Novel power system variable component sub-electromagnetic transient simulation method and system

Abstract

The invention discloses a novel power system variable component quantum electromagnetic transient simulation method and system, and the method comprises the steps: firstly building an EMTP quantization model based on a constant admittance model, carrying out the quantum circuit parameter calculation through a matrix low-dimensional quantum projection method, building a quantum circuit based on a pure real part quantum circuit reduction principle, and carrying out the equation solving, and finally, solving an electromagnetic transient simulation result at each moment through an iteration method. According to the method, a quantum calculation method is used, the problem of frequent transformation of simulation topology of a power electronic device is avoided through a constant admittance modeling method, exponential acceleration of a matrix mapping preprocessing part is realized in combination with a matrix tensor splitting method, and redundant circuits during quantum calculation are reduced through a pure real part reduction principle.

Description

Technical Field

[0001] The present invention belongs to the technical field of electromagnetic transient simulation of power systems, and in particular relates to a novel method and system for variational quantum electromagnetic transient simulation of power systems. Background Art

[0002] In recent years, with the increasing proportion of renewable energy and power electronic devices connected to the power grid, the wide-area interconnection of power systems has led to the continuous expansion of the dimensionality of new power systems, and a sharp increase in the scale of problems to be solved. However, the curse of dimensionality brought about by the increased system size and its inherently highly nonlinear characteristics have led to increasingly prominent problems such as the long time required for small-step electromagnetic transient simulations and the difficulty in converging stochastic simulations and risk analysis. Due to their binary nature, classical computers are unable to cope with the exponential growth of problem sizes. The curse of dimensionality renders current electromagnetic transient simulation algorithms unscalable, resulting in the inability to provide the real-time, high-fidelity results required to manage large-scale electronic devices and ensure the resilient operation of power systems.

[0003] With the recent development of quantum computing technology, quantized solutions to electromagnetic transient problems have become possible. Given the inherent parallel superposition and entanglement properties of quantum computers, the dimensionality dilemma of electromagnetic transient problems is expected to be fundamentally resolved on quantum computers. Performing electromagnetic transient computations on quantum computers can exponentially speed up algorithms and significantly increase the matrix dimensions of electromagnetic transient computations, enabling high-fidelity simulations of rapidly changing electromagnetic transient processes in power systems and addressing the large-scale computational complexity that classical computing cannot handle.

[0004] However, as we are still in the era of noisy medium-scale quantum computers, the variational quantum algorithms used still face many problems. The complex computational complexity of matrix preprocessing and the enormous quantum computer resource consumption caused by the large number of parallel quantum circuits have hindered the practical application of quantum algorithms. Summary of the Invention

[0005] This invention addresses the existing problem of electromagnetic transient simulation computation time increasing exponentially with the problem size. It provides a novel method and system for variational quantum electromagnetic transient simulation of power systems. First, a quantized EMTP model based on a constant admittance model is established. A matrix low-dimensional quantum projection method is then used to calculate quantum circuit parameters. Based on the principle of pure real part quantum circuit reduction, a quantum circuit is constructed and equations are solved. Finally, an iterative method is used to solve the electromagnetic transient simulation results at each moment. This method utilizes quantum computing methods, employing a constant admittance modeling approach to avoid the frequent topology changes in power electronic device simulations. Combined with a matrix tensor splitting method, this method achieves exponential acceleration of the matrix mapping preprocessing phase, while also reducing redundant circuits during quantum computations through the principle of pure real part reduction.

[0006] To achieve the above objectives, the present invention adopts a technical solution: a novel power system variational quantum electromagnetic transient simulation method and system, comprising the following steps:

[0007] S1: Establish an EMTP quantization model based on a constant admittance model. The equation of the EMTP quantization model is specifically:

[0008]

[0009] in, Represents the N-dimensional admittance matrix constructed by the EMTP model, v(t) represents the node voltage vector; i(t) represents the combined node injection current vector including the current source and the history term; By expanding the N-dimensional admittance matrix to dimensionality, thereby mapping the mathematical expression of EMTP to Hilbert space and converting the mathematical model into a physical model that can be compiled by an actual quantum computer. The specific expression of the EMTP physical equation is:

[0010] G|v>=|i>

[0011] Where G is G q The unit determinant matrix after scaling; |v> and |i> represent the definitions in Normalized quantum representation of v(t) and i(t) on the qubit; through vector normalization, the probability amplitude conversion of the current and voltage vectors is achieved, and the quantized EMTP equation is constructed;

[0012] S2: Calculate quantum circuit parameters using a matrix low-dimensional quantum projection method. The specific method for calculating the parameters is as follows: first, for the EMTP physical equation constructed in S1, the matrix G is split into tensors based on the generalized Kronecker decomposition, and the resulting low-dimensional submatrices are subjected to Pauli basis mapping. After calculating the Kronecker product of the corresponding basis parameters according to the splitting method, the mapping parameters of the corresponding physical equation on the Pauli basis are obtained. Secondly, based on the top-down coding principle, the quantum circuit coding parameters of |i> are calculated. Finally, based on the hardware efficient structure, the gate circuit parameters are selected to implement the quantum circuit expression of |v>.

[0013] S3. Construct a quantum circuit based on the pure real part quantum circuit reduction principle and solve the quantized EMTP node admittance equation. The quantum equation is constructed based on the EMTP method and quantized modeling is performed through the steps in S1. The quantized EMTP equation is converted into an optimization problem. Combining the Hadamard test principle with the characteristics of the EMTP equation, only the real part quantum computing circuit is constructed to solve the loss function according to the mapping parameters calculated in S2 and the corresponding Pauli basis. Finally, the variational circuit parameters are optimized based on the gradient descent method. After obtaining the optimal parameters of the variational circuit, quantum state tomography is performed to obtain the corresponding node voltage initial value.

[0014] S4. Based on the node voltage initial value obtained in step S3, the current correction amount and the node voltage correction amount are calculated in sequence to obtain a new node voltage value, and the new node voltage value is compared with the preset accuracy. If the new node voltage value is not satisfied, step S3 is repeated until the preset accuracy is satisfied, and the node voltage value is obtained to obtain the electromagnetic transient simulation result at each moment.

[0015] As an improvement of the present invention, in the EMTP quantization model of step S1, the EMTP node equation is specifically:

[0016] G0v(t)=i s (t)+i h (t)≡i(t)

[0017] Among them, i s (t) represents the current source vector; i h (t) represents the historical current vector; G0 is the equivalent admittance matrix with dimension N; the EMTP equation is mapped to the Hilbert space to obtain the equation of the EMTP quantized model.

[0018] As an improvement of the present invention, step S2 uses the admittance matrix low-dimensional quantum projection method to calculate the quantum circuit parameters, which specifically includes the following steps:

[0019] S21. Perform low-dimensional splitting of the matrix G obtained in step S1 based on generalized Kronecker decomposition:

[0020]

[0021] Where G is the quantized admittance matrix, γ r and ξ r is the low-dimensional matrix after splitting, is the tensor product, r is the generalized Kronecker splitting precision, R is the preset splitting precision, and the upper limit is the minimum dimension of the split submatrix;

[0022] S22. Perform Pauli basis mapping on the low-dimensional matrix:

[0023]

[0024] Among them, G i is the corresponding split i-th sub-matrix, g i is the Pauli basis consisting of four basic Pauli matrices; c i is the calculated corresponding basis parameter, and n is the number of quantum bits that the corresponding circuit will use; the specific calculation formula is:

[0025]

[0026] Where Tr represents the trace of the matrix; the Kronecker product operation is performed on the basis parameters of the corresponding submatrix according to the splitting method in S21 to obtain the corresponding parameters of the matrix G under the corresponding Pauli basis;

[0027] S23, based on the Top-down coding principle, the current vector is encoded into the quantum circuit, and the quantum coding method is characterized in that: for the N-dimensional classical current vector I∈R N , through the cooperative mechanism of orthonormal basis decomposition and quantum amplitude mapping, the vector is encoded into In a quantum circuit with 10 qubits, the circuit structure is gradually decomposed from the target quantum state through the Top-Down coding strategy. The complex state is expressed as a superposition or tensor product of low-dimensional sub-states through recursive decomposition. Each sub-state gradually approaches the target state through rotation gates and controlled operations, and finally the circuit depth is O(n 2 / log n) constraint, achieving fidelity encoding of the current vector in the quantum state amplitude;

[0028] S24. Construct hardware-efficient variational quantum circuits to represent the node voltages to be solved: Through the collaborative architecture of controlled quantum operations and parameterized rotation gates, the grid node voltage solution problem is mapped to a quantum circuit expression. The implementation process includes: using a parameterized rotation gate sequence and adjacently coupled controlled Z gates to interleave and construct a variational quantum circuit, while reducing the number of quantum bits and circuit depth while ensuring the accurate expression of the node voltage characteristics.

[0029] As another improvement of the present invention, the step S3 further includes:

[0030] S31. Construction of the quantum circuit of the variational quantum linear solver. Convert the EMTP equation in S1 into an optimization problem. The optimal solution is the result of the equation calculation. Use the Hamiltonian to calculate the corresponding loss function in the quantum circuit. The specific steps are as follows:

[0031] S311. Convert the EMTP equation into an optimization model and construct the corresponding loss function. The specific calculation formula is:

[0032]

[0033] Among them, V(α) is the unitary matrix corresponding to the variational quantum circuit, α is the corresponding variational circuit parameter to be trained, G is the quantized admittance matrix, H L The Hamiltonian constructed for the loss function in Hilbert space is:

[0034]

[0035] Where U is the encoding unitary matrix corresponding to the current vector, I is the identity matrix, Apply the identity matrix gate on the corresponding circuit other than the j-th qubit;

[0036] S312. Based on the loss function constructed in S311, a quantum circuit is constructed using the Hadamard test principle and the Pauli matrix basis calculated in S2, the current vector quantum circuit encoding parameters, and the hardware efficient variational circuit parameters. The quantum circuit construction method is characterized by: based on the real symmetric admittance matrix characteristics of the EMTP equation, a purely real part quantum circuit architecture is used to achieve efficient calculation of the loss function; due to the real symmetry of the admittance matrix, the imaginary part calculation component is always zero, and by omitting the imaginary part calculation in the traditional Hadamard test, a large amount of quantum computer resources is saved;

[0037] S32. Solve the EMTP equation based on the variational quantum circuit: Continuously optimize the circuit parameters in S311 using the calculated loss function value to obtain the optimal solution to the corresponding optimization problem, thereby obtaining the solution to the corresponding EMTP admittance equation. The specific steps are as follows:

[0038] S321. Calculate the loss function gradient based on the parameter displacement principle. The specific gradient calculation method is:

[0039]

[0040] Where, L(α) is each Hadamard test circuit;

[0041] S322. Train the variational circuit parameter α based on the gradient descent method according to the gradient value of the loss function. The specific gradient update method is:

[0042]

[0043] in, is the gradient of the loss function, η is the learning rate;

[0044] S323. Substitute the optimal variational circuit parameter α obtained through training into the variational quantum circuit, and use the quantum state tomography method to obtain the final node equation solution.

[0045] As another improvement of the present invention, in step S4, the calculation formula of the current correction amount is:

[0046] △i (k) =i-Gv (k-1)

[0047] Among them, △i (k) is the current correction value of the kth iteration, v (k-1) is the node voltage vector after the k-1th correction;

[0048] According to the current correction amount, the node voltage correction amount is calculated, specifically:

[0049] G△v (k) =△i (k)

[0050] Among them, △v (k) is the node voltage correction amount.

[0051] In order to achieve the above-mentioned purpose, the present invention also adopts the following technical solution: a new power system variational quantum electromagnetic transient simulation method and system, including a computer program, which, when executed by a processor, implements the steps of the method described in any one of claims 1 to 5 above.

[0052] Compared with the existing technology, the present invention has the following beneficial effects: the present invention discloses a novel method and system for variational quantum electromagnetic transient simulation of power systems. This method, through a constant admittance model, converts the repeated quantization process of the time-varying admittance matrix in traditional EMTP simulation into a single mapping operation, solving the problem of frequent changes in the admittance matrix of the electromagnetic transient network and repeated quantization mapping. At the same time, by utilizing the generalized Kronecker decomposition, the high-dimensional matrix to be processed is split into a low-dimensional Kronecker product form similar to a multi-qubit system, exponentially reducing the computational complexity of the preprocessing part. Finally, an innovative quantum circuit real part completeness theorem is proposed, proving that for quantum simulation of real symmetric admittance matrices, the loss function calculation can be strictly equivalently implemented through pure real part quantum circuits. Based on this, a pure real part redundant circuit reduction principle is proposed, which greatly saves quantum computer resources. In summary, the present method has greatly promoted the practical application of quantum electromagnetic transient simulation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flowchart of the steps of a novel power system variational quantum electromagnetic transient simulation method of the present invention;

[0054] Figure 2 Schematic diagram of simulation calculation results of Example 1 of the present invention;

[0055] Figure 31 is a schematic diagram of the calculation error results of Example 1 of the present invention;

[0056] Figure 4 1 is a schematic diagram of the simulation calculation and error results of Phase A of Example 2 of the present invention;

[0057] Figure 5 1 is a schematic diagram of the simulation calculation and error results of phase B of Example 2 of the present invention;

[0058] Figure 6 1 is a schematic diagram of the simulation calculation and error results of phase C of Example 2 of the present invention; DETAILED DESCRIPTION

[0059] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0060] Example 1

[0061] This embodiment is applied to the electromagnetic transient simulation of a Buck circuit, with an input voltage of 50V, a duty cycle of 0.8, a low-voltage side load of 5Ω, an inductor of 100mH, a capacitor of 500μF, a simulation duration of 0.14 seconds, and a time step of 25μs.

[0062] A new power system variational quantum electromagnetic transient simulation method and system, such as Figure 1 As shown, the following steps are included:

[0063] S1: Establish an EMTP quantization model based on a constant admittance model. The equation of the EMTP quantization model is specifically:

[0064]

[0065] in, Represents the N-dimensional admittance matrix constructed by the EMTP model, v(t) represents the node voltage vector; i(t) represents the combined node injection current vector including the current source and the history term; By expanding the N-dimensional admittance matrix to dimensionality, thereby mapping the mathematical expression of EMTP to Hilbert space and converting the mathematical model into a physical model that can be compiled by an actual quantum computer. In this embodiment, the original admittance model is a 3rd-order model, and the augmented EMTP equation is:

[0066]

[0067] Among them, Y sw is the switch equivalent admittance, r is the power supply internal resistance, L is the circuit inductance, C is the circuit capacitance, R is the low-voltage side load, and △t is the simulation step size. The corresponding physical equation is:

[0068] G|v>=|i>

[0069] Where G is G q The unit determinant matrix after scaling; |v> and |i> represent the definitions in The normalized quantum representation of v(t) and i(t) on the qubit. By normalizing the vectors, the probability amplitude conversion of the current and voltage vectors is achieved, and the quantized EMTP equation is constructed.

[0070] In the EMTP quantized model, the EMTP node equation is specifically:

[0071] G0v(t)=i s (t)+i h (t)≡i(t)

[0072] Among them, i s represents the current source vector; i h represents the historical current vector; G0 is the equivalent conductivity matrix with dimension N; in this case, it is:

[0073]

[0074] Mapping the EMTP equations into Hilbert space, we obtain the equations of the EMTP quantized model.

[0075] S2: Calculate the quantum circuit parameters using the matrix low-dimensional quantum projection method; the specific method of parameter calculation is: first, the matrix G is split into tensors based on the generalized Kronecker decomposition, and the low-dimensional sub-matrices obtained by the split are subjected to Pauli basis mapping. After recombination, the quantum circuit parameter calculation results corresponding to the loss function are obtained.

[0076] The calculation of quantum circuit parameters using the low-dimensional quantum projection method of the admittance matrix specifically includes the following steps:

[0077] S21. Perform low-dimensional splitting of the matrix G obtained in step S1 based on generalized Kronecker decomposition:

[0078]

[0079] Where G is the quantized admittance matrix, γ r and ξ r is the low-dimensional matrix after splitting, is the tensor product, r is the generalized Kronecker splitting precision, R is the preset splitting precision, and the upper limit is the minimum dimension of the split submatrix;

[0080] S22. Perform Pauli basis mapping on the low-dimensional matrix:

[0081]

[0082] Among them, G i is the corresponding split i-th sub-matrix, g i is the Pauli basis consisting of four basic Pauli matrices; c i is the calculated corresponding basis parameter, and n is the number of quantum bits that the corresponding circuit will use. The specific calculation formula is:

[0083]

[0084] Where Tr represents the trace of the matrix. Performing Kronecker product operations on the basis parameters of the corresponding submatrix according to the splitting method in S21 can obtain the corresponding parameters of the matrix G under the corresponding Pauli basis;

[0085] S23, based on the Top-down coding principle, the current vector is encoded into the quantum circuit, and the quantum coding method is characterized in that: for the N-dimensional classical current vector I∈R N , through the cooperative mechanism of orthonormal basis decomposition and quantum amplitude mapping, the vector is encoded into In a quantum circuit with 100 qubits, the top-down encoding strategy is used to gradually decompose the circuit structure from the target quantum state, and the complex state is expressed as a superposition or tensor product of low-dimensional sub-states through recursive decomposition. Each sub-state gradually approaches the target state through rotation gates and controlled operations, and finally the quantum circuit depth is O(n 2 / log n) constraint, achieving fidelity encoding of the current vector in the quantum state amplitude;

[0086] S24. Construct a hardware-efficient variational quantum circuit to represent the node voltages to be solved. Through a collaborative architecture of controlled quantum operations and parameterized rotation gates, the grid node voltage problem is mapped to a quantum circuit representation. This implementation involves constructing a variational quantum circuit using a sequence of parameterized rotation gates interleaved with adjacently coupled controlled Z-gates. This ensures accurate representation of node voltage characteristics while reducing the number of qubits and circuit depth.

[0087] S3. Based on the principle of purely real quantum circuit reduction, a quantum circuit is constructed and the quantized EMTP node admittance equation is solved. This quantum equation is constructed using the EMTP method and quantized using the steps in S1. The quantized EMTP equation is transformed into an optimization problem. Combining the Hadamard test principle with the characteristics of the EMTP equation, a real-only quantum computation circuit is constructed to solve the loss function. Finally, the variational circuit parameters are optimized using gradient descent. After obtaining the optimal variational circuit parameters, quantum state tomography is performed to obtain the corresponding node voltage initial values.

[0088] The construction of quantum circuits using the pure real part quantum circuit reduction principle further includes:

[0089] S31. Construct a quantum circuit for the variational quantum linear solver. Convert the EMTP equation in S1 into an optimization problem. The optimal solution is the result of the equation calculation. Use the Hamiltonian to calculate the corresponding loss function in the quantum circuit. The specific steps are as follows:

[0090] S311. Convert the EMTP equation into an optimization model and construct the corresponding loss function. The specific calculation formula is:

[0091]

[0092] Among them, V(α) is the unitary matrix corresponding to the variational quantum circuit, α is the corresponding variational circuit parameter to be trained, G is the quantized admittance matrix, H L The Hamiltonian constructed for the loss function in Hilbert space is:

[0093]

[0094] Where U is the encoding unitary matrix corresponding to the current vector, I is the identity matrix, Apply the identity matrix gate on the corresponding lines other than the j-th quantum bit.

[0095] S312. Based on the loss function constructed in S311, a quantum circuit is constructed using the Hadamard test principle and the Pauli matrix basis calculated in S2, the current vector quantum circuit encoding parameters, and the hardware efficient variational circuit parameters. The quantum circuit construction method is characterized by: based on the real symmetric admittance matrix characteristics of the EMTP equation, a purely real quantum circuit architecture is used to achieve efficient calculation of the loss function. Due to the real symmetry of the admittance matrix, the imaginary part calculation component is always zero, and by omitting the imaginary part calculation in the traditional Hadamard test, a large amount of quantum computer resources can be saved. The specific circuit construction formula is:

[0096]

[0097] Where Z j To apply the Pauli Z gate on the j-th quantum bit. The corresponding loss function is calculated as:

[0098]

[0099] Where C L (α) is the loss function value, c i and c i′ are the Pauli basis projection parameters calculated in S2.

[0100] S32. Solve the EMTP equation based on the variational quantum circuit. The circuit parameters in S311 are continuously optimized using the calculated loss function value to obtain the optimal solution to the corresponding optimization problem, thereby obtaining the solution to the corresponding EMTP admittance equation. The specific steps are as follows:

[0101] S321. Calculate the loss function gradient based on the parameter displacement principle. The specific gradient calculation method is:

[0102]

[0103] Where, L(α) is each Hadamard test circuit;

[0104] S322. Train the variational circuit parameter α based on the gradient descent method according to the gradient value of the loss function. The specific gradient update method is:

[0105]

[0106] in, is the gradient of the loss function, η is the learning rate;

[0107] S323. Substitute the optimal variational circuit parameter α obtained through training into the variational quantum circuit, and use the quantum state tomography method to obtain the final node equation solution.

[0108] S4. Based on the initial node voltage value obtained in step S3, calculate the current correction amount and the node voltage correction amount in sequence to obtain a new node voltage value, and compare it with the preset accuracy; if it is not satisfied, repeat step S3 until the preset accuracy is met, obtain the node voltage value, and obtain the electromagnetic transient simulation result at each moment.

[0109] The calculation formula for the current correction is:

[0110] △i (k) =i-Gv (k-1)

[0111] Among them, △i (k) is the current correction value of the kth iteration, v (k-1) is the node voltage vector after the k-1th correction;

[0112] According to the current correction amount, the node voltage correction amount is calculated, specifically:

[0113] G△v (k) =△i (k)

[0114] Among them, △v (k) is the node voltage correction amount.

[0115] The improved variational quantum linear solver can effectively complete electromagnetic transient simulation calculations. Figure 2 The results of electromagnetic transient analysis of the Buck circuit are shown. The figure shows the simulation waveforms of the inductor current and output voltage within 0-0.14 seconds, and also shows the simulation waveforms of the classic, unimproved variational quantum algorithm, and the improved variational quantum algorithm. Figure 3 The simulation error of quantum electromagnetic transient simulation and classical computer before and after improvement is shown. Figure 3 It can be seen that the simulation errors of both methods for each switch are within 1e-9, and there is no error accumulation.

[0116] Example 2

[0117] This embodiment is applied to electromagnetic transient simulation calculations of a three-phase full-bridge inverter. The AC input voltage is 326.6V, the DC side resistance is 50Ω, the circuit inductance is 500μH, the DC side capacitance is 4mF, the simulation step size is 10μs, and the carrier frequency is 2.5kHz.

[0118] According to the steps of the present invention, quantum electromagnetic transient simulation calculation is performed, and the electromagnetic transient results of the three phases ABC are obtained as follows: Figure 4 、 5 , as shown in 6. Figure 4 、 5 6 show the simulation results of the switch voltages of the upper and lower arms of the three-phase switch bridges A, B, and C respectively, and the calculation errors of the two variational quantum algorithms before and after improvement compared with the classical electromagnetic transient simulation results are given on the right. As can be seen from the figure, the simulation results of the three phases have an error of 10 -7 The results show that the error of the two QEMTP methods is within 100%, and there is no obvious error accumulation. At the same time, there is no obvious error difference between the two QEMTP methods. It can be seen that even in more complex AC-DC networks, the present invention can also achieve accurate simulation calculations.

[0119] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0120] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

[0121] In summary, the method of the present invention solves the problems of frequent changes in the admittance matrix of the electromagnetic transient network and repeated quantization mapping through the constant admittance model; uses the generalized Kronecker decomposition to exponentially reduce the computational complexity of the preprocessing part; finally, proposes the principle of reducing purely real redundant circuits, which greatly saves quantum computer resources. The method of the present invention is useful for solving the dimensionality curse problem encountered when current classical computers solve electromagnetic transient simulation problems.

[0122] It should be noted that the above content merely illustrates the technical idea of the present invention and cannot be used to limit the scope of protection of the present invention. For ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications all fall within the scope of protection of the claims of the present invention.

Claims

1. A new power system variational quantum electromagnetic transient simulation method, characterized by: The steps include: S1: Establish an EMTP quantization model based on a constant admittance model. The mathematical equation of the EMTP quantization model is specifically: in, G represents the N-dimensional admittance matrix constructed by the EMTP model; q Indicates expansion to dimensional EMTP admittance matrix; v(t) represents the node voltage vector; i(t) represents the combined node injection current vector including the current source and history term; By expanding the N-dimensional admittance matrix to Dimension, the mathematical equation of the EMTP quantized model equation is mapped to the Hilbert space to obtain the corresponding physical equation; the expression of the physical equation is specifically: G|v>=|i> Where G is G q The unit determinant matrix after scaling; |v> and |i> represent the definitions in The physical space representation of the quantum states of v(t) and i(t) on the quantum bit; S2: Calculate quantum circuit parameters using the matrix low-dimensional quantum projection method. For the EMTP physical equation constructed in step S1, perform tensor splitting on the matrix G based on the generalized Kronecker decomposition. Perform Pauli basis mapping on the resulting low-dimensional submatrices. After calculating the Kronecker product of the corresponding basis parameters according to the splitting method, obtain the mapping parameters of the corresponding physical equation on the Pauli basis. Based on the top-down coding principle, calculate the quantum circuit coding parameters of |i> and select the gate circuit parameters to implement the quantum circuit expression of |v>. S3. Construct a quantum circuit based on the principle of pure real part quantum circuit reduction and solve the quantized EMTP node admittance equation; transform the quantized EMTP equation into an optimization problem. Combining the Hadamard test principle with the characteristics of the EMTP equation, based on the mapping parameters calculated in step S2 and the corresponding Pauli basis, only construct a real part quantum computing circuit to solve the loss function, optimize the variational circuit parameters based on the gradient descent method, and after obtaining the optimal variational circuit parameters, perform quantum state tomography to obtain the corresponding node voltage initial value; S4. Based on the initial node voltage value obtained in step S3, calculate the current correction amount and the node voltage correction amount in sequence to obtain a new node voltage value, and compare it with the preset accuracy; if it is not satisfied, repeat step S3 until the preset accuracy is met, obtain the node voltage value, and obtain the electromagnetic transient simulation result at each moment.

2. The novel power system variational quantum electromagnetic transient simulation method according to claim 1, characterized in that: In the EMTP quantization model of step S1, the EMTP node equation is specifically: G0v(t)=i s (t)+i h (t)≡i(t) Among them, i s (t) represents the current source vector; i h (t) represents the historical current vector; G0 is the equivalent admittance matrix with dimension N.

3. The novel power system variational quantum electromagnetic transient simulation method according to claim 2, characterized in that: The step S2 specifically includes the following steps: S21. Perform low-dimensional splitting of the matrix G obtained in step S1 based on generalized Kronecker decomposition: Where G is the quantized admittance matrix, γ r and ξ r is the low-dimensional matrix after splitting, is the tensor product, r is the generalized Kronecker splitting accuracy, R is the preset splitting accuracy, and the upper limit is the minimum dimension of the split submatrix; S22. Perform Pauli basis mapping on the low-dimensional matrix: Among them, G i is the corresponding split i-th sub-matrix, g i is the Pauli basis consisting of four basic Pauli matrices; c i is the corresponding basis parameter obtained by calculation, n is the number of quantum bits to be used in the corresponding circuit; S23, based on the top-down coding principle, the current vector is encoded into the quantum circuit, specifically: for the N-dimensional classical current vector I∈R N , through the cooperative mechanism of orthonormal basis decomposition and quantum amplitude mapping, the vector is encoded into In a quantum circuit with 10 qubits, the Top-Down coding strategy is used to start from the target quantum state and gradually decompose the circuit structure. The complex state is expressed as a superposition or tensor product of low-dimensional sub-states through recursive decomposition. Each sub-state gradually approaches the target state through rotation gates and controlled operations, and finally the quantum circuit depth is O(n 2 / log n) constraint, achieving fidelity encoding of the current vector in the quantum state amplitude; S24. Construct a hardware-efficient variational quantum circuit to represent the node voltage to be solved: Through the collaborative architecture of controlled quantum operations and parameterized rotation gates, the grid node voltage solution problem is mapped to a quantum circuit expression.

4. The novel power system variational quantum electromagnetic transient simulation method according to claim 3, characterized in that: The step S3 specifically includes the following steps: S31. Construction of a variational quantum linear solver quantum circuit: Convert the EMTP equation in step S1 into an optimization problem, and obtain the optimal solution as the result of the equation calculation; use the Hamiltonian to calculate the corresponding loss function in the quantum circuit. The specific steps are as follows: S311. Convert the EMTP equation into an optimization model and construct the corresponding loss function. The specific calculation formula is: Among them, V(α) is the unitary matrix corresponding to the variational quantum circuit, α is the corresponding variational circuit parameter to be trained, G is the quantized admittance matrix, H L Hamiltonian constructed for the loss function in Hilbert space; S312. According to the loss function constructed in step S311, using the Hadamard test principle, constructing a quantum circuit based on the Pauli matrix basis calculated in step S2, the current vector quantum circuit encoding parameters, and the hardware efficient variational circuit parameters, and based on the real symmetric admittance matrix characteristics of the EMTP equation, using a purely real part quantum circuit architecture to achieve efficient calculation of the loss function; S32. Solve the EMTP equation based on the variational quantum circuit: Optimize the circuit parameters in S311 through the loss function value to obtain the optimal solution to the corresponding optimization problem, thereby obtaining the solution to the corresponding EMTP admittance equation: S321. Calculate the loss function gradient based on the parameter displacement principle: The specific gradient calculation method is: Where, L(α) is each Hadamard test circuit; S322. Train the variational circuit parameter α using the gradient descent method based on the gradient value of the loss function. The specific gradient update method is: in, is the loss function gradient, η is the learning rate; S323. Substitute the optimal variational circuit parameter α obtained through training into the variational quantum circuit, and use the quantum state tomography method to obtain the final node equation solution.

5. The novel power system variational quantum electromagnetic transient simulation method according to claim 4, characterized in that: In step S4, the calculation formula of the current correction amount is: △i( k )=i-Gv( k-1 ) Among them, △i (k) is the current correction value of the kth iteration, v (k-1) is the node voltage vector after the k-1th correction; According to the current correction amount, the node voltage correction amount is calculated, specifically: G△v( k )=△i( k ) Among them, △v (k) is the node voltage correction amount.

6. A novel power system variational quantum electromagnetic transient simulation system, including a computer program, characterized by: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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