Multi-state N-out-of-K power generation system reliability detection method based on parallel architecture

Abstract

The invention discloses a multi-state N-out-of-K power generation system reliability detection method based on a parallel architecture. The method comprises the following steps: firstly, establishing a multi-state reliability function corresponding to each element in the large-scale N-out-of-K power generation system by using a general generation function method; then, according to the multi-state reliability function corresponding to each element, multi-state reliability functions corresponding to subsystems from the first hierarchy to the (A-1) th hierarchy of the power generation system are calculated in sequence by utilizing a multi-layer parallel subsystem construction method; and then, based on the multi-state reliability function corresponding to the (A-1) th hierarchy subsystem, generating the reliability of the large-scale N-out-of-K power generation system by adopting a general function generation method, and completing the detection of the reliability. According to the method, the multi-state reliability function of each subsystem is subjected to parallel calculation by adopting a parallel method, so that the time accumulation effect in traditional serial calculation is avoided, and the time period of reliability detection of the large-scale N-K power generation system considering parallel elements is shortened.

Description

Technical Field

[0001] The present invention relates to a method for detecting the reliability of a power generation system, and particularly to a method for detecting the reliability of a multi-state N-out-of-K power generation system based on a parallel architecture. Background Art

[0002] Equipment in a power generation system, such as a generator, etc., may encounter various forms of failures or performance degradation phenomena during its complex and long operation cycle, which affects the stable operation of the power generation system and may also lead to the interruption of power supply, affecting the continuous and reliable power supply to a large number of power users. Therefore, in the planning, design, and subsequent operation and maintenance management stages of the power generation system, the reliability issue of the power generation system must be highly emphasized and systematically considered.

[0003] The reliability of a power generation system measures the ability of the power generation system to continuously and stably provide power services to power users according to the preset power quality and quantity requirements that meet industry and national standards. Improving the reliability of the power generation system can ensure the stable operation of the social economy and the electricity demand of people's daily lives, and is an indispensable part of the sustainable and healthy development of the power industry.

[0004] A power generation system is usually composed of components such as generators in parallel and has an N-out-of-K structure. However, with the increasing scale of the power generation system, the introduction of a large number of power components has led to a sharp increase in the number of components, and at the same time, the number of possible states of the system has also increased. This sharp increase in the order of magnitude (usually exponential) poses a challenge to the demand for computing resources and becomes a key factor restricting the further development of the reliability of large-scale N-out-of-K power generation systems. For traditional methods for detecting the reliability of power generation systems, when the system scale reaches a certain level, the processing time and computing resources required by these methods will increase sharply. Generally, it is considered that if the system reliability calculation time is greater than 30 minutes, the system is large-scale. When facing a large-scale N-out-of-K power generation system containing a large number of parallel components, these methods are often difficult to effectively and quickly evaluate the reliability of the system.

[0005] Therefore, in order to address this challenge, it is urgent to explore new reliability detection technologies and methods. These methods need to be able to significantly improve the computational efficiency of reliability detection while ensuring accuracy to meet the requirements of the stable operation of large-scale N-out-of-K power generation systems. Summary of the Invention

[0006] In order to solve the problems and requirements in the background art, the present invention provides a method for detecting the reliability of a multi-state N-out-of-K power generation system based on a parallel architecture. The present invention combines computer parallel computing, system structure division strategies, and methods for detecting the reliability of power generation systems, which helps to quickly detect the reliability of large-scale power generation systems.

[0007] The technical solution adopted by the present invention is as follows:

[0008] 1. A reliability detection method for a multi-state N-out-of-K power generation system based on a parallel architecture

[0009] The first step: Use the general generating function method to establish the multi-state reliability functions corresponding to each component in a large-scale N-out-of-K power generation system;

[0010] The second step: According to the multi-state reliability functions corresponding to each component, use the method of constructing multi-layer parallel subsystems to sequentially calculate the multi-state reliability functions corresponding to each hierarchical subsystem of the power generation system, and finally obtain the multi-state reliability function corresponding to the (A - 1)-th hierarchical subsystem, where A is the number of levels of the power generation system;

[0011] The third step: Based on the multi-state reliability function corresponding to the (A - 1)-th layer subsystem, use the general generating function method to generate the reliability of the large-scale N-out-of-K power generation system and complete the reliability detection.

[0012] The specific content of the second step is as follows:

[0013] S21: After factoring the number n of parallel components in the power generation system, obtain a factor list, satisfying n = n1 × n2 × … × n a … × n A , n a is the a-th factor, a = 1, …, A, where A is the number of factors in the factor list and is used as the number of levels of the power generation system;

[0014] S22: Form a first-level subsystem S1 from every n1 components in the power generation system, thereby obtaining n2 × … × n A first-level subsystems, and calculate the multi-state reliability functions corresponding to each first-level subsystem based on the multi-state reliability functions of all components;

[0015] S23: After combining the current-level subsystems according to the next factor in the factor list, obtain several next-level subsystems, that is, form a next-level subsystem S a+1 from every n a+1 current-level subsystems, and calculate the multi-state reliability functions corresponding to each next-level subsystem S a based on the multi-state reliability function corresponding to the current-level subsystem S a+1 ;

[0016] S24: Repeat S23 to sequentially construct each hierarchical subsystem and calculate the corresponding multi-state reliability functions until the multi-state reliability function corresponding to the (A - 1)-th hierarchical subsystem is calculated.

[0017] The multi-state reliability function corresponding to the (A - 1)-th hierarchical subsystem Satisfy the following formula:

[0018]

[0019] Wherein, K P,A-1 +1 represents the number of states of the subsystem S at the (A - 1)th level A-1 ; represents the output power of the subsystem S at the (A - 1)th level A-1 in the state ; represents the probability of the subsystem S at the (A - 1)th level A-1 in the state ;

[0020] In the third step, the reliability of the power generation system taking K from large - scale N satisfies the following formula:

[0021]

[0022] Wherein, U P (z) is the multi - state reliability function of the power generation system represented in the UGF form; (K + 1) represents the number of states of the power generation system after merging of the same type; w s,j represents the output power of the power generation system in the state j, j = 0,..., K; p s,j represents the probability of the power generation system in the state j; D represents the system requirement of the power generation system taking K from N; R represents the reliability of the power generation system with system requirement D in the power generation system taking K from large - scale N, and χ(w s,j , D) represents the comparison function of the state of the power generation system and the system requirement;

[0023] In S22 or S23, the calculation process of the multi - state reliability function of each subsystem is taken as a task and assigned to a computing node of the computer. After all computing nodes are assigned or all tasks are assigned to the computer, the computer processes all computing nodes in parallel to achieve the parallel processing of the multi - state reliability functions of multiple subsystems.

[0024] II. A multi - state power generation system reliability detection device taking K from N based on a parallel architecture

[0025] A multi - layer parallel subsystem partitioning unit, used to construct the structure of the subsystems from the first level to the A level;

[0026] An element multi - state reliability function generation unit, used to establish the multi - state reliability functions corresponding to each element in the power generation system taking K from large - scale N by using the general generating function method;

[0027] The subsystem multi-state reliability function generation unit is used to calculate the multi-state reliability functions corresponding to the subsystems of the first level to the (A - 1)th level of the power generation system in sequence according to the multi-state reliability functions corresponding to each component by using the method of computer parallel computing;

[0028] The system reliability detection unit is used to generate the reliability of the large-scale k-out-of-N power generation system by using the general generating function method based on the multi-state reliability function corresponding to the (A - 1)th level subsystem.

[0029] III. A computer device

[0030] The device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method for detecting the reliability of a multi-state k-out-of-N power generation system based on a parallel architecture are implemented.

[0031] IV. A computer-readable storage medium

[0032] A computer program is stored on the medium, and when the computer program is executed by a processor, the steps of the method for detecting the reliability of a multi-state k-out-of-N power generation system based on a parallel architecture are implemented.

[0033] V. A computer program product

[0034] The product includes a computer program / instructions, and when the computer program / instructions are executed by a processor, the steps of the method for detecting the reliability of a multi-state k-out-of-N power generation system based on a parallel architecture are implemented.

[0035] The beneficial effects of the present invention are as follows:

[0036] First of all, the present invention makes a fine division of the system structure of the complex power generation system, disassembling it into multiple subsystems at different levels and relatively independent. Each subsystem is composed of multiple parallel generators. Such a system structure division not only retains the logical integrity of the internal structure of the power generation system but also facilitates subsequent calculation and processing.

[0037] Secondly, at the subsystem level, the present invention uses computer parallel computing technology to achieve parallel calculation of the multi-state reliability functions of each subsystem. The introduction of this step means that the reliability detection of subsystems can be carried out simultaneously, reducing the time accumulation effect caused by processing subsystems one by one in traditional serial computing. In this way, the time period of subsystem reliability detection is shortened, laying an efficiency foundation for the rapid reliability detection of the overall system.

[0038] Furthermore, the present invention extends the idea of parallel computing and system structure partitioning strategy to different levels of the entire power generation system, constructing a multi-level parallel computing framework, that is, the reliability data of the subsystem is used as input information for the reliability calculation of the next level. This parallel computing framework from local to global and progressive layer by layer not only improves the computing efficiency but also ensures the comprehensiveness and accuracy of the reliability detection of the entire power generation system. Description of the Drawings

[0039] Figure 1 It is a logic block diagram of the method of the present invention. Detailed Embodiment

[0040] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for the purpose of illustrating and explaining the present invention and are not intended to limit the present invention.

[0041] The present invention proposes a reliability detection method for a multi-state k-out-of-N power generation system based on a parallel architecture, as Figure 1 shown, the method includes the following steps:

[0042] Step 1: Use the universal generating function method (UGF) to establish the multi-state reliability functions corresponding to each component in a large-scale k-out-of-N power generation system;

[0043] Specifically, Step 1 is as follows:

[0044] The large-scale k-out-of-N power generation system is composed of n components connected in parallel. The components include generators, etc., for generating electrical energy. The number of states of each component is (l + 1), and the state refers to the different output powers of the component. The state distributions of the components are independent of each other. The reliability of the k-out-of-N power generation system refers to the probability that the output power of the system is greater than or equal to the known system demand. The output power of the system is determined by the sum of the output powers of the components in the corresponding states. Therefore, the multi-state reliability function of each component of this power generation system is expressed in the form of UGF, and the formula is as follows:

[0045]

[0046] Among them, u i (z) represents the multi-state reliability function of component i expressed in the form of UGF; represents the output power of a single component i in state j i ; represents the probability of a single component i in state j i ; j i = 0, 1,..., l, i = 1,..., n, n is the number of parallel components in the power generation system, and z is a representation method specified by the UGF algorithm, which has no meaning in itself.

[0047] Step 2: According to the multi-state reliability functions corresponding to each component, use the method of constructing multi-layer parallel subsystems to calculate the multi-state reliability functions corresponding to each hierarchical subsystem of the power generation system in turn, and finally obtain the multi-state reliability function corresponding to the (A - 1)-th hierarchical subsystem, where A is the number of levels of the power generation system;

[0048] Specifically, Step 2 is as follows:

[0049] S21: After factorizing the number n of parallel components in the power generation system, obtain a factor list that satisfies n = n1 × n2 × … × n a … × n A n a is the a-th factor, a = 1, …, A, where A is the number of factors in the factor list and is used as the number of levels of the power generation system;

[0050] S22: Combine every n1 components in the power generation system to form a first-level subsystem S1, thereby obtaining n2 × … × n A first-level subsystems, and calculate the multi-state reliability functions corresponding to each first-level subsystem based on the multi-state reliability functions of all components;

[0051] S23: After combining the current-level subsystems according to the next factor in the factor list, obtain several next-level subsystems, that is, form a next-level subsystem S a+1 by every n a+1 current-level subsystems, and calculate the multi-state reliability function corresponding to the next-level subsystem S a based on the multi-state reliability function corresponding to the current-level subsystem S a+1 ;

[0052] S24: Repeat S23 to construct each hierarchical subsystem and calculate the corresponding multi-state reliability functions in turn until the multi-state reliability function corresponding to the (A - 1)-th hierarchical subsystem is obtained.

[0053] According to the UGF technology and the parallel component state mapping function it can be known that the multi-state reliability function of the first-level subsystem S1 satisfies the following formula:

[0054]

[0055] The first-level subsystem S1 is composed of components such as generators, so its multi-state reliability function is determined by the states of components such as generators.

[0056] Among them, represents the multi-state reliability function of the first-level subsystem S1 expressed in the form of UGF; u i(z) represents the multi-state reliability function of a single generator element represented in the form of UGF; Represents the parallel element state mapping function, and in this step, it can realize the mapping from the states of n1 generator elements to the state of subsystem S1; Represents the output power of a single generator element i in state j i Under the output power; Represents the output power of a single engine element i in state j i Under the probability; (K P,1 +1) represents the number of states of the first-level subsystem S1, Represents the output power of the first-level subsystem S1 in state Under the output power; Represents the output power of the first-level subsystem S1 in state Under the probability.

[0057] The multi-state reliability function of the second-level subsystem S2 satisfies the following formula:

[0058]

[0059] And so on, repeating continuously. After obtaining the multi-state reliability functions of each subsystem at the i-1th layer, combined with the UGF technology and the parallel element state mapping function, when the i-th layer is in parallel, the multi-state reliability function of each i-th level subsystem S i Satisfies the following formula: Satisfies the following formula:

[0060]

[0061] The i-th level subsystem S i Consists of n i The i-1th layer subsystems S i-1 Composed, so its multi-state reliability function is determined by the multi-state reliability function of the i-1th layer subsystem S i-1 Of the multi-state reliability function.

[0062] Among them, Is the multi-state reliability function of the subsystem S i-1 Obtained by parallel calculation at the i-1th layer. Represents the multi-state reliability function of the subsystem S i Represented in the form of UGF; Represents the parallel element state mapping function, and in this step, it can realize the mapping from the states of n i The i-1th level subsystems S i-1 To the state of the i-th level subsystem S i State; (K P,i +1) represents the number of states of the i-th level subsystem S i State number, Denote the output power of the $i$-th level subsystem $S$ i under the state ; Denote the probability of the $i$-th level subsystem $S$ i under the state ; Denote the output power of the $b$-th $(i - 1)$-th level subsystem $S$ i-1 under the state ; Denote the probability of the $b$-th $(i - 1)$-th level subsystem $S$ i-1 under the state ; $(K$ P,i-1 + 1) denotes the number of states of the $(i - 1)$-th level subsystem $S$ i-1 .

[0063] Therefore, the multi-state reliability function A-1 corresponding to the $(A - 1)$-th level subsystem $S$ satisfies the following formula:

[0064]

[0065] The $(A - 1)$-th level subsystem $S$ A-1 is composed of $n$ A-1 $(A - 2)$-th level subsystems $S$ A-2 . Therefore, its multi-state reliability function is determined by the multi-state reliability functions of the $(A - 2)$-th level subsystems $S$ A-2 .

[0066] Among them, $K$ P,A-1 + 1 denotes the number of states of the $(A - 1)$-th level subsystem $S$ A-1 , denotes the output power of the $(A - 1)$-th level subsystem $S$ A-1 under the state , denotes the probability of the $(A - 1)$-th level subsystem $S$ A-1 under the state ; $K$ P,A-2 + 1 denotes the number of states of the $(A - 2)$-th level subsystem $S$ A-2 . Denote the output power of the $b$-th $(A - 2)$-th level subsystem $S$ A-2 under the state ; Denote the probability of the $b$-th $(i - 2)$-th level subsystem $S$ i-2 under the state .

[0067] The idea of parallel computing in the present invention is extended to all structures of the power generation system. Under the framework of the structural frameworks of the subsystems divided in the power generation system, first, focus on the smaller-level subsystems in the power generation system. These subsystems are usually composed of components such as parallel-connected generators. The multi-state reliability functions of these smaller-level subsystems are accurately obtained by using the universal generating function. Since the number of components in the smaller-level subsystems is small, the calculation time of the multi-state reliability functions is very small. Based on the multi-state reliability functions of the smaller-level subsystems, the present invention proposes a method of computer parallel computing in higher-level subsystems to perform the reliability calculation of the next layer (which may be a higher-level subsystem or the entire system). This step-by-step system structure division strategy can realize the reliability detection from the local to the whole, and at the same time, it also makes full use of the advantages of computer parallel computing in processing large-scale data, can improve the calculation efficiency, and ensure the calculation accuracy in large-scale systems.

[0068] In S22 or S23, the calculation process of the multi-state reliability function of each subsystem is taken as a task and assigned to a computing node of the computer. After all computing nodes are assigned or all tasks are assigned to the computer, the computer processes all computing nodes in parallel to achieve the parallel processing of the multi-state reliability functions of multiple subsystems.

[0069] For example, for the multi-state reliability function of the first-level subsystem S1, the computer has m computing nodes, and all m computing nodes can work normally at the same time to execute the corresponding parallel tasks. The calculation of the multi-state reliability function of the first-level subsystem S1 composed of every n1 components is taken as a parallel task, and there are a total of n2×n3×…×n A parallel tasks, then a total of times of parallel task allocation and execution are required. First, m subtasks are simultaneously allocated to the parallel computing platform. When the calculations of all m subtasks are completed, the calculation results of each subtask are returned to the main process. Then, m subtasks are simultaneously allocated to the parallel computing platform again to continue executing each subtask and return. And so on, repeating the operation times, the parallel computing of the first layer ends. Thus, the number of times of calculating the multi-state reliability function of the subsystem S1 that should originally be calculated n2×n3×…×n A times is reduced to calculating times. Through the parallel computing of the computer, the number of calculation times of the multi-state reliability functions of all subsystems S1 is reduced, that is, the calculation time of the S1 subsystem is reduced.

[0070] The second division is for n2×n3×…×n A first-level subsystems S1. Every n2 first-level subsystems S1 form a second-level subsystem S2, and n3×…×n An3 × … × n second - level subsystems S2 A The multi - state reliability functions of these n3 × … × n second - level subsystems S2 represented by UGF are calculated simultaneously in parallel on the computer. Considering that there are m computing nodes in the computer that can calculate simultaneously, the originally supposed to be calculated n3 × … × n A times of the multi - state reliability functions of the second - level subsystems S2 is reduced to calculating times. Through the parallel computing of the computer, the calculation times of the multi - state reliability functions of all subsystems S2 are reduced, that is, the calculation time of subsystem S2 is reduced.

[0071] And so on, until the (A - 1) - th layer of division. The (A - 1) - th division is for n A-1 ×n A (A - 2) - th level subsystems S A-2 , every n A-1 (A - 2) - th level subsystems S A-2 constitute an (A - 1) - th level subsystem S A-1 . After the (A - 1) - th layer of division, n A (A - 1) - th level subsystems S A-1 can be obtained. The multi - state reliability functions of these n A (A - 1) - th level subsystems S A-1 represented by UGF are calculated simultaneously in parallel on the computer. Considering that there are m computing nodes in the computer, the originally supposed to be calculated n A times of the multi - state reliability functions of the (A - 1) - th level subsystems S A-1 is reduced to calculating times.

[0072] In the traditional serial computing mode, the multi - state reliability functions of each subsystem need to be calculated sequentially. This means that in the serial computing mode, the reliability detection time of the entire system will increase as the number of subsystems increases. By introducing computer parallel computing, the present invention can simplify the reliability detection process of large - scale k - out - of - N power generation systems. Computer parallelism allows simultaneous processing of the calculation of the multi - state reliability functions of each subsystem at the same level. This means that within the same time period, multiple subsystems can perform reliability analysis simultaneously, thus reducing the time accumulation effect caused by processing subsystems one by one in traditional serial computing. Therefore, when dealing with large - scale multi - state k - out - of - N power generation systems with large - scale data sets, its performance advantages such as computing efficiency are more obvious compared with the traditional serial computing mode.

[0073] In addition, through the system structure division strategy of the present invention, a multi - level parallel computing framework is constructed, which can progressively process the reliability assessment from the smallest subsystem to the entire power generation system layer by layer. This strategy makes the system reliability detection process more modular and easy to manage.

[0074] Step 3: Based on the multi-state reliability function corresponding to the A-1 level subsystems, the universal generating function method is used to generate the reliability of the large-scale K-out-of-N power generation system, and the reliability detection is completed.

[0075] Specifically, the reliability of the large-scale K-out-of-N power generation system satisfies the following formula:

[0076]

[0077] The K-out-of-N power generation system consists of n A subsystems S of the A-1 level A-1 . Therefore, its reliability is determined by the multi-state reliability function of the subsystem S of the A-1 level A-1 .

[0078] Among them, U P (z) is the multi-state reliability function of the power generation system represented in the UGF form; is the multi-state reliability function of the subsystem S of the A-1 level represented in the UGF form A-1 ; is the state mapping function of the parallel components. In this step, the state mapping from n A subsystems S A-1 to the state of the integrated energy supply system can be realized. n A is the A-th factor, and K P,A-1 +1 represents the number of states of the subsystem S of the A-1 level A-1 ; represents the output power of the subsystem S of the A-1 level A-1 in the state ; represents the probability of the subsystem S of the A-1 level A-1 in the state ; represents the output power of the b-th subsystem S of the A-1 level A-1 in the state ; represents the probability of the b-th subsystem S of the i-1 level in the state i-1 in the state ; (K + 1) represents the number of states of the power generation system after merging of the same type; w s,j represents the output power of the power generation system in the state j, where j = 0,..., K; p s,j represents the probability of the power generation system in the state j; D represents the system demand of the K-out-of-N power generation system; R represents the reliability of the power generation system with the system demand D in the large-scale K-out-of-N power generation system, χ(w s,j, (D) represents a comparison function of the state of the power generation system and the system requirements.

[0079] The present invention also provides a reliability detection device for a multi-state N-out-of-K power generation system based on a parallel architecture, including:

[0080] A multi-layer parallel subsystem division unit for constructing the structures (including the number of parallel components) of the subsystems from the first layer to the A-th layer by using a system structure division strategy;

[0081] A component multi-state reliability function generation unit for establishing the multi-state reliability functions corresponding to each component in a large-scale N-out-of-K power generation system by using the universal generating function method;

[0082] A subsystem multi-state reliability function generation unit for successively calculating the multi-state reliability functions corresponding to the subsystems from the first layer to the (A - 1)-th layer of the power generation system by using the method of computer parallel calculation according to the multi-state reliability functions corresponding to each component;

[0083] A system reliability detection unit for generating the reliability of a large-scale N-out-of-K power generation system by using the universal generating function method based on the multi-state reliability function corresponding to the (A - 1)-th layer subsystem.

[0084] The present invention also provides a computer device, including a memory and a processor. When the processor executes the computer program, the steps of a reliability detection method for a multi-state N-out-of-K power generation system based on a parallel architecture are implemented.

[0085] The present invention also provides a computer-readable storage medium, on which a computer program is stored. The computer program, when executed by a processor, implements the steps of a reliability detection method for a multi-state N-out-of-K power generation system based on a parallel architecture.

[0086] The present invention also provides a computer program product, including a computer program / instructions. When the computer program / instructions are executed by a processor, the steps of a reliability detection method for a multi-state N-out-of-K power generation system based on a parallel architecture are implemented.

[0087] Finally, it should be noted that the above embodiments and descriptions are only used to illustrate the technical solutions of the present invention and not to limit them. Those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced. Without departing from the spirit and scope of the disclosure of the technical solutions of the present invention, they should all be covered by the protection scope of the claims of the present invention.

Claims

1. A multi-state K-out-of-N power generation system reliability detection method based on parallel architecture, characterized in that: The steps include: Step 1: Use the general generating function method to establish the multi-state reliability function corresponding to each component in the large-scale N out of K power generation system; Step 2: According to the multi-state reliability function corresponding to each component, the multi-state reliability function corresponding to each level subsystem of the power generation system is calculated in turn by using the method of multi-layer parallel subsystem construction, and finally the multi-state reliability function corresponding to the A-1 level subsystem is obtained, where A is the number of levels of the power generation system; Step 3: Based on the multi-state reliability function corresponding to the A-1 layer subsystem, the general generating function method is used to generate the reliability of the large-scale N-out-of-K power generation system to complete the reliability detection.

2. According to the parallel architecture-based multi-state K-out-of-N power generation system reliability detection method of claim 1, characterized in that: The second step is specifically as follows: S21: After factoring the number n of parallel elements in the power generation system, a factor list is obtained, satisfying n = n1×n2×…×n a …×n A , n a is the ath factor, a=1,…,A, A is the number of factors in the factor list and serves as the number of levels of the power generation system; S22: Every n1 components in the power generation system form a first-level subsystem S1, thus obtaining n2×…×n A first-level subsystems, and respectively calculating the multi-state reliability functions corresponding to each first-level subsystem based on the multi-state reliability functions of all components; S23: After combining the current level subsystems according to the next factor in the factor list, several next level subsystems are obtained, that is, each n a+1 The current level subsystems form a next level subsystem S a+1 , based on the current level subsystem S a The corresponding multi-state reliability function calculates each next-level subsystem S a+1 The corresponding multi-state reliability function; S24: Repeat S23, construct each level subsystem in turn and calculate the corresponding multi-state reliability function, until the multi-state reliability function corresponding to the A-1 level subsystem is calculated and obtained.

3. The reliability detection method of a multi-state K-out-of-N power generation system based on a parallel architecture according to claim 1 is characterized in that: The multi-state reliability function corresponding to the A-1-th level subsystem Satisfies the following formula: Among them, K P,A-1 +1 indicates the A-1 level subsystem S A-1 The number of states, Indicates the A-1 level subsystem S A-1 In Status Output power under Indicates the A-1 level subsystem S A-1 In Status The probability of the following.

4. The reliability detection method of a multi-state K-out-of-N power generation system based on a parallel architecture according to claim 1 is characterized in that: In the third step, the reliability of the large-scale K-out-of-N power generation system satisfies the following formula: Among them, U P (z) is the multi-state reliability function of the power generation system expressed in the form of UGF; (K+1) represents the number of states of the power generation system after the same type of merging; w s,j represents the output power of the power generation system in state j, j = 0, ..., K; p s,j represents the probability of the power generation system in state j; D represents the system demand of the K-out-of-N power generation system; R represents the reliability of the power generation system with a system demand of D in a large-scale K-out-of-N power generation system, χ(w s,j ,D) represents the comparison function between the state of the power generation system and the system demand.

5. The reliability detection method of a multi-state K-out-of-N power generation system based on a parallel architecture according to claim 2 is characterized in that: In S22 or S23, the calculation process of the multi-state reliability function of each subsystem is taken as a task and assigned to a computing node of the computer. After all computing nodes are assigned or all tasks are assigned to the computer, the computer processes all computing nodes in parallel to realize parallel processing of multi-state reliability functions of multiple subsystems.

6. A multi-state K-out-of-N power generation system reliability detection device based on parallel architecture, characterized in that: include: A multi-layer parallel subsystem partitioning unit is used to construct the structure of the first to A-th level subsystems; A component multi-state reliability function generation unit is used to establish a multi-state reliability function corresponding to each component in a large-scale K-out-of-N power generation system using a general generation function method; A subsystem multi-state reliability function generating unit is used to sequentially calculate the multi-state reliability functions corresponding to the first level to the A-1 level subsystems of the power generation system using a computer parallel computing method according to the multi-state reliability functions corresponding to each component; The system reliability detection unit is used to generate the reliability of a large-scale K-out-of-N power generation system using a general generating function method based on the multi-state reliability function corresponding to the A-1-th level subsystem.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of a multi-state K-out-of-N power generation system reliability detection method based on a parallel architecture as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a reliability detection method for a multi-state K-out-of-N power generation system based on a parallel architecture as described in any one of claims 1 to 5 are implemented.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of a reliability detection method of a multi-state K-out-of-N power generation system based on a parallel architecture as described in any one of claims 1 to 5 are implemented.

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