A method, device, medium and equipment for synchronous simulation of multiple time intervals of power grid

Abstract

The present application discloses a method, device, medium and equipment for synchronous simulation of multiple time intervals of a power grid. The time interval to be simulated is divided into several small time sub-intervals. The initial value of each interval is predicted by multi-dimensional interpolation and swarm optimization tracking prediction methods. Based on the predicted initial value, all sub-intervals can synchronously implement dynamic simulation. The simulation result is transmitted in each sub-interval through a ring network. According to the predicted initial value and the simulation result of the previous interval, the correction amount of the initial value can be obtained, the predicted initial value is corrected, and the dynamic simulation is re-performed until the predicted initial value of all intervals is consistent with the simulation result of the previous interval, and the simulation results of all intervals can be output. It is possible to implement dynamic simulation of power grids synchronously in multiple time intervals, and the predicted initial value can be quickly corrected and converged to a stable simulation result through iteration, thereby improving the efficiency of dynamic simulation.

Description

Technical Field

[0001] The present invention relates to the field of operation and control technology of power systems, and in particular to a method, device, medium and equipment for synchronous simulation of multiple time intervals of a power grid. Background Art

[0002] Grid dynamic simulation is a powerful means to evaluate the operating status of the grid. Grid dynamic simulation has been widely used in many aspects such as grid planning, operation and control. Through grid dynamic simulation, operators can calculate whether the grid can remain stable under a given disturbance and what kind of stability control measures need to be taken. This information is very important reference information for grid planning, operation and control. Grid dynamic simulation needs to be deduced step by step from a given initial state to obtain the process of grid change over time. This deduction process is interlocking and carried out step by step. The state of the previous moment must be known before the state of the next moment can be deduced. Therefore, the process of grid dynamic simulation in the prior art cannot realize simultaneous or parallel calculation at multiple moments, and it takes a lot of time to perform long-term dynamic grid simulation. Summary of the invention

[0003] In order to solve the above problems, an embodiment of the present invention proposes a method, device, medium and equipment for synchronous simulation of multiple time intervals of a power grid, which divides the time interval to be simulated into several small time sub-intervals, and performs dynamic simulation of multiple time intervals synchronously, thereby greatly saving the time of dynamic simulation of the power grid.

[0004] An embodiment of the present invention provides a method for synchronous simulation of multiple time intervals of a power grid, the method comprising:

[0005] Calculating a differential algebraic equation system of the power grid according to the time variables of the power grid, and dividing the time interval to be simulated into a plurality of sub-intervals;

[0006] According to the initial value of the time interval, the initial value of each sub-interval is predicted by using a multi-dimensional interpolation prediction method and a swarm optimization tracking prediction method;

[0007] Simulating each subinterval separately according to the differential algebraic equation group and the initial value of each subinterval to obtain the simulation result of each subinterval;

[0008] The simulation result of each sub-interval is transmitted through the ring network formed by each sub-interval to obtain the boundary value corresponding to each sub-interval;

[0009] The correction amount is calculated according to the initial value of each sub-interval and the corresponding boundary value, and the initial value of each sub-interval is corrected;

[0010] According to the corrected initial value, each sub-interval is re-simulated until the boundary value corresponding to each interval obtained by simulation is the same as the initial value of simulation, and the simulation result is output.

[0011] As an improvement of the above solution, the differential algebraic equations for the power grid simulation are calculated according to the time variables of the power grid, and the time interval to be simulated is divided into several sub-intervals, specifically including:

[0012] Calculating a differential algebraic equation group for dynamic simulation of the power grid according to a time variable of the power grid, wherein the differential algebraic equation group includes differential equations and algebraic equations;

[0013] Dividing the time interval of the power grid to be simulated into a plurality of sub-intervals according to a preset time length;

[0014] The time variables include state variables x(t), algebraic variables y(t) and control variables u(t), and the differential equation is: The algebraic equation is: 0=g(x(t), y(t), u(t)), where f and g represent the algebraic expressions of the differential equation and the algebraic equation respectively.

[0015] Furthermore, the method of predicting the initial value of each sub-interval according to the initial value of the time interval by using a multi-dimensional interpolation prediction method and a swarm optimization tracking prediction method specifically includes:

[0016] Inserting a number of arrangement points in each subinterval according to a preset relative position;

[0017] The prediction factor W is calculated each time according to the tracking prediction method of swarm optimization. j+1 ;

[0018] The initial value of the time interval is used as the initial value of the first subinterval, and for any subinterval of the plurality of subintervals, the initial value of each subinterval is calculated by using a tensor product according to the initial value of the first subinterval and the distribution of the time variable;

[0019] Among them, the prediction factor W each time j+1 =W j +L(r)·(W j -W j-1 )+L′(r)·(W j -W j-1 ), L(r) is the Legendre polynomial, L′(r) is the first-order derivative of the Legendre polynomial; the initial value of each subinterval p x(t0) is the initial value of the state variable in the initial value of the first sub-interval, is the initial value of subinterval p, h is the interval length of the subinterval, W is the prediction factor of subinterval p, n is the number of subintervals, p is the integral dimension of the subinterval, q is the number of arranged points in subinterval p, i n is the interpolation level of the nth state variable x(n), is the interpolation set of the nth state variable x(n).

[0020] As a preferred solution, the step of simulating each sub-interval separately according to the differential algebraic equations and the initial value of each sub-interval to obtain the simulation result of each sub-interval specifically includes:

[0021] Calculate the Omega function coefficients and property conditions of the arrangement points on each subinterval;

[0022] Calculate the time integral of the coefficient of the omega function according to the property conditions;

[0023] According to the Omega function coefficients, the state variable polynomial and the algebraic variable polynomial of each subinterval, the state variable and the algebraic variable of each subinterval are calculated as simulation results;

[0024] Among them, the Omega function coefficient is The property conditions are: The state variable polynomial for subinterval p is The polynomial of the algebraic variable in the subinterval p is The time integral of the coefficients of the omega function is Lagrange polynomials x p,r ,y p,r , and u p are the values ​​of the state variable, algebraic variable and control variable of the rth arrangement point in the subinterval p. The number of arrangement points on the subinterval p is 3, which are τ p,1 ,τ p,2 and τ p,3 , δ r,q represents the Kronecker delta, τ p,q The relative positions of the arrangement points are in the range of [0,1], and the control variable u(t) of the differential algebraic equation system remains constant in each subinterval.

[0025] Preferably, the ring network formed by each sub-interval transmits the simulation result of each sub-interval to obtain the boundary value corresponding to each sub-interval, specifically including:

[0026] Each sub-interval is arranged in chronological order to form a ring network, and each sub-interval in the ring network communicates with adjacent sub-intervals through the ring network;

[0027] The boundary value of the previous subinterval is used as the communication data R(z), and normalized to obtain the normalized data P(z). Through the bidirectional mapping relationship, the planned data P(z) is converted into binary data S(z);

[0028] The key K(T) is dynamically generated through the current system clock, and the encrypted data Q(Z) is obtained based on the Pony-ARM core technology;

[0029] The boundary value of the previous sub-interval is passed to the next sub-interval through the encrypted data Q(Z). The next sub-interval obtains the encrypted data Q(Z), and decrypts the encrypted data according to the key K(T) to obtain the decrypted data S(Z) as the boundary value corresponding to the next sub-interval.

[0030] in, R(z) and P(z) are the information data before and after normalization, respectively. e is the natural index. T is the system clock time, T0 is the starting time, Remainder

[0031] Furthermore, the calculation of the correction amount according to the initial value of each sub-interval and the corresponding boundary value, and correction of the initial value of each sub-interval, specifically includes:

[0032] Solve the first-order differential equation according to the initial value of each sub-interval and the corresponding boundary value to obtain the correction value;

[0033] The initial value of each sub-interval is corrected according to the correction amount;

[0034] Among them, the first-order differential equation is R(z) is the variable value at the end of a time interval obtained by synchronous simulation of several time intervals, and is transmitted to each interval through a ring information path. is the initial value of the variable at the end of the interval, ΔR(z) is the correction value of the simulation result, ΔR′(z) is the first-order derivative of ΔR(z), T h is the period of the correction process, is the time constant, K p is the proportional coefficient of the correction amount.

[0035] As a preferred solution, the method of re-simulating each sub-interval according to the corrected initial value until the boundary value corresponding to each interval obtained by simulation is the same as the initial value of simulation, and outputting the simulation result specifically includes:

[0036] According to the obtained correction value, the initial value of each sub-interval is corrected, and each sub-interval is simulated again according to the corrected initial value to obtain the updated boundary value of each sub-interval, and the simulated boundary value at the end of the time interval is compared with the corrected initial value again;

[0037] If the revised initial value is different from the boundary value after simulation, the revised value is calculated, and the initial value is revised again, and each sub-interval is simulated again according to the revised initial value, and the updated boundary value of each sub-interval is obtained again, until the obtained boundary value is the same as the revised initial value;

[0038] If the corrected initial value is the same as the boundary value after simulation, the boundary value after simulation is output as the simulation result.

[0039] The embodiment of the present invention further provides a power grid multiple time interval synchronization simulation device, the device comprising:

[0040] A partitioning module, used for calculating a differential algebraic equation system of the power grid according to the time variables of the power grid, and dividing the time interval to be simulated into a plurality of sub-intervals;

[0041] A prediction module, used to predict the initial value of each sub-interval according to the initial value of the time interval by using a multi-dimensional interpolation prediction method and a swarm optimization tracking prediction method;

[0042] A simulation module, used for separately simulating each sub-interval according to the differential algebraic equation group and the initial value of each sub-interval, and obtaining the simulation result of each sub-interval;

[0043] An information transmission module is used to transmit the simulation result of each sub-interval through the ring network formed by each sub-interval to obtain the boundary value corresponding to each sub-interval;

[0044] A correction module, used for calculating a correction amount according to an initial value of each sub-interval and a corresponding boundary value, and correcting the initial value of each sub-interval;

[0045] The control module is used to re-simulate each sub-interval according to the corrected initial value until the boundary value corresponding to each interval obtained by simulation is the same as the initial value of simulation, and output the simulation result.

[0046] Furthermore, the partition module is specifically used for:

[0047] Calculating a differential algebraic equation system of the power grid according to the time variables of the power grid, and dividing the time interval to be simulated into a plurality of sub-intervals;

[0048] According to the initial value of the time interval, the initial value of each sub-interval is predicted by using a multi-dimensional interpolation prediction method and a swarm optimization tracking prediction method;

[0049] Simulating each subinterval separately according to the differential algebraic equation group and the initial value of each subinterval to obtain the simulation result of each subinterval;

[0050] The simulation result of each sub-interval is transmitted through the ring network formed by each sub-interval to obtain the boundary value corresponding to each sub-interval;

[0051] The correction amount is calculated according to the initial value of each sub-interval and the corresponding boundary value, and the initial value of each sub-interval is corrected;

[0052] According to the corrected initial value, each sub-interval is re-simulated until the boundary value corresponding to each interval obtained by simulation is the same as the initial value of simulation, and the simulation result is output.

[0053] Preferably, the prediction module is specifically used for:

[0054] Calculating a differential algebraic equation group for dynamic simulation of the power grid according to a time variable of the power grid, wherein the differential algebraic equation group includes differential equations and algebraic equations;

[0055] Dividing the time interval of the power grid to be simulated into a plurality of sub-intervals according to a preset time length;

[0056] The time variables include state variables x(t), algebraic variables y(t) and control variables u(t), and the differential equation is: The algebraic equation is: 0=g(x(t), y(t), u(t)).

[0057] Preferably, the prediction module is specifically used for:

[0058] Inserting a number of arrangement points in each subinterval according to a preset relative position;

[0059] The prediction factor W is calculated each time according to the tracking prediction method of swarm optimization. j+1 ;

[0060] The initial value of the time interval is used as the initial value of the first subinterval, and for any subinterval of the plurality of subintervals, the initial value of each subinterval is calculated by using a tensor product according to the initial value of the first subinterval and the distribution of the time variable;

[0061] Among them, the prediction factor W each time j+1 =W j +L(r)·(W j -W j-1 )+L′(r)·(W j -W j-1), L(r) is the Legendre polynomial, L′(r) is the first-order derivative of the Legendre polynomial; the initial value of each subinterval p x(t0) is the initial value of the state variable in the initial value of the first sub-interval, is the initial value of subinterval p, h is the interval length of the subinterval, W is the prediction factor of subinterval p, n is the number of subintervals, p is the integral dimension of the subinterval, q is the number of arranged points in subinterval p, i n is the interpolation level of the nth state variable x(n), is the interpolation set of the nth state variable x(n).

[0062] Preferably, the simulation module is specifically used for:

[0063] Calculate the Omega function coefficients and property conditions of the arrangement points on each subinterval;

[0064] Calculate the time integral of the coefficient of the omega function according to the property conditions;

[0065] According to the Omega function coefficients, the state variable polynomial and the algebraic variable polynomial of each subinterval, the state variable and the algebraic variable of each subinterval are calculated as simulation results;

[0066] Among them, the Omega function coefficient is The property conditions are: The state variable polynomial for subinterval p is The polynomial of the algebraic variable in the subinterval p is The time integral of the coefficients of the omega function is Lagrange polynomials x p,r ,y p,r , and u p are the values ​​of the state variable, algebraic variable and control variable of the rth arrangement point in the subinterval p. The number of arrangement points on the subinterval p is 3, which are τ p,1 ,τ p,2 and τ p,3 , δ r,q represents the Kronecker delta, τ p,q The relative positions of the arrangement points are in the range of [0,1], and the control variable u(t) of the differential algebraic equation system remains constant in each subinterval.

[0067] Preferably, the information transmission module is specifically used for:

[0068] Each sub-interval is arranged in chronological order to form a ring network, and each sub-interval in the ring network communicates with adjacent sub-intervals through the ring network;

[0069] The boundary value of the previous subinterval is used as the communication data R(z), and normalized to obtain the normalized data P(z). Through the bidirectional mapping relationship, the planned data P(z) is converted into binary data S(z);

[0070] The key K(T) is dynamically generated through the current system clock, and the encrypted data Q(Z) is obtained based on the Pony-ARM core technology;

[0071] The boundary value of the previous sub-interval is passed to the next sub-interval through the encrypted data Q(Z). The next sub-interval obtains the encrypted data Q(Z), and decrypts the encrypted data according to the key K(T) to obtain the decrypted data S(Z) as the boundary value corresponding to the next sub-interval.

[0072] in, R(z) and P(z) are the information data before and after normalization, respectively. e is the natural index. T is the system clock time, T0 is the starting time, Remainder

[0073] Preferably, the correction module is specifically used for:

[0074] Solve the first-order differential equation according to the initial value of each sub-interval and the corresponding boundary value to obtain the correction value;

[0075] The initial value of each sub-interval is corrected according to the correction amount;

[0076] Among them, the first-order differential equation is R(z) is the variable value at the end of a time interval obtained by synchronous simulation of several time intervals, and is transmitted to each interval through a ring information path. is the initial value of the variable at the end of the interval, ΔR(z) is the correction value of the simulation result, ΔR′(z) is the first-order derivative of ΔR(z), T h is the period of the correction process, is the time constant, K p is the proportional coefficient of the correction amount.

[0077] Preferably, the control module is specifically used for:

[0078] According to the obtained correction value, the initial value of each sub-interval is corrected, and each sub-interval is simulated again according to the corrected initial value to obtain the updated boundary value of each sub-interval, and the simulated boundary value at the end of the time interval is compared with the corrected initial value again;

[0079] If the revised initial value is different from the boundary value after simulation, the revised value is calculated, and the initial value is revised again, and each sub-interval is simulated again according to the revised initial value, and the updated boundary value of each sub-interval is obtained again, until the obtained boundary value is the same as the revised initial value;

[0080] If the corrected initial value is the same as the boundary value after simulation, the boundary value after simulation is output as the simulation result.

[0081] Another embodiment of the present invention provides a storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the power grid multiple time interval synchronous simulation method described in any one of the above-mentioned invention embodiments.

[0082] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the power grid multiple time interval synchronous simulation method described in any one of the above-mentioned embodiments of the invention.

[0083] Compared with the prior art, the present application provides a method, device, medium and equipment for synchronous simulation of multiple time intervals of a power grid, which divides the time interval to be simulated into several small time sub-intervals, and predicts the initial value of each interval through multi-dimensional interpolation and swarm optimization tracking prediction methods. Based on the predicted initial value, all sub-intervals can synchronously implement dynamic simulation. The simulation result is transmitted in each sub-interval through a ring network. According to the predicted initial value and the simulation result of the previous interval, the correction amount of the initial value can be obtained, the predicted initial value is corrected, and the dynamic simulation is performed again until the predicted initial value of all intervals is consistent with the simulation result of the previous interval, and the simulation results of all intervals can be output. It is possible to implement dynamic simulation of power grids synchronously in multiple time intervals, and the predicted initial value can be quickly corrected and converged to a stable simulation result through iteration, thereby improving the efficiency of dynamic simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 It is a flowchart of a method for synchronous simulation of multiple time intervals of a power grid provided by an embodiment of the present invention;

[0085] Figure 2 It is a schematic diagram of dividing the time interval of a differential algebraic equation system provided by an embodiment of the present invention;

[0086] Figure 3 It is a structural schematic diagram of a ring network provided by an embodiment of the present invention;

[0087] Figure 4It is a structural schematic diagram of a power grid multiple time interval synchronization simulation device provided by an embodiment of the present invention;

[0088] Figure 5 It is a structural diagram of a terminal device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0089] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0090] The embodiment of the present invention provides a method for synchronous simulation of multiple time intervals of a power grid, see Figure 1 , is a flow chart of a method for synchronous simulation of multiple time intervals of a power grid provided by an embodiment of the present invention, the method comprising steps S1 to S6;

[0091] S1, calculating a differential algebraic equation group of the power grid according to the time variables of the power grid, and dividing the time interval to be simulated into a plurality of sub-intervals;

[0092] S2, predicting the initial value of each sub-interval using a multi-dimensional interpolation prediction method and a swarm optimization tracking prediction method according to the initial value of the time interval;

[0093] S3, performing a separate simulation on each sub-interval according to the differential algebraic equation group and the initial value of each sub-interval, and obtaining a simulation result for each sub-interval;

[0094] S4, transmitting the simulation result of each sub-interval through the ring network formed by each sub-interval to obtain the boundary value corresponding to each sub-interval;

[0095] S5, calculating a correction amount according to the initial value of each sub-interval and the corresponding boundary value, and correcting the initial value of each sub-interval;

[0096] S6, re-simulate each sub-interval according to the corrected initial value, until the boundary value corresponding to each interval obtained by simulation is the same as the initial value of simulation, and output the simulation result.

[0097] In the specific implementation of this embodiment, for the problem of dynamic simulation of the power grid, the differential algebraic equations of the power grid are calculated according to the time variables of the power grid, and the simulation time interval can be divided into sub-intervals of the same length. In each integral interval, time variables such as state variables, algebraic variables and control variables can change dynamically;

[0098] Based on the multi-dimensional interpolation prediction method, the arrangement points are inserted at unequal intervals in each sub-interval, and the initial value of each sub-interval is predicted according to the swarm optimization tracking prediction;

[0099] According to the initial value and the differential algebraic equation group, multiple time interval synchronous simulation is implemented on each sub-interval to obtain the interval boundary value.

[0100] The simulation boundary values ​​are transmitted between each sub-interval through a ring network, and data encryption, decryption and repair can be achieved through the Pony-ARM encryption scheme to ensure data security.

[0101] The correction amount of the predicted initial value is calculated by solving the differential equation, and the initial value is corrected. Dynamic simulation, information transmission and initial value correction are repeated until the initial value predicted in each sub-interval is the same as the simulation result of the previous sub-interval, and the simulation result is output.

[0102] The embodiment of the present invention provides a method for synchronous simulation of multiple time intervals of a power grid, which can solve the problem that the dynamic simulation of the power grid in the prior art cannot simulate multiple time intervals at the same time, the dynamic simulation process has a large amount of calculation and a long calculation time. By synchronously implementing the dynamic simulation of the power grid in multiple time intervals, the initial value can be quickly corrected and converged to a stable simulation result through an iterative method, thereby improving the efficiency of dynamic simulation.

[0103] In another embodiment provided by the present invention, the S1 specifically includes:

[0104] Calculating a differential algebraic equation group for dynamic simulation of the power grid according to a time variable of the power grid, wherein the differential algebraic equation group includes differential equations and algebraic equations;

[0105] Dividing the time interval of the power grid to be simulated into a plurality of sub-intervals according to a preset time length;

[0106] The time variables include state variables x(t), algebraic variables y(t) and control variables u(t), and the differential equation is: The algebraic equation is: 0 = g(x(t), y(t), u(t)).

[0107] In the specific implementation of this embodiment, when the dynamic simulation of the power grid is performed, the model equations describing the components such as the power grid generator, asynchronous motor, FACTs device, the machine-grid interface equation and the differential algebraic equations of the power flow equation are:

[0108]

[0109] 0=g(x(t),y(t),u(t)), (2)

[0110] Among them, formula (1) is a differential equation, formula (2) is an algebraic equation; x(t) is a state variable, which cannot change suddenly during the simulation process; y(t) is an algebraic variable; u(t) is a control variable; algebraic variables and control variables can change suddenly during the simulation process. The so-called power grid simulation process is actually to solve the equations of formula (1) and formula (2) to obtain the changes of each variable over time.

[0111] Furthermore, the simulation time interval needs to be divided, see Figure 2 , is a schematic diagram of dividing the time interval of a differential algebraic equation system provided by an embodiment of the present invention;

[0112] The state variable x(t) cannot change suddenly in each subinterval, and the algebraic variable y(t) and the control variable u(t) change suddenly in the simulation process time t. f It can change when it changes; the initial value of the integral interval p is t p-1 , the interval length is T p , the interval boundary value is t p , the boundary point between each integral interval and the next subinterval is the interval boundary; except for the first subinterval whose initial value is known, the initial values ​​of other integral subintervals are unknown.

[0113] For the problem of dynamic simulation of power grid, the simulation time interval can be divided into sub-intervals of 1 second in length. In each integral interval, the state variables, algebraic variables and control variables can change dynamically. At the interval boundary, the state variables cannot change suddenly, so they must remain continuous; the control variables and algebraic variables can change suddenly, so they can be discontinuous and have steps.

[0114] In another embodiment provided by the present invention, the S2 further includes:

[0115] Inserting a number of arrangement points in each subinterval according to a preset relative position;

[0116] The prediction factor W is calculated each time according to the tracking prediction method of swarm optimization. j+1 ;

[0117] The initial value of the time interval is used as the initial value of the first subinterval, and for any subinterval of the plurality of subintervals, the initial value of each subinterval is calculated by using a tensor product according to the initial value of the first subinterval and the distribution of the time variable;

[0118] Among them, the prediction factor W each time j+1 =W j +L(r)·(W j -W j-1 )+L′(r)·(W j -W j-1), L(r) is the Legendre polynomial, L′(r) is the first-order derivative of the Legendre polynomial; the initial value of each subinterval p x(t0) is the initial value of the state variable in the initial value of the first sub-interval, is the initial value of subinterval p, h is the interval length of the subinterval, W is the prediction factor of subinterval p, n is the number of subintervals, p is the integral dimension of the subinterval, q is the number of arranged points in subinterval p, i n is the interpolation level of the nth state variable x(n), is the interpolation set of the nth state variable x(n).

[0119] When this embodiment is implemented, see Figure 2 , insert arrangement points at the relative positions of 0.1550625, 0.6449948 and 1 in each subinterval respectively.

[0120] For the integral interval P, its initial value can be predicted by the tensor product method based on the initial value of the first interval and the distribution of each state variable, that is:

[0121]

[0122] Among them, x(t0) is the state variable at the start of the simulation, that is, the initial value of the state variable, is the predicted initial value of subinterval p, h is the interval length of the subinterval, W is the prediction factor, n is the number of subintervals, p is the dimension of the integral interval, q is the number of arrangement points, i n is the interpolation level of the nth state variable, is the interpolation set of the nth state variable.

[0123] For the prediction factors, a tracking prediction method based on swarm optimization can be used to iteratively update the prediction factors each time:

[0124] W j+1 =W j +L(r)·(W j -W j-1 )+L′(r)·(W j -W j-1 ) (4)

[0125] Where L(r) is the Legendre polynomial, and L′(r) is the first-order derivative of the Legendre polynomial. If the prediction accuracy needs to be improved, the second-order derivative can be further used.

[0126] In another embodiment provided by the present invention, the step S3 specifically includes:

[0127] Calculate the Omega function coefficients and property conditions of the arrangement points on each subinterval;

[0128] Calculate the time integral of the coefficient of the omega function according to the property conditions;

[0129] According to the Omega function coefficients, the state variable polynomial and the algebraic variable polynomial of each subinterval, the state variable and the algebraic variable of each subinterval are calculated as simulation results;

[0130] Among them, the Omega function coefficient is The property conditions are: The state variable polynomial for subinterval p is The polynomial of the algebraic variable in the subinterval p is The time integral of the coefficients of the omega function is Lagrange polynomials x p,r ,y p,r , and u p are the values ​​of the state variable, algebraic variable and control variable of the rth arrangement point in the subinterval p. The number of arrangement points on the subinterval p is 3, which are τ p,1 ,τ p,2 and τ p,3 , δ r,q represents the Kronecker delta, τ p,q The relative positions of the arrangement points are in the range of [0,1], and the control variable u(t) of the differential algebraic equation system remains constant in each subinterval.

[0131] In the specific implementation of this embodiment, based on the predicted initial value, the simulation of each sub-interval is independent of each other and is carried out synchronously; the simulation is to obtain the accurate value of the interval boundary by using a family of finite element-based polynomial linear superposition calculations on the arrangement points, and dynamic simulation needs to be implemented separately in each sub-interval.

[0132] The specific process is as follows: when the initial value of each subinterval is known, the value of each variable in equations (1) and (2) is solved separately in each subinterval. The state variable cannot change suddenly at the interval boundary and must remain continuous, while the algebraic variable can change suddenly at the interval boundary and does not need to remain continuous. Three arrangement points are inserted in each subinterval, and then the state variable and the algebraic variable can be calculated at the three arrangement points using a family of finite element-based polynomial linear superposition:

[0133]

[0134]

[0135] Among them, x p,r ,y p,r,u p are the values ​​of the state variable, algebraic variable and control variable of the rth arrangement point in the pth subinterval respectively; is the coefficient of the Omega function, representing the importance of the corresponding arrangement point in the linear superposition process. The properties of the product must satisfy the following conditions:

[0136]

[0137] Among them, τ p,q is the normalized relative position of the arrangement points in the range of [0,1]. For the case where three arrangement points are used in each interval of the present invention, τ p,1 ,τ p,2 and τ p,3 There are three positions: 0.1550625, 0.6449948 and 1; δ r,q Stands for Kronecker delta.

[0138] In order to satisfy the condition of formula (7), the present invention takes Ω r (·) is the integral of the Lagrange polynomial with respect to time:

[0139]

[0140] in is the Lagrange polynomial.

[0141] From equations (1) and (2), it can be seen that in addition to the state variables and algebraic variables, the differential algebraic equations also contain the control variable u(t), which remains constant in each subinterval and jumps at the interval boundary, so there is no need to solve and fit the control variable. Solving equations (1)-(2) through equations (5)-(8), the state variables and algebraic variables of each subinterval are calculated as simulation results, and dynamic simulation is implemented separately in each subinterval, that is, multiple time interval synchronous simulation.

[0142] In another embodiment provided by the present invention, the step S4 specifically includes:

[0143] Each sub-interval is arranged in chronological order to form a ring network, and each sub-interval in the ring network communicates with adjacent sub-intervals through the ring network;

[0144] The boundary value of the previous subinterval is used as the communication data R(z), and normalized to obtain the normalized data P(z). Through the bidirectional mapping relationship, the planned data P(z) is converted into binary data S(z);

[0145] The key K(T) is dynamically generated through the current system clock, and the encrypted data Q(Z) is obtained based on the Pony-ARM core technology;

[0146] The boundary value of the previous sub-interval is passed to the next sub-interval through the encrypted data Q(Z). The next sub-interval obtains the encrypted data Q(Z), and decrypts the encrypted data according to the key K(T) to obtain the decrypted data S(Z) as the boundary value corresponding to the next sub-interval.

[0147] in, R(z) and P(z) are the information data before and after normalization, respectively. e is the natural index. T is the system clock time, T0 is the starting time, Remainder

[0148] In another embodiment provided by the present invention, simulation results are mutually transmitted within each sub-interval through a ring network, and a Pony-ARM communication encryption scheme is proposed to achieve data encryption, decryption and repair.

[0149] See also Figure 3 , which is a schematic diagram of the structure of a ring network provided by an embodiment of the present invention; the ring network includes a first sub-interval 1, a second sub-interval 2, a third sub-interval 3, a fourth sub-interval 4, a fifth sub-interval 5, a sixth sub-interval 6, a seventh sub-interval 7 and an eighth sub-interval 8;

[0150] After the multiple time interval synchronous simulation is completed, the simulation results of each interval need to be transmitted. Because solving the differential algebraic equation system is only related to the initial value, it is only necessary to transmit the simulation results of the multiple time interval synchronous simulation on the interval boundary, and the data transmission volume is small. Figure 3 The ring network transmission mode (without a control center server) shown in the figure is used to transmit data. Assuming that each small circle is a sub-interval, the numbers in the small circles represent the order of the sub-intervals. Then, the sub-intervals can only communicate with the adjacent sub-intervals, which can realize communication without a control center and improve the security of communication.

[0151] The network parameters, generator output, load level, etc. of the power grid are all confidential data of the power grid. Figure 3 When transmitting multiple time interval synchronous simulation results through a ring communication method, data encryption must be used to ensure that the results cannot be stolen externally.

[0152] Aiming at the ring communication mode, a Pony-ARM communication encryption scheme which can be used for multiple time interval synchronous simulation is proposed.

[0153] Taking the boundary value of the previous subinterval as the communication data R(z), Pony-ARM communication encryption first needs to normalize the transmitted data, that is:

[0154]

[0155] Among them, R(z) and P(z) are the information data before and after normalization respectively; e is the natural index.

[0156] Through the bidirectional mapping relationship, the normalized data P(z) is converted into binary data S(z):

[0157]

[0158] Among them, S (2) (z) and P (10) (z) are the normalized data of binary and decimal values ​​respectively.

[0159] Pony-ARM communication encryption requires the encryption and decryption keys to be defined in advance. This invention uses dynamic keys and dynamically generates keys using the current system clock:

[0160]

[0161] Among them, T is the system clock time, and T0 is the starting time. After the secret key is generated, it is transmitted to the computing cores of each sub-interval through broadcasting.

[0162] Then, Pony-ARM core technology is used to implement communication encryption:

[0163]

[0164] Among them, Q(z) is the encrypted data, which can be transmitted freely in the ring path, and will not cause information leakage even if it is intercepted; Δ is the remainder, which can repair the lost data when a disconnection occurs in the ring network. The specific calculation method is:

[0165]

[0166] like Figure 3 After each sub-interval obtains the encrypted data information Q(z), it can use the original broadcast key to decrypt the information:

[0167]

[0168] By using equations (9)-(13), the boundary value corresponding to each sub-interval is obtained, and the data encryption and decryption in the ring information transmission process and the repair function of disconnection and lost data can be realized.

[0169] In another embodiment provided by the present invention, the step S5 specifically includes:

[0170] Solve the first-order differential equation according to the initial value of each sub-interval and the corresponding boundary value to obtain the correction value;

[0171] The initial value of each sub-interval is corrected according to the correction amount;

[0172] Among them, the first-order differential equation is R(z) is the variable value at the end of a time interval obtained by synchronous simulation of several time intervals, and is transmitted to each interval through a ring information path. is the initial value of the variable at the end of the interval, ΔR(z) is the correction value of the simulation result, ΔR′(z) is the first-order derivative of ΔR(z), T h is the period of the correction process, is the time constant, K p is the proportional coefficient of the correction amount.

[0173] When this embodiment is implemented, it is necessary to compare the predicted initial value with the simulation result of the previous interval, calculate the correction amount and correct the initial value. The correction amount can be obtained by solving the differential equation of formula (15):

[0174]

[0175] Among them, R(z) is the interval end variable value obtained by synchronous simulation of multiple time intervals, which is transmitted to each interval through the ring information path; is the initial value of the variable at the end of the interval; ΔR(z) is the correction value of the simulation result; ΔR′(z) is the first-order derivative of ΔR(z); T h is the period of the correction process (time constant); K p is the proportional coefficient of the correction amount.

[0176] It can be seen from formula (15) that the correction amount ΔR(z) changes dynamically with the iterative process of correction. When the difference between the boundary value and the initial value is large, the correction amount is also large; when the difference between the boundary value and the initial value is small, the correction amount is also small.

[0177] Therefore, the corrected initial value is obtained

[0178] In another embodiment provided by the present invention, the step S6 specifically includes:

[0179] According to the obtained correction value, the initial value of each sub-interval is corrected, and each sub-interval is simulated again according to the corrected initial value to obtain the updated boundary value of each sub-interval, and the simulated boundary value at the end of the time interval is compared with the corrected initial value again;

[0180] If the revised initial value is different from the boundary value after simulation, the revised value is calculated, and the initial value is revised again, and each sub-interval is simulated again according to the revised initial value, and the updated boundary value of each sub-interval is obtained again, until the obtained boundary value is the same as the revised initial value;

[0181] If the corrected initial value is the same as the boundary value after simulation, the boundary value after simulation is output as the simulation result.

[0182] In the specific implementation of this embodiment, the corrected initial value R'(z) is used to repeat multiple time interval synchronous simulation and correction until the initial value predicted in each interval is the same as the simulation result of the previous interval, that is, ΔR(z) converges to a smaller value such as 0.0001, and the simulation result can be output.

[0183] See also Figure 4 , is a schematic diagram of the structure of a power grid multiple time interval synchronization simulation device provided by an embodiment of the present invention, the device comprising:

[0184] A partitioning module, used for calculating a differential algebraic equation system of the power grid according to the time variables of the power grid, and dividing the time interval to be simulated into a plurality of sub-intervals;

[0185] A prediction module, used to predict the initial value of each sub-interval according to the initial value of the time interval by using a multi-dimensional interpolation prediction method and a swarm optimization tracking prediction method;

[0186] A simulation module, used for separately simulating each sub-interval according to the differential algebraic equation group and the initial value of each sub-interval, and obtaining the simulation result of each sub-interval;

[0187] An information transmission module is used to transmit the simulation result of each sub-interval through the ring network formed by each sub-interval to obtain the boundary value corresponding to each sub-interval;

[0188] A correction module, used for calculating a correction amount according to an initial value of each sub-interval and a corresponding boundary value, and correcting the initial value of each sub-interval;

[0189] The control module is used to re-simulate each sub-interval according to the corrected initial value until the boundary value corresponding to each interval obtained by simulation is the same as the initial value of simulation, and output the simulation result.

[0190] It should be noted that a power grid multiple time interval synchronization simulation device provided in an embodiment of the present invention is used to execute all process steps of a power grid multiple time interval synchronization simulation method in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be elaborated upon.

[0191] See also Figure 5, is a schematic diagram of the structure of a terminal device provided in an embodiment of the present invention. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a power grid multiple time interval synchronization simulation program. When the processor executes the computer program, the steps in each of the above-mentioned power grid multiple time interval synchronization simulation method embodiments are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are realized, for example Figure 4 The modules shown.

[0192] Exemplarily, the computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device. For example, the computer program may be divided into an information reading module, an acquisition module, a search module, a judgment module, an important line set acquisition module, and a stability analysis module, and the specific functions of each module are not described in detail here.

[0193] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the terminal device may also include an input / output device, a network access device, a bus, etc.

[0194] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0195] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0196] Wherein, the module / unit integrated in the terminal device can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0197] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0198] It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for synchronous simulation of multiple time intervals of a power grid, characterized in that: The method comprises: Calculating a differential algebraic equation system of the power grid according to the time variables of the power grid, and dividing the time interval to be simulated into a plurality of sub-intervals; According to the initial value of the time interval, the initial value of each sub-interval is predicted by using a multi-dimensional interpolation prediction method and a swarm optimization tracking prediction method; Simulating each subinterval separately according to the differential algebraic equation group and the initial value of each subinterval to obtain the simulation result of each subinterval; The simulation result of each sub-interval is transmitted through the ring network formed by each sub-interval to obtain the boundary value corresponding to each sub-interval; The correction amount is calculated according to the initial value of each sub-interval and the corresponding boundary value, and the initial value of each sub-interval is corrected; According to the corrected initial value, each sub-interval is re-simulated until the boundary value corresponding to each interval obtained by simulation is the same as the initial value of simulation, and the simulation result is output; The method of predicting the initial value of each sub-interval according to the initial value of the time interval by using a multi-dimensional interpolation prediction method and a swarm optimization tracking prediction method specifically includes: Inserting a number of arrangement points in each subinterval according to a preset relative position; The prediction factor W is calculated each time according to the tracking prediction method of swarm optimization. j+1 ; The initial value of the time interval is used as the initial value of the first subinterval, and for any subinterval of the plurality of subintervals, the initial value of each subinterval is calculated by using a tensor product according to the initial value of the first subinterval and the distribution of the time variable; The step of simulating each sub-interval separately according to the differential algebraic equation group and the initial value of each sub-interval to obtain the simulation result of each sub-interval specifically includes: Calculate the Omega function coefficients and property conditions of the arrangement points on each subinterval; Calculate the time integral of the coefficient of the omega function according to the property conditions; According to the Omega function coefficients, the state variable polynomial and the algebraic variable polynomial of each subinterval, the state variable and the algebraic variable of each subinterval are calculated as simulation results; The simulation result of each sub-interval is transmitted through the ring network formed by each sub-interval to obtain the boundary value corresponding to each sub-interval. The transmission process includes encryption, decryption and repair of data, which specifically includes: Each sub-interval is arranged in chronological order to form a ring network, and each sub-interval in the ring network communicates with adjacent sub-intervals through the ring network; The boundary value of the previous subinterval is used as the communication data R(z), and normalized to obtain the normalized data P(z). Through the bidirectional mapping relationship, the normalized data P(z) is converted into binary data S(z); The key K(T) is dynamically generated through the current system clock, and the encrypted data Q(Z) is obtained based on the Pony-ARM core technology; When a line is broken in a ring network, the remainder Δ can be used to repair the lost data; The boundary value of the previous sub-interval is passed to the next sub-interval through the encrypted data Q(Z). The next sub-interval obtains the encrypted data Q(Z), and decrypts the encrypted data according to the key K(T) to obtain the decrypted data S(Z) as the boundary value corresponding to the next sub-interval. The calculation of the correction amount according to the initial value of each sub-interval and the corresponding boundary value, and correction of the initial value of each sub-interval, specifically includes: Solve the first-order differential equation according to the initial value of each subinterval and the corresponding boundary value to obtain the correction value ΔR(z); The initial value of each sub-interval is corrected according to the correction amount.

2. The method for synchronous simulation of multiple time intervals of a power grid according to claim 1, characterized in that: The differential algebraic equations for the power grid simulation are calculated according to the time variables of the power grid, and the time interval to be simulated is divided into a plurality of sub-intervals, specifically including: Calculating a differential algebraic equation group for dynamic simulation of the power grid according to a time variable of the power grid, wherein the differential algebraic equation group includes differential equations and algebraic equations; Dividing the time interval of the power grid to be simulated into a plurality of sub-intervals according to a preset time length; When dividing the interval, the subintervals are divided at equal intervals, and the arrangement points inside the subintervals are inserted at unequal intervals; at the interval boundary, the state variables cannot mutate and remain continuous; the control variables and algebraic variables can mutate and step at the interval boundary; except for the first integral subinterval whose initial value is known, the initial values ​​of other integral subintervals are unknown; The time variables include state variables x(t), algebraic variables y(t) and control variables u(t), and the differential equation is: The algebraic equation is: 0=g(x(t), y(t), u(t)), where f and g represent the algebraic expressions of the differential equation and the algebraic equation respectively.

3. The method for synchronous simulation of multiple time intervals of a power grid according to claim 1, characterized in that: The predictor W for each j+1 =W j +L(r)·(W j -W j-1 )+L′(r)·(W j -W j-1 ), L(r) is the Legendre polynomial, L′(r) is the first-order derivative of the Legendre polynomial; the initial value prediction of each subinterval p is x(t0) is the initial value of the state variable in the initial value of the first sub-interval, is the initial value of subinterval p, h is the interval length of the subinterval, W is the prediction factor of subinterval p, n is the number of subintervals, p is the integral dimension of the subinterval, q is the number of arranged points in subinterval p, i n is the interpolation level of the nth state variable x(n), is the interpolation set of the nth state variable x(n).

4. The method for synchronous simulation of multiple time intervals of a power grid according to claim 1, characterized in that: The coefficients of the Omega function are The property conditions are: The Omega function coefficients are the integrals of the Lagrange polynomials: The Lagrange polynomial is The state variable polynomial of subinterval p is approximately The polynomial approximation of the algebraic variable in the subinterval p is x p,r ,y p,r , and u p are the values ​​of the state variable, algebraic variable and control variable of the rth arrangement point in the subinterval p. The number of arrangement points on the subinterval p is 3, which are τ p,1 ,τ p,2 and τ p,3 , δ r,q represents the Kronecker delta, τ p,q is the relative position of the arrangement points with values ​​in the interval [0,1], and the control variable u(t) of the differential algebraic equation system remains constant in each subinterval.

5. The method for synchronous simulation of multiple time intervals of a power grid according to claim 1, characterized in that: Normalized data R(z) and P(z) are the information data before and after normalization, respectively. e is the natural index. T is the system clock time, T0 is the starting time, Remainder 6. The method for synchronous simulation of multiple time intervals of a power grid according to claim 1, characterized in that: The first-order differential equation is R(z) is the variable value at the end of a time interval obtained by synchronous simulation of several time intervals, and is transmitted to each interval through a ring information path. is the initial value of the variable at the end of the interval, ΔR(z) is the correction value of the simulation result, ΔR′(z) is the first-order derivative of ΔR(z), T h is the period of the correction process, is the time constant, K p is the proportional coefficient of the correction amount, and the correction process is 7. The method for synchronous simulation of multiple time intervals of a power grid according to claim 1, characterized in that: The method of re-simulating each sub-interval according to the corrected initial value until the boundary value corresponding to each interval obtained by simulation is the same as the initial value of simulation, and outputting the simulation result specifically includes: According to the obtained correction value, the initial value of each sub-interval is corrected, and each sub-interval is simulated again according to the corrected initial value to obtain the updated boundary value of each sub-interval, and the simulated boundary value at the end of the time interval is compared with the corrected initial value again; If the revised initial value is different from the boundary value after simulation, the revised value is calculated, and the initial value is revised again, and each sub-interval is simulated again according to the revised initial value, and the updated boundary value of each sub-interval is obtained again, until the obtained boundary value is the same as the revised initial value; If the corrected initial value is the same as the boundary value after simulation, the boundary value after simulation is output as the simulation result.

8. A power grid multiple time interval synchronization simulation device, characterized in that: The device comprises: A partitioning module, used for calculating a differential algebraic equation system of the power grid according to the time variables of the power grid, and dividing the time interval to be simulated into a plurality of sub-intervals; A prediction module, used to predict the initial value of each sub-interval according to the initial value of the time interval by using a multi-dimensional interpolation prediction method and a swarm optimization tracking prediction method; A simulation module, used for separately simulating each sub-interval according to the differential algebraic equation group and the initial value of each sub-interval, and obtaining the simulation result of each sub-interval; An information transmission module is used to transmit the simulation result of each sub-interval through the ring network formed by each sub-interval to obtain the boundary value corresponding to each sub-interval; A correction module, used for calculating a correction amount according to an initial value of each sub-interval and a corresponding boundary value, and correcting the initial value of each sub-interval; A control module is used to re-simulate each sub-interval according to the corrected initial value until the boundary value corresponding to each interval obtained by simulation is the same as the initial value of simulation, and output the simulation result; The method of predicting the initial value of each sub-interval according to the initial value of the time interval by using a multi-dimensional interpolation prediction method and a swarm optimization tracking prediction method specifically includes: Inserting a number of arrangement points in each subinterval according to a preset relative position; The prediction factor W is calculated each time according to the tracking prediction method of swarm optimization. j+1 ; The initial value of the time interval is used as the initial value of the first subinterval, and for any subinterval of the plurality of subintervals, the initial value of each subinterval is calculated by using a tensor product according to the initial value of the first subinterval and the distribution of the time variable; The step of simulating each sub-interval separately according to the differential algebraic equation group and the initial value of each sub-interval to obtain the simulation result of each sub-interval specifically includes: Calculate the Omega function coefficients and property conditions of the arrangement points on each subinterval; Calculate the time integral of the coefficient of the omega function according to the property conditions; According to the Omega function coefficients, the state variable polynomial and the algebraic variable polynomial of each subinterval, the state variable and the algebraic variable of each subinterval are calculated as simulation results; The simulation result of each sub-interval is transmitted through the ring network formed by each sub-interval to obtain the boundary value corresponding to each sub-interval. The transmission process includes encryption, decryption and repair of data, specifically including: Each sub-interval is arranged in chronological order to form a ring network, and each sub-interval in the ring network communicates with adjacent sub-intervals through the ring network; The boundary value of the previous subinterval is used as the communication data R(z), and normalized to obtain the normalized data P(z). Through the bidirectional mapping relationship, the normalized data P(z) is converted into binary data S(z); The key K(T) is dynamically generated through the current system clock, and the encrypted data Q(Z) is obtained based on the Pony-ARM core technology; When a line is broken in a ring network, the remainder can be used Δ Repair lost data; The boundary value of the previous sub-interval is passed to the next sub-interval through the encrypted data Q(Z). The next sub-interval obtains the encrypted data Q(Z), and decrypts the encrypted data according to the key K(T) to obtain the decrypted data S(Z) as the boundary value corresponding to the next sub-interval. The calculation of the correction amount according to the initial value of each sub-interval and the corresponding boundary value, and correction of the initial value of each sub-interval, specifically includes: Solve the first-order differential equation according to the initial value of each subinterval and the corresponding boundary value to obtain the correction value ΔR(z); The initial value of each sub-interval is corrected according to the correction amount.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the power grid multiple time interval synchronization simulation method according to any one of claims 1 to 7.

10. A terminal device, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for synchronous simulation of multiple time intervals of a power grid as described in any one of claims 1 to 7 is implemented.

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