Power grid cascading failure development prediction method based on new energy access and terminal

Abstract

The invention discloses a new energy access-based power grid cascading failure development prediction method and terminal, and the method comprises the steps: determining the initial power flow data of a wind turbine generator set and a photovoltaic generator set through random power flow calculation, and calculating the safety degree and comprehensive power transfer degree of an initial fault power grid branch according to the initial power flow data; the safety degree and the comprehensive power transfer degree of the power grid branches serve as sample input values to be input into a fuzzy clustering algorithm, the power grid branches are grouped according to the size and combination of all indexes through the fuzzy clustering algorithm, and the branch corresponding to the highest clustering center index serves as a lower-level fault branch set for predicting fault development. Therefore, the safety degree and the comprehensive power transfer degree of the power grid branch under the initial fault serve as the basis for predicting the lower-level fault link, the lower-level fault branch set is determined in a clustering mode, the uncertainty and volatility of wind power and photovoltaic power are comprehensively considered, the actual operation condition is met, and meanwhile prediction rapidness and accuracy are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid security analysis, and in particular to a method and a terminal for predicting the development of power grid cascading failures based on new energy access. Background Art

[0002] In recent years, with the massive access of new energy sources to the power system, many power system blackouts have occurred frequently. These accidents show that cascading failures are still the main cause of blackouts. Power system cascading failures are caused by various source faults leading to large-scale power flow transfer, which in turn causes a series of branch overload tripping, and ultimately leads to blackouts. Therefore, before the fault propagation process, if the possible development path of the fault can be predicted quickly and accurately, it is of great significance to take effective control measures in time to prevent the occurrence of large-scale power outages.

[0003] At present, many experts and scholars in related fields have conducted in-depth research on the prediction of cascading failure development paths and have achieved certain results. However, the set of Nk expected accidents in the power system is huge, and the cascading failure propagation paths are diverse, which makes it difficult to improve the robustness of complex large systems to deal with Nk failures.

[0004] Moreover, previous cascading failure models were mostly studied based on traditional power systems, which makes it difficult to reflect the randomness and volatility of renewable energy output. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method and terminal for predicting the development of cascading failures in a power grid based on the access of new energy, which can quickly and accurately predict the development path of cascading failures when new energy is accessed.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for predicting the development of power grid cascading failures based on new energy access comprises the following steps:

[0008] Determine the initial power flow data of wind turbines and photovoltaic generators through random power flow calculation;

[0009] Calculate the safety degree and comprehensive power transfer degree of the initial fault power grid branch according to the initial power flow data;

[0010] The safety degree and comprehensive power transfer degree of power grid branches are input as sample input values ​​into the fuzzy clustering algorithm. The power grid branches are grouped according to the size and combination of each indicator using the fuzzy clustering algorithm. The branch with the highest cluster center indicator is used as the lower-level fault branch set for predicting fault development.

[0011] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0012] A power grid cascading failure development prediction terminal based on new energy access includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0013] Determine the initial power flow data of wind turbines and photovoltaic generators through random power flow calculation;

[0014] Calculate the safety degree and comprehensive power transfer degree of the initial fault power grid branch according to the initial power flow data;

[0015] The safety degree and comprehensive power transfer degree of power grid branches are input as sample input values ​​into the fuzzy clustering algorithm. The power grid branches are grouped according to the size and combination of each indicator using the fuzzy clustering algorithm. The branch with the highest cluster center indicator is used as the lower-level fault branch set for predicting fault development.

[0016] The beneficial effects of the present invention are: determining the initial power flow data of wind turbines and photovoltaic generators through random power flow calculation, calculating the safety and comprehensive power transfer of the initial fault power grid branch according to the initial power flow data; inputting the safety and comprehensive power transfer of the power grid branch as sample input values ​​into the fuzzy clustering algorithm, using the fuzzy clustering algorithm to group the power grid branches according to the size and combination of each index, and taking the branch of a class corresponding to the highest cluster center index as the lower-level fault branch set for predicting the development of the fault. Therefore, the safety and comprehensive power transfer of the power grid branch under the initial fault are used as the basis for predicting the lower-level fault link, and the lower-level fault branch set is determined by clustering. Compared with the existing chain fault development path prediction method, it comprehensively considers the uncertainty and volatility of wind power and photovoltaics, conforms to the actual operation situation, and has both rapidity and accuracy, providing intuitive decision-making basis for dispatchers, and making up for the shortcomings of the current chain fault development path prediction method. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a method for predicting the development of power grid cascading failures based on new energy access according to an embodiment of the present invention;

[0018] Figure 2 A schematic diagram of a power grid cascading failure development prediction terminal based on new energy access according to an embodiment of the present invention;

[0019] Figure 3 The operating characteristic diagram of the quasi-quadrilateral impedance relay used in the embodiment of the present invention;

[0020] Figure 4 This is a flowchart of cascading failure development path prediction according to an embodiment of the present invention.

[0021] Description of labels:

[0022] 1. A terminal for predicting the development of power grid cascading failures based on new energy access; 2. Memory; 3. Processor. DETAILED DESCRIPTION

[0023] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in conjunction with the implementation modes and the accompanying drawings.

[0024] Please refer to Figure 1 The embodiment of the present invention provides a method for predicting the development of power grid cascading failures based on new energy access, comprising the steps of:

[0025] Determine the initial power flow data of wind turbines and photovoltaic generators through random power flow calculation;

[0026] Calculate the safety degree and comprehensive power transfer degree of the initial fault power grid branch according to the initial power flow data;

[0027] The safety degree and comprehensive power transfer degree of power grid branches are input as sample input values ​​into the fuzzy clustering algorithm. The power grid branches are grouped according to the size and combination of each indicator using the fuzzy clustering algorithm. The branch with the highest cluster center indicator is used as the lower-level fault branch set for predicting fault development.

[0028] From the above description, it can be seen that the beneficial effects of the present invention are: determining the initial power flow data of wind turbines and photovoltaic generators through random power flow calculation, calculating the safety and comprehensive power transfer of the initial fault power grid branch according to the initial power flow data; inputting the safety and comprehensive power transfer of the power grid branch as sample input values ​​into the fuzzy clustering algorithm, using the fuzzy clustering algorithm to group the power grid branches according to the size and combination of each indicator, and taking the branch with the highest cluster center indicator as the lower-level fault branch set for predicting the development of the fault. Therefore, the safety and comprehensive power transfer of the power grid branch under the initial fault are used as the basis for predicting the lower-level fault link, and the lower-level fault branch set is determined by clustering. Compared with the existing chain fault development path prediction method, it comprehensively considers the uncertainty and volatility of wind power and photovoltaics, conforms to the actual operation situation, and has both rapidity and accuracy, providing intuitive decision-making basis for dispatchers, and making up for the shortcomings of the current chain fault development path prediction method.

[0029] Furthermore, the initial power flow data of the wind turbine and photovoltaic generator are determined by random power flow calculation, including:

[0030] Determine a first probability density function of wind speed in the wind farm and a second probability density function of light intensity in the photovoltaic field, randomly sample the wind speed in the wind farm according to the first probability density function, and randomly sample the light intensity in the photovoltaic field according to the second probability density function;

[0031] The power flow calculation is performed in sequence according to the wind speed of the wind farm and the light intensity of the photovoltaic field obtained by multiple random samplings, and the initial power flow data is recorded.

[0032] From the above description, it can be seen that the random power flow method is used to introduce wind power and photovoltaic generators, and the Monte Carlo random power flow calculation model is used to provide initial power flow data for the cascading failure path prediction model. In this way, the random power flow method can be introduced to dynamically reveal the power flow changes of the branch, so as to fully consider the uncertainty of power.

[0033] Further, calculating the safety degree of the initial fault power grid branch according to the initial power flow data includes:

[0034] When the power grid branch is disconnected due to an initial fault, calculating the measured resistance and the measured reactance according to the initial power flow data;

[0035] Calculate the branch safety degree D of the remaining branch l zl :D zl =D Rl +D Xl , where D Rl Indicates the distance between the measured resistor and the protection action boundary, D Xl Indicates the distance between the measured reactance and the protection action boundary.

[0036] From the above description, it can be seen that by defining the branch safety of the remaining branches after the initial fault occurs, it can essentially reflect the degree of influence of each branch of the system after the branch fault is disconnected. The smaller the value, the more likely it is to fail due to disturbance, which is convenient for predicting the subsequent fault development.

[0037] Further, calculating the initial fault comprehensive power transfer degree according to the initial power flow data includes:

[0038] Combine the power flow transfer change and node voltage change to calculate the comprehensive power transfer degree F l :F l =V l ′×U l ′, where V l ' represents the normalized power flow transfer change value, U l ' represents the normalized node voltage change value;

[0039]

[0040] Where P kl represents the active power flow of branch l after branch k fails, P l represents the original power flow of branch l before branch k fails, P max Indicates the maximum operating power of the branch; Ul represents the total node voltage offset after a fault occurs in branch k; represents the voltage of node y after a fault occurs in branch k, Represents the voltage of node y before the branch fault.

[0041] From the above description, it can be seen that since the change in active power mainly depends on the system flow and the change in reactive power mainly depends on the system node voltage, the flow transfer and node voltage change are further considered comprehensively to define the comprehensive power transfer degree, which can comprehensively consider the changes in active power and reactive power of the system after a chain failure occurs in the power grid, intuitively reflect the operating status of the power grid after the fault, and help to further screen out vulnerable branches.

[0042] Furthermore, a type of branch corresponding to the highest cluster center index is used as a lower-level fault branch set for predicting fault development, and further includes:

[0043] The power flow equation of the power grid is decomposed by eigenvalues, and the power flow Jacobian determinant is established. Whether the power grid is unstable is determined by judging the singularity of the power flow Jacobian determinant. If the power grid is unstable, the search for the lower-level fault branch set is stopped.

[0044] From the above description, it can be seen that by performing eigenvalue decomposition on the power grid flow equation and using the singularity of the Jacobian matrix, it is possible to determine whether the system is unstable. If it is unstable, the fault mode search is terminated, and the fault development can be predicted based on the development law of cascading fault accidents, thereby improving the prediction reliability.

[0045] Please refer to Figure 2 Another embodiment of the present invention provides a power grid cascading failure development prediction terminal based on new energy access, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, it implements the various steps of the above-mentioned power grid cascading failure development prediction method based on new energy access.

[0046] The above-mentioned method and terminal for predicting the development of cascading failures in a power grid based on the access of new energy sources are suitable for quickly and accurately predicting the development path of cascading failures when new energy sources are accessed. The following is an explanation through specific implementation methods:

[0047] Please refer to Figure 1 , Embodiment 1 of the present invention is:

[0048] A method for predicting the development of power grid cascading failures based on new energy access comprises the following steps:

[0049] S1. Determine the initial power flow data of wind turbines and photovoltaic generators through random power flow calculation.

[0050] S11. Determine a first probability density function of wind speed in the wind farm and a second probability density function of light intensity in the photovoltaic field, randomly sample the wind speed in the wind farm according to the first probability density function, and randomly sample the light intensity in the photovoltaic field according to the second probability density function.

[0051] Specifically, in the new power system, the proportion of renewable energy output is increasing, so the random power flow method is introduced to dynamically reveal the power flow changes of the branch and fully consider the uncertainty of power. The random power flow method is used to introduce wind power and photovoltaic generators, and the Monte Carlo random power flow calculation model is used to provide initial power flow data for the cascading fault path prediction model.

[0052] In this embodiment, the wind speed of the wind farm obeys the Weibull distribution, and its first probability density function is:

[0053]

[0054] Where k and c represent the shape factor and scale factor of the Weibull distribution, respectively, and v represents the wind speed of the wind farm.

[0055] The functional relationship between the wind turbine output power P(v) and wind speed v can be expressed as:

[0056]

[0057] In the formula, v a 、v b and v c Respectively represent the cut-in wind speed, rated wind speed and cut-out wind speed, P 1 Indicates the rated output power of the wind farm.

[0058] In this embodiment, the illumination intensity of the photovoltaic field obeys the Beta distribution, and its second probability density function is:

[0059]

[0060] In the formula, α and β represent the shape factors of the Beta distribution based on historical data, r and r max represent the actual value and maximum value of the light intensity respectively, and Γ(·) represents the Gamma function.

[0061] Factors such as light intensity and ambient temperature affect photovoltaic output power, and its output power function P(r) is:

[0062] P(r)=rAλ

[0063] Where A represents the total area of ​​PV panels, and λ represents the photoelectric conversion rate.

[0064] S12. Calculate the power flow in sequence according to the wind speed of the wind farm and the light intensity of the photovoltaic field obtained by multiple random samplings, and record the initial power flow data.

[0065] Specifically, the probability density functions of wind speed in wind farms and light intensity in photovoltaic fields are taken as input parameters, and multiple samplings are performed to obtain specific values ​​of wind power and photovoltaics. The obtained random samples are input into the power system model to perform flow calculations and operation simulations. During the simulation process, the output power of wind power and photovoltaics will be taken according to the random samples, and flow calculations will be performed for each value taken. At this time, the voltage, branch current, power and other information of each node are recorded. The sampling and flow calculations are repeated, and multiple simulations are performed to ensure the stability of the data. After completing all simulations, the simulation results are statistically analyzed, and statistical indicators such as the mean, variance, and probability distribution of node voltage and power can be calculated.

[0066] S2. Calculate the safety degree and comprehensive power transfer degree of the initial fault power grid branch according to the initial power flow data.

[0067] Specifically, due to the uncertainty of new energy access, there is also uncertainty about the overload of each branch in the system, which further leads to the possibility of misoperation of protection in a branch under normal operation of the power grid. Therefore, this embodiment considers the grid cascading failure scenario of new energy access, and predicts the development path of the fault based on safety, power flow transfer index, and voltage offset index.

[0068] S21. Calculate the safety degree of the initial fault power grid branch according to the initial power flow data.

[0069] In this embodiment, a cascading fault in the power grid refers to an initial fault in a system branch. At this time, the power grid current is readjusted, causing the remaining branches to trip due to the electrical quantity entering the action zone, resulting in a new branch failure. This embodiment uses a quasi-quadrilateral impedance relay as a branch backup protection device. For its action characteristics, please refer to Figure 3 Among them, X 1 =(X set cotα 4 -R set )tanα 3 / (1+cotα 4 tanα 3 ); α 1 —α 4 is the set offset angle of each line segment; R set , X set They are the setting values ​​of resistance and reactance respectively, which are calculated from the information recorded in step S2.

[0070] The measured impedance Z m The measured resistance R m and measure the reactance Xm The distance from the protection action boundary is calculated separately, where the measured resistance R m Denoted as D R , measure reactance X m Denoted as D X . Then we can get:

[0071]

[0072] When the power grid branch is disconnected due to an initial fault, calculating the measured resistance and the measured reactance according to the initial power flow data;

[0073] Calculate the branch safety degree D of the remaining branch l zl :D zl =D Rl +D Xl , where D Rl Indicates the distance between the measured resistor and the protection action boundary, D Xl Indicates the distance between the measured reactance and the protection action boundary. zl = 0, Z m Just at the border of causing cascading failures; when D zl <0, Z m Outside the boundary of causing cascading failures; D zl >0,Z m Within the boundary of causing cascading failures. zl It essentially reflects the degree to which each branch of the system is affected by the disconnection of branch k due to a fault. The smaller the value, the more likely it is to fail due to disturbance.

[0074] S22. Calculate the initial fault comprehensive power transfer degree according to the initial power flow data.

[0075] From the perspective of system operation, after a cascading failure occurs in the power grid, the active and reactive power of the system will change accordingly. Since the change in active power mainly depends on the system flow and the change in reactive power mainly depends on the system node voltage, the flow transfer and node voltage change are further considered comprehensively.

[0076] Therefore, the comprehensive power transfer degree F is calculated by combining the power flow transfer change and the node voltage change. l :F l =V l ′×U l ′, where V l ' represents the normalized power flow transfer change value, U l ' represents the normalized node voltage change value;

[0077]

[0078] Where P klrepresents the active power flow of branch l after branch k fails, P l represents the original power flow of branch l before branch k fails, P max Indicates the maximum operating power of the branch; U l represents the total node voltage deviation after the branch k fails; U l y represents the voltage of node y after a fault occurs in branch k, U 0 y Represents the voltage of node y before the branch fault.

[0079] S3. Input the safety degree and comprehensive power transfer degree of the power grid branches as sample input values ​​into the fuzzy clustering algorithm, use the fuzzy clustering algorithm to group the power grid branches according to the size and combination of each indicator, and use the branch type corresponding to the highest cluster center indicator as the lower-level fault branch set for predicting fault development.

[0080] Specifically, in this embodiment, the branch safety degree and the comprehensive power transfer degree are used as sample input values, and the fuzzy clustering algorithm is used to group the branches according to the size and combination of each index. The branch with the highest cluster center index is used as the subsequent fault branch set, which can quickly and accurately predict the possible development path of the chain failure. Based on the above calculation model and prediction method, please refer to the chain failure development path prediction flow chart Figure 4 It should be noted that according to the development law of cascading failure accidents, the eigenvalue decomposition of the power system flow equation is performed and the singularity of the Jacobian matrix is ​​used to determine whether the system is unstable. If it is unstable, the fault mode search ends.

[0081] In this embodiment, the singularity of the power flow Jacobian matrix is ​​expressed by the power flow Jacobian determinant being equal to zero, and the equation is:

[0082]

[0083] Where f(x) represents the power flow Jacobian, σ represents the parameter of the node injection power change, and e represents the vector of the system node injection power change direction; |f x (x)| represents the Jacobian determinant of the power flow, if f x (x) has a non-zero solution, then f x (x) Strange.

[0084] The power flow equation of the power grid is decomposed by eigenvalues, and the power flow Jacobian determinant is established. The singularity of the power flow Jacobian determinant is judged to determine whether the power grid is unstable. If the power grid is unstable, the search for the lower-level fault branch set is stopped.

[0085] Therefore, this embodiment proposes a new method for rapid prediction of the development path of chain failures in view of the chain failure phenomenon caused by potential branch failures in the new energy power system, defines the branch safety degree and the comprehensive power transfer degree as the basis for predicting the lower-level fault link, and uses the fuzzy clustering algorithm to take the branch with the highest cluster center index as the lower-level fault branch set, which significantly reduces the calculation workload. Compared with the existing chain failure development path prediction method, it comprehensively considers the uncertainty and volatility of wind power and photovoltaic power, conforms to the actual operation situation, and has both rapidity and accuracy, providing dispatchers with an intuitive decision-making basis, making up for the shortcomings of the current chain failure development path prediction method.

[0086] Please refer to Figure 2 , Embodiment 2 of the present invention is:

[0087] A power grid cascading failure development prediction terminal 1 based on new energy access includes a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, each step of a power grid cascading failure development prediction method based on new energy access in embodiment 1 is implemented.

[0088] In summary, the present invention provides a method and terminal for predicting the development of power grid chain failures based on new energy access. The method determines the initial power flow data of wind turbines and photovoltaic generators through random power flow calculation, and calculates the safety and comprehensive power transfer of the initial fault power grid branch according to the initial power flow data; the safety and comprehensive power transfer of the power grid branch are input as sample input values ​​into the fuzzy clustering algorithm, and the power grid branches are grouped according to the size and combination of each indicator using the fuzzy clustering algorithm, and the branch type corresponding to the highest cluster center indicator is used as the lower-level fault branch set for predicting the development of the fault. Therefore, the safety and comprehensive power transfer of the power grid branch under the initial fault are used as the basis for predicting the lower-level fault link, and the lower-level fault branch set is determined by clustering, which comprehensively considers the uncertainty and volatility of wind power and photovoltaic power, conforms to the actual operation situation, and has both rapidity and accuracy in prediction.

[0089] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for predicting the development of power grid cascading failures based on new energy access, characterized in that: Includes steps: Determine the initial power flow data of wind turbines and photovoltaic generators through random power flow calculation; Calculate the safety degree and comprehensive power transfer degree of the initial fault power grid branch according to the initial power flow data; The safety degree and comprehensive power transfer degree of power grid branches are input as sample input values ​​into the fuzzy clustering algorithm. The power grid branches are grouped according to the size and combination of each indicator using the fuzzy clustering algorithm. The branch with the highest cluster center indicator is used as the lower-level fault branch set for predicting fault development.

2. A method for predicting the development of power grid cascading failures based on new energy access according to claim 1, characterized in that: The initial power flow data of wind turbines and photovoltaic generators are determined by random power flow calculation, including: Determine a first probability density function of wind speed in the wind farm and a second probability density function of light intensity in the photovoltaic field, randomly sample the wind speed in the wind farm according to the first probability density function, and randomly sample the light intensity in the photovoltaic field according to the second probability density function; The power flow calculation is performed in sequence according to the wind speed of the wind farm and the light intensity of the photovoltaic field obtained by multiple random samplings, and the initial power flow data is recorded.

3. A method for predicting the development of power grid cascading failures based on new energy access according to claim 1, characterized in that: Calculating the safety degree of the initial fault power grid branch according to the initial power flow data includes: When the power grid branch is disconnected due to an initial fault, calculating the measured resistance and the measured reactance according to the initial power flow data; Calculate the branch safety degree D of the remaining branch l zl :D zl =D Rl +D Xl , where D Rl Indicates the distance between the measuring resistor and the protection action boundary, D Xl Indicates the distance between the measured reactance and the protection action boundary.

4. A method for predicting the development of power grid cascading failures based on new energy access according to claim 3, characterized in that: Calculating the initial fault comprehensive power transfer degree according to the initial power flow data includes: Combine the power flow transfer change and node voltage change to calculate the comprehensive power transfer degree F l : F l =V l ′×U l ′, where V l ' represents the normalized power flow transfer change value, U l ' represents the normalized node voltage change value; Where P kl represents the active power flow of branch l after branch k fails, P l represents the original power flow of branch l before branch k fails, P max Indicates the maximum operating power of the branch; U l represents the total node voltage offset after a fault occurs in branch k; represents the voltage of node y after a fault occurs in branch k, Represents the voltage of node y before the branch fault.

5. A method for predicting the development of power grid cascading failures based on new energy access according to claim 1, characterized in that: The branch with the highest cluster center index is used as the lower fault branch set for predicting the development of faults, and also includes: The power flow equation of the power grid is decomposed by eigenvalues, and the power flow Jacobian determinant is established. Whether the power grid is unstable is determined by judging the singularity of the power flow Jacobian determinant. If the power grid is unstable, the search for the lower-level fault branch set is stopped.

6. A power grid cascading failure development prediction terminal based on new energy access, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: Determine the initial power flow data of wind turbines and photovoltaic generators through random power flow calculation; Calculate the safety degree and comprehensive power transfer degree of the initial fault power grid branch according to the initial power flow data; The safety degree and comprehensive power transfer degree of power grid branches are input as sample input values ​​into the fuzzy clustering algorithm. The power grid branches are grouped according to the size and combination of each indicator using the fuzzy clustering algorithm. The branch with the highest cluster center indicator is used as the lower-level fault branch set for predicting fault development.

7. A power grid cascading failure development prediction terminal based on new energy access according to claim 6, characterized in that: The initial power flow data of wind turbines and photovoltaic generators are determined by random power flow calculation, including: Determine a first probability density function of wind speed in the wind farm and a second probability density function of light intensity in the photovoltaic field, randomly sample the wind speed in the wind farm according to the first probability density function, and randomly sample the light intensity in the photovoltaic field according to the second probability density function; The power flow calculation is performed in sequence according to the wind speed of the wind farm and the light intensity of the photovoltaic field obtained by multiple random samplings, and the initial power flow data is recorded.

8. A power grid cascading failure development prediction terminal based on new energy access according to claim 6, characterized in that: Calculating the safety degree of the initial fault power grid branch according to the initial power flow data includes: When the power grid branch is disconnected due to an initial fault, calculating the measured resistance and the measured reactance according to the initial power flow data; Calculate the branch safety degree D of the remaining branch l zl :D zl =D Rl +D Xl , where D Rl Indicates the distance between the measuring resistor and the protection action boundary, D Xl Indicates the distance between the measured reactance and the protection action boundary.

9. A power grid cascading failure development prediction terminal based on new energy access according to claim 8, characterized in that: Calculating the initial fault comprehensive power transfer degree according to the initial power flow data includes: Combine the power flow transfer change and node voltage change to calculate the comprehensive power transfer degree F l : F l =V l ′×U l ′, where V l ' represents the normalized power flow transfer change value, U l ' represents the normalized node voltage change value; Where P kl represents the active power flow of branch l after branch k fails, P l represents the original power flow of branch l before branch k fails, P max Indicates the maximum operating power of the branch; U l represents the total node voltage offset after a fault occurs in branch k; represents the voltage of node y after a fault occurs in branch k, Represents the voltage of node y before the branch fault.

10. A power grid cascading failure development prediction terminal based on new energy access according to claim 6, characterized in that: The branch with the highest cluster center index is used as the lower fault branch set for predicting the development of faults, and also includes: The power flow equation of the power grid is decomposed by eigenvalues, and the power flow Jacobian determinant is established. Whether the power grid is unstable is determined by judging the singularity of the power flow Jacobian determinant. If the power grid is unstable, the search for the lower-level fault branch set is stopped.

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