Method, device and equipment for reliability evaluation of multi-terminal flexible direct current transmission system

Abstract

The application provides a reliability evaluation method, device and equipment of a multi-terminal flexible DC power transmission system, and relates to the technical field of power systems. The method comprises the following steps: determining the converter station and the DC line topology in the multi-terminal flexible DC power transmission system respectively; determining the theoretical failure rate of the converter station based on the theoretical failure rate of each subsystem in the converter station; determining the theoretical failure rate of the DC line topology based on the DC line topology; correcting the theoretical failure rates of the converter station and the DC line topology interactively, and determining the corrected failure rate of each converter station and the corrected failure rate of the DC line topology; and calculating the reliability index of the multi-terminal flexible DC power transmission system based on the corrected failure rates of the converter station and the DC line topology, so as to determine the reliability of the multi-terminal flexible DC power transmission system. The application can improve the reliability evaluation accuracy of the multi-terminal flexible DC power transmission system.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a reliability assessment method, apparatus and equipment for a multi-terminal flexible DC transmission system. Background Technology

[0002] Reliability assessment is a fundamental issue in power systems, providing guidance for grid planning and operation. Traditional AC power grids have a mature reliability assessment system. However, compared to AC grids, multi-terminal flexible DC transmission systems introduce more power electronic devices and have a more complex topology, making traditional reliability assessment systems unsuitable. Therefore, using traditional reliability evaluation systems when assessing the reliability of multi-terminal flexible DC transmission systems results in poor assessment accuracy. Summary of the Invention

[0003] This invention provides a reliability assessment method, apparatus, and equipment for multi-terminal flexible DC transmission systems to address the problem of poor accuracy in reliability assessment of such systems.

[0004] In a first aspect, embodiments of the present invention provide a reliability assessment method for a multi-terminal flexible DC transmission system, comprising:

[0005] Determine the topology of the converter station and DC line in the multi-terminal flexible DC transmission system respectively;

[0006] The theoretical failure rate of the converter station is determined based on the theoretical failure rate of each subsystem in the converter station.

[0007] Based on the DC line topology, determine the theoretical failure rate of the DC line topology;

[0008] The theoretical failure rates of the converter stations and the DC line topology are interactively corrected to determine the corrected failure rates of each converter station and the corrected failure rates of the DC line topology.

[0009] Based on the corrected failure rate of the converter station and the DC line topology, the reliability index of the multi-terminal flexible DC transmission system is calculated to determine the reliability of the multi-terminal flexible DC transmission system.

[0010] In one possible implementation, the theoretical failure rates of the converter stations and the DC line topology are interactively corrected to determine the corrected failure rates of each converter station and the corrected failure rate of the DC line topology, including:

[0011] The number of converter stations and the DC line topology between each converter station in the multi-terminal flexible DC transmission system are input into a pre-trained neural network model to obtain the joint probability of converter station faults and DC line topology faults output by the neural network model.

[0012] The theoretical failure rates of the converter station and the DC line topology, as well as the joint occurrence probability, are input into a Bayesian network to obtain the corrected failure probabilities of the converter station and the DC line topology output by the Bayesian network.

[0013] In one possible implementation, before inputting the number of converter stations in the multi-terminal flexible DC transmission system and the DC line topology between the converter stations into the pre-trained neural network model, the following steps are also included:

[0014] Monte Carlo simulation was used to simulate different fault scenarios in different multi-terminal flexible DC transmission systems, and the joint occurrence probability of converter station faults and DC line topology faults was statistically obtained.

[0015] Based on different multi-terminal flexible DC transmission systems and their corresponding joint occurrence probabilities, a neural network model is trained to obtain a well-trained neural network model; wherein, the number of converter stations in different multi-terminal flexible DC transmission systems is different, and / or the DC line topology between each converter station is different.

[0016] In one possible implementation, the reliability metric includes: annual equivalent downtime metric;

[0017] The reliability index of the multi-terminal flexible DC transmission system is determined based on the corrected failure rate of each converter station and the DC line topology, including:

[0018] According to the state-space method, a transition probability matrix is ​​constructed based on the corrected failure rate of the converter station and the DC line topology;

[0019] Based on the transition probability matrix, the steady-state probability of the multi-terminal flexible DC transmission system in different states is determined.

[0020] Based on the steady-state probability, the annual equivalent downtime index is calculated.

[0021] In one possible implementation, determining the steady-state probabilities of the multi-terminal flexible DC transmission system in different states based on the transition probability matrix includes:

[0022] according to Solve for the steady-state probability of the multi-terminal flexible DC transmission system under different conditions;

[0023] Where A represents the transition probability matrix, This represents the steady-state probability of a multi-terminal flexible DC transmission system in state 1. This represents the steady-state probability of a multi-terminal flexible DC transmission system in the nth state. This represents the steady-state probability of a multi-terminal flexible DC transmission system in the Nth state, where N represents the number of states.

[0024] In one possible implementation, calculating the annual equivalent downtime index based on the steady-state probability includes:

[0025] according to The power supply availability index is calculated;

[0026] according to The annual equivalent downtime index is calculated;

[0027] Wherein, EA represents the power supply availability index. Let N represent the steady-state probability in the nth state, and let N represent the number of states. EOH represents the available capacity of a multi-terminal flexible DC transmission system in the nth state, and EOH represents the annual equivalent outage time index.

[0028] Secondly, embodiments of the present invention provide a reliability assessment device for a multi-terminal flexible DC transmission system, comprising:

[0029] The determination module is used to determine the topology of converter stations and DC lines in a multi-terminal flexible DC transmission system, respectively.

[0030] The calculation module is used for:

[0031] The theoretical failure rate of the converter station is determined based on the theoretical failure rate of each subsystem in the converter station.

[0032] Based on the DC line topology, determine the theoretical failure rate of the DC line topology;

[0033] The evaluation module is used for:

[0034] The theoretical failure rates of the converter stations and the DC line topology are interactively corrected to determine the corrected failure rates of each converter station and the corrected failure rates of the DC line topology.

[0035] Based on the corrected failure rate of the converter station and the DC line topology, the reliability index of the multi-terminal flexible DC transmission system is calculated to determine the reliability of the multi-terminal flexible DC transmission system.

[0036] In one possible implementation, the evaluation module interactively corrects the theoretical failure rates of the converter stations and the DC line topology to determine the corrected failure rates of each converter station and the DC line topology, including:

[0037] The number of converter stations and the DC line topology between each converter station in the multi-terminal flexible DC transmission system are input into a pre-trained neural network model to obtain the joint probability of converter station faults and DC line topology faults output by the neural network model.

[0038] The theoretical failure rates of the converter station and the DC line topology, as well as the joint occurrence probability, are input into a Bayesian network to obtain the corrected failure probabilities of the converter station and the DC line topology output by the Bayesian network.

[0039] In one possible implementation, before inputting the number of converter stations in the multi-terminal flexible DC transmission system and the DC line topology between the converter stations into the pre-trained neural network model, the following steps are also included:

[0040] Monte Carlo simulation was used to simulate different fault scenarios in different multi-terminal flexible DC transmission systems, and the joint occurrence probability of converter station faults and DC line topology faults was statistically obtained.

[0041] Based on different multi-terminal flexible DC transmission systems and their corresponding joint occurrence probabilities, a neural network model is trained to obtain a well-trained neural network model; wherein, the number of converter stations in different multi-terminal flexible DC transmission systems is different, and / or the DC line topology between each converter station is different.

[0042] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0043] In this embodiment of the invention, when conducting reliability assessment of a multi-terminal flexible DC transmission system, the theoretical failure rates of the converter station and the DC line topology are determined independently. Based on this, the mutual influence between converter station failures and DC line topology failures is considered, thereby interactively correcting the theoretical failure rates of the two. The reliability of the multi-terminal flexible DC transmission system is then assessed based on the corrected failure rates, thus improving the accuracy of reliability assessment. Attached Figure Description

[0044] Figure 1 This is an application scenario diagram of the reliability assessment method for multi-terminal flexible DC transmission systems provided in this embodiment of the invention;

[0045] Figure 2 These are simplified structural diagrams of the symmetrical bipolar flexible DC transmission system provided in this embodiment of the invention.

[0046] Figure 3 This is a schematic diagram of the structure of the reliability assessment device for a multi-terminal flexible DC transmission system provided in an embodiment of the present invention;

[0047] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0048] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0049] Compared to traditional AC power grids, multi-terminal flexible DC transmission systems introduce more power electronic devices and have a more complex topology, making traditional reliability assessment systems unsuitable for multi-terminal flexible DC transmission networks.

[0050] In order to accurately assess the reliability of multi-terminal flexible DC transmission systems, this invention determines the theoretical failure rates of converter stations and DC line topologies in the multi-terminal flexible DC transmission system, and takes into account the mutual influence between converter station failures and DC line topology failures. The theoretical failure rates of converter stations and DC line topologies are interactively corrected, and reliability indicators are calculated based on the corrected failure rates, thereby achieving the goal of accurately assessing the reliability of multi-terminal flexible DC transmission systems.

[0051] See Figure 1 The document illustrates a flowchart of the reliability assessment method for a multi-terminal flexible DC transmission system provided in an embodiment of the present invention, which is described in detail below:

[0052] Step 101: Determine the converter station and DC line topology in the multi-terminal flexible DC transmission system.

[0053] The main components of a multi-terminal flexible DC transmission system include: AC circuit breakers, converter transformers, grounding reactors, VSC converters, control and protection systems for converter stations, cooling equipment, DC transmission lines, and pole equipment.

[0054] Because flexible DC transmission systems have numerous components and complex structures, this invention divides them into subsystems based on function and connection relationships to simplify the flexible DC transmission system. The specific division is as follows:

[0055] 1) Converter transformer subsystem: converter transformer, AC circuit breaker;

[0056] 2) Converter subsystem: converter valve assembly, valve control, valve cooling equipment, protection devices, etc.;

[0057] 3) Control and protection subsystem;

[0058] 4) Electrode equipment subsystem: neutral point grounding branch and related switching components;

[0059] 5) DC line subsystem: DC transmission lines and DC circuit breakers.

[0060] The aforementioned converter transformer subsystem, converter subsystem, control and protection subsystem, and pole equipment subsystem can constitute a single converter station. This division simplifies the multi-terminal flexible DC transmission system, which includes multiple converter stations and the DC line topology between them. For example, see [link to example]. Figure 2 , Figure 2 The diagram shows the structure of a symmetrical bipolar flexible DC transmission system before and after simplification. The simplified symmetrical bipolar flexible DC transmission system includes a converter station at each end and the DC line topology between the converter stations.

[0061] Step 102: Determine the theoretical failure rate of the converter station based on the theoretical failure rate of each subsystem in the converter station;

[0062] Based on the above, the converter transformer subsystem, converter subsystem, control and protection subsystem, and pole equipment subsystem can form a single converter station. A failure in any of these subsystems will lead to a failure of the entire converter station.

[0063] For each converter station, when calculating the theoretical failure rate of the converter station, the state-space method can be used to establish a transition probability matrix based on Markov state transitions. By solving the transition probability matrix, the steady-state probability of the converter station in different states can be determined, and the theoretical failure rate of the converter station can be obtained.

[0064] Specifically, the transition probability matrix corresponding to the converter station can be expressed as:

[0065] ;

[0066] in, , , , ,and These represent the theoretical failure rates of each subsystem. , , , ,and The diagonal elements represent the recovery rates of each subsystem. According to The calculation is complete. Here, This represents the value of the element in the i-th row and j-th column of the aforementioned transition probability matrix.

[0067] Based on the above transition probability matrix, we can obtain:

[0068] ;

[0069] in, , This represents the steady-state probability of the converter station being in the k-th state.

[0070] Here, state 1 is the normal operating state, corresponding to a transport capacity of 100%. States 2 to 5 represent the fault states of the four subsystems: converter transformer, converter, control and protection, and pole equipment, respectively, corresponding to a transport capacity of 0%.

[0071] The equivalent failure rate of the converter station is: ;

[0072] in, Let I represent the equivalent failure rate of the converter station, and let I represent the set of states where the corresponding transport capacity of the converter station is 100%. Let J represent the steady-state probability of the converter station being in the i-th state, and let J represent the set of states where the corresponding transport capacity of the converter station is 0. This represents the failure rate of the converter station from state i to state j.

[0073] The equivalent repair rate of the converter station is: ;

[0074] in, This indicates the equivalent repair rate of the converter station. Let J represent the steady-state probability of the converter station being in the j-th state, where J represents the set of states where the corresponding transport capacity of the converter station is 0. This represents the repair rate of the converter station from state j to state i.

[0075] Step 103: Determine the theoretical failure rate of the DC line topology based on the DC line topology;

[0076] Here, when calculating the theoretical failure rate of a DC line topology, the following three aspects are mainly considered:

[0077] Firstly, the average annual number of faults for the same type of DC line topology can be statistically analyzed beforehand. Then, based on the statistically obtained average annual number of faults, a preliminary estimate of the primary failure rate can be determined. .

[0078] Secondly, considering that the line failure rate is also affected by voltage stress and has an exponential relationship with voltage stress, a second initial failure rate is determined based on the actual DC line voltage: .in, This represents the baseline failure rate corresponding to the rated voltage. Here, v represents the material constant, and v represents the actual voltage of the DC line topology. This indicates the rated voltage of a DC line topology.

[0079] Next, considering the impact of the external environment on the DC line topology, the second initial failure rate is corrected based on the environmental factors recommended in IEEE Std 493 (IEEE's recommended practice for reliability design of industrial and commercial power systems), thus determining the second failure rate: .in, This represents the environmental coefficient.

[0080] Thirdly, different fault scenarios such as lightning strikes and switching overvoltages can be simulated to statistically determine the DC line topology failure rate under different fault scenarios. Then, by statistically analyzing the occurrence frequency of different fault scenarios and assigning different weights according to the frequency, a weighted calculation is performed to obtain the third failure rate under each fault scenario.

[0081] Finally, the theoretical failure rate of the DC line topology is calculated by assigning different weights to the first failure rate, the second failure rate, and the third failure rate.

[0082] Step 104: Perform interactive correction on the theoretical failure rates of converter stations and DC line topologies to determine the corrected failure rates of each converter station and the corrected failure rates of the DC line topologies.

[0083] It is understandable that converter station failures and DC line topology failures between converter stations are not independent events. This invention, through statistical analysis of the combined occurrence probability of these two events, reveals their coupling relationship, thereby correcting the theoretical failure rate mentioned above.

[0084] Optionally, in this embodiment of the invention, the number of converter stations in the multi-terminal flexible DC transmission system and the DC line topology between each converter station can be input into a pre-trained neural network model to obtain the joint occurrence probability of converter station faults and DC line topology faults output by the neural network model. Then, the theoretical fault rates of converter stations and DC line topologies, as well as the joint occurrence probability, can be input into a Bayesian network to obtain the corrected fault probability of converter stations and the corrected fault probability of DC line topologies output by the Bayesian network.

[0085] This invention utilizes Monte Carlo simulation to simulate different fault scenarios in various multi-terminal flexible DC transmission systems and statistically obtains the joint occurrence probability of converter station faults and DC line topology faults. Based on the different multi-terminal flexible DC transmission systems and their corresponding joint occurrence probabilities, a neural network model is trained to obtain a well-trained neural network model.

[0086] Here, the number of converter stations varies in different multi-terminal flexible DC transmission systems, and / or the DC line topologies between the converter stations differ. By using the Monte Carlo method to simulate fault scenarios in multi-terminal flexible DC transmission systems with different topologies, the joint probability of converter station faults and DC line faults in different multi-terminal flexible DC transmission systems is statistically obtained, which is then used to train a neural network model.

[0087] The trained neural network model is used to output the joint probability of converter station faults and DC line topology faults based on the number of converter stations in the input multi-terminal flexible DC transmission system and the DC line topology between the converter stations.

[0088] Based on the determination of the joint occurrence probability, this embodiment of the invention utilizes a Bayesian network to determine the corrected failure rate of the converter station and DC line topology based on the theoretical failure rate and joint occurrence probability of the converter station and DC line topology.

[0089] Step 105: Based on the corrected failure rate of the converter station and DC line topology, calculate the reliability index of the multi-terminal flexible DC transmission system to determine the reliability of the multi-terminal flexible DC transmission system.

[0090] Here, the reliability index can be the annual equivalent outage time index. When determining the annual equivalent outage time index, we can first construct a transition probability matrix based on the corrected failure rate of the converter station and DC line topology using the state-space method; then, based on the transition probability matrix, we can solve for the steady-state probability of the multi-terminal flexible DC transmission system under different states; finally, based on the steady-state probability, we can calculate the annual equivalent outage time index.

[0091] The transition probability matrix corresponding to a multi-terminal flexible DC transmission system can be expressed as:

[0092] ;

[0093] The matrix elements mentioned above can be expressed using a general expression. and Characterization. Among them, This represents the failure rate from state i to state j. This represents the recovery rate from state j to state i.

[0094] Here, the failure rate and recovery rate mentioned above are defined as follows:

[0095] If state j has one more converter station failure than state i, then ;in, This indicates the corrected failure rate of the converter station.

[0096] If state j has one more line fault than state i, then ; This indicates the corrected failure rate of a DC line.

[0097] If state j has one less converter station failure than state i, then ; This indicates the recovery rate of the converter station.

[0098] If state j has one less line fault than state i, then ; This indicates the recovery rate of the DC line.

[0099] Based on the above transition probability matrix, we can... Solve for the steady-state probability of a multi-terminal flexible DC transmission system under different conditions;

[0100] Where A represents the transition probability matrix, This represents the steady-state probability of a multi-terminal flexible DC transmission system in state 1. This represents the steady-state probability of a multi-terminal flexible DC transmission system in the nth state. This represents the steady-state probability of a multi-terminal flexible DC transmission system in the Nth state, where N represents the number of states.

[0101] Optionally, based on the calculated steady-state probability, embodiments of the present invention can be based on... The power supply availability index is calculated;

[0102] according to The equivalent downtime index was calculated;

[0103] EA represents the power availability index. Let N represent the steady-state probability in the nth state, and let N represent the number of states. EOH represents the available capacity of a multi-terminal flexible DC transmission system in the nth state, and EOH represents the equivalent outage time index.

[0104] In this embodiment of the invention, when conducting reliability assessment of a multi-terminal flexible DC transmission system, the theoretical failure rates of the converter station and the DC line topology are determined independently. Based on this, the mutual influence between converter station failures and DC line topology failures is considered, thereby interactively correcting the theoretical failure rates of the two. The reliability of the multi-terminal flexible DC transmission system is then assessed based on the corrected failure rates, thus improving the accuracy of reliability assessment.

[0105] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0106] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0107] Figure 3 A schematic diagram of the reliability assessment device for a multi-terminal flexible DC transmission system provided in an embodiment of the present invention is shown. For ease of explanation, only the parts relevant to the embodiment of the present invention are shown, and are described in detail below:

[0108] like Figure 3 As shown, the reliability assessment device 3 for a multi-terminal flexible DC transmission system includes: a determination module 31, a calculation module 32, and an assessment module 33.

[0109] Module 31 is used to determine the topology of the converter station and the DC line in the multi-terminal flexible DC transmission system, respectively.

[0110] Calculation module 32 is used for:

[0111] The theoretical failure rate of the converter station is determined based on the theoretical failure rate of each subsystem in the converter station.

[0112] Based on the DC line topology, determine the theoretical failure rate of the DC line topology;

[0113] Evaluation module 33 is used for:

[0114] The theoretical failure rates of converter stations and DC line topologies are interactively corrected to determine the corrected failure rates of each converter station and the corrected failure rates of the DC line topology.

[0115] Based on the corrected failure rate of the converter station and DC line topology, the reliability index of the multi-terminal flexible DC transmission system is calculated to determine the reliability of the multi-terminal flexible DC transmission system.

[0116] In one possible implementation, the evaluation module 33 is specifically used for:

[0117] The number of converter stations and the DC line topology between each converter station in the multi-terminal flexible DC transmission system are input into a pre-trained neural network model to obtain the joint probability of converter station faults and DC line topology faults output by the neural network model.

[0118] The theoretical failure rates of the converter station and the DC line topology, as well as their joint occurrence probabilities, are input into a Bayesian network to obtain the corrected failure probabilities of the converter station and the DC line topology output by the Bayesian network.

[0119] In one possible implementation, the evaluation module 33 is also used for:

[0120] Monte Carlo simulation was used to simulate different fault scenarios in different multi-terminal flexible DC transmission systems, and the joint occurrence probability of converter station faults and DC line topology faults was statistically obtained.

[0121] Based on different multi-terminal flexible DC transmission systems and their corresponding joint occurrence probabilities, a neural network model is trained to obtain a well-trained neural network model; wherein, the number of converter stations in different multi-terminal flexible DC transmission systems is different, and / or the DC line topology between each converter station is different.

[0122] In one possible implementation, reliability metrics include: annual equivalent downtime.

[0123] Evaluation module 33 is specifically used for:

[0124] Using the state-space method, a transition probability matrix is ​​constructed based on the corrected failure rate of the converter station and DC line topology;

[0125] Based on the transition probability matrix, the steady-state probability of a multi-terminal flexible DC transmission system under different states is determined.

[0126] Based on steady-state probability, the annual equivalent downtime index is calculated.

[0127] In one possible implementation, the evaluation module 33 is specifically used for:

[0128] according to Solve for the steady-state probability of a multi-terminal flexible DC transmission system under different conditions;

[0129] Where A represents the transition probability matrix, This represents the steady-state probability of a multi-terminal flexible DC transmission system in state 1. This represents the steady-state probability of a multi-terminal flexible DC transmission system in the nth state. This represents the steady-state probability of a multi-terminal flexible DC transmission system in the Nth state, where N represents the number of states.

[0130] In one possible implementation, the evaluation module 33 is specifically used for:

[0131] according to The power supply availability index is calculated;

[0132] according to The annual equivalent downtime index is calculated;

[0133] EA represents the power availability index. Let N represent the steady-state probability in the nth state, and let N represent the number of states. EOH represents the available capacity of a multi-terminal flexible DC transmission system in the nth state, and EOH represents the annual equivalent outage time index.

[0134] This device embodiment can be used to implement the above method embodiment, and its technical principle and implementation effect are the same as those of the above method embodiment, so they will not be repeated here.

[0135] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 4 As shown, the electronic device 4 in this embodiment includes a processor 40 and a memory 41. The memory 41 stores a computer program 42. When the processor 40 executes the computer program 42, it implements the steps in the various method embodiments described above. Alternatively, when the processor 40 executes the computer program 42, it implements the functions of each module / unit in the various device embodiments described above.

[0136] For example, computer program 42 may be divided into one or more modules / units, which are stored in memory 41 and executed by processor 40 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 42 in electronic device 4.

[0137] Electronic device 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 4 and does not constitute a limitation on electronic device 4. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 4 may also include input / output devices, network access devices, buses, etc.

[0138] The processor 40 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 any conventional processor.

[0139] The memory 41 can be an internal storage unit of the electronic device 4, such as a hard disk or RAM. The memory 41 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 41 can include both internal and external storage units of the electronic device 4. The memory 41 is used to store the computer program 42 and other programs and data required by the electronic device 4. The memory 41 can also be used to temporarily store data that has been output or will be output.

[0140] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0141] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0142] This invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0143] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0144] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0145] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A reliability assessment method for a multi-terminal flexible DC transmission system, characterized in that, include: Determine the topology of the converter station and DC line in the multi-terminal flexible DC transmission system respectively; The theoretical failure rate of the converter station is determined based on the theoretical failure rate of each subsystem in the converter station. Based on the DC line topology, determine the theoretical failure rate of the DC line topology; The theoretical failure rates of the converter stations and the DC line topology are interactively corrected to determine the corrected failure rates of each converter station and the corrected failure rates of the DC line topology. Based on the corrected failure rate of the converter station and the DC line topology, the reliability index of the multi-terminal flexible DC transmission system is calculated to determine the reliability of the multi-terminal flexible DC transmission system. The theoretical failure rates of the converter stations and the DC line topology are interactively corrected to determine the corrected failure rates of each converter station and the DC line topology, including: The number of converter stations and the DC line topology between each converter station in the multi-terminal flexible DC transmission system are input into a pre-trained neural network model to obtain the joint probability of converter station faults and DC line topology faults output by the neural network model. The theoretical failure rates of the converter station and the DC line topology, as well as the joint occurrence probability, are input into a Bayesian network to obtain the corrected failure probability of the converter station and the corrected failure probability of the DC line topology output by the Bayesian network. Before inputting the number of converter stations and the DC line topology between the converter stations in the multi-terminal flexible DC transmission system into the pre-trained neural network model, the following steps are also included: Monte Carlo simulation was used to simulate different fault scenarios in different multi-terminal flexible DC transmission systems, and the joint occurrence probability of converter station faults and DC line topology faults was statistically obtained. Based on different multi-terminal flexible DC transmission systems and their corresponding joint occurrence probabilities, a neural network model is trained to obtain a well-trained neural network model; wherein, the number of converter stations in different multi-terminal flexible DC transmission systems is different, and / or the DC line topology between each converter station is different.

2. The reliability assessment method for a multi-terminal flexible DC transmission system according to claim 1, characterized in that, The reliability metrics include: annual equivalent downtime; The reliability index of the multi-terminal flexible DC transmission system is determined based on the corrected failure rate of each converter station and the DC line topology, including: According to the state-space method, a transition probability matrix is ​​constructed based on the corrected failure rate of the converter station and the DC line topology; Based on the transition probability matrix, the steady-state probability of the multi-terminal flexible DC transmission system in different states is determined. Based on the steady-state probability, the annual equivalent downtime index is calculated.

3. The reliability assessment method for a multi-terminal flexible DC transmission system according to claim 2, characterized in that, The step of determining the steady-state probability of the multi-terminal flexible DC transmission system under different states based on the transition probability matrix includes: according to Solve for the steady-state probability of the multi-terminal flexible DC transmission system under different conditions; Where A represents the transition probability matrix, This represents the steady-state probability of a multi-terminal flexible DC transmission system in state 1. This represents the steady-state probability of a multi-terminal flexible DC transmission system in the nth state. This represents the steady-state probability of a multi-terminal flexible DC transmission system in the Nth state, where N represents the number of states.

4. The reliability assessment method for a multi-terminal flexible DC transmission system according to claim 2, characterized in that, The annual equivalent downtime index, calculated based on the steady-state probability, includes: according to The power supply availability index is calculated; according to The annual equivalent downtime index is calculated; Wherein, EA represents the power supply availability index. Let N represent the steady-state probability in the nth state, and let N represent the number of states. EOH represents the available capacity of a multi-terminal flexible DC transmission system in the nth state, and EOH represents the annual equivalent outage time index.

5. A reliability assessment device for a multi-terminal flexible DC transmission system, characterized in that, A reliability assessment method for implementing a multi-terminal flexible DC transmission system as described in any one of claims 1 to 4 includes: The determination module is used to determine the topology of converter stations and DC lines in a multi-terminal flexible DC transmission system, respectively. The calculation module is used for: The theoretical failure rate of the converter station is determined based on the theoretical failure rate of each subsystem in the converter station. Based on the DC line topology, determine the theoretical failure rate of the DC line topology; The evaluation module is used for: The theoretical failure rates of the converter stations and the DC line topology are interactively corrected to determine the corrected failure rates of each converter station and the corrected failure rates of the DC line topology. Based on the corrected failure rate of the converter station and the DC line topology, the reliability index of the multi-terminal flexible DC transmission system is calculated to determine the reliability of the multi-terminal flexible DC transmission system. The evaluation module interactively corrects the theoretical failure rates of the converter stations and the DC line topology to determine the corrected failure rates of each converter station and the DC line topology, including: The number of converter stations and the DC line topology between each converter station in the multi-terminal flexible DC transmission system are input into a pre-trained neural network model to obtain the joint probability of converter station faults and DC line topology faults output by the neural network model. The theoretical failure rates of the converter station and the DC line topology, as well as the joint occurrence probability, are input into a Bayesian network to obtain the corrected failure probability of the converter station and the corrected failure probability of the DC line topology output by the Bayesian network. Before inputting the number of converter stations and the DC line topology between the converter stations in the multi-terminal flexible DC transmission system into the pre-trained neural network model, the following steps are also included: Monte Carlo simulation was used to simulate different fault scenarios in different multi-terminal flexible DC transmission systems, and the joint occurrence probability of converter station faults and DC line topology faults was statistically obtained. Based on different multi-terminal flexible DC transmission systems and their corresponding joint occurrence probabilities, a neural network model is trained to obtain a well-trained neural network model; wherein, the number of converter stations in different multi-terminal flexible DC transmission systems is different, and / or the DC line topology between each converter station is different.

6. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 4.

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