A method and system for reliability analysis of a direct current power transmission system based on task division

Abstract

The application discloses a kind of based on task division's reliability analysis method and system of direct current transmission system, it is related to power transmission system field, the method includes: the Monte Carlo simulation process of direct current transmission system is divided into several subtask modules, to parallel task processing architecture;According to the parameter of each element in power transmission system, sample and obtain the failure and repair time of each element in the set simulation period and integrate into time sequence combination sequence;Read the failure element of failure event in time sequence combination sequence, judge the standby state of failure element, and determine the failure combination period in the current simulation period with the judgment result;Iterative execution is carried out in multiple rounds Monte Carlo simulation, the failure combination period of each round output is counted to determine the reliability analysis result of the power transmission system, and reliable support is provided for the safe and stable operation of power grid.

Description

Technical Field

[0001] This invention relates to the field of DC transmission systems, and in particular to a reliability analysis method and system for DC transmission systems based on task partitioning. Background Technology

[0002] Ultra-high voltage (UHVDC) flexible direct current (HVDC) transmission systems are a crucial component of modern power grids. They not only bear enormous power transmission burdens but also play an irreplaceable role in maintaining grid stability. Failures in any part of these systems can lead to widespread power outages, causing significant economic losses and social impact. Therefore, conducting a comprehensive reliability assessment of UHVDC transmission systems is of profound significance for ensuring the safe and stable operation of the power grid, improving power supply quality, and optimizing operation and maintenance strategies.

[0003] However, traditional reliability assessment methods, such as analytical methods, are difficult to establish mathematical models for ultra-high voltage flexible DC transmission systems with multiple components and multiple states. The solution process is complex and it is difficult to achieve accurate assessment.

[0004] Therefore, how to effectively assess the reliability of DC transmission systems and ensure accuracy and efficiency has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention provides a method and system for reliability analysis of DC transmission systems based on task partitioning, in order to solve how to achieve accurate evaluation and improve the efficiency of subsequent system maintenance by partitioning and decoupling multiple task modules.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide a reliability analysis method for DC transmission systems based on task partitioning, comprising: The Monte Carlo simulation process of the target DC transmission system is divided into several sub-task modules, and a parallel task processing architecture is constructed using the sub-task modules. In the parallel task processing architecture, based on the original parameters of each component in the target DC transmission system, the fault time and repair time of each component within a set simulation period are sampled. The fault time and the repair time are integrated to generate a time-series combination sequence; Read the faulty components that have experienced fault events in the time sequence combination, determine the standby status of the faulty components, and determine the fault combination period in the current simulation cycle based on the determination result; The Monte Carlo simulation is performed iteratively for multiple rounds, and the fault combination time periods output in each round are statistically analyzed to determine the reliability analysis results of the target DC transmission system.

[0007] Furthermore, the plurality of subtask modules include: The system includes a fault time calculation module, a repair time calculation module, a time series combination module, a component fault event processing module, a simulation cutoff time judgment module, a system state mapping module, and a reliability index calculation module.

[0008] Furthermore, the step of determining the standby status of the faulty component and using the determination result to determine the fault combination period within the current simulation cycle includes: Determine whether the currently faulty component belongs to a shared backup component; If so, query the availability status of the backup resources of the faulty component, and determine the first fault duration of the faulty component based on the availability status of the backup resources; If not, the repair time corresponding to the faulty component shall be taken as the first fault duration. When an additional faulty component is detected within the first fault duration, the second fault duration corresponding to the additional faulty component is obtained; The duration of the first fault and the duration of the second fault are combined to determine the combined fault period.

[0009] Furthermore, determining the first fault duration of the faulty component based on the availability of the backup resources includes: When a spare component is available, the first installation time of the spare component shall be taken as the first fault duration of the faulty component. When no spare component is available, the second installation time is determined according to the order in which the faulty component and the spare component are repaired. The second installation time is taken as the first fault duration of the currently faulty component.

[0010] Furthermore, after determining the fault combination period within the current simulation cycle based on the judgment result, the method further includes: Based on the topology of the target DC transmission system, identify the associated components that are in the same branch as the currently faulty component; At the repair time corresponding to the current faulty component, the time axis state of the associated component is updated and adjusted.

[0011] Furthermore, the process of dividing the Monte Carlo simulation process of the target DC transmission system into several sub-task modules includes: Obtain the theoretical time consumption value for each of the subtask modules; Based on the current total computing resources and the theoretical time consumption value, allocate target computing resources to each of the subtask modules.

[0012] Furthermore, the iterative execution of multiple rounds of Monte Carlo simulation includes: When the end of a Monte Carlo simulation is detected, the state of all components in the target DC transmission system is reset. Based on the reset state, execute the next round of Monte Carlo simulation.

[0013] Another embodiment of the present invention provides a reliability analysis system for DC transmission systems based on task partitioning, comprising: The architecture design module is used to divide the Monte Carlo simulation process of the target DC transmission system into several sub-task modules, and to build a parallel task processing architecture using the sub-task modules. The timing determination module is used to sample the fault time and repair time of each component within a set simulation period based on the original parameters of each component in the target DC transmission system in the parallel task processing architecture. The timing combination module is used to integrate the fault time and the repair time to generate a timing combination sequence; The standby status determination module is used to read the faulty components that have experienced fault events in the time series combination sequence, determine the standby status of the faulty components, and determine the fault combination period in the current simulation cycle based on the determination result. The reliability result output module is used to iteratively execute multiple rounds of Monte Carlo simulation and statistically analyze the fault combination time periods output in each round to determine the reliability analysis results of the target DC transmission system.

[0014] Another embodiment of the present invention provides a computer device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the task-based DC transmission system reliability analysis method as described above.

[0015] In another embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the task-based DC transmission system reliability analysis method as described above.

[0016] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following: This invention decomposes the Monte Carlo simulation process for DC transmission systems into a parallel task processing architecture consisting of multiple executable sub-task modules. This significantly accelerates the operation of each task module, more realistically reflects the actual working state of components, and improves the efficiency of reliability analysis. Within this architecture, fault / repair times are generated by sampling original component parameters and integrated into a time-series combination sequence, providing a complete time reference for subsequent fault handling. It provides a method to traverse the time-series sequence to determine the standby status of faulty components, generating fault combination time periods that reflect standby competition relationships, accurately simulating the complex standby resource call logic and scenarios of the system. Finally, it iterative execution of multiple rounds of simulation outputs reliability indicators, providing stronger support for the safe and stable operation of the power grid. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of a DC transmission system reliability analysis method based on task partitioning in one embodiment of the present invention; Figure 2 This is an execution flowchart of a parallel task processing architecture in one embodiment of the present invention; Figure 3 This is a logic flowchart of the component failure event handling module in one embodiment of the present invention; Figure 4 This is a structural diagram of the ultra-high voltage flexible DC transmission system in Example 1 provided by the present invention; Figure 5 This is a schematic diagram of the structure of a DC transmission system reliability analysis system based on task partitioning in one embodiment of the present invention; Figure 6 This is a structural block diagram of a preferred embodiment of a computer device provided by the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0021] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0022] One embodiment of the present invention provides a reliability analysis method for DC transmission systems based on task partitioning. For details, please refer to [link to relevant documentation]. Figure 1 , Figure 1 The diagram shown is a flowchart of a task-based DC transmission system reliability analysis method according to one embodiment of the present invention, including the following steps: S1. Divide the Monte Carlo simulation process of the target DC transmission system into several sub-task modules, and use the sub-task modules to build a parallel task processing architecture.

[0023] This embodiment aims to perform multiple rounds of Monte Carlo simulation on a target DC transmission system to output indicators reflecting system reliability. Based on this, this embodiment divides the Monte Carlo simulation process and designs a task-parallel processing architecture. For the specific processing flow of this architecture, please refer to [reference needed]. Figure 2 As shown, the architecture is divided into seven sub-task modules, including: fault time calculation module, repair time calculation module, time series combination module, component fault event processing module, simulation deadline judgment module, system state mapping module, and reliability index calculation module.

[0024] In one implementation of the present invention, the fault time calculation module and the repair time calculation module are used to calculate the fault occurrence time and repair completion time of each component in the system, respectively. The time series combination module is used to integrate fault and repair events to form a system time sequence. The component fault event processing module is used to determine the standby status of the component to record the fault duration of each faulty component. The simulation deadline judgment module is used to verify whether the time sequence has reached the preset period and trigger the annual inspection mechanism for verification. The system state mapping module is used to map the fault combination to the system state. Finally, the system reliability index is output through the reliability index calculation module.

[0025] To achieve fine-grained parallel acceleration of this parallel task processing architecture, this embodiment allocates target computing resources to each subtask module based on the constructed resource allocation function, according to the current total computing resources and theoretical time consumption, as specifically shown below: In the formula, T represents the computing resources used by module i; i τ is the theoretical computation time of module i; τ is the theoretical computation time baseline value of the smallest task computation unit; N total Total computing resources (CPU cores).

[0026] As can be seen from the above formula, in the process of allocating computing resources, it is necessary to obtain the theoretical time consumption value τ of each subtask module. This value mainly serves to unify the time consumption of each module (T). i This standardizes and normalizes computation, ensuring that in complex heterogeneous computing environments (multi-core CPUs, mixed GPU stream processors), the computing power of different architectures can be represented by a unified unit. Resource equivalence calculation is performed to make the allocation function platform-adaptive. In one implementation of the present invention, the value of τ is a dynamic calibration process that strictly depends on the target hardware platform. The determination method is as follows: First, on a single CPU physical core of the target computing platform, a preset set of atomic tasks is executed. This set must cover three representative types of operations: computationally intensive, random processes, and logical judgments. For example, performing floating-point matrix multiplication of a certain scale, generating a certain number of exponentially distributed random numbers, and verifying the topological connectivity of a certain number of nodes. Each type of task needs to be run continuously 100,000 times, and its minimum execution time is recorded to eliminate external interference such as operating system scheduling. Finally, the arithmetic mean of the minimum execution times of these three types of tasks is taken as the benchmark value. Subsequently, an integer between 5 and 20 is selected as the scaling factor K based on the hardware platform type. Specifically, K=5 is used for GPU-accelerated platforms to match their fine-grained parallel architecture, K=10 is used for servers or workstations to balance scheduling precision and overhead, and K=20 is used for other computing devices to avoid excessive resource fragmentation. Thus, the initial value of τ can be obtained through the formula... The calculation yielded the result.

[0027] N total It is a normalized total number of abstract computing units, which comprehensively quantifies the capabilities of all available computing components on the hardware platform, such as CPU physical cores, logical threads, and heterogeneous resources such as GPU stream processors.

[0028] S2. In the parallel task processing architecture, based on the original parameters of each component in the target DC transmission system, the fault time and repair time of each component within the set simulation period are sampled.

[0029] This step involves executing a sequential Monte Carlo simulation process based on a pre-designed parallel task processing architecture, following seven sub-task modules. First, the failure time calculation module and the repair time calculation module are triggered. Specifically, input system parameters are acquired, including the failure rate, repair rate, and installation time of the constituent components, and the system component states are initialized. For example, these input parameters can be derived from published literature or reliability engineering reports. These components may include key components such as converter transformers, converters, DC reactors, neutral reactors, grounding electrodes, and transmission lines. In this embodiment, considering that the evaluation focuses on the annual availability rate and that the transmission system undergoes annual maintenance (during which all components are inspected, and the evaluation assumes all components have returned to normal after the annual maintenance), the simulation period is set to one year.

[0030] In this embodiment, random number sampling is used to calculate the failure time of each component within the simulation period based on the component failure rate λ. And calculate the repair time when the corresponding component completes repair based on the repair rate μ. For example, the failure time can be extracted according to the proportion of the component's failure rate. For instance, if the failure rate of the grounding electrode is 0.05 and the severe failure rate of the converter transformer is 0.015, the failure time calculation module extracts the failure time according to a 10:3 ratio during calculation. In one implementation of the invention, the failure time and repair time follow an exponential distribution, and the component's failure rate λ can be calculated using the following formula: In the formula, t represents the running time. , where U is a uniform distribution following [0,1].

[0031] By sampling U, the random failure process of a component can be simulated.

[0032] Similarly μ represents the component's repair rate. By sampling U, the random repair process of the component can be simulated. For example, if the failure rate of a component is 0.05 and the randomly selected number is 0.1, then by substituting into the above formula, we can see that in this sampling, the component fails after 2.1 years of normal operation. Since the calculated index is 1 year, it is considered that this component does not fail in this simulation. Through multiple simulations, the stable value that the reliability index converges to is considered the system's reliability index.

[0033] S3. Integrate the fault time and repair time to generate a time-series combination sequence.

[0034] This step is the execution process of the time series combination module, which combines the fault time and repair time within the simulation period. For example: Suppose the existing fault times include a severe fault in converter transformer 1 on day 5 of the simulation period, a severe fault in converter transformer 2 on day 8, a repair time of 100 days for converter transformer 1, a repair time of 98 days for converter transformer 2, and a converter transformer installation time of 4 days (a fixed value). Other components will also have corresponding data in this step. Integrating this data on a timeline yields a combined sequence reflecting the operating status of each component throughout the year: Days 5-8: Severe fault in converter transformer 1; Days 8-109: Simultaneous severe faults in converter transformers 1 and 2; Days 109-110: Severe fault in converter transformer 2. This time series combination continues until it exceeds 365 days.

[0035] Specifically, the time series combination module sorts the fault-repair time pairs of all components along the time axis to generate a globally ordered system-level critical event time chain, denoted as T = {t1, t2, ..., t...}. n This timing chain drives the simulation process into the component fault event handling module. Specifically, when the event pointer advances to the fault moment of a certain component, it drives the component fault event handling module to start the corresponding processing logic.

[0036] S4. Read the faulty components that have experienced fault events in the timing combination sequence, determine the standby status of the faulty components, and determine the fault combination period in the current simulation cycle based on the determination result.

[0037] This step aims to detail the execution process of the component failure event handling module. Please refer to the following for the specific flow. Figure 3 As shown.

[0038] As can be seen, the first step is to determine whether the currently faulty component is a shared backup component at the time of the failure. This can be determined by the component type; for example, if the component is a converter transformer or a DC reactor, it is a shared backup component. If the currently faulty component is not a shared backup component, then its corresponding repair time is directly taken as the fault duration of that component.

[0039] If the currently faulty component is a shared backup component, then the availability status of the backup resources of the currently faulty component is further queried to determine whether a backup replacement is needed. Then, the duration of the fault is determined based on the availability status of the backup resources.

[0040] If a faulty component requires replacement (e.g., due to a serious fault), the system further checks if a spare component is available in inventory. If a spare component exists, its installation time is used as the fault duration for that component. If no spare component exists, the installation time is determined based on the repair completion order of the current faulty component and the spare component, and this installation time is used as the fault duration for the current faulty component. Specifically, in this case, the system waits for either the spare component or the current faulty component to be repaired. The fault duration is determined according to the order in which they are repaired. If the spare component is repaired first, it is installed at the time of repair completion to obtain the fault duration for the current faulty component, which is the repair completion time plus the installation time. It is understood that the availability of a spare component is related to the number of components. In the parallel architecture of this embodiment, the number of spare components decreases by one when a spare is used. The replaced component is then used as a spare after repair.

[0041] Then, the system event chain is continuously scanned throughout the entire failure duration of the faulty component to detect whether there are any additional components experiencing concurrent failures. Their corresponding failure durations can be recorded in the same way. Finally, the additional faulty components that occurred concurrently with the faulty component are identified using a combined identifier (e.g., ...). Record them in a way that combines the duration of each fault to determine the fault combination period.

[0042] For example, first, identify the first faulty component, such as converter transformer No. 1. A critical fault is detected, requiring a backup replacement. Records show the backup transformer is used on day 5 and will be repaired on day 105, with an installation time of 4 days. Therefore, days 5-9 represent the fault time of converter transformer No. 1. The time series is updated: days 5-8 show a critical fault in converter transformer No. 1; days 8-9 show simultaneous critical faults in converter transformers No. 1 and No. 2; days 9-110 show a critical fault in converter transformer No. 2. The next fault of converter transformer No. 1 is adjusted accordingly. Since other components experience concurrent fault events during the first component's fault period, processing continues. For example, a critical fault is detected in converter transformer No. 2, also requiring a backup replacement. However, the backup is unavailable on day 8. Converter transformer No. 2 will be repaired on day 106, while the backup transformer will be available on day 105. The time series is updated again: days 5-8 show a critical fault in converter transformer No. 1; days 8-9 show simultaneous critical faults in converter transformers No. 1 and No. 2; days 9-119 show a critical fault in converter transformer No. 2.

[0043] It is worth noting that in a series-connected DC transmission system, the failure of one component on a branch will cause all other components on that branch to fail as well. Therefore, in this embodiment, based on the topology of the target DC transmission system, the associated components on the same branch as the currently faulty component are determined.

[0044] Once the associated components are identified, the timeline state of the associated components is updated and adjusted at the repair time corresponding to the currently faulty component. In one implementation of the invention, subsequent fault events of these passively shut-down components can be reset on the timeline, and the fault / repair time can be resampled.

[0045] S5. Iteratively execute multiple rounds of Monte Carlo simulation, and statistically analyze the fault combination time periods output in each round to determine the reliability analysis results of the target DC transmission system.

[0046] Next, the simulation deadline judgment module is entered. This module continuously monitors the cumulative simulation time, and when the preset cycle limit (such as 1 year) is reached, the annual inspection mechanism verification logic is triggered. Specifically, when the end of a round of Monte Carlo simulation is detected, the state of all components in the target DC transmission system is reset (such as repairing latent defects and resetting component lifespan). Based on the reset state, the next round of Monte Carlo simulation is executed.

[0047] After verification, the system state mapping module will base its analysis on the recorded fault combination time periods: fault combination The duration of the fault, combined with predefined system topology logic rules, is mapped to a vector representing the overall system operating state, such as: full-capacity operation, 50% derating operation, and complete shutdown.

[0048] Finally, the reliability index calculation module aggregates and analyzes the duration distribution of the system under various states, and outputs quantitative evaluation indicators, including but not limited to the probability, frequency and average availability of each state, to complete the full-cycle evaluation of the Monte Carlo simulation.

[0049] This embodiment will further illustrate the above reliability analysis process with the following examples: Example 1: This example selects a ±800kV UHV flexible DC transmission system as the research object. This system consists of sending-end and receiving-end converter stations, employing a symmetrical bipolar DC connection. Each converter station has two multi-level flexible DC converter valves connected in parallel, each matched with a corresponding transformer group. The converter station mainly involves the following components: converter transformer system, converter valve system, DC reactor, neutral line assembly, and DC transmission line. The specific structure is shown in the attached figure. Figure 4 As shown, the power transmission system is divided into positive and negative poles, each further divided into high-end and low-end, with a total of four lines (H1, L1, H2, L2). Each branch can operate independently, transmitting 25% of the total capacity. Additionally, there are grounding electrodes and metallic return lines to ensure a circuit is formed with the other pole in the event of a fault in one pole, guaranteeing power delivery. After any equipment failure occurs on the line, the line's protection system responds rapidly, ensuring timely disconnection of the faulty line and adjusting the operating mode to ensure the normal operation of other lines, with a response speed in the millisecond range.

[0050] Based on the number of faulty branches disconnected, the power transmission system has 7 operating states: (1) Complete bipolar balanced operation mode (2) 1 / 2 bipolar balanced operation mode (3) Complete single-polarity loop operation mode (4) 1 / 2 monopolar extreme loop operation mode (5) Complete single-pole metal circuit operation mode (6) 1 / 2 single-pole metal circuit operation mode (7) Operation mode with one pole intact and the other pole 1 / 2 unbalanced For reliability and economic reasons, most large substations now opt for three-phase transformer banks consisting of three single-phase transformers, plus a standby single-phase transformer that cannot be automatically connected. Based on this, the standby logic for the standby transformers is set to provide one standby transformer for each high / low end of each converter station, and the standby logic for the DC reactors is to provide one standby DC reactor for each of the four DC reactors in each converter station. The redundancy for the converter valves and DC reactors is shared redundancy, so their installation time needs to be considered during reliability assessment. The basic parameters of each component are shown in Table 1.

[0051] Table 1: Basic Parameters of Components This example assumes the computing hardware contains 10 processing units. The theoretical computation time of each sub-task module in the sequential Monte Carlo simulation can be obtained from serial calculation. This example uses... =1×10 -5 There are a total of 9 computing units. By substituting the resource allocation mechanism function, we can obtain the computing unit allocation of each subtask module, as shown in Table 2.

[0052] Table 2: Allocation of Computation Units for Subtask Modules After performing Monte Carlo simulation on the power transmission system of this example using the parallel processing architecture described in this embodiment, the output reliability evaluation results are shown in Table 3.

[0053] Table 3. Probability and frequency of operating states of the UHV flexible DC transmission system It can be seen that when using the computation method without considering task parallelism, the evaluation time is 145 minutes, but after adding the computation method with task parallelism, the task time is 29.5 minutes, and the time to perform tasks by parallelism is reduced by 79.7%.

[0054] In summary, this embodiment decouples the reliability assessment process of the DC transmission system into a parallel processing architecture of multiple sub-task modules, introduces the Monte Carlo simulation algorithm for reliability assessment, and allows each sub-task module to execute the corresponding simulation process, thereby achieving accurate and efficient reliability analysis of the DC transmission system.

[0055] One embodiment of the present invention provides a reliability analysis system for DC transmission systems based on task partitioning. For details, please refer to [link to relevant documentation]. Figure 5 , Figure 5 The diagram shown is a schematic of a task-based DC transmission system reliability analysis system structure according to one embodiment of the present invention, including: The architecture design module M1 is used to divide the Monte Carlo simulation process of the target DC transmission system into several sub-task modules and use the sub-task modules to build a parallel task processing architecture. The timing determination module M2 is used to sample the fault time and repair time of each component within a set simulation period based on the original parameters of each component in the target DC transmission system in the parallel task processing architecture. The timing combination module M3 is used to integrate the fault time and the repair time to generate a timing combination sequence; The standby status determination module M3 is used to read the faulty components that have experienced fault events in the time sequence combination sequence, determine the standby status of the faulty components, and determine the fault combination period in the current simulation cycle based on the determination result. The reliability result output module M5 is used to iteratively execute multiple rounds of Monte Carlo simulation and statistically analyze the fault combination time periods output in each round to determine the reliability analysis results of the target DC transmission system.

[0056] This invention also provides a computer device; for details, please refer to [link / reference needed]. Figure 6 The diagram shown is a structural block diagram of a preferred embodiment of a computer device provided by the present invention. The computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method described above.

[0057] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2, ...), and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0058] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can be any conventional processor. The processor is the control center of the terminal device, connecting various parts of the terminal device through various interfaces and lines.

[0059] The memory mainly includes a program storage area and a data storage area. The program storage area can store the operating system, applications required for at least one function, etc., while the data storage area can store related data, etc. Furthermore, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard drive, a SmartMedia Card (SMC), a Secure Digital (SD) card, and a Flash Card, or other volatile solid-state storage devices.

[0060] It should be noted that the aforementioned terminal devices may include, but are not limited to, processors and memory, as will be understood by those skilled in the art. Figure 6 The structural block diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown, or combine certain components, or use different components. Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0061] Accordingly, embodiments of the present invention provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the steps in the method of the above embodiments, for example... Figure 1 Steps S1 to S5 as described above.

[0062] The technical features and effects of the DC transmission system reliability analysis system based on task partitioning proposed in this embodiment of the invention are the same as those of the DC transmission system reliability analysis method based on task partitioning proposed in this embodiment of the invention, and will not be repeated here.

[0063] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A reliability analysis method for DC transmission systems based on task partitioning, characterized in that, include: The Monte Carlo simulation process of the target DC transmission system is divided into several sub-task modules, and a parallel task processing architecture is constructed using the sub-task modules. In the parallel task processing architecture, based on the original parameters of each component in the target DC transmission system, the fault time and repair time of each component within a set simulation period are sampled. The fault time and the repair time are integrated to generate a time-series combination sequence; Read the faulty components that have experienced fault events in the time-series combination sequence, determine the standby status of the faulty components, and use the determination result to determine the fault combination period in the current simulation cycle; The Monte Carlo simulation is performed iteratively for multiple rounds, and the fault combination time periods output in each round are statistically analyzed to determine the reliability analysis results of the target DC transmission system.

2. The reliability analysis method for DC transmission systems based on task partitioning as described in claim 1, characterized in that, The several sub-task modules include: The system includes a fault time calculation module, a repair time calculation module, a time series combination module, a component fault event processing module, a simulation cutoff time judgment module, a system state mapping module, and a reliability index calculation module.

3. The reliability analysis method for DC transmission systems based on task partitioning as described in claim 1, characterized in that, The step of determining the standby status of the faulty component and using the determination result to determine the fault combination period within the current simulation cycle includes: Determine whether the currently faulty component belongs to a shared backup component; If so, query the availability status of the backup resources of the faulty component, and determine the first fault duration of the faulty component based on the availability status of the backup resources; If not, the repair time corresponding to the faulty component shall be taken as the first fault duration. When an additional faulty component is detected within the first fault duration, the second fault duration corresponding to the additional faulty component is obtained; The duration of the first fault and the duration of the second fault are combined to determine the combined fault period.

4. The reliability analysis method for DC transmission systems based on task partitioning as described in claim 3, characterized in that, Determining the first fault duration of the faulty component based on the availability of the backup resources includes: When a spare component is available, the first installation time of the spare component shall be taken as the first fault duration of the faulty component. When no spare component is available, the second installation time is determined according to the order in which the faulty component and the spare component are repaired. The second installation time is taken as the first fault duration of the currently faulty component.

5. The reliability analysis method for DC transmission systems based on task partitioning as described in claim 1, characterized in that, After determining the fault combination period within the current simulation cycle based on the judgment result, the method further includes: Based on the topology of the target DC transmission system, identify the associated components that are in the same branch as the currently faulty component; At the repair time corresponding to the current faulty component, the time axis state of the associated component is updated and adjusted.

6. The reliability analysis method for DC transmission systems based on task partitioning as described in claim 1, characterized in that, The process of dividing the Monte Carlo simulation process of the target DC transmission system into several sub-task modules includes: Obtain the theoretical time consumption value for each of the subtask modules; Based on the current total computing resources and the theoretical time consumption value, allocate target computing resources to each of the subtask modules.

7. The reliability analysis method for DC transmission systems based on task partitioning as described in claim 6, characterized in that, The iterative execution of multiple rounds of Monte Carlo simulation includes: When the end of a Monte Carlo simulation is detected, the state of all components in the target DC transmission system is reset. Based on the reset state, execute the next round of Monte Carlo simulation.

8. A reliability analysis system for DC transmission systems based on task partitioning, characterized in that, include: The architecture design module is used to divide the Monte Carlo simulation process of the target DC transmission system into several sub-task modules, and to build a parallel task processing architecture using the sub-task modules. The timing determination module is used to sample the fault time and repair time of each component within a set simulation period based on the original parameters of each component in the target DC transmission system in the parallel task processing architecture. The timing combination module is used to integrate the fault time and the repair time to generate a timing combination sequence; The standby status determination module is used to read the faulty components that have experienced fault events in the time-series combination sequence, determine the standby status of the faulty components, and determine the fault combination period in the current simulation cycle based on the determination result. The reliability result output module is used to iteratively execute multiple rounds of Monte Carlo simulation and statistically analyze the fault combination time periods output in each round to determine the reliability analysis results of the target DC transmission system.

9. A computer device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the task-based DC transmission system reliability analysis method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the task-based DC transmission system reliability analysis method as described in any one of claims 1 to 7.

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