment index calculation module for taking into account the volatility of new energy and the operation characteristics of direct current, determining the calculation algorithms of various risk assessment indexes in the new-type power system weak branch risk evaluation index system to quantify the risks caused by different line outages to the system; A comprehensive risk index calculation module for assigning weights to the calculated values of all risk assessment indexes and performing weighted summation to obtain the calculated value of the comprehensive risk index that synthesizes system security and stability. Identify the weak branches in the new-type power system according to the magnitude of the calculated value of the comprehensive risk index.

9. The novel weak branch identification system for a power system according to claim 8, characterized in that The risk assessment index system establishment module includes a voltage violation line overload index calculation unit, and the voltage violation line overload index calculation unit is used to calculate according to the following method: Define the severity of voltage violation and the severity of line overload: Sev(L i )=(P ij -P ij,max ) / P ij,max where Sev(U i ) is the severity of voltage violation at node i; Sev(L ij ) is the severity of overload of line ij; are the upper and lower limits of the voltage amplitude at node i; U i is the most severe value of the voltage amplitude at node i during the fluctuation of new energy output; P ij,max is the transmission power limit value of line ij; P ij is the maximum value that the transmission power of line ij may appear with the fluctuation of new energy output; When defining the severity, both the node voltage violation index and the line overload index take the most severe case; Obtain the risk \(R\) of system voltage violation caused by the outage of a line in the new power system VV and the risk \(R\) of line overload LO respectively as follows: Where, n1 is the number of nodes in the new power system; n2 is the number of remaining lines after one line outage; P(U i ) is the probability of voltage violation; P(L i ) is the probability of line overload.

10. The novel power system weak branch identification system according to claim 9 is characterized in that: The risk assessment index system establishment module includes a static security index calculation unit, and the static security index calculation unit is used to calculate according to the following method: Risk R of the system losing static security after a line outage in a new power system SSA , as follows: R SSA = R VV + R LO .

11. The novel power system weak branch identification system according to claim 8, characterized in that: The risk assessment index system establishment module includes a static frequency stability index calculation unit, and the static frequency stability index calculation unit is used to calculate according to the following method: Assume that the new power system with high proportion of new energy and AC / DC interconnection has K eigenvalues, where the kth eigenvalue is λ k =ξ k +jω k , linearizing the functional relationship between the new power system characteristic value and the wind turbine output power at the benchmark operating point, the linear relationship between the two can be established as follows: Where W i represents the output power of the i-th fan; w represents the number of fans; Δλ k Indicates the small change of the kth eigenvalue; ΔW i Indicates the slight change in the output power of the i-th wind turbine; The sensitivity of the kth eigenvalue of the system to the output power of the i-th wind turbine can be solved numerically: A linear relationship between the system eigenvalue and the fan output power is established through the above two formulas; the probability distribution of the fan output power has been modeled by GMM, which is weighted by multiple Gaussian distributions with a mean of μ m , and a variance of Σ m . Since the Gaussian distribution still follows the Gaussian distribution after linear transformation, each Gaussian sub-component of the system eigenvalue probability distribution should follow a Gaussian distribution with a mean of K2μ m + B2 and a variance of K2Σ m K2 T , where K2 and B2 are the coefficient matrices of the linear expressions obtained through the above calculation formulas. Thus, the cumulative distribution function expression of the m-th Gaussian sub-component is as follows: Then, through the total probability formula, the probability distribution function F(λ) expression of the eigenvalue λ of the new power system can be finally obtained as follows: where M is the number of Gaussian subcomponents; ω m is the weight coefficient of each Gaussian subcomponent; When the real part of the eigenvalue is greater than 0, the system static frequency is unstable. Therefore, the probability P(SFS) of the system static frequency instability can be obtained through the probability distribution function expression of the eigenvalue of the new power system. When static frequency instability occurs, its severity Sev(SFS) is defined as 1, and the risk R of system static frequency instability after a line in the new power system is out of operation is obtained. SFS It can be defined as: R SFS = P(SFS) × Sev(SFS).

12. The novel weak branch identification system for a power system according to claim 8, characterized in that, The risk assessment index system establishment module includes a static power angle stability index calculation unit, and the static power angle stability index calculation unit is used to calculate according to the following method: When a line in the new power system is out of operation, the static power angle instability risk R SRAS for: Where n3 is the number of generators; P(K P,i ) is the probability that the static power angle stability reserve coefficient of the i-th generator is less than 10%; Ser(K P,i ) is the severity of the static power angle instability of the i-th generator; K P,i = (P max,i - P 0,i ) / P 0,i Sev(K P,i ) = (0.1 - K P,i,min ) / 0.1 Where P max,i is the static power limit of the i-th generator; P 0,i is the output power of the i-th generator under normal operation; K P,i is the static power angle stability reserve coefficient of the i-th generator; K P,i,min is the minimum value that the static limit power of generator i may reach during the wind turbine power fluctuation.

13. The novel weak branch identification system for a power system according to claim 8, characterized in that, The risk assessment index system establishment module includes a static voltage stability index calculation unit, and the static voltage stability index calculation unit is used to calculate according to the following method: Define the static stability reserve coefficient of the node voltage as: K U,i =(U 0,i -IN cr,i ) / IN cr,i where K U,i is the static voltage stability reserve coefficient of the i-th node; U 0,i is the voltage amplitude of the i-th node under normal operation; U cr,i is the voltage instability critical point of the i-th node; Use the stochastic AC-DC power flow calculation method to obtain the probability distribution of the voltage instability critical points of each node; The probability distribution of the critical point of voltage instability at each node and the static voltage stability reserve coefficient K of the i-th node are used to calculate the voltage stability of the node. U,i , solve the probability P(K U,i ); Define the severity function of static voltage instability of the i-th node Ser(K U,i ): Sev(K U,i ) = (0.08 - K U,i,min ) / 0.08 where K U,i,min is the minimum value that the static voltage stability reserve coefficient may reach during the new energy output fluctuation period; Therefore, the static voltage instability risk R caused by the outage of a line in the new power system is SVS It can be defined as:

14. The novel power system weak branch identification system according to claim 8, characterized in that: The comprehensive risk index calculation module is used to assign weights to the calculated values of all risk assessment indexes and perform weighted summation to obtain the calculated value of the comprehensive risk index of the comprehensive system security and stability. According to the magnitude of the calculated value of the comprehensive risk index, the weak branches in the new power system are identified, including: The calculation results of each risk assessment index are normalized and weighted summed to obtain the comprehensive risk index R of the comprehensive system security and stability. c : where α1 to α6 are the weights of each risk assessment index; is the normalized index of node voltage violation; is the normalized line overload index; is the normalized static security index; is the normalized static frequency stability index; is the normalized static power angle stability index; is the normalized static voltage stability index. Among them, the weights α1 to α6 of each risk assessment index are calculated by the comprehensive weighting method that combines subjective weighting and objective weighting, and the calculation method is as follows: Obtain the subjective weight C1 of each risk assessment index based on the analytic hierarchy process, and obtain the objective weight C2 of each risk assessment index based on the entropy weight method; linearly combine the subjective weight C1 and the objective weight C2 to obtain the weight C of the comprehensive weighting method = β1C1 + β2C2, where β1 and β2 are the comprehensive weight combination coefficients.

15. A novel device for identifying weak branches in a power system, characterized in that, It includes a processor and a memory. Computer instructions are stored in the memory, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the new power system weak branch identification method as described in any one of claims 1 to 7.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the program is executed by the processor, it implements the steps of the new power system weak branch identification method as described in any one of claims 1 to 7.

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