A power generation system reliability evaluation method based on quantum computing theory

Abstract

The application discloses a power generation system reliability evaluation method based on quantum computing theory, comprising the following steps: 1, preparing a quantum reliability model of an element, and generating all system states at one time; 2, establishing a quantum model for calculating power generation and load shedding of each system state; 3, establishing a quantum model for distinguishing load shedding states, calculating reliability indexes and converting the reliability indexes into a form convenient for measurement; and 4, establishing a quantum reliability index measurement model based on an IQAE algorithm, obtaining high-precision reliability indexes and completing a reliability evaluation process. Through the research on the application of quantum computing in power system reliability evaluation, the quantum reliability evaluation process is summarized, the quantum reliability index calculation model is established, the accurate reliability evaluation indexes are solved through the IQAE algorithm, and thus the power system reliability evaluation is completed, a new reference for the power system reliability evaluation is provided, and then the power grid reliability and stability are improved.

Description

Technical Field

[0001] This invention relates to the field of reliability assessment, and more specifically to a reliability assessment method for power generation systems based on quantum computing. Background Technology

[0002] Power system reliability assessment is a crucial step in ensuring power quality and optimizing system planning and operation. However, this assessment process is complex and cumbersome, facing numerous challenges such as the integration of renewable energy, energy storage systems, electric vehicles, and load redistribution attacks. These changes not only increase the complexity of the assessment but also place higher demands on its accuracy. Therefore, seeking new methods to improve assessment efficiency is essential. Quantum computing, with its highly efficient parallel computing capabilities, offers a new perspective for solving highly complex problems. For example, Shor's algorithm, through its innovative method of large number factorization, challenges classical communication cryptography. Similarly, Grover's algorithm's advantage in search speed demonstrates the potential of quantum computing to surpass classical computing in solving certain problems. Therefore, applying quantum computing to power system reliability assessment holds great promise.

[0003] Regarding the application of quantum computing in power system computation and analysis, some scholars both domestically and internationally have conducted research: They have solved fast decoupling power flow problems using an enhanced Harrow-Hassidim-Lloyd (HHL) algorithm based on quantum computing; leveraged the excellent search capabilities and convergence speed of quantum particle swarm optimization (QSA) to solve economic load dispatching problems; applied quantum computing to transient stability assessment and developed a quantum natural gradient descent algorithm to train low-depth quantum circuits; and utilized the quantum amplitude estimation (QAE) algorithm to evaluate the reliability indices of distribution systems. However, research on reliability assessment of power generation systems based on quantum computing and specific quantum models is currently scarce both domestically and internationally. Therefore, it is essential to study the approach to calculating reliability indices of quantum power generation systems and the specific quantum models used. Summary of the Invention

[0004] This invention aims to fill the gap in the field of reliability assessment of quantum power generation systems by providing a reliability assessment method for power generation systems based on quantum computing theory. It aims to leverage the advantages of quantum parallel computing to reduce the complexity of classical sampling and reliability assessment processes. The reliability indicators of the power generation system are solved using quantum computing theory, and the reliability indicators are measured using the IQAE algorithm to achieve higher accuracy. This provides a basis and reference for the application of quantum computing in the reliability assessment of power systems.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] The reliability assessment method for a power generation system based on quantum computing theory, as described in this invention, is characterized by the following steps:

[0007] Step 1: Let each qubit represent a generator, construct the element quantum reliability model F1, and thus generate all the state vectors of the power generation system at once;

[0008] Step 2: Based on all state vectors of the power generation system, establish quantum models for calculating the probability of power shortage (LOLP) and quantum models for calculating the power generation and load shedding in each state of the power generation system using quantum adders.

[0009] Step 3: Based on the state vectors of power generation and load shedding in each state of the power generation system, establish a quantum model to calculate the expected value of insufficient power EENS, and convert the reliability indices LOLP and EENS into quantum state vectors that are easy to measure.

[0010] Step 4: Based on the converted quantum state vector, establish a quantum reliability index measurement model based on the IQAE algorithm, and measure the reliability index from the quantum state vector containing LOLP and EENS to complete the reliability assessment.

[0011] The reliability assessment method for a power generation system based on quantum computing theory described in this invention is also characterized in that step 1 is performed as follows:

[0012] Step 1.1: Calculate the normal operating probability A of the j-th generator using equations (1) and (2) respectively. j Probability of abnormal operation U j :

[0013]

[0014]

[0015] In equations (1) and (2), ξ j Let μ be the failure rate of the j-th generator. j Let j be the repair time for the j-th generator;

[0016] Step 1.2: Calculate the angle θ of the Y-rotation gate used by the j-th qubit in the element quantum reliability model F1 using equation (3). j :

[0017]

[0018] In equation (3), n represents the total number of qubits in the element quantum reliability model;

[0019] Step 1.3: Use the state probabilities of the ground states |0> and |1> to represent the normal operation probability A of the j-th generator. j Probability of abnormal operation Uj Based on the Y-rotation gate angle obtained in step 1.2, a quantum reliability model F1 is constructed, and the state vector q of the j-th qubit output by the quantum reliability model F1 is obtained using equation (4). j :

[0020]

[0021] Step 1.4: Use equation (5) to generate the state vector F1·|0> of the power generation system. n :

[0022]

[0023] In equation (5), Denotes tensor product, |0> n Let represent the tensor product of n ground states |0>.

[0024] Step 2 is performed as follows:

[0025] Step 2.1: Calculate the number of qubits λ required to generate electricity using equations (6) and (7):

[0026]

[0027]

[0028] In equations (6) and (7), S j This represents the power output of the j-th generator. Let L represent the scaled output of the j-th generator, and L represent the load of the power generation system. represents the scaled load of the power generation system, c represents the greatest common factor of power generation and load, and N represents a positive integer;

[0029] Step 2.2: Construct the quantum adder U using equation (8). add :

[0030] U add (|001>|010>)=|011>|010> (8)

[0031] In equation (8), |010> represents the addend of the three qubits, |001> represents the addend of the three qubits, and |011> represents the result of the addition of the three qubits;

[0032] Step 2.3, based on quantum adder U add Constructing a controlled quantum model U m and F1|0> n+λ Input controlled quantum model U mThus, the controlled quantum model U is obtained using equation (9). m Output quantum system state vector |ψ>′ n+λ :

[0033]

[0034] In equation (9), g i |g represents the power generation of the power generation system in state |i>. i > λ The amount of electricity generated, g, is represented by λ qubits. i p i This represents the probability that the power generation system is in state |i>. n Represents a state |i>, represented by n qubits;

[0035] Step 2.4, based on quantum adder U add Constructing a quantum comparator U cp and |ψ>′ n+λ Input quantum comparator U cp Thus, the quantum comparator U is obtained using equation (10). cp Output the quantum system state vector containing the probability of low power (LOLP):

[0036]

[0037] In equation (10), Let L(LOLP) represent the probability of insufficient power, X represent the set of states where the power generation system is underloaded, and Y represent the set of states where the power generation system is operating normally.

[0038] Step 2.5, calculate the power generation |g i > λ and the scaled load of the power generation system Construct a quantum subtractor U, using it as the minuend and subtrahend respectively. sub And the quantum comparator U cp The output quantum system state vector is input to build a quantum subtractor U. sub Thus, the quantum subtractor U can be obtained using equation (11). sub The load shedding amount S′ of the output power generation system under state |i> i :

[0039]

[0040] In equation (11), |S′ i > λ The load S′ represents the load in λ qubits. i .

[0041] Step 3 is performed as follows:

[0042] Step 3.1: Use equation (12) to calculate the load shedding amount S′ of the power generation system under state |i>. i With scaled load Perform a second scaling:

[0043]

[0044] In equation (12), This represents the second scaled value of the load shedding amount of the power generation system in state |i>. c′ represents the second scaling value of the power generation system load, and c′ represents the second scaling factor;

[0045] Step 3.2: Based on the controlled Y-revolving door with an angle of 2·π / 4=π / 2, calculate the second scaling value of the load shearing amount. Transformed into a sinusoidal form that conforms to the probability amplitude of a quantum bit.

[0046] Step 3.3: Use equation (13) to scale the load shearing amount a second time. sinusoidal form Perform a Taylor expansion:

[0047]

[0048] In equation (13), Represents the remainder term in the Taylor series expansion;

[0049] Step 3.4: Calculate the angle θ′ of the t-th controlled Y-revolving door using equation (14). t ,t∈{1,...,λ+1}:

[0050]

[0051] Step 3.5: Construct operator R using λ+1 controlled Y-rotation gates. o Thus, the operator R is obtained using equation (15). o The output quantum state vector contains the expected value of the insufficient charge EENS:

[0052]

[0053] In equation (15), The square of represents the reliability index EENS. Represents operator R o Input to output.

[0054] Step 4 is performed as follows:

[0055] Step 4.1: Construct operator Q using equation (16):

[0056]

[0057] In equation (16), |ψ0> represents the target quantum state vector, Ι represents the identity matrix, and P represents the quantum model used to output the target quantum state vector |ψ0>. This represents the conjugate transpose of the quantum model P. S0 represents the quantum model that multiplies the target quantum state vector |ψ0> by -1; S0 represents the quantum model that multiplies the |0> state by -1.

[0058] Step 4.2: Use equation (17) to obtain the quantum models P and Q. k Output target quantum state vector |ψ0>:

[0059]

[0060] In equation (17), Q k This indicates the kth application of the Q operator, where θ represents the angle by which the target quantum state vector |ψ0> is rotated around the Y-axis.

[0061] Step 4.3: Transform equation (17) into the cosine form shown in equation (18):

[0062] sin 2 ((2k+1)θ)=(1-cos((4k+2)θ)) / 2 (18)

[0063] Step 4.4: Assume variable K = 4k + 2, and use equation (19) to calculate the range of values ​​for variable K:

[0064]

[0065] In equation (19), K max and K min These represent the maximum and minimum values ​​of variable K, respectively. Represents rounding down, θ u and θ l These represent the upper and lower limits of θ, respectively, and K0 represents the initial value of variable K;

[0066] Step 4.5, within the maximum value K max and minimum value K min Internal measurement quantum models P and Q k The current output, and the upper limit a of the current measurement value a. max and lower limit a min Therefore, θ is calculated using equation (20) for the current measured value a. u and θ l :

[0067]

[0068] In equation (20), θ′ u and θ′ l These represent the upper and lower limits of θ obtained in the previous measurement, respectively; mod 2π represents the function that takes the remainder when divided by 2π; cos -1 Represents the inverse cosine function;

[0069] Step 4.6, Determine θ u -θ l Does it satisfy the accuracy ε? If it does, then it means that a confidence interval [θ] is obtained for the current measured value a, with a value range of [0,π] or [π,2π]. l ,θ u If the estimated value is not obtained, return to step 4.4 to remeasure and calculate.

[0070] Step 4.7: Use equation (21) to obtain the current measured value a, and use it as the reliability indices LOLP and EENS to conduct a reliability assessment of the power generation system.

[0071]

[0072] In equation (21), a u and a l These represent the upper and lower limits of the current measured value 'a', respectively.

[0073] The present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the power generation system reliability assessment method, and the processor is configured to execute the program stored in the memory.

[0074] The present invention discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, performs the steps of the power generation system reliability assessment method.

[0075] Compared with existing technologies, the beneficial effects of this invention are reflected in:

[0076] 1. Based on quantum computing theories, this invention establishes a quantum reliability model for components. This model can generate all system states at once, giving full play to the advantages of quantum parallelism and providing a reference for the combination of quantum computing and component shutdown models.

[0077] 2. This invention uses quantum binary numbers to represent power generation, reduces the number of qubits used by the greatest common factor, establishes a quantum computing model for power generation in each state, combines the quantum reliability model of the components with power generation, and obtains the state of the entire system carrying power generation and state probability, providing a basis for the calculation of quantum reliability index.

[0078] 3. This invention utilizes a quantum adder to construct a quantum comparator model. By comparing power generation and load, the system's unloaded state is identified in the quantum circuit. The probability of the unloaded state is accumulated and converted into the probability amplitude of the auxiliary qubit, thus successfully calculating the reliability index LOLP.

[0079] 4. This invention constructs a quantum subtractor model to convert the power generation of each system state into load shedding, thereby reducing the number of qubits used and establishing a one-to-one correspondence between load shedding and state probabilities, thus providing a basis for calculating the reliability index EENS.

[0080] 5. Based on the Taylor expansion and simplification formula of the cosine function, this invention constructs a quantum model with controlled operators. This quantum model can realize the product of the probability of each unloaded state and the load shedding amount, and eliminate the normal operating state to accurately obtain the reliability index EENS.

[0081] 6. This invention establishes a quantum reliability index measurement model based on the IQAE algorithm, which converts the reliability index calculated in the quantum circuit into a real number form of a classical computer. By comparing the accuracy with that of the enumeration method, the correctness of the quantum reliability evaluation model of this invention is reflected. Attached Figure Description

[0082] Figure 1 This is a quantum model diagram of the present invention used to identify the unloaded state and calculate LOLP;

[0083] Figure 2 This is a quantum model diagram for calculating EENS in this invention;

[0084] Figure 3 This is a flowchart of the IQAE algorithm of the present invention. Detailed implementation method:

[0085] In this embodiment, a reliability assessment method for a power generation system based on quantum computing theory is performed according to the following steps:

[0086] Step 1: Let each qubit represent a generator, construct a quantum reliability model of the components, and thus generate all the state vectors of the power generation system at once;

[0087] Step 1.1: Calculate the non-fault probability A of the j-th generator using equations (1) and (2) respectively. j and failure probability Uj :

[0088]

[0089]

[0090] In equations (1) and (2), ξ j Let μ be the failure rate of the j-th generator. j Let be the repair time for the j-th generator.

[0091] Step 1.2: Calculate the R value used by the j-th qubit in the quantum reliability model of the element using equation (3). y (θ) Angle of the door θ j :

[0092]

[0093] In equation (3), n represents the total number of qubits in the element quantum reliability model.

[0094] Step 1.3: Use the state probabilities of the ground states |0> and |1> to represent the non-fault probability A of the j-th generator. j and failure probability U j Thus, the quantum reliability model of the component corresponding to the j-th generator is constructed using equation (4):

[0095]

[0096] In equation (4), q j Let represent the state vector of the j-th qubit.

[0097] Step 1.4: Use equation (5) to generate all state vectors F1·|0> of the power generation system. n :

[0098]

[0099] In equation (5), Let F1 denote the tensor product, and let F1 denote the element quantum reliability model of steps 1.2 and 1.3.

[0100] Step 2: Establish a quantum model for calculating the power generation and load shedding in each state of the power generation system:

[0101] Step 2.1: Calculate the number of qubits λ required to generate electricity using equations (6) and (7):

[0102]

[0103]

[0104] In equations (6) and (7), This represents the scaled-down power generation. This represents the scaled load, c represents the greatest common factor of power generation and load, and N represents a positive integer.

[0105] Step 2.2: Construct a quantum adder using equation (8) to realize binary addition in a quantum circuit:

[0106] U add (|001>010>)=|011>|010> (8)

[0107] In equation (8), U add This represents a quantum adder circuit.

[0108] Step 2.3: Using the number of qubits λ required for power generation calculated by equation (7) and the quantum adder constructed by equation (8), a controlled quantum model U is constructed using equation (9). m Calculate the power generation and load shedding in each state of the power generation system, and convert the quantum system state vector F1|0> n+λ As input, the output quantum system state vector |ψ>′ n+λ for:

[0109]

[0110] In equation (9), λ represents the number of qubits required to generate electricity, and g i p represents the total power generation of the power generation system in state |i>. i This represents the probability that the power generation system is in state |i>.

[0111] Step 2.4: Use the quantum adder constructed by equation (8) to construct a quantum comparator, and use the quantum system state vector |ψ>′ output by equation (8) n+λ As input, the quantum system state vector containing the reliability index "power shortage probability" LOLP is calculated and output using Equation (10), and the quantum model of LOLP is calculated as follows. Figure 1 As shown:

[0112]

[0113] In equation (10), U represents the probability of insufficient power (LOLP). cp Let X represent the set of states of the power generation system under load, Y represent the set of states of the power generation system under normal operation, λ represent the number of qubits required to generate power, and g represent the quantum comparator. i p represents the total power generation of the power generation system in state |i>. i This represents the probability that the power generation system is in state |i>.

[0114] Step 2.5: Using equation (11), obtain the formula for the reliability index "expected power shortage value" EENS under classical calculation:

[0115]

[0116] In equation (11), p x S′ is the probability of the power generation system being in state x; X is the set of states where the power generation system is unloaded; S′ x It represents the load shedding amount when the power generation system is in state x; T is the time interval.

[0117] Step 2.6: Use the output of equation (10) as the input for this step, by measuring the power generation |g i > λ+1 and load Using these as the minuend and subtrahend respectively, a quantum subtractor is constructed, and the load shedding amount S′ of each state of the power generation system is output using equation (12). i :

[0118]

[0119] In equation (12), U sub S' represents a quantum subtractor. i This indicates the load shedding amount under various conditions of the power generation system.

[0120] Step 3: Establish a quantum model to calculate reliability indicators and transform the reliability indicators into a form that is easy to measure:

[0121] Step 3.1: Use equation (13) to calculate the load shedding amount S′ of the power generation system under state |i>. i With scaled load Perform a second scaling:

[0122]

[0123] In equation (12), This represents the second scaled value of the load shedding amount of the power generation system in state |i>. c′ represents the second scaling value of the power generation system load, and c′ represents the second scaling factor;

[0124] Step 3.2: Based on the controlled Y-revolving door with an angle of 2·π / 4=π / 2, calculate the second scaling value of the load shearing amount. Transformed into a sinusoidal form that conforms to the probability amplitude of a quantum bit.

[0125] Step 3.3: Use equation (14) to scale the load shearing amount a second time. sinusoidal form Perform a Taylor expansion:

[0126]

[0127] In equation (14), Represents the remainder term in the Taylor series expansion;

[0128] Step 3.4: Calculate the angle θ′ of the t-th controlled Y-revolving door using equation (15). t ,t∈{1,...,λ+1}:

[0129]

[0130] Step 3.5: Construct operator R using λ+1 controlled Y-rotation gates. o Thus, the operator R is obtained using equation (16). o The output is a quantum state vector containing the expected value of the insufficient charge EENS. The quantum model for calculating EENS is as follows: Figure 2 As shown:

[0131]

[0132] In equation (16), The square of represents the reliability index EENS. Represents operator R o Input to output;

[0133] Step 4: Establish a quantum reliability index measurement model based on the IQAE algorithm, and measure the reliability index from the quantum state vector containing LOLP and EENS to complete the reliability assessment.

[0134] Step 4.1: Construct operator Q using equation (17):

[0135]

[0136] In equation (17), |ψ0> represents the target quantum state vector, Ι represents the identity matrix, and P represents the quantum model used to output the target quantum state vector |ψ0>. This represents the conjugate transpose of the quantum model P. S0 represents the quantum model that multiplies the target quantum state vector |ψ0> by -1; S0 represents the quantum model that multiplies the |0> state by -1.

[0137] Step 4.2: Obtain the quantum models P and Q using equation (18). k Output target quantum state vector |ψ0>:

[0138]

[0139] In equation (18), Qk This indicates the kth application of the Q operator, where θ represents the angle by which the target quantum state vector |ψ0> is rotated around the Y-axis.

[0140] Step 4.3: Transform equation (18) into the cosine form shown in equation (19):

[0141] sin 2 ((2k+1)θ)=(1-cos((4k+2)θ)) / 2 (19)

[0142] Step 4.4: Assume variable K = 4k + 2, and use equation (20) to calculate the range of values ​​for variable K:

[0143]

[0144] In equation (20), K max and K min These represent the maximum and minimum values ​​of variable K, respectively. Represents rounding down, θ u and θ l These represent the upper and lower limits of θ, respectively, and K0 represents the initial value of variable K;

[0145] Step 4.5, within the maximum value K max and minimum value K min Internal measurement quantum models P and Q k The current output, and the upper limit a of the current measurement value a. max and lower limit a min Therefore, θ is calculated using equation (21) for the current measured value a. u and θ l :

[0146]

[0147] In equation (21), θ′ u and θ′ l These represent the upper and lower limits of θ obtained in the previous measurement, respectively; mod 2π represents the function that takes the remainder when divided by 2π; cos -1 Represents the inverse cosine function;

[0148] Step 4.6, Determine θ u -θ l Does it satisfy the accuracy ε? If it does, then it means that a confidence interval [θ] is obtained for the current measured value a, with a value range of [0,π] or [π,2π]. l ,θ u If the estimated value is not obtained, return to step 4.4 to remeasure and calculate.

[0149] Step 4.7: Use equation (22) to obtain the current measured value a, and use it as the reliability indices LOLP and EENS to conduct reliability assessment of the power generation system. The IQAE algorithm flowchart is as follows. Figure 3 As shown:

[0150]

[0151] In equation (22), a u and a l These represent the upper and lower limits of the current measured value 'a', respectively.

[0152] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0153] In this embodiment, a computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the above-described method.

Claims

1. A reliability assessment method for a power generation system based on quantum computing theory, characterized in that, The procedure is as follows: Step 1: Let each qubit represent a generator, and construct a quantum reliability model for the components. This allows all state vectors of the power generation system to be generated at once. Step 2: Based on all state vectors of the power generation system, establish quantum models for calculating the probability of power shortage (LOLP) and quantum models for calculating the power generation and load shedding in each state of the power generation system using quantum adders. Step 2.1: Calculate the number of qubits λ required to generate electricity using equations (6) and (7): (6) (7) In equations (6) and (7), This represents the power output of the j-th generator. This represents the scaled output of the j-th generator. Indicates the load of the power generation system. represents the scaled load of the power generation system, c represents the greatest common factor of power generation and load, and N represents a positive integer; Step 2.2: Construct a quantum adder using equation (8) : (8) In equation (8), This represents the addend of three qubits. This represents the addend of three qubits. This represents the result of adding three qubits; Step 2.3: Based on quantum adders Constructing a controlled quantum model and the state vector of the power generation system Input controlled quantum model Thus, the controlled quantum model is obtained using equation (9). Output quantum system state vector : (9) In equation (9), g i Represents the state of the power generation system The power generation below, The amount of electricity generated, g, is represented by λ qubits. i , This indicates that the power generation system is in a certain state. The probability, Representing a state in terms of n qubits ; Step 2.4: Based on quantum adders Building a quantum comparator and will Input quantum comparator Thus, the quantum comparator is obtained using equation (10). Output the quantum system state vector containing the probability of low power (LOLP): (10) In equation (10), Let L(LOLP) represent the probability of insufficient power, X represent the set of states where the power generation system is underloaded, and Y represent the set of states where the power generation system is operating normally. Step 2.5: Calculate the power generation. and the scaled load of the power generation system Construct a quantum subtractor, using the minuend and subtrahend respectively. and quantum comparator The output quantum system state vector is used as input to build a quantum subtractor. Thus, the quantum subtractor can be constructed using equation (11). The output power generation system is in state under load : (11) In equation (11), This represents the load amount in λ qubits. ; Step 3: Based on the state vectors of power generation and load shedding in each state of the power generation system, establish a quantum model to calculate the expected value of insufficient power EENS, and convert the reliability indices LOLP and EENS into quantum state vectors that are easy to measure. Step 3.1: Use equation (12) to determine the state of the power generation system. under load With scaled load Perform a second scaling: (12) In equation (12), Indicates the state of the power generation system The second scaling value of the load shedding amount. This represents the second scaling value of the power generation system load. Indicates the second scaling factor; Step 3.2, based on the angle... The controlled Y-turn door will scale the load amount a second time. Transformed into a sinusoidal form that conforms to the probability amplitude of a quantum bit. ; Step 3.3: Use equation (13) to scale the load shearing amount a second time. sinusoidal form Perform a Taylor expansion: (13) In equation (13), Represents the remainder term in the Taylor series expansion; Step 3.4: Calculate the angle of the t-th controlled Y-revolving door using equation (14). : (14) Step 3.5: Construct operator R using λ+1 controlled Y-rotation gates. o Thus, the operator R is obtained using equation (15). o The output quantum state vector contains the expected value of the insufficient charge EENS: (15) In equation (15), The square of represents the reliability index EENS. Represents operator R o Input to output; Step 4: Based on the converted quantum state vector, establish a quantum reliability index measurement model based on the IQAE algorithm, and measure the reliability index from the quantum state vector containing LOLP and EENS to complete the reliability assessment.

2. The reliability assessment method for a power generation system based on quantum computing theory according to claim 1, characterized in that, Step 1 is performed as follows: Step 1.1: Calculate the normal operating probability A of the j-th generator using equations (1) and (2) respectively. j Probability of abnormal operation U j : (1) (2) In equations (1) and (2), ξ j Let μ be the failure rate of the j-th generator. j Let j be the repair time for the j-th generator; Step 1.2: Calculate the quantum reliability model of the component using equation (3). The angle θ of the Y-rotation gate used in the j-th qubit j : (3) In equation (3), n represents the total number of qubits in the element quantum reliability model; Step 1.3: Utilizing the ground state and The state probability represents the normal operation probability A of the j-th generator. j Probability of abnormal operation U j Based on the Y-rotation gate angle obtained in step 1.2, a quantum reliability model for the components is constructed. Thus, the quantum reliability model of the component is obtained using equation (4). The output state vector q of the j-th qubit j : (4) Step 1.4: Generate the state vector of the power generation system using equation (5). : (5) In equation (5), Represents the tensor product. Represents n ground states The tensor product.

3. The reliability assessment method for a power generation system based on quantum computing theory according to claim 2, characterized in that, Step 4 is performed as follows: Step 4.1: Construct operator Q using equation (16): (16) In equation (16), Represents the target quantum state vector. Let P represent the identity matrix, and let P represent the target quantum state vector used for output. The quantum model, This represents the conjugate transpose of the quantum model P. This indicates that the target quantum state vector The quantum model multiplied by -1 Indicates will A quantum model where the state is multiplied by -1; Step 4.2: Use equation (17) to obtain the quantum model P and Output target quantum state vector : (17) In equation (17), This indicates that the Q operator is applied k times. Represents the target quantum state vector The angle of rotation about the Y-axis; Step 4.3: Transform equation (17) into the cosine form shown in equation (18): (18) Step 4.4: Assume variable K = 4k + 2, and use equation (19) to calculate the range of values ​​for variable K: (19) In equation (19), and These represent the maximum and minimum values ​​of variable K, respectively. This represents rounding down. and Represent The upper and lower limits, This represents the initial value of variable K; Step 4.5, at the maximum value and minimum value Internal measurement quantum model P and The current output, and the upper limit of the current measurement value a. and lower limit Therefore, equation (20) is used to calculate the corresponding value at the current measurement value a. of and : (20) In equation (20), and These represent the results obtained in the previous measurement. The upper and lower limits, Meaning to divide by Functions that take the remainder Represents the inverse cosine function; Step 4.6, Judgment Does it meet the accuracy requirement? If satisfied, it means that a confidence interval [θ] is obtained where the value range is [0, π] or [π, 2π] under the current measured value a. l , θ u If the estimated value is not obtained, return to step 4.4 to remeasure and calculate. Step 4.7: Use equation (21) to obtain the current measured value a, and use it as the reliability indices LOLP and EENS to conduct a reliability assessment of the power generation system. (21) In equation (21), and These represent the upper and lower limits of the current measured value 'a', respectively.

4. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store programs that support the processor in executing the reliability assessment method for any of the power generation systems described in claims 1-3, and the processor is configured to execute the programs stored in the memory.

5. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is run by the processor, it performs the steps of the reliability assessment method for any of the power generation systems described in claims 1-3.

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