Method and device for evaluating reliability of substation-level protection system of transformer substation based on Markov model

Abstract

The invention relates to the technical field of power system reliability evaluation, and particularly provides a Markov model-based substation-level protection system reliability evaluation method and device, and the method comprises the steps: obtaining a historical failure rate, a historical fault recovery rate and a historical fault self-detection rate corresponding to target equipment in a target system; the target system comprises a plurality of subsystems of different types, and each subsystem comprises a plurality of target devices; calculating the system failure rate and the system repair rate of the corresponding subsystem according to the historical failure rate and the historical fault repair rate of the target equipment; the system repair rate comprises a hardware repair rate and a software repair rate; calculating the state transition probability and the state probability of the target system according to the system failure rate, the hardware repair rate and the software repair rate of the subsystem; and determining a corresponding reliability evaluation result according to the state probability of the target system. The method comprehensively considers the fault mode and the self-inspection influence, and effectively improves the evaluation precision.

Description

Technical Field

[0001] This invention relates to the field of power system reliability assessment technology, and in particular to a method and apparatus for assessing the reliability of substation-level protection systems based on Markov models. Background Technology

[0002] Substation-level protection systems are a key factor in the safe operation of power systems, and their reliability directly affects the stability and fault handling capabilities of the power grid. In existing technologies, the reliability assessment method for substation-level protection systems mainly adopts the Reliability Block Diagram (RBD) method, which directly models the equipment in the protection system based on the reliability block diagram. This method treats different states of the substation-level protection system as completely independent, ignoring the correlation between states. This leads to biased assessment results, making it difficult to accurately reflect the actual system's reliability level, resulting in low accuracy. Summary of the Invention

[0003] The reliability assessment method and apparatus for substation-level protection systems based on Markov models provided in this invention at least solves the problem of low accuracy in related assessment methods and effectively improves the assessment accuracy.

[0004] In a first aspect, the present invention provides a reliability assessment method for a substation-level protection system based on a Markov model, comprising the steps of: obtaining the historical failure rate, historical fault repair rate, and historical fault self-check rate of target equipment in a target system; wherein the target system includes multiple subsystems of different types, and each subsystem includes multiple target equipment; calculating the system failure rate and system repair rate of the corresponding subsystem based on the historical failure rate and historical fault repair rate of the target equipment; wherein the system repair rate includes hardware repair rate and software repair rate; calculating the state transition probability and state probability of the target system based on the system failure rate, hardware repair rate, and software repair rate of the subsystem; and determining the corresponding reliability assessment result based on the state probability of the target system.

[0005] In one embodiment of the present invention, the state transition probability and state probability of the target system are calculated based on the system failure rate, hardware repair rate, and software repair rate of the subsystem, including the steps of: calculating the state transition probability based on the system failure rate, hardware repair rate, and software repair rate; establishing a corresponding system state transition matrix based on the state transition probability, and calculating the state probability based on the system state transition matrix; wherein, the state of the target system includes a normal state, a single-type single-fault self-check state, a single-type single-fault non-self-check state, a similar-type multiple-fault self-check state, a similar-type multiple-fault non-self-check state, a similar-type single-self-check single-non-self-check state, a different-type single-self-check single-non-self-check state, a different-type multiple-fault self-check state, and a different-type multiple-fault non-self-check state.

[0006] In one embodiment of the present invention, the state transition probability is calculated based on the system failure rate, the hardware repair rate, and the software repair rate, including the steps of: obtaining a state transition probability O1 based on the product of the number of subsystems of a corresponding class, the system failure rate, and the historical fault self-check rate; wherein, the state transition probability O1 is the probability that the target system transitions from the normal state to the single-type single-fault self-check state; obtaining a state transition probability O2 based on the hardware repair rate of the subsystems of a corresponding class; wherein, the state transition probability O2 is the probability that the target system transitions from the single-type single-fault self-check state to the normal state; and obtaining a state transition probability O2 based on the product of the number of subsystems of a corresponding class, the system failure rate, and the historical fault self-check rate. The state transition probability 03 is obtained by multiplying the system failure rate and the historical failure-to-self-check rate; wherein the historical failure-to-self-check rate is the difference between one and the historical failure-to-self-check rate; the state transition probability 03 is the probability that the target system will transition from the normal state to the single-type single-fault failure-to-self-check state; the state transition probability 04 is obtained based on the software repair rate of the corresponding subsystem; wherein the state transition probability 04 is the probability that the target system will transition from the single-type single-fault failure-to-self-check state to the normal state; the state transition probability 05 is obtained by multiplying the number of subsystems of the corresponding subsystem by one, the system failure rate, and the historical failure-to-self-check rate; wherein the state transition probability 05 is the probability that the target system will transition from the single-type single-fault failure-to-self-check state to the normal state. The probability of the system transitioning from the single-type single-fault self-test state to the same-type multi-fault self-test state; based on the hardware repair rate of the corresponding subsystem, a state transition probability 06 is obtained; wherein, the state transition probability 06 is the probability of the target system transitioning from the same-type multi-fault self-test state to the single-type single-fault self-test state; based on the product of the number of subsystems of the corresponding subsystem minus one, the system failure rate, and the historical failure-to-self-test rate, a state transition probability 07 is obtained; wherein, the state transition probability 07 is the probability of the target system transitioning from the single-type single-fault self-test state to the same-type single self-test single non-self-test state; based on the hardware repair rate of the corresponding subsystem, a state transition probability 0 is obtained. 8; wherein, the state transition probability 08 is the probability that the target system will transition from the same type of single self-test and single non-self-test state to the single type of single fault self-test state; the state transition probability 09 is obtained by multiplying the number of non-self-tested subsystems, the historical fault non-self-test rate, and the system failure rate of the non-self-tested subsystems; wherein, the state transition probability 09 is the probability that the target system will transition from the single type of single fault self-test state to the different type of single self-test and single non-self-test state; the state transition probability 10 is obtained by multiplying the software repair rate of the non-self-tested subsystems; wherein, the state transition probability 10 is the probability that the target system will transition from the different type of single self-test and single non-self-test state to the single type of single fault self-test state;Based on the product of the number of subsystems that performed the second fault self-check, the system failure rate of the subsystems that performed the second fault self-check, and the historical fault self-check rate, a state transition probability 11 is obtained; wherein, the state transition probability 11 is the probability that the target system will transition from the single-type single-fault self-check state to the heterogeneous multi-fault self-check state; based on the hardware repair rate of the subsystems that performed fault self-check and repair, a state transition probability 12 is obtained; wherein, the state transition probability 12 is the probability that the target system will transition from the heterogeneous multi-fault self-check state to the single-type single-fault self-check state; based on the number of subsystems that did not perform the second fault self-check, the system failure rate of the subsystems that performed the second fault self-check, and the historical fault self-check rate... The state transition probability 13 is obtained by multiplying the non-self-check rates; where the state transition probability 13 is the probability that the target system will transition from the single-type single-fault non-self-check state to the heterogeneous multi-fault non-self-check state; the state transition probability 14 is obtained based on the software repair rate of the subsystems that have not self-checked and repaired the faults; where the state transition probability 14 is the probability that the target system will transition from the heterogeneous multi-fault non-self-check state to the single-type single-fault non-self-check state; the state transition probability 15 is obtained by multiplying the number of subsystems of the corresponding type by one, the system failure rate, and the historical fault self-check rate; where the state transition probability 15 is the probability that the target system will transition from the single-type single-fault non-self-check state to the single-type single-fault non-self-check state. The probability of a single self-test and single non-self-test state of the same type is described; based on the hardware repair rate of the subsystem that has performed fault self-test and repair, a state transition probability 16 is obtained; wherein, the state transition probability 16 is the probability that the target system changes from the single self-test and single non-self-test state of the same type to the single fault non-self-test state of the same type; based on the product of the number of subsystems of the corresponding type minus one, the system failure rate, and the historical fault non-self-test rate, a state transition probability 17 is obtained; wherein, the state transition probability 17 is the probability that the target system changes from the single fault non-self-test state of the same type to the multiple fault non-self-test state of the same type; based on the software repair rate of the subsystems of the corresponding type, a state transition probability 18 is obtained; wherein, the state Transition probability 18 is the probability that the target system will transition from the state of multiple faults of the same type without self-check to the state of a single fault of a single type without self-check; state transition probability 19 is obtained by multiplying the number of subsystems that have performed fault self-checks, the system failure rate of the second subsystem that has performed fault self-checks, and the historical fault self-check rate; wherein, state transition probability 19 is the probability that the target system will transition from the state of a single fault of a single type without self-check to the state of a different type with a single self-check and a single fault without self-check; state transition probability 20 is obtained by multiplying the hardware repair rate of the subsystems that have performed fault self-checks and repairs; wherein, state transition probability 20 is the probability that the target system will transition from the state of a different type with a single self-check and a single fault without self-check to the state of a single fault of a single type without self-check.

[0007] In one embodiment of the present invention, a corresponding system state transition matrix is ​​established based on the state transition probabilities, and the state probabilities are calculated based on the system state transition matrix, and are expressed as follows: In the formula, Let be the system state transition matrix. For the first The state probability of each state. The total number of states of the target system. This is the state probability vector.

[0008] In one embodiment of the present invention, the system failure rate and system repair rate of the corresponding subsystem are calculated based on the historical failure rate and historical fault repair rate of the target device, including the steps of: summing the historical failure rates of multiple target devices corresponding to the subsystem to obtain the corresponding system failure rate; and performing a weighted average calculation based on the historical failure rates and historical fault repair rates of multiple target devices corresponding to the subsystem to obtain the corresponding system repair rate.

[0009] In one embodiment of the present invention, the corresponding system failure rate is obtained by summing the historical failure rates of the multiple target devices corresponding to the subsystem, and is expressed as follows: In the formula, The failure rate of the system. For the first The historical failure rate of the target device. This refers to the total number of target devices corresponding to the subsystem.

[0010] In one embodiment of the present invention, the corresponding system repair rate is obtained by weighted averaging the historical failure rates and historical fault repair rates of the multiple target devices corresponding to the subsystem, and is expressed as follows: In the formula, The system repair rate, For the first The historical fault repair rate of the target device.

[0011] Secondly, the present invention also provides a substation-level protection system reliability assessment device based on a Markov model, applied to the substation-level protection system reliability assessment method based on a Markov model as described in any one of the above claims, comprising a first module for acquiring the historical failure rate, historical fault repair rate, and historical fault self-check rate of target equipment in a target system; wherein the target system includes multiple subsystems of different types, and each subsystem includes multiple target equipment; a second module for calculating the system failure rate and system repair rate of the corresponding subsystem based on the historical failure rate and historical fault repair rate of the target equipment; wherein the system repair rate includes hardware repair rate and software repair rate; a third module for calculating the state transition probability and state probability of the target system based on the system failure rate, hardware repair rate, and software repair rate of the subsystem; and a fourth module for determining the corresponding reliability assessment result based on the state probability of the target system.

[0012] Thirdly, the present invention also provides an electronic device, including a processor and a memory storing a program, the program including instructions that, when executed by the processor, cause the processor to perform the reliability assessment method for a substation-level protection system based on a Markov model as described in any of the preceding claims.

[0013] Fourthly, the present invention also provides a non-transient machine-readable medium storing computer instructions for causing the computer to execute the substation-level protection system reliability assessment method based on the Markov model as described in any of the preceding claims.

[0014] Compared with the prior art, the above-described technical solution of the present invention has the following advantages:

[0015] The reliability assessment method for substation-level protection systems based on Markov models described in this invention comprehensively considers the impact of historical failure rates, historical fault repair rates, and historical fault self-check rates of each target device in the target system on the target system's state. During the assessment, the system failure rate and system repair rate of the corresponding subsystem are calculated based on the historical failure rate and the historical fault repair rate of the target devices. Then, based on the system failure rate and system repair rate of each subsystem, and simultaneously using a pre-constructed Markov multi-state model, the state transition probability and state probability of the target system are directly calculated. Finally, the corresponding reliability assessment result can be determined based on the state probability of the target system. Compared to existing methods that do not consider equipment self-check characteristics and state correlations, this method comprehensively considers the impact of fault modes and self-checks, achieving a more refined assessment and effectively improving assessment accuracy. It is applicable to substations with different wiring configurations. Attached Figure Description

[0016] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort. In the drawings...

[0017] Figure 1 This is a flowchart illustrating the reliability assessment method for substation-level protection systems based on Markov models in a preferred embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of state model 28 in a preferred embodiment of the present invention.

[0019] Figure 3 This is a schematic diagram illustrating the impact of the fault self-check rate on the reliability of the protection system.

[0020] Figure 4 This is a schematic diagram illustrating the impact of replacing the chip with a custom chip on the reliability of the protection system.

[0021] Figure 5 This is a schematic diagram of the structure of a substation-level protection system reliability assessment device based on a Markov model in a preferred embodiment of the present invention.

[0022] Figure 6 This is a schematic diagram of the structure of an electronic device in a preferred embodiment of the present invention.

[0023] The above-mentioned figures include the following reference numerals: 11, First module; 12, Second module; 13, Third module; 14, Fourth module; 201, Computing unit; 202, ROM; 203, RAM; 204, Bus; 205, I / O interface; 206, Input unit; 207, Output unit; 208, Storage unit; 209, Communication unit. Detailed Implementation

[0024] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0025] It should be noted that, among related technologies, the RBD method does not consider the impact of failure modes and other factors on the reliability of the protection system, resulting in low accuracy of the evaluation results. Besides the RBD method, there are also methods based on the Markov method to calculate reliability. Specifically, it constructs a 10-state Markov model. This model is relatively simple and does not distinguish details such as whether a device failure is detected by self-testing, leading to insufficient evaluation accuracy.

[0026] To solve the above problems, refer to Figure 1 As shown, this embodiment of the invention provides a reliability assessment method for substation-level protection systems based on Markov models. The reliability assessment method for substation-level protection systems based on Markov models includes the following steps.

[0027] Step S10: Obtain the historical failure rate, historical fault repair rate, and historical fault self-test rate of the target device in the target system. The target system includes multiple subsystems of different types, and each subsystem includes multiple target devices.

[0028] Step S20: Calculate the system failure rate and system repair rate of the corresponding subsystem based on the historical failure rate and historical fault repair rate of the target device. The system repair rate includes both hardware repair rate and software repair rate.

[0029] Step S30: Calculate the state transition probability and state probability of the target system based on the system failure rate, hardware repair rate, and software repair rate of the subsystem.

[0030] Step S40: Determine the corresponding reliability assessment result based on the state probability of the target system.

[0031] In this embodiment of the invention, the target system is the station-level protection system of a 110kV substation. Before conducting a reliability assessment, the station-level protection system is first divided into subsystems according to the bay type, and the number of subsystems can be determined based on the actual wiring; at the same time, the equipment composition of each subsystem is confirmed.

[0032] For example, the target system includes four L-type subsystems, three B-type subsystems, and four T-type subsystems. This subsystem partitioning provides a foundation for subsequent state-space modeling, ensuring that the model is consistent with the actual system structure.

[0033] The L-type subsystem corresponds to the line bay and includes line merging units, line intelligent terminals, and line protection equipment. The B-type subsystem corresponds to the segmented bay and includes segment merging units, segment intelligent terminals, and segment protection equipment. The T-type subsystem corresponds to the main transformer bay and includes high-voltage side merging units, high-voltage side intelligent terminals, low-voltage side merging units, low-voltage side intelligent terminals, and main transformer protection equipment.

[0034] Next, for each subsystem, based on the self-testing characteristics of its devices, a Markov state model for a single device is established and simplified into a state model for the subsystem.

[0035] For each device, the Markov state model includes three states: State 1, the device is operating normally; State 2, the device has failed and the failure has been detected by a self-test; and State 3, the device has failed but the failure has not been detected by a self-test. The state transition parameter is determined by the device's historical failure self-test rate. Historical failure rate and historical fault repair rate Definition: Historical fault repair rate Including device hardware repair rate and device software repair rate The fault self-test rate represents the probability that a fault is detected by the self-test, and it is a universally applicable term.

[0036] In this way, the state transition probabilities between different states can be determined. Specifically, the state transition probability from state 1 to state 2 is: This can be understood as the probability that a device malfunction is detected by its self-test. The probability of transitioning from state 2 to state 1 is... This can be understood as the repair rate of equipment malfunctions detected by self-testing. The probability of transitioning from state 1 to state 3 is... This can be understood as the probability that a device malfunctions and goes undetected by its self-test. The probability of transitioning from state 3 to state 1 is... This can be understood as the repair rate of equipment malfunctions that are not detected by self-inspection.

[0037] Based on this, a multi-state Markov state model is established for the corresponding subsystems. Taking the L-type subsystem as an example, which includes multiple serially connected devices, a Markov state model is established for the L-type subsystem. The model includes seven states: State 1: All components are operating normally; State 2: The line merging unit malfunctions and is detected by self-test; State 3: The line merging unit malfunctions but is not detected by self-test; State 4: The line intelligent terminal malfunctions and is detected by self-test; State 5: The line intelligent terminal malfunctions but is not detected by self-test; State 6: The line protection device malfunctions and is detected by self-test; State 7: The line protection device malfunctions but is not detected by self-test.

[0038] Based on the independence of equipment failures, the seven states can be simplified to only three states: State 1. The L-type subsystem is operating normally; State 2. The L-type subsystem has failed and the failure has been detected by self-test; State 3. The L-type subsystem has failed but the failure has not been detected by self-test. The system failure rate of the L-type subsystem; the system repair rate of the L-type subsystem includes the hardware repair rate. and software repair rate .

[0039] In this way, the state transition probabilities between different states can be determined. Specifically, the state transition probability from state 1 to state 2 is: This can be understood as the probability that a fault in the L-type subsystem is detected by self-testing. The probability of transitioning from state 2 to state 1 is... This can be understood as the repair rate of a fault detected by self-checking in a subsystem of type L. The transition probability from state 1 to state 3 is... This can be understood as the probability that a fault in the L-type subsystem fails to be detected by self-check. The probability of transitioning from state 3 to state 1 is... This can be understood as the repair rate of subsystems of type L that fail to be detected by self-checking. The same applies to other types of subsystems, so further details will not be provided.

[0040] Next, a state-space model of multiple subsystems of the same type is established. Simultaneously, based on state merging rules, a simplified Markov model is built, and the possibility of faults being detected by self-checks is considered. Specifically, considering the high reliability requirements of the protection system, the probability of three or more subsystems failing simultaneously is relatively small, therefore these cases are ignored.

[0041] Taking four L-type subsystems as an example, an 11-state model is established, including state 1, where all four L-type subsystems are operating normally; states 2 to 5 correspond to the failure of the first to fourth L-type subsystems, respectively; states 6 to 11 correspond to the failure of a second L-type subsystem in addition to the failure of the first L-type subsystem. For example, in the case of the failure of the first L-type subsystem, the second, third, and fourth L-type subsystems also fail.

[0042] Based on the state merging rule, the 11-state model is simplified to a 3-state model. The three states are: State 1, all L-type subsystems are operating normally; State 2, one L-type subsystem fails; State 3, two L-type subsystems fail. Furthermore, by introducing self-checking effects, the 3-state model is extended to a 6-state model.

[0043] Specifically, the six states are as follows: State 1. All L-type subsystems are operating normally; State 2. One L-type subsystem malfunctions and is detected by self-test; State 3. One L-type subsystem malfunctions but is not detected by self-test; State 4. Two L-type subsystems malfunction and are detected by self-test; State 5. Two L-type subsystems malfunction but are not detected by self-test; State 6. One L-type subsystem malfunctions and is detected by self-test, and one L-type subsystem malfunctions but is not detected by self-test.

[0044] In this way, the state transition probabilities between different states can be determined. Specifically, the state transition probability from state 1 to state 2 is... The state transition probability from state 2 to state 1 is The state transition probability from state 1 to state 3 is The state transition probability from state 3 to state 1 is The state transition probability from state 2 to state 4 is The state transition probability from state 4 to state 2 is The state transition probability from state 2 to state 6 is The state transition probability from state 6 to state 2 is The state transition probability from state 3 to state 6 is The state transition probability from state 6 to state 3 is The state transition probability from state 3 to state 5 is The state transition probability from state 5 to state 3 is .

[0045] Reference Figure 2As shown, a 28-state model can be similarly established for a complete substation protection system. The twenty-eight states are as follows: State 1: All subsystems of the protection system are operating normally; State 2: One L-class subsystem has a fault detected by self-test; State 3: One B-class subsystem has a fault detected by self-test; State 4: One T-class subsystem has a fault detected by self-test; State 5: One L-class subsystem has a fault that is not detected by self-test; State 6: One B-class subsystem has a fault that is not detected by self-test; State 7: One T-class subsystem has a fault that is not detected by self-test; State 8: Two L-class subsystems have faults detected by self-test; State 9: Two B-class subsystems have faults detected by self-test; State 10: Two T-class subsystems have faults detected by self-test; State 11: Two L-class subsystems have faults detected by self-test. State 12. Two Class B subsystems failed to detect the fault during self-testing; State 13. Two Class T subsystems failed to detect the fault during self-testing; State 14. One Class L subsystem failed to detect the fault during self-testing, and one Class L subsystem failed to detect the fault during self-testing; State 15. One Class B subsystem failed to detect the fault during self-testing, and one Class B subsystem failed to detect the fault during self-testing; State 16. One Class T subsystem failed to detect the fault during self-testing, and one Class T subsystem failed to detect the fault during self-testing; State 17. One Class L subsystem failed to detect the fault during self-testing, and one Class B subsystem failed to detect the fault during self-testing; State 18. One L-type subsystem malfunctioned but was not detected by self-test, and one B-type subsystem malfunctioned but was detected by self-test; State 19. One B-type subsystem malfunctioned but was detected by self-test, and one T-type subsystem malfunctioned but was not detected by self-test; State 20. One B-type subsystem malfunctioned but was not detected by self-test, and one T-type subsystem malfunctioned but was detected by self-test; State 21. One T-type subsystem malfunctioned but was detected by self-test, and one L-type subsystem malfunctioned but was not detected by self-test; State 22. One T-type subsystem malfunctioned but was not detected by self-test, and one L-type subsystem malfunctioned but was detected by self-test; State 23. One L-type subsystem malfunctioned but was detected by self-test. Currently, one Class B subsystem has failed and been detected by self-test; State 24. One Class B subsystem and one Class T subsystem have failed and been detected by self-test; State 25. One Class T subsystem and one Class L subsystem have failed and been detected by self-test; State 26. One Class L subsystem and one Class B subsystem have failed and were not detected by self-test; State 27. One Class B subsystem and one Class T subsystem have failed and were not detected by self-test; State 28. One Class T subsystem and one Class L subsystem have failed and were detected by self-test.

[0046] Thus, the state transition probability from state 1 to state 2 is . Let L be the system failure rate of the subsystem. The state transition probability from state 1 to state 3 is... . This represents the system failure rate of subsystem B. The state transition probability from state 1 to state 4 is... . Let T be the system failure rate of the subsystem. The state transition probability from state 1 to state 5 is... . This represents the historical failure rate of self-checking. The state transition probability from state 1 to state 6 is... The state transition probability from state 1 to state 7 is... The state transition probability from state 2 to state 1 is... . The hardware repair rate of the L-class subsystem. The state transition probability from state 2 to state 8 is... The state transition probability from state 2 to state 14 is... The state transition probability from state 2 to state 17 is... The state transition probability from state 2 to state 22 is... The state transition probability from state 2 to state 23 is: The state transition probability from state 2 to state 25 is... The state transition probability from state 3 to state 1 is... The state transition probability from state 3 to state 9 is... The state transition probability from state 3 to state 15 is... The state transition probability from state 3 to state 18 is... The state transition probability from state 3 to state 19 is... The state transition probability from state 3 to state 23 is... The state transition probability from state 3 to state 24 is... The state transition probability from state 4 to state 1 is... . The hardware repair rate of subsystem T. The state transition probability from state 4 to state 10 is... The state transition probability from state 4 to state 16 is... The state transition probability from state 4 to state 20 is... The state transition probability from state 4 to state 21 is... The state transition probability from state 4 to state 24 is... The state transition probability from state 4 to state 25 is... The state transition probability from state 5 to state 1 is... . The software repair rate of subsystem L. The state transition probability from state 5 to state 11 is... The state transition probability from state 5 to state 14 is... The state transition probability from state 5 to state 18 is... The state transition probability from state 5 to state 21 is... The state transition probability from state 5 to state 26 is... The state transition probability from state 5 to state 28 is... The state transition probability from state 6 to state 1 is... The state transition probability from state 6 to state 12 is... The state transition probability from state 6 to state 15 is... The state transition probability from state 6 to state 17 is... The state transition probability from state 6 to state 20 is... The state transition probability from state 6 to state 26 is... The state transition probability from state 6 to state 27 is... The state transition probability from state 7 to state 1 is... . The software repair rate of subsystem T. The state transition probability from state 7 to state 13 is... The state transition probability from state 7 to state 16 is... The state transition probability from state 7 to state 19 is... The state transition probability from state 7 to state 22 is... The state transition probability from state 7 to state 27 is... The state transition probability from state 7 to state 28 is... The state transition probability from state 8 to state 2 is... The state transition probability from state 9 to state 3 is... . The hardware repair rate of subsystem B. The state transition probability from state 10 to state 4 is... The state transition probability from state 11 to state 5 is... The state transition probability from state 12 to state 6 is... . The software repair rate for subsystem B. The state transition probability from state 13 to state 7 is... The state transition probability from state 14 to state 2 is... The state transition probability from state 14 to state 5 is... The state transition probability from state 15 to state 3 is... The state transition probability from state 15 to state 6 is... The state transition probability from state 16 to state 4 is... The state transition probability from state 16 to state 7 is... The state transition probability from state 17 to state 2 is... The state transition probability from state 17 to state 6 is... The state transition probability from state 18 to state 3 is... The state transition probability from state 18 to state 5 is... The state transition probability from state 19 to state 3 is... The state transition probability from state 19 to state 7 is... The state transition probability from state 20 to state 4 is... The state transition probability from state 20 to state 6 is... The state transition probability from state 21 to state 4 is... The state transition probability from state 21 to state 5 is... The state transition probability from state 22 to state 2 is... The state transition probability from state 22 to state 7 is... The state transition probability from state 23 to state 2 is: The state transition probability from state 23 to state 3 is: The state transition probability from state 24 to state 3 is... The state transition probability from state 24 to state 4 is... The state transition probability from state 25 to state 2 is... The state transition probability from state 25 to state 4 is... The state transition probability from state 26 to state 5 is... The state transition probability from state 26 to state 6 is... The state transition probability from state 27 to state 6 is... The state transition probability from state 27 to state 7 is... The state transition probability from state 28 to state 5 is... The state transition probability from state 28 to state 7 is... .

[0047] After modeling is completed, reliability assessment can be performed based on the established system-level Markov state model. In step S10, the historical failure rate, historical fault repair rate, and historical fault self-check rate of the target equipment in the target system are obtained. This allows for the acquisition of national-level substation-level protection system failure data from the previous year. For example, the failure rate of the merging unit is 0.00159 times / (unit•year), and the repair rate is 717 times / (unit•year); the failure rate of the intelligent terminal is 0.00094 times / (unit•year), and the repair rate is 512 times / (unit•year); the failure rate of protection equipment varies depending on the type, such as 0.02801 times / (unit•year) for line protection equipment and 232 times / (unit•year) for internal bridge protection equipment; and 0.0283 times / (unit•year) for main transformer protection devices and 232 times / (unit•year) for main transformer protection devices.

[0048] Step S20: Calculate the system failure rate and system repair rate of the corresponding subsystem based on the historical failure rate and historical fault repair rate of the target equipment. This includes the following steps.

[0049] Step S21: Sum the historical failure rates of the multiple target devices corresponding to the subsystem to obtain the corresponding system failure rate, which is expressed as .

[0050] .

[0051] In the formula, For system failure rate, For the first The historical failure rate of the target equipment. This represents the total number of target devices corresponding to the subsystem.

[0052] Step S22: Calculate the corresponding system repair rate by weighted averaging the historical failure rates and historical fault repair rates of multiple target devices corresponding to the subsystem.

[0053] .

[0054] In the formula, For system repair rate, For the first The historical fault repair rate of the target equipment.

[0055] In this way, parameter calculations ensure that the model is consistent with the actual equipment reliability data.

[0056] Step S30 involves calculating the state transition probability and state probability of the target system based on the system failure rate, hardware repair rate, and software repair rate of the subsystem. This step includes the following steps.

[0057] Step S31: Calculate the state transition probability based on the system failure rate, hardware repair rate, and software repair rate.

[0058] Step S32: Establish the corresponding system state transition matrix based on the state transition probabilities, and calculate the state probabilities based on the system state transition matrix.

[0059] The target system's states include normal state, single-type single-fault self-check state, single-type single-fault no-self-check state, same-type multiple-fault self-check state, same-type multiple-fault no-self-check state, same-type single-self-check single-no-self-check state, different-type single-self-check single-no-self-check state, different-type multiple-fault self-check state, and different-type multiple-fault no-self-check state.

[0060] Step S31 involves calculating the state transition probability based on the system failure rate, hardware repair rate, and software repair rate, and includes specific steps.

[0061] The state transition probability 01 is obtained by multiplying the number of subsystems of a corresponding class, the system failure rate, and the historical fault self-check rate. Here, state transition probability 01 represents the probability that the target system transitions from a normal state to a single-class, single-fault self-check state. For example, using a 28-state model, the transition could be from state 1 to states 2, 3, or 4.

[0062] Based on the hardware repair rate of the corresponding subsystem, the state transition probability 02 is obtained. Here, state transition probability 02 represents the probability that the target system transitions from a single-fault self-test state to a normal state. For example, using a 28-state model, the transition could be from state 2, 3, or 4 to state 1.

[0063] The state transition probability O3 is obtained by multiplying the number of subsystems of the corresponding class, the system failure rate, and the historical failure rate. The historical failure rate is the difference between one and the historical failure rate. The state transition probability O3 represents the probability that the target system transitions from a normal state to a single-class, single-fault failure state. For example, using a 28-state model, the transition could be from state 1 to states 5, 6, or 7.

[0064] Based on the software repair rate of the corresponding subsystem, the state transition probability 04 is obtained. Here, state transition probability 04 represents the probability that the target system transitions from a single-fault, non-self-checking state to a normal state. For example, using a 28-state model, the transition could be from state 5, 6, or 7 to state 1.

[0065] The state transition probability 05 is obtained by subtracting one from the number of subsystems of the corresponding class, multiplying the system failure rate by the historical fault self-check rate. Here, state transition probability 05 represents the probability that the target system transitions from a single-class, single-fault self-check state to a class-wide, multi-fault self-check state. For example, using a 28-state model, the transition could be from state 2 to state 8, from state 3 to state 9, and from state 4 to state 10.

[0066] Based on the hardware repair rate of the corresponding subsystem, the state transition probability 06 is obtained. Here, state transition probability 06 is the probability that the target system transitions from a multi-fault self-test state of the same type to a single-fault self-test state of a single type. For example, using a 28-state model, the transition could be from state 8 to state 2, from state 9 to state 3, and from state 10 to state 4.

[0067] The state transition probability 07 is obtained by multiplying the number of subsystems of the corresponding category by one, the system failure rate, and the historical failure-to-self-check rate. Here, state transition probability 07 represents the probability that the target system transitions from a single-category, single-fault self-check state to a single-category, single-self-check, single-fault non-self-check state. For example, using a 28-state model, the transition could be from state 2 to state 14, from state 3 to state 15, and from state 4 to state 16.

[0068] Based on the hardware repair rate of the corresponding subsystem, the state transition probability 08 is obtained. Here, state transition probability 08 is the probability that the target system transitions from a single self-test / single non-self-test state to a single fault self-test state. For example, using a 28-state model, the transition could be from state 14 to state 2, from state 15 to state 3, and from state 16 to state 4.

[0069] The state transition probability 09 is obtained by multiplying the number of subsystems that did not self-check, the historical failure rate of failure to self-check, and the system failure rate of the subsystems that did not self-check. Here, state transition probability 09 represents the probability that the target system will transition from a single-type, single-fault self-check state to a heterogeneous, single-self-check, single-fault non-self-check state. For example, using a 28-state model, the transition could be from state 2 to state 17, state 2 to state 22, state 3 to state 19, state 3 to state 18, state 4 to state 21, and state 4 to state 20.

[0070] Based on the software repair rate of the subsystems that did not undergo self-checking, the state transition probability 10 is obtained. Here, state transition probability 10 represents the probability that the target system transitions from a heterogeneous single self-checking / single non-self-checking state to a single-type single-fault self-checking state. For example, using a 28-state model, the transition could be from state 17 to state 2, from state 22 to state 2, from state 19 to state 3, from state 18 to state 3, from state 20 to state 4, and from state 21 to state 4.

[0071] The state transition probability 11 is obtained by multiplying the number of subsystems undergoing the second fault self-check, the system failure rate of the second fault self-check subsystem, and the historical fault self-check rate. Here, state transition probability 11 represents the probability that the target system transitions from a single-type, single-fault self-check state to a heterogeneous, multi-fault self-check state. For example, using a 28-state model, the transition could be from state 2 to state 23, from state 2 to state 25, from state 3 to state 23, from state 3 to state 24, from state 4 to state 24, and from state 4 to state 25.

[0072] Based on the hardware repair rate of the subsystem that performs fault self-check and repair, the state transition probability 12 is obtained. Here, state transition probability 12 is the probability that the target system transitions from a heterogeneous multi-fault self-check state to a single-fault self-check state. For example, using a 28-state model, the transition could be from state 23 to state 2, from state 25 to state 2, from state 23 to state 3, from state 24 to state 3, from state 24 to state 4, and from state 25 to state 4.

[0073] The state transition probability 13 is obtained by multiplying the number of subsystems that failed to self-check for the second fault, the system failure rate of the subsystems that self-checked for the second fault, and the historical failure rate of failure to self-check. Here, state transition probability 13 represents the probability that the target system will transition from a single-type, single-fault failure state to a heterogeneous, multi-fault failure state. For example, using a 28-state model, the transition could be from state 5 to state 26, state 5 to state 28, state 6 to state 26, state 6 to state 27, state 7 to state 27, and state 7 to state 28.

[0074] Based on the software repair rate of subsystems that fail to self-check and repair faults, the state transition probability 14 is obtained. Here, state transition probability 14 represents the probability that the target system transitions from a heterogeneous multi-fault non-self-check state to a single-fault non-self-check state. For example, using a 28-state model, the transition could be from state 26 to state 5, state 28 to state 5, state 26 to state 6, state 27 to state 6, state 27 to state 7, and state 28 to state 7.

[0075] The state transition probability 15 is obtained by subtracting one from the number of subsystems of the corresponding class, multiplying the system failure rate by the historical fault self-check rate, and so on. Here, state transition probability 15 represents the probability that the target system transitions from a single-class, single-fault, non-self-check state to a single-class, single-self-check, single-non-self-check state. For example, using a 28-state model, the transition could be from state 5 to state 14, from state 6 to state 15, and from state 7 to state 16.

[0076] Based on the hardware repair rate of the subsystem that performs fault self-check and repair, the state transition probability 16 is obtained. Here, state transition probability 16 is the probability that the target system transitions from a state of single self-check and single non-self-check for the same type to a state of single fault and non-self-check for the same type. For example, using a 28-state model, the transition could be from state 14 to state 5, from state 15 to state 6, and from state 16 to state 7.

[0077] The state transition probability 17 is obtained by multiplying the number of subsystems of the corresponding class by one, the system failure rate, and the historical failure rate of self-checking. Here, state transition probability 17 represents the probability that the target system transitions from a single-class, single-fault failure-to-self-check state to a class-wide, multi-fault failure-to-self-check state. For example, using a 28-state model, the transition could be from state 5 to state 11, from state 6 to state 12, and from state 7 to state 13.

[0078] Based on the software repair rate of the corresponding subsystem, the state transition probability 18 is obtained. Here, state transition probability 18 is the probability that the target system will transition from a state of multiple faults without self-checking of the same type to a state of a single fault without self-checking of a single type. For example, using a 28-state model, the transition could be from state 11 to state 5, from state 12 to state 6, and from state 13 to state 7.

[0079] The state transition probability 19 is obtained by multiplying the number of subsystems undergoing fault self-checks, the system failure rate of the second fault self-checking subsystem, and the historical fault self-check rate. Here, state transition probability 19 represents the probability that the target system transitions from a single-type, single-fault, non-self-checking state to a heterogeneous, single-self-checking, single-non-self-checking state. For example, using a 28-state model, the transition could be from state 5 to state 18, state 5 to state 21, state 6 to state 17, state 6 to state 20, state 7 to state 19, and state 7 to state 22.

[0080] Based on the hardware repair rate of the subsystem that performs fault self-check and repair, the state transition probability 20 is obtained. Here, state transition probability 20 is the probability that the target system transitions from a heterogeneous single self-check single non-self-check state to a single-type single fault non-self-check state. For example, using a 28-state model, the transition could be from state 18 to state 5, from state 21 to state 5, from state 17 to state 6, from state 20 to state 6, from state 19 to state 7, and from state 22 to state 7.

[0081] Step S32 involves establishing the corresponding system state transition matrix based on the state transition probabilities, and calculating the state probabilities based on the system state transition matrix. This includes the following steps.

[0082] Establish the corresponding system state transition matrix based on the state transition probabilities, and calculate the state probabilities based on the system state transition matrix, which are expressed as follows.

[0083] .

[0084] In the formula, Here is the system state transition matrix. For the first The state probability of each state. The total number of states of the target system, for example It is 28. This is the state probability vector.

[0085] Taking the 28-state model as an example, the system state transition matrix It is a matrix with 28 rows and 28 columns. The element a_(y, x) in the y-th row and x-th column represents the probability of transitioning from state y to state x, where y and x are both integers with values ​​ranging from 1 to 28. Specifically, the elements on the main diagonal of the matrix are the negatives of the sum of the probabilities of transitioning from one state to another.

[0086] For example, a_(1,1) is... a_(1,2) is a_(1,3) is a_(1,4) is, a_(1,5) is a_(1,6) is a_(1,7) is The values ​​from a_(1, 8) to a_(1, 28) are all 0. Based on this, the solution can be obtained. to .

[0087] Step S40: Determine the corresponding reliability assessment result based on the state probability of the target system.

[0088] Taking the 28-state model as an example, the L-type and B-type subsystems are single-configuration systems, and a single point of failure will cause the system to fail, while the T-type subsystem is a dual-configuration system with fault redundancy capability.

[0089] When one of the T-type subsystems fails, the other intact T-type subsystem can still perform its main protection function normally, and the main transformer remains in a protected state. Therefore, the overall system function is not lost, only the redundancy is reduced. In this situation, the system is considered to be operating in degraded mode but is still usable.

[0090] If a Class L or Class B subsystem fails, the corresponding line or section loses the protection function of the main system in that bay, and the entire system should be deemed unusable.

[0091] Therefore, the reliability assessment results of the system corresponding to the 28-state model It is represented as.

[0092] .

[0093] In the formula, This represents the probability that the target system is in a normal state. This represents the state probability of a self-checking process detecting a fault in a subsystem of type T within the target system. The indicator system represents the state probability of a fault occurring in a T-type subsystem that was not detected by self-testing. This represents the state probability of the target system having two T-type subsystems experiencing a fault that is detected by self-testing. This represents the state probability of the target system having two T-type subsystems experiencing a failure that was not detected by self-checking. This represents the state probability of the target system being either a fault detected by self-testing in one T-type subsystem or a fault not detected by self-testing in another T-type subsystem.

[0094] The weighting factor can be between 1 / 3 and 1 / 2. A weight of 1 / 2 implies that the severity of the failure in the dualized system is considered low, and the system still has 50% reliability. A weight of 1 / 3 implies a more cautious assessment, believing that this failure state is more detrimental to the system, and therefore contributing less to its effectiveness.

[0095] The reliability assessment method for substation-level protection systems based on Markov models described in this invention comprehensively considers the impact of historical failure rates, historical fault repair rates, and historical fault self-check rates of each target device in the target system on the target system's state. During the assessment, the system failure rate and system repair rate of the corresponding subsystem are calculated based on the historical failure rate and the historical fault repair rate of the target devices. Then, based on the system failure rate and system repair rate of each subsystem, and simultaneously using a pre-constructed Markov multi-state model, the state transition probability and state probability of the target system are directly calculated. Finally, the corresponding reliability assessment result can be determined based on the state probability of the target system. Compared to existing methods that do not consider equipment self-check characteristics and state correlations, this method comprehensively considers the impact of fault modes and self-checks, achieving a more refined assessment and effectively improving assessment accuracy. It is applicable to substations with different wiring configurations.

[0096] To verify the effectiveness of this method, reliability assessments are conducted below using both the RBD method and this method.

[0097] The RBD method divides substation wiring bays into line bays, section bays, and main transformer bays. Based on the equipment contained in each bay, reliability block diagrams for different protection functions are constructed. Taking a line bay as an example, its protection function reliability block diagram includes the connection from the merging unit to the intelligent terminal, and then from the intelligent terminal to the line protection equipment. On this basis, assuming that the operation of different bays is completely independent, a reliability block diagram for the overall protection system is constructed, including line 1 bay to line 2 bay, then from line 2 bay to line 3 bay, and line 3 bay to both main transformer bay set A and main transformer bay set B.

[0098] By setting the failure rate change factor to 0.6, 1, 1.2, 1.6, 2 and 3 respectively, we can see that...

[0099] When the failure rate change factor is 0.6, the failure probability value determined by the RBD method is 6.64 × 10⁻⁶. -4 The failure probability value determined by this method is 8.01 × 10⁻⁶. -4 The probability difference between the two is 1.37 × 10⁻⁶. -4 The probability deviation is 17.10%.

[0100] When the failure rate change factor is 1, the failure probability value determined by the RBD method is 1.11 × 10⁻⁶. -3 The failure probability value determined by this method is 1.33 × 10⁻⁶. -3 The probability difference between the two is 2.28 × 10⁻⁶. -4 The probability deviation is 17.10%.

[0101] When the failure rate variation factor is 1.2, the failure probability value determined by the RBD method is 1.33 × 10⁻⁶. -4 The failure probability value determined by this method is 1.60 × 10⁻⁶. -4 The probability difference between the two is 2.74 × 10⁻⁶. -4 The probability deviation is 17.10%.

[0102] When the failure rate change factor is 1.6, the failure probability value determined by the RBD method is 1.77 × 10⁻⁶. -4 The failure probability value determined by this method is 2.14 × 10⁻⁶. -4 The probability difference between the two is 3.65 × 10⁻⁶. -4 The probability deviation is 17.10%.

[0103] When the failure rate changes by a factor of 2, the failure probability value determined by the RBD method is 2.21 × 10⁻⁶. -4 The failure probability value determined by this method is 2.67 × 10⁻⁶. -4 The probability difference between the two is 4.56 × 10⁻⁶. -4 The probability bias is 17.11%.

[0104] When the failure rate changes by a factor of 3, the failure probability value determined by the RBD method is 3.32 × 10⁻⁶. -4 The failure probability value determined by this method is 3.99 × 10⁻⁶. -4 The probability difference between the two is 6.84 × 10⁻⁶. -4 The probability bias is 17.11%.

[0105] Thus, it can be determined that, compared with the traditional reliability block diagram method, the evaluation accuracy of the method described in this invention is improved by approximately 17.1%.

[0106] Furthermore, the method described in this invention also supports parametric analysis, which helps to identify weaknesses in reliability and provides data support for the optimized design and operation and maintenance strategy formulation of substation protection systems. For example, sensitivity analysis can be used to assess the impact of factors such as equipment self-test rate, autonomous chip replacement, and equipment failure rate on system reliability, providing a basis for optimized design.

[0107] For example, sensitivity analysis quantifies the impact of device self-test rate and chip reliability on overall system reliability. Specifically, sensitivity analysis is performed by changing key parameters, including historical fault self-test rate. Variations within the range of 0.8 to 0.999, refer to Figure 3 As shown, increased self-inspection rate can significantly reduce unavailability. Historical failure rate. The simulation measures the impact of replacing the original chip with a domestically developed one, varying the value from 0.6 to 10 times the baseline. Figure 4 As shown, an increased failure rate of the self-testing chip deteriorates reliability. Therefore, it can be determined that improving the self-test rate or selecting high-reliability chips can improve the overall reliability of the system.

[0108] On the other hand, refer to Figure 5 As shown, this embodiment of the invention also provides a reliability assessment device for a substation-level protection system based on a Markov model, which is applied to the reliability method for a substation-level protection system based on a Markov model as described in any of the above embodiments.

[0109] The reliability assessment device for substation-level protection systems based on Markov models includes a first module 11, a second module 12, a third module 13, and a fourth module 14.

[0110] The first module 11 is used to obtain the historical failure rate, historical fault repair rate, and historical fault self-test rate of the target devices in the target system. The target system includes multiple subsystems of different types, and each subsystem includes multiple target devices.

[0111] The second module 12 is used to calculate the system failure rate and system repair rate of the corresponding subsystem based on the historical failure rate and historical fault repair rate of the target device. The system repair rate includes both hardware repair rate and software repair rate.

[0112] The third module 13 is used to calculate the state transition probability and state probability of the target system based on the system failure rate, hardware repair rate and software repair rate of the subsystem.

[0113] Module 4, 14, is used to determine the corresponding reliability assessment results based on the state probabilities of the target system.

[0114] This invention also provides a non-transitory machine-readable medium storing a computer program. When executed by a computer's processor, the computer program causes the computer to perform the Markov model-based substation-level protection system reliability assessment method described in any of the above embodiments.

[0115] This invention also provides a computer program product, including a computer program. When executed by a computer's processor, the computer program causes the computer to perform the substation-level protection system reliability assessment method based on the Markov model described in any of the above embodiments.

[0116] This invention also provides an electronic device, including at least one processor and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor. When executed by the at least one processor, the computer program causes the electronic device to perform the substation-level protection system reliability assessment method based on the Markov model described in any of the above embodiments.

[0117] Reference Figure 6 The diagram illustrates a structural block diagram of an electronic device that can serve as an embodiment of the present invention, representing an example of a hardware device applicable to various aspects of the invention. The term "electronic device" is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0118] Reference Figure 6As shown, the electronic device includes a computing unit 201, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 202 or a computer program loaded from a storage unit 208 into a random access memory (RAM) 203. The RAM 203 may also store various programs and data required for the operation of the electronic device. The computing unit 201, ROM 202, and RAM 203 are interconnected via a bus 204. An input / output (I / O) interface 205 is also connected to the bus 204.

[0119] Multiple components in the electronic device are connected to I / O interface 205, including: an input unit 206, an output unit 207, a storage unit 208, and a communication unit 209. Input unit 206 can be any type of device capable of inputting information into the electronic device; it can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 207 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 208 may include, but is not limited to, a hard disk and an optical disk. Communication unit 209 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, and / or wireless communication transceivers, such as Bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0120] The computing unit 201 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 201 include, but are not limited to, CPUs, graphics processing units (GPUs), various special-purpose artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. The computing unit 201 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention can be implemented as a computer program tangibly contained in a machine-readable medium, such as storage unit 208. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 202 and / or communication unit 209. In some embodiments, the computing unit 201 can be configured to perform the methods described above by any other suitable means (e.g., by means of firmware).

[0121] Computer programs for implementing the methods of embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0122] In the context of embodiments of the present invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0123] It should be noted that the term "comprising" and its variations used in the embodiments of the present invention are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of the present invention are illustrative and not restrictive, and those skilled in the art should understand that unless explicitly indicated otherwise in the context, they should be understood as "one or more".

[0124] The steps described in the method embodiments provided by the present invention can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0125] The term "embodiment" in this specification refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of the invention. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply independence or alternativeity from other embodiments. The various embodiments in this specification are described in a related manner, with reference to each other for similar or identical parts. In particular, for apparatus, device, and system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and relevant details are referred to in the description of the method embodiments.

[0126] 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 protection. 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 all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A reliability assessment method for substation-level protection systems based on Markov models, characterized in that, Including the following steps: The historical failure rate, historical fault repair rate, and historical fault self-test rate of the target devices in the target system are obtained; wherein the target system includes multiple subsystems of different types, and each subsystem includes multiple target devices; Based on the historical failure rate and historical fault repair rate of the target device, calculate the system failure rate and system repair rate of the corresponding subsystem; wherein, the system repair rate includes hardware repair rate and software repair rate; Based on the system failure rate, hardware repair rate, and software repair rate of the subsystem, calculate the state transition probability and state probability of the target system; The corresponding reliability assessment result is determined based on the state probability of the target system.

2. The reliability assessment method for substation-level protection systems based on Markov models according to claim 1, characterized in that, Based on the system failure rate, hardware repair rate, and software repair rate of the subsystem, calculate the state transition probability and state probability of the target system, including the following steps: The state transition probability is calculated based on the system failure rate, the hardware repair rate, and the software repair rate. A corresponding system state transition matrix is ​​established based on the state transition probabilities, and the state probabilities are obtained by calculation based on the system state transition matrix. The target system's states include normal state, single-type single-fault self-check state, single-type single-fault no-self-check state, same-type multiple-fault self-check state, same-type multiple-fault no-self-check state, same-type single-self-check single-no-self-check state, different-type single-self-check single-no-self-check state, different-type multiple-fault self-check state, and different-type multiple-fault no-self-check state.

3. The reliability assessment method for substation-level protection systems based on Markov models according to claim 2, characterized in that, The state transition probability is calculated based on the system failure rate, the hardware repair rate, and the software repair rate, including the following steps: The state transition probability 01 is obtained by multiplying the number of subsystems of the corresponding class, the system failure rate, and the historical fault self-check rate; wherein, the state transition probability 01 is the probability that the target system changes from the normal state to the single-class single-fault self-check state. Based on the hardware repair rate of the corresponding subsystem, the state transition probability 02 is obtained; wherein, the state transition probability 02 is the probability that the target system changes from the single-type single-fault self-test state to the normal state; The state transition probability 03 is obtained by multiplying the number of subsystems of the corresponding class, the system failure rate, and the historical failure rate; wherein the historical failure rate is the difference between one and the historical failure rate; and the state transition probability 03 is the probability that the target system will transition from the normal state to the single-class single-fault failure state. Based on the software repair rate of the corresponding subsystem, the state transition probability 04 is obtained; wherein, the state transition probability 04 is the probability that the target system changes from the single-fault non-self-check state to the normal state; The state transition probability 05 is obtained by multiplying the number of subsystems of the corresponding class by one, the system failure rate, and the historical fault self-check rate; wherein, the state transition probability 05 is the probability that the target system changes from the single-type single-fault self-check state to the same-type multi-fault self-check state. Based on the hardware repair rate of the corresponding subsystem, the state transition probability 06 is obtained; wherein, the state transition probability 06 is the probability that the target system changes from the same type of multi-fault self-test state to the single type of single-fault self-test state. The state transition probability 07 is obtained by multiplying the number of subsystems of the corresponding category by one, the system failure rate, and the historical failure rate; wherein, the state transition probability 07 is the probability that the target system changes from the single-category single-fault self-check state to the same-category single self-check single non-self-check state. Based on the hardware repair rate of the corresponding subsystem, the state transition probability 08 is obtained; wherein, the state transition probability 08 is the probability that the target system changes from the same type of single self-test single non-self-test state to the single type of single fault self-test state. The state transition probability 09 is obtained by multiplying the number of subsystems that did not self-test, the historical failure rate of failure to self-test, and the system failure rate of the subsystems that did not self-test; wherein, the state transition probability 09 is the probability that the target system will change from the single-type single-fault self-test state to the heterogeneous single self-test single non-self-test state. Based on the software repair rate of the subsystem that has not undergone self-testing, the state transition probability 10 is obtained; wherein, the state transition probability 10 is the probability that the target system changes from the heterogeneous single self-test single non-self-test state to the single-type single fault self-test state; The state transition probability 11 is obtained by multiplying the number of subsystems in the second fault self-check, the system failure rate of the subsystems in the second fault self-check, and the historical fault self-check rate; wherein, the state transition probability 11 is the probability that the target system will change from the single-type single-fault self-check state to the heterogeneous multi-fault self-check state. Based on the hardware repair rate of the subsystem that performs fault self-check and repair, the state transition probability 12 is obtained; wherein, the state transition probability 12 is the probability that the target system changes from the heterogeneous multi-fault self-check state to the single-fault self-check state. The state transition probability 13 is obtained by multiplying the number of subsystems that failed the second fault self-check, the system failure rate of the subsystems that failed the second fault self-check, and the historical fault self-check rate; wherein, the state transition probability 13 is the probability that the target system will change from the single-type single-fault self-check state to the heterogeneous multi-fault self-check state. Based on the software repair rate of the subsystem that has not self-checked and repaired the fault, the state transition probability 14 is obtained; wherein, the state transition probability 14 is the probability that the target system changes from the heterogeneous multi-fault non-self-check state to the single-fault non-self-check state. The state transition probability 15 is obtained by subtracting one from the number of subsystems of the corresponding category, the system failure rate, and the historical fault self-check rate; wherein, the state transition probability 15 is the probability that the target system changes from the single-category single-fault non-self-check state to the same-category single-self-check single-non-self-check state. Based on the hardware repair rate of the subsystem that performs fault self-check and repair, the state transition probability 16 is obtained; wherein, the state transition probability 16 is the probability that the target system changes from the same type of single self-check single non-self-check state to the single type of single fault non-self-check state. The state transition probability 17 is obtained by subtracting one from the number of subsystems of the corresponding class, the system failure rate, and the historical failure rate of self-checking. The state transition probability 17 is the probability that the target system will change from the single-type single-fault failure state to the same-type multi-fault failure state. Based on the software repair rate of the corresponding subsystem, the state transition probability 18 is obtained; wherein, the state transition probability 18 is the probability that the target system changes from the state of multiple faults without self-checking of the same type to the state of single faults without self-checking of a single type. The state transition probability 19 is obtained by multiplying the number of subsystems that perform fault self-checks, the system failure rate of the second fault self-check subsystem, and the historical fault self-check rate; wherein, the state transition probability 19 is the probability that the target system changes from the single-type single-fault non-self-check state to the heterogeneous single-self-check single-non-self-check state. Based on the hardware repair rate of the subsystem that performs fault self-check and repair, the state transition probability 20 is obtained; wherein, the state transition probability 20 is the probability that the target system changes from the heterogeneous single self-check single non-self-check state to the single-type single fault non-self-check state.

4. The reliability assessment method for substation-level protection systems based on Markov models according to claim 2, characterized in that, A corresponding system state transition matrix is ​​established based on the state transition probabilities, and the state probabilities are calculated based on the system state transition matrix, expressed as: ， In the formula, Let be the system state transition matrix. For the first The state probability of each state. The total number of states of the target system. This is the state probability vector.

5. The method for reliability assessment of substation-level protection systems based on Markov models according to claim 1, characterized in that, Based on the historical failure rate and historical fault repair rate of the target device, calculate the corresponding system failure rate and system repair rate of the subsystem, including the following steps: The corresponding system failure rate is obtained by summing the historical failure rates of multiple target devices corresponding to the subsystem. The corresponding system repair rate is obtained by weighted averaging the historical failure rates and historical fault repair rates of the multiple target devices corresponding to the subsystem.

6. The reliability assessment method for substation-level protection systems based on Markov models according to claim 5, characterized in that, The system failure rate is obtained by summing the historical failure rates of the multiple target devices corresponding to the subsystem, and is expressed as follows: ， In the formula, The failure rate of the system. For the first The historical failure rate of the target device. This refers to the total number of target devices corresponding to the subsystem.

7. The method for reliability assessment of substation-level protection systems based on Markov models according to claim 5, characterized in that, The system repair rate is calculated by weighting the historical failure rates and historical fault repair rates of the multiple target devices corresponding to the subsystem, and is expressed as follows: ， In the formula, The system repair rate, For the first The historical fault repair rate of the target device.

8. A reliability assessment device for a substation-level protection system based on a Markov model, applied to the reliability assessment method for a substation-level protection system based on a Markov model as described in any one of claims 1 to 7, characterized in that, include: The first module is used to obtain the historical failure rate, historical fault repair rate, and historical fault self-test rate of the target devices in the target system; wherein, the target system includes multiple subsystems of different types, and each subsystem includes multiple target devices; The second module is used to calculate the system failure rate and system repair rate of the corresponding subsystem based on the historical failure rate and historical fault repair rate of the target device; wherein the system repair rate includes hardware repair rate and software repair rate; The third module is used to calculate the state transition probability and state probability of the target system based on the system failure rate, hardware repair rate and software repair rate of the subsystem; The fourth module is used to determine the corresponding reliability assessment result based on the state probability of the target system.

9. An electronic device comprising a processor and a memory storing a program, characterized in that, The program includes instructions that, when executed by the processor, cause the processor to perform the reliability assessment method for a substation-level protection system based on a Markov model according to any one of claims 1 to 7.

10. A non-transitory machine-readable medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the reliability assessment method for substation-level protection systems based on Markov models according to any one of claims 1 to 7.

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