A power system potential risk source identification method and system based on power flow entropy

Abstract

This invention discloses a method and system for identifying potential risk sources in power systems based on power flow entropy. First, time-series simulation is used to obtain the power flow distribution at various time points within a certain time period. Second, using the power flow calculation results from the time-series simulation as initial values, the Newton-GMRES method is used to perform N-1 power flow calculations on the entire system, obtaining the N-1 power flow for each node throughout the entire time period. Next, combining the concept of energy entropy, a power system risk source assessment index based on power flow distribution entropy is constructed. Finally, a comprehensive assessment model for power system risk sources considering both power flow transfer entropy and power flow distribution entropy is proposed. This invention calculates the power system power flow entropy and compares the entropy values ​​of different branches and nodes to accurately locate potential risks in the system, solving the problems of topological complexity and inconsistent measurement indicators inherent in traditional methods based on complex network theory, thus improving the accuracy of identifying potential risk sources in power systems.

Description

Technical Field

[0001] This invention relates to the technical field of identification methods for potential risk sources in power systems, specifically to the establishment of mixed-integer linear programming models, determination of constraints, Krylov subspace concept, Newton-GMRES method for calculating N-1 power flow, calculation of power system energy entropy, identification of potential risk sources in power systems, and assessment of the comprehensive vulnerability of components. Background Technology

[0002] In recent years, with the rapid development of new energy sources and their high grid integration, the operational pattern of the power system has undergone significant changes. Clean energy sources such as wind power and photovoltaics are gradually replacing traditional thermal power units, and the proportion of power electronic equipment in the system has increased substantially, resulting in a power grid characterized by both high penetration of new energy sources and a high proportion of power electronics. Against this backdrop, the power system faces a series of new challenges in operation, including supply and demand imbalances caused by fluctuations in new energy sources, the risk of cascading failures triggered by branch overloads and protection actions, the uncertainty brought about by power flow shifts, and the threat of large-scale blackouts to grid security. Research shows that some vulnerable links have an amplifying effect in fault propagation; therefore, accurately identifying potential risk sources has become a key issue in ensuring the safe and stable operation of the power system.

[0003] Regarding the identification of potential risk sources in power systems, the power flow entropy method has the advantages of clear physical meaning, ability to quantify the concentration of power flow distribution and system disorder. However, traditional methods often only target a single disturbance scenario, neglect the differences in the shock resistance of components, and the assessment results are easily affected by changes in operating conditions, resulting in the disadvantage of not being able to fully reflect the degree of risk. Summary of the Invention

[0004] The technical problem to be solved by this invention is the identification of potential risk sources in power systems based on power flow entropy, and the identification of key vulnerable components that may cause cascading failures and large-scale power outages during power grid operation.

[0005] This invention improves upon existing technologies by combining nodal power flow distribution entropy and branch power flow transfer entropy to jointly assess the power flow clustering and dispersion of the system under disturbance. The improved method not only considers the dual mechanisms of nodes and branches, enhancing the comprehensiveness of risk source identification, but also maintains high adaptability under various operating conditions, effectively solving the problems of one-sided assessment results and insufficient ranking rationality in traditional methods.

[0006] To achieve the above objectives, the present invention proposes a power system potential risk source identification method based on power flow entropy, which mainly considers the following two aspects: First, it establishes a power flow distribution and transfer entropy model under power system disturbances to comprehensively reflect the risk level of nodes and branches in the propagation of cascading faults; Second, it combines an improved power flow entropy evaluation mechanism to quantitatively rank and identify potential risk sources, thereby providing a scientific basis for the safety assessment and control strategy of power grid operation.

[0007] The technical solution adopted in this invention is: a method for identifying potential risk sources in a power system based on power flow entropy, comprising the following steps: Step 1: Establish the objective function for the time-series power system production simulation and add relevant constraints; Step 2: Based on the Krylov subspace concept, the Newton-GMRES method is used to solve the N-1 power flow of the system to obtain the N-1 power flow of the entire system at each time period; Step 3: Define the energy entropy of the power system and establish a potential risk source identification model for the power system based on the concept of energy entropy; Step 4: Based on the component power flow transfer entropy and component power flow distribution entropy, establish a comprehensive assessment model for potential risk sources in the power system, including a component consequence assessment model, a component impact assessment model, and a power flow entropy-based model.

[0008] This invention also provides a power system potential risk source identification system based on power flow entropy, comprising: The time-series production simulation module is used to establish the objective function for power system time-series production simulation and add relevant constraints to obtain the time-series power flow distribution. The N-1 power flow calculation module is used to solve the N-1 power flow of the system based on the Krylov subspace concept and combined with the Newton-GMRES method, so as to obtain the N-1 power flow of the whole system at each time period; The risk source identification module is used to define the energy entropy of the power system and establish a potential risk source identification model for the power system based on the concept of energy entropy. The comprehensive assessment module is used to establish a comprehensive assessment model for potential risk sources in the power system, including a component consequence assessment model, a component impact assessment model, and a power flow entropy-based model, based on the component power flow transfer entropy and the component power flow distribution entropy.

[0009] Compared with the prior art, the technical solution adopted in this invention has the following beneficial effects: 1. This invention includes a method for calculating the power flow distribution entropy at nodes, a quantitative model for the power flow transfer entropy of branches, a construction of a comprehensive vulnerability index for components, and a mechanism for ranking and identifying potential risk sources.

[0010] 2. This invention combines power flow distribution entropy and power flow transfer entropy to quantitatively identify potential risk sources in power systems under time-series simulation. This solves the problem that traditional methods rely solely on topology or a single indicator, resulting in biased assessment results and insufficient discriminativeness. It achieves accurate identification of potential risk sources in power systems and improves the scientific rigor and effectiveness of power grid security assessment. Attached Figure Description

[0011] Figure 1 This is a flowchart of the power system potential risk source identification method based on power flow entropy in this embodiment.

[0012] Figure 2 This is a model diagram of the IEEE 30 system in this embodiment.

[0013] Figure 3 This is a histogram of the overall vulnerability index of IEEE30 system nodes in this embodiment.

[0014] Figure 4 This is a distribution diagram of IEEE30 system nodes R1-R2 in this embodiment.

[0015] Figure 5 This is a distribution diagram of the comprehensive vulnerability index of IEEE30 system nodes in this embodiment. Detailed Implementation

[0016] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art fall within the scope defined by the appended claims.

[0017] like Figure 1 As shown, this invention proposes a method for identifying potential risk sources in power systems based on power flow entropy. This method includes the following steps: Step 1: Establish the objective function for time-series production simulation, and add power balance constraints, reserve constraints, output and ramp-up constraints. Solve the above mixed-integer linear programming model and obtain the system power flow distribution over a certain time period. Step 2: Based on the Krylov subspace concept, the Newton-GMRES method is used to solve the N-1 power flow of the system to obtain the N-1 power flow of the entire system at each time period; Step 3: Define the power system energy entropy based on the concept and characteristics of information entropy, and establish a potential risk source identification model for the power system based on the power system energy entropy; Step 4: Based on the branch power flow transfer entropy and branch power flow distribution entropy, establish a comprehensive assessment model for potential risk sources in the power system, including a component consequence assessment model, a component impact assessment model, and a power flow entropy-based model. Furthermore, in step 1, the mixed-integer linear programming model of the power system and the constraints are established, and the specific methods are as follows: 1) Objective function

[0018]

[0019] In the formula: F is the objective function; T is the total running time; G is the total number of generators; The cost of generating electricity from the generator; The starting cost of the generator; This refers to the downtime cost of the generator.

[0020] Mode This is the objective function for time-series simulation, used to solve for the system operating state with the minimum total power generation cost.

[0021] 2) Power balance constraints

[0022]

[0023] In the formula, Let i be the active power output of generator i during time period t; and denoted as , respectively, are the absorption capacity of photovoltaic unit j and wind farm k during time period t; D(t) is the load size of the system during time period t.

[0024] Mode The power balance constraint represents the requirement that the power output of the power source and the power output of the load should remain balanced at all times in the system.

[0025] 3) Alternate constraints

[0026]

[0027] In the formula, u i (t) is a 0-1 variable representing the start-up state of unit i, which is 1 when the generator is running and 0 when it is stopped; α is the upper limit of generator i's output; α is the system's maximum reserve demand coefficient.

[0028] Mode As a backup constraint, the maximum output of the operating generator set is required to be higher than the maximum backup capacity of the system.

[0029] 4) Generator output and climbing constraints

[0030]

[0031]

[0032]

[0033]

[0034]

[0035] in, This represents the lower limit of the output power of generator i; and These are the uphill and downhill ramp rates of generator i, respectively; and These refer to the capacities of the system's wind farm and photovoltaic units, respectively.

[0036] Mode - To constrain the output of the generators, limiting the output of each generator to within the system's allowable range; (Equation) and To constrain generator ramping, the generator ramping power must not exceed the system's allowable range.

[0037] 5) Current flow grid constraint

[0038]

[0039]

[0040]

[0041] Among them, P i (t) represents the power injected into the node; G ik B is the branch conductance between node i and node k; ik θ is the susceptance of the branch between node i and node k; ik (t) represents the branch phase difference between node i and node k; P ik (t) represents the power transmitted in the branch between node i and node k; Minimum power transmitted in the branch between node i and node k; The maximum power transmitted in the branch between node i and node k; U i (t) represents the voltage at node i; This is the lower limit of the voltage at node i; This represents the upper limit of the voltage at node i.

[0042] Mode For node power balance constraints, it means that the power flowing into and out of a node is equal to the sum of the power flowing through all branches connected to the node; Equation Branch power constraints indicate that the power flowing through a branch is within the system's allowable range; Equation For node voltage constraints, the node voltage must be kept within the allowable range of the system.

[0043] Furthermore, in step 2, the Krylov subspace concept and the Newton-GMRES method are specifically described as follows: 1) Krylov subspace concept

[0044]

[0045]

[0046]

[0047] Where J is the Jacobian matrix. b is a constant vector. L m For another m-dimensional subspace; K m 3D Krylov subspace; It is a linear span.

[0048] Mode This is a system of linear equations, represented by the conventional Newton-Raphson method. The power flow calculation results can be obtained by solving these equations. For the Galerkin condition, when applying the Krylov subspace method, the residual r should be such that... m This condition is satisfied; Equation A Krylov subspace is defined, which represents the Krylov subspace generated by the initial residual r0 and the matrix J.

[0049] 2) Newton-GMRES method

[0050] S1: Initialization: Given an initial guess x0, external iteration convergence tolerance ε, and maximum internal iteration tolerance threshold. Backtracking parameters And the upper and lower bounds of the backtracking step scaling factor: 0 < θ f < θ g If the value is less than 1, set the external iteration count k = 0; S2: Check for external convergence: Calculate the nonlinear residual F(x) k ), that is, the power imbalance, if If the algorithm converges, the output x will be obtained.k If the solution is correct, then proceed to S3; otherwise, go to S3. S3: Form the Jacobian matrix J for the k-th outer iteration. k ; S4: Set l=0, calculate the initial residual r0:

[0051]

[0052] S5: Calculate the Euclidean norm of r0:

[0053] S6: Normalize the residual vector of the l-th iteration, and update the iteration count l = l + 1:

[0054] S7: Update residual r l :

[0055] S8: Perform orthogonalization, setting i=1, for i=1 to l, calculate:

[0056]

[0057] S9: Calculate the Euclidean norm of the new residual:

[0058] S10: If h l+1 If the value is greater than 0, return to S6; otherwise, proceed to S11. S11: Solve the least squares problem and update the approximate solution:

[0059]

[0060]

[0061] Among them, y l H is the objective function; l Let e1 be the upper Hessenberg matrix; e1 be the standard basis vectors; Q be the lower Hessenberg matrix. l For q iAn orthogonal basis matrix consisting of (i=1, 2,…, l).

[0062] S12: Output the final solution x=x of the GMRES method. l ; S13: Order ; Where s k This is a correction amount; This is the approximate correction amount obtained from the inner iteration. η k For iteration tolerance; S14: If

[0063]

[0064] Then proceed to S15; otherwise proceed to S16.

[0065] S15: Select a scaling factor Update correction amount s k+1 = θs k Update iteration tolerance η k+1 =1 − θ(1 − η k ), switch to S5;

[0066] S16: Accept the correction amount and update the solution vector x k+1 = x k + s k Set k = k + 1, and go to S2.

[0067] Mode For the calculation of the initial residual; Equation The Euclidean norm is used to calculate the initial residuals; (Equation) This indicates that the residuals are normalized to obtain an orthogonal basis; the formula... By using the Jacobi matrix J k With orthogonal basis vector q l Multiply to update the residual vector; Equation , This indicates orthogonalization of the residual vector from 1 to l; Equation This indicates the Euclidean norm of the updated residuals; the formula is used to calculate the Euclidean norm. Let be the objective function of the least squares method; Equation To update the state variable x l ;Mode Indicates that it is derived from the orthogonal basis q i Construct an orthogonal basis matrix Q l ;Mode This means substituting the updated solution into the formula for calculating the nonlinear residual F. If the norm of the nonlinear residual is greater than 1, then... This indicates the correction amount s k The correction did not result in a sufficient decrease in residuals, and the correction amount needs to be updated.

[0068] Furthermore, in step 3, the definition of power system energy entropy and the construction of the power system potential risk source identification model are specifically implemented as follows: 1) Power system energy entropy:

[0069]

[0070] In the formula, H sys η is the energy entropy of the power system. i Where i is the energy distribution rate of system component i; N is the total number of system components; It is the natural logarithm.

[0071] Mode and The energy entropy of a power system is defined, describing the dynamic equilibrium of stability within the system through the entropy change process of energy distribution. The more uniform the energy, the greater the entropy value, and the more stable the system. When a disturbance occurs in the system, the entropy value decreases. If the external environment provides an increase in entropy that cannot balance the decrease in entropy, it will lead to system collapse.

[0072] 2) Power System Risk Source Identification Model

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] In the formula, P in (t) represents the power flow of element i connected to node n during time period t; ΔE in (t) represents the power flow change of component i connected to node n during the time period t; M is the number of system components; ΔE n (t) represents the power flow impact of node n on element i; H n (t) represents the power flow distribution entropy of the system; η in(t) represents the power flow impact rate of component i; Let n be the node vulnerability index.

[0079] Mode - A model for identifying risk sources in power systems; This describes the power flow relationship between the previous time period and the current time period through the component; (Equation) The power flow changes of all components connected to node n from the previous time period to the next time period are quantified; Equation This describes the proportion of power flow variation of element i among all elements connected to node n; Equation The power flow distribution entropy of n nodes is defined, reflecting the distribution of power flow impacts in the system at different time periods; Equation A risk source index for a node is defined, representing the probability that node n will become a potential risk source within time period t. The larger the value, the more likely node n is to become a source of risk.

[0080] Furthermore, in step 4, the specific method for constructing the quantitative indicators of power system operation mode is as follows: 1) Component consequence vulnerability assessment indicators

[0081]

[0082]

[0083]

[0084]

[0085] Among them, P ki (t) represents the power flow through element k after element i is disconnected during time period t; ΔP ki (t) represents the power flow increment transferred from component i to component k after component i is disconnected; γ ki (t) represents the power flow transfer impact rate of element i on element k; R is the power flow transfer entropy of element i; 1i (t) represents the vulnerability index of the component consequences.

[0086] Mode - Defined the component consequence vulnerability assessment index; formula This describes the power flow transferred from component i to component k after component i exits; Equation The power flow transfer impact rate of element i on element k is defined; Equation The power flow transfer entropy of element i is defined; Equation The component consequence vulnerability index, R, is defined. 1i The higher the value, the greater the impact of the disconnection, making the fault more likely to occur.

[0087] 2) Component impact vulnerability assessment indicators

[0088]

[0089]

[0090]

[0091] in, H represents the power flow distribution entropy of element n on element i during time period t; i(g, l) (t) represents the power flow distribution entropy of the load disturbance in element i for the generator-load node pair; g represents the g-th generator node; l represents the l-th load node; R 2i (t) represents the component impact vulnerability assessment index; G and L are the number of generator nodes and load nodes, respectively.

[0092] Mode - A comprehensive assessment index for power system risk sources was defined; formula This describes the power flow distribution entropy of element i connected to node n; Equation The equation describes the power flow distribution entropy of the "generator-load" node pair at element i; A component impact vulnerability assessment index is defined. The larger the index value, the greater the power flow fluctuation of component i when generator output changes or load disturbances occur, and the more likely it is to become a risk source. 3) Comprehensive vulnerability assessment model for potential risk sources in power systems

[0093]

[0094]

[0095] In the formula, For comprehensive vulnerability assessment indicators.

[0096] Mode Combined with component consequence vulnerability index Component impact vulnerability index This characterizes the potential risk of element i in the current time period. (Equation) By calculating the potential risk source index of a component for the current period and comparing it with other indicators, it is possible to identify the component with the highest potential risk in the system at that time and take appropriate measures to prevent failures. The comprehensive vulnerability assessment indicators were normalized.

[0097] This invention proposes a method for identifying potential risk sources in power systems based on power flow entropy, using the formula... To evaluate the objective, the mixed-integer linear programming model for power system operation is established as follows: The system constraints are determined as follows: - The Krylov subspace concept is expressed as follows: -Mode The Newton-GMRES method consists of steps S1-S16 in step 2, where the energy entropy of the power system is defined as equation [equation missing]. and The power system risk source identification model is as follows: - The component consequence vulnerability assessment index is: - The component impact vulnerability assessment index is: - The comprehensive assessment index for potential risk sources in the power system is: .

[0098] Case Analysis

[0099] To verify the reliability and accuracy of the method proposed in the invention, the following methods were employed: Figure 2 The IEEE 30-node system shown is being deployed for verification. The system comprises 12 generators: 7 synchronous generators, 2 photovoltaic units, and 3 wind turbines. System unit parameters are shown in Table 1.

[0100] Table 1 Generator Set Parameters

[0101] G1 550 G8 90 G2 256 W1 80 G3 245 W2 80 G5 180 W3 80 G6 283 PV1 100 G7 216 PV2 100

[0102] A time-series production simulation was performed on the IEEE 30 system, using the power flow data at that moment as the initial value for the N-1 power flow calculation. The Newton-GMRES method from step 2 was applied for the calculation. The calculation results were then used in steps 3 and 4 to rank the vulnerability of all system components and nodes at one given moment using the power flow entropy-based power system potential risk source identification model. The 10 nodes and components with the highest vulnerability indices were selected and sorted from highest to lowest, as shown in Tables 2 and 3, respectively. The comprehensive vulnerability index of each node is as follows: Figures 3-5 As shown.

[0103] Table 2 Comparison of Vulnerable Nodes

[0104] 1 6 1.00 0.95 0.92 2 10 0.85 0.88 0.88 3 4 0.72 0.82 0.80 4 2 0.64 0.78 0.75 5 1 0.53 0.72 0.68 6 8 0.45 0.68 0.60 7 9 0.38 0.62 0.55 8 7 0.29 0.58 0.45 9 5 0.21 0.52 0.36 10 3 0.12 0.45 0.25

[0105] Table 3 Comparison of fragile components

[0106] 1 1-2 1.00 0.98 0.95 2 2-4 0.88 0.95 0.88 3 2-5 0.76 0.92 0.78 4 2-6 0.67 0.88 0.72 5 4-6 0.58 0.85 0.65 6 6-7 0.49 0.82 0.56 7 6-8 0.42 0.78 0.50 8 6-9 0.34 0.75 0.42 9 6-10 0.27 0.70 0.35 10 9-10 0.18 0.65 0.26

[0107] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying potential risk sources in a power system based on power flow entropy, characterized in that, Includes the following steps: Step 1: Establish the objective function for the time-series power system production simulation and add relevant constraints; Step 2: Based on the Krylov subspace concept, the Newton-GMRES method is used to solve the N-1 power flow of the system to obtain the N-1 power flow of the entire system at each time period; Step 3: Define the energy entropy of the power system and establish a potential risk source identification model for the power system based on the concept of energy entropy; Step 4: Based on the component power flow transfer entropy and component power flow distribution entropy, establish a comprehensive assessment model for potential risk sources in the power system, including a component consequence assessment model, a component impact assessment model, and a power flow entropy-based model.

2. The method for identifying potential risk sources in a power system based on power flow entropy according to claim 1, characterized in that, In step 1, the objective function and constraints are as follows: Objective function: ， In the formula: F is the objective function; T is the total running time; G is the total number of generators; The cost of generating electricity from the generator; The starting cost of the generator; The cost of generator downtime; Power balance constraints: ， In the formula, Let i be the active power output of generator i during time period t; and These represent the absorption capacity of photovoltaic unit j and wind farm k during time period t, respectively. Let be the system load during time period t; Alternative constraints: ， In the formula, This is a 0-1 variable representing the startup state of unit i; it is 1 when the generator is running and 0 when it is shut down. This represents the upper limit of the output power of generator i; This is the maximum reserve requirement factor for the system; Generator output and climbing constraints: ， ， ， ， ， in, This represents the lower limit of the output power of generator i; and These are the uphill and downhill ramp rates of generator i, respectively; and These refer to the capacities of the system's wind farm and photovoltaic units, respectively. Trendline space frame constraints: ， ， ， Among them, P i (t) represents the power injected into the node; G ik B is the branch conductance between node i and node k; ik θ is the susceptance of the branch between node i and node k; ik (t) represents the branch phase difference between node i and node k; P ik (t) represents the power transmitted in the branch between node i and node k; Minimum power transmitted in the branch between node i and node k; The maximum power transmitted in the branch between node i and node k; U i (t) represents the voltage at node i; This is the lower limit of the voltage at node i; This represents the upper limit of the voltage at node i.

3. The method for identifying potential risk sources in a power system based on power flow entropy according to claim 1, characterized in that: In step 2, the Krylov subspace concept is specifically defined as follows: ， ， ， Where J is the Jacobian matrix. b is a constant vector. L m For another m-dimensional subspace; K m 3D Krylov subspace; It is a linear span.

4. The method for identifying potential risk sources in a power system based on power flow entropy according to claim 1, characterized in that: Step 2, the Newton-GMRES method specifically includes: S1: Initialization: Given an initial guess x0, external iteration convergence tolerance ε, and maximum internal iteration tolerance threshold. Backtracking parameters And the upper and lower bounds of the backtracking step scaling factor: 0 < θ f < θ g If the value is less than 1, set the external iteration count k = 0; S2: Check for external convergence: Calculate the nonlinear residual F(x) k ),like If the algorithm converges, the output x will be obtained. k If the solution is correct, then proceed to S3; otherwise, go to S3. S3: Form the Jacobian matrix J for the k-th outer iteration. k ; S4: Set l=0, calculate the initial residual r0: ， S5: Calculate the Euclidean norm of r0: ， S6: Normalize the residual vector of the l-th iteration, and update the iteration count l = l + 1: ， S7: Update residual r l : ， S8: Perform orthogonalization, setting i=1, for i=1 to l, calculate: ， ， S9: Calculate the Euclidean norm of the new residual: ， S10: If h l+1 If the value is greater than 0, return to S6; otherwise, proceed to S11. S11: Solve the least squares problem and update the approximate solution: ， ， ， Among them, y l H is the objective function; l Let e1 be the upper Hessenberg matrix; e1 be the standard basis vectors; Q be the lower Hessenberg matrix. l For q i An orthogonal basis matrix consisting of (i=1, 2, …,l); S12: Output the final solution x=x of the GMRES method. l ; S13: Order ; Where s k Step size; This is the approximate correction amount obtained from the inner iteration. η k For iteration tolerance; S14: If ， Then proceed to S15; otherwise proceed to S16. S15: Select a scaling factor Update correction amount s k+1 = θs k Update iteration tolerance η k+1 = 1 −θ(1 − η k ), switch to S5; S16: Accept the correction and update the solution vector x k+1 = x k + s k Set k = k + 1, and go to S2.

5. The method for identifying potential risk sources in a power system based on power flow entropy according to claim 1, characterized in that, In step 3, the energy entropy of the power system is defined as follows: ， ， In the formula, H sys η is the energy entropy of the power system. i Where i is the energy distribution rate of system component i; N is the total number of system components; It is the natural logarithm.

6. The method for identifying potential risk sources in a power system based on power flow entropy according to claim 1, characterized in that, In step 3, the risk source identification model is as follows: ， ， ， ， ， In the formula, P in (t) represents the power flow of element i connected to node n during time period t; ΔE in (t) represents the power flow change of element i connected to node n during the time period t; M is the number of system branches; ΔE n (t) represents the power flow impact of node n on element i; H n (t) represents the power flow distribution entropy of the system; η in (t) represents the power flow impact rate of component i; Let n be the node vulnerability index.

7. The method for identifying potential risk sources in a power system based on power flow entropy according to claim 1, characterized in that, In step 4, the component consequence vulnerability assessment index is: ， ， ， ， Among them, P ki (t) represents the power flow through element k after element i is disconnected during time period t; ΔP ki (t) represents the power flow increment transferred from component i to component k after component i is disconnected; γ ki (t) represents the power flow transfer impact rate of element i on element k; R is the power flow transfer entropy of element i; 1i (t) represents the vulnerability index of the component consequences.

8. The method for identifying potential risk sources in a power system based on power flow entropy according to claim 1, characterized in that, In step 4, the component impact vulnerability assessment index is: ， ， ， in, H represents the power flow distribution entropy of element n on element i during time period t; i(g, l) (t) represents the power flow distribution entropy of the load disturbance in element i for the "generator-load" node pair; g is the g-th generator node; l is the l-th load node; R 2i (t) represents the component impact vulnerability assessment index; G and L are the number of generator nodes and load nodes, respectively.

9. The method for identifying potential risk sources in a power system based on power flow entropy according to claim 1, characterized in that, In step 4, the comprehensive vulnerability assessment model for components is as follows: ， In the formula, R i For comprehensive vulnerability assessment indicators.

10. A power system potential risk source identification system based on power flow entropy, using the method described in any one of claims 1-9, characterized in that, include: The time-series production simulation module is used to establish the objective function for power system time-series production simulation and add relevant constraints to obtain the time-series power flow distribution. The N-1 power flow calculation module is used to solve the N-1 power flow of the system based on the Krylov subspace concept and combined with the Newton-GMRES method, so as to obtain the N-1 power flow of the whole system at each time period; The risk source identification module is used to define the energy entropy of the power system and establish a potential risk source identification model for the power system based on the concept of energy entropy. The comprehensive assessment module is used to establish a comprehensive assessment model for potential risk sources in the power system, including a component consequence assessment model, a component impact assessment model, and a power flow entropy-based model, based on the component power flow transfer entropy and the component power flow distribution entropy.

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