Device failure rate calculation method and system for offshore converter station

Abstract

The present invention relates to a device failure rate calculation method and system for an offshore converter station. The method comprises the following steps: acquiring failure data of an offshore converter station device within a preset time, and dividing the offshore converter station device into components according to functions; using an autocorrelation function to perform correlation analysis on a failure time series, so as to obtain the type of a failure; if the failure is a static failure, using the law of large numbers and a normal distribution to construct a confidence interval, so as to obtain an initial failure rate of each component; if the failure is a dynamic failure, using a time series analysis model to obtain the initial failure rate of each component; constructing an environment correction coefficient model, and correcting the initial failure rate of each component, so as to obtain a corrected component failure rate; using an analytic hierarchy process to analyze each component to obtain a failure weight, and combining the failure weights and the corrected component failure rates to obtain an overall failure rate of the overall offshore converter station device; and on the basis of the overall failure rate, obtaining an expected value of an annual average failure time.

Description

A method and system for calculating equipment failure rate of offshore converter stations Technical Field

[0001] This invention relates to the field of power technology, and mainly to a method and system for calculating the equipment failure rate of an offshore converter station. Background Technology

[0002] With the large-scale development and construction of deep-sea wind farms, offshore converter stations, as key hubs connecting offshore wind farms and the main power grid, are crucial to the reliability of the entire power transmission system. Offshore converter stations operate in harsh environments, facing numerous adverse factors such as high humidity, high salt spray, and strong winds and waves, making them more prone to failure compared to onshore converter stations. Accurately calculating the failure rate and expected annual mean time between failures (MTBF) of offshore converter stations provides crucial information for operation and maintenance strategy formulation, spare parts configuration, and power system reliability assessment. However, existing failure rate calculation methods are mostly designed for conventional onshore power facilities and are difficult to apply precisely to the unique scenario of offshore converter stations.

[0003] For example, CN117407788A, "Method, Device, Equipment, Storage Medium and Program Product for Fault Monitoring of Converter Stations," discloses a method for "acquiring real-time operating data of a converter station, then processing the real-time operating data based on multiple fault identification algorithms to obtain fault identification results output by each algorithm, each fault identification result including the probability of fault occurrence corresponding to different converter station fault types, then performing evidence body synthesis processing on multiple fault identification results based on an evidence chain synthesis diagnostic algorithm to determine the fault type of the target converter station from different converter station fault types, and finally, based on the target converter station..." The method of "using fault types to handle the operation and maintenance of converter stations" only focuses on the real-time working data of converter stations, and does not make sufficient use of historical fault data. Furthermore, it relies solely on multiple fault identification algorithms to process real-time data without considering factors such as the functional structure of converter station equipment. This leads to an incomplete understanding of faults and makes it difficult to quickly and accurately find the root cause of faults during operation and maintenance. In addition, the method does not involve the analysis of the overall failure rate of converter stations, making it difficult to grasp the operational reliability of converter stations from a macro perspective. This makes it impossible for operation and maintenance personnel to accurately assess the long-term stability and risk level of converter stations. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, this application provides a method and system for calculating the equipment failure rate of an offshore converter station.

[0005] The technical solution of this application is as follows:

[0006] On one hand, this invention proposes a method for calculating the equipment failure rate of an offshore converter station, the method comprising:

[0007] Fault data of offshore converter station equipment within a preset time period is obtained, and the offshore converter station equipment is divided into components according to function; the autocorrelation function is used to perform correlation analysis on the fault time series to obtain the fault type; if it is a static fault, the confidence interval is constructed using the law of large numbers and normal distribution to obtain the initial fault rate of each component; if it is a dynamic fault, the initial fault rate of each component is obtained using a time series analysis model.

[0008] An environmental correction coefficient model is constructed to correct the initial failure rate of each component, resulting in the corrected component failure rate.

[0009] The fault weights of each component are obtained by analyzing the analytic hierarchy process (AHP). The overall fault rate of the offshore converter station is obtained by combining the fault weights with the corrected component fault rates. The expected value of the annual average failure time is obtained based on the overall fault rate.

[0010] Preferably, the method further includes data cleaning of the fault data, the data cleaning including processing missing values, outliers and data format standardization.

[0011] Preferably, the fault data includes the number of faults, the average humidity of the offshore converter station, the salt spray concentration of the offshore converter station, the wind and wave level of the offshore converter station, and the component type.

[0012] Preferably, the autocorrelation function is used to perform correlation analysis on the fault time series to obtain the fault type, which is expressed by the formula:

[0013] In the formula, R(k) represents the value of the autocorrelation function; t i Let represent the failure time series of the i-th component; t represent the mean of the failure time series; k represent the lag order; n i This represents the number of failures of the i-th component;

[0014] Based on hypothesis testing, different autocorrelation function values ​​are analyzed. If the autocorrelation function value corresponding to the current k value is not zero, then there is a correlation between the fault times, and the fault type is a dynamic fault; otherwise, the fault type is a static fault.

[0015] The dynamic fault is specifically determined by using a time series analysis model to obtain the initial failure rate of each component, expressed by the formula: φ p =1-φ1B-φ2B 2 -...φ j B j -...-φ p B p j = 1, 2, ..., p;

[0016] In the formula, λi y represents the initial failure rate of the i-th component; t Indicates the predicted time interval; c represents the preset constant term; φ p B represents the autoregressive function of the p-th order autoregressive term; j Let represent the j-th order lag operator; B represents the lag operator; P represents the order of the autoregressive term; p represents the index value of the p-th order autoregressive term; θ q Let represent the moving average coefficient of the q-th order moving average term; Q represent the order of the moving average term; q represent the index value of the order of the q-th order moving average term; j represent the index value of the j-th order lag operator; ε t The noise figure is represented by h; the preset time step is represented by h.

[0017] The static failure is specifically determined by constructing confidence intervals using the law of large numbers and the normal distribution to obtain the initial failure rate of each component, expressed by the formula:

[0018] In the formula, T represents the preset failure time; α represents the preset significance level of the normal distribution; z α / 2 α represents the critical value when the significance level of the normal distribution is α / 2; i represents the index value of the i-th component.

[0019] Preferably, an environmental correction coefficient model is constructed to correct the initial failure rate of each component, expressed by the formula: K = a0 + a h H+a s S+a w W; kλ i =λ i ×K;

[0020] In the formula, kλ i Represents the corrected failure rate of the i-th component; K represents the environmental correction coefficient model; a0 represents the initial coefficients of the preset environmental correction coefficient model; a h Indicates the preset average humidity coefficient; H represents the average humidity; a s This represents the preset salt spray concentration coefficient; S represents the salt spray concentration; a w This indicates the preset wind and wave level coefficient; W represents the wind and wave level.

[0021] Preferably, the overall failure rate of the offshore converter station is obtained by combining the fault weights and the corrected component failure rate, specifically as follows:

[0022] The analytic hierarchy process (AHP) is used to analyze each component to obtain fault weights. Based on an expert scoring table, the importance of each pair of components is scored, resulting in a fault score matrix A = {a...} iz}, where a izThis represents the element value of the fault score matrix in the i-th row and z-th column, i.e., the score for the importance of every two components, where i represents the index of the i-th row and z represents the index of the z-th column. a ii =a zz =1;

[0023] The fault weights are calculated using the following formula:

[0024] In the formula, M i ω represents the product of the scores in the fault score matrix of the i-th row; i This represents the fault weight of the i-th component; This represents the fault weight of the i-th component after normalization; N represents the number of component categories.

[0025] The overall failure rate of the offshore converter station equipment is expressed by the formula:

[0026] Preferably, the expected mean time to failure (MTTF) is obtained based on the overall failure rate, expressed by the formula:

[0027] On the other hand, the present invention also proposes a system for calculating the equipment failure rate of an offshore converter station. The system includes a data acquisition module, a failure calculation module, a failure correction module, an expected annual average failure time calculation module, and a result output module, wherein:

[0028] The data acquisition module is used to acquire fault data of the offshore converter station equipment within a preset time period, and divide the offshore converter station equipment into components according to function; and transmit the fault data to the initial fault calculation module;

[0029] The fault calculation module is used to perform correlation analysis on the fault time series using the autocorrelation function to obtain the fault type; if it is a static fault, it uses the law of large numbers and normal distribution to construct confidence intervals to obtain the initial fault rate of each component; if it is a dynamic fault, it uses a time series analysis model to obtain the initial fault rate of each component.

[0030] The fault correction module is used to construct an environmental correction coefficient model to correct the initial failure rate of each component and obtain the corrected component failure rate.

[0031] The annual average failure time expectation calculation module is used to analyze each component using the analytic hierarchy process to obtain failure weights, and combine the failure weights with the corrected component failure rates to obtain the overall failure rate of the entire offshore converter station equipment; and obtain the annual average failure time expectation based on the overall failure rate.

[0032] The results output module is used to display the expected value of the average annual failure time.

[0033] In another aspect, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for calculating the equipment failure rate of an offshore converter station as described in any one of the embodiments.

[0034] In another aspect, the present invention also proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for calculating the equipment failure rate of an offshore converter station as described in any one of the embodiments.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] 1) This invention provides a method and system for calculating the equipment failure rate of an offshore converter station. By acquiring failure data of the offshore converter station equipment within a preset time period, the time dimension of the data is improved, the ability to mine failure patterns from a long-term perspective is enhanced, and the comprehensive understanding of failures is strengthened. Furthermore, the offshore converter station equipment is divided into components according to function, which improves the granularity of failure analysis, enhances the accuracy of failure location, and improves the efficiency of failure troubleshooting.

[0037] 2) This invention provides a method and system for calculating the equipment failure rate of an offshore converter station. It clearly distinguishes between static and dynamic failures and uses different models to calculate the initial failure rate, which enhances the pertinence of handling failures of different natures and improves the effectiveness of failure handling. By constructing an environmental correction coefficient model and using the analytic hierarchy process (AHP) to analyze the failure weights, the overall failure rate of the offshore converter station and the expected value of the annual average failure time are obtained. This improves the macroscopic assessment capability of the reliability of converter station operation, enhances the systematicness and comprehensiveness of the assessment, and improves the scientific nature of operation and maintenance decisions.

[0038] 3) This invention provides a method and system for calculating the equipment failure rate of an offshore converter station. By using quantitative indicators such as the overall failure rate and the expected annual average failure time, the accuracy of operation and maintenance decision-making is enhanced, the rationality of operation and maintenance resource allocation is improved, and the accuracy of operation and maintenance plan formulation is increased. Attached Figure Description

[0039] Figure 1 is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0040] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0041] This invention provides the following technical solution: a method and system for calculating the equipment failure rate of an offshore converter station.

[0042] Example 1

[0043] This embodiment provides a method for calculating the equipment failure rate of an offshore converter station, the specific steps of which include:

[0044] S1. Obtain fault data of the offshore converter station equipment within a preset time period, and divide the offshore converter station equipment into components according to function;

[0045] The method also includes data cleaning, which includes handling missing values, outliers, and data format standardization.

[0046] S11. The fault data includes the number of faults, the average humidity of the offshore converter station, the salt spray concentration of the offshore converter station, the wind and wave level of the offshore converter station, and the component type.

[0047] S2. Use the autocorrelation function to perform correlation analysis on the fault time series to obtain the fault type, expressed by the formula:

[0048] In the formula, R(k) represents the value of the autocorrelation function; t i Let represent the failure time series of the i-th component; t represent the mean of the failure time series; k represent the lag order; n i This represents the number of failures of the i-th component;

[0049] Based on hypothesis testing, different autocorrelation function values ​​are analyzed. If the autocorrelation function value corresponding to the current k value is not zero, then there is a correlation between the fault times, and the fault type is a dynamic fault; otherwise, the fault type is a static fault.

[0050] S21. If it is a static fault, the initial failure rate of each component is obtained by constructing confidence intervals using the law of large numbers and the normal distribution, expressed by the formula:

[0051] In the formula, T represents the preset failure time; α represents the preset significance level of the normal distribution; z α / 2 α represents the critical value when the significance level of the normal distribution is α / 2; i represents the index value of the i-th component;

[0052] S22. If the fault is dynamic, the initial failure rate of each component is obtained using a time series analysis model, expressed by the formula: φ p =1-φ1B-φ2B 2 -...φ j B j -...-φ p B p j = 1, 2, ..., p;

[0053] In the formula, λ i y represents the initial failure rate of the i-th component; t Indicates the predicted time interval; c represents the preset constant term; φ p B represents the autoregressive function of the p-th order autoregressive term; j Let represent the j-th order lag operator; B represents the lag operator; P represents the order of the autoregressive term; p represents the index value of the p-th order autoregressive term; θ q Let represent the moving average coefficient of the q-th order moving average term; Q represent the order of the moving average term; q represent the index value of the order of the q-th order moving average term; j represent the index value of the j-th order lag operator; ε t The noise figure is represented by h; the preset time step is represented by h.

[0054] S3. Construct an environmental correction coefficient model to correct the initial failure rate of each component, obtaining the corrected component failure rate, expressed by the formula: K = a0 + a h H+a s S+a w W; kλ i =λ i ×K;

[0055] In the formula, kλ i Represents the corrected failure rate of the i-th component; K represents the environmental correction coefficient model; a0 represents the initial coefficients of the preset environmental correction coefficient model; a h Indicates the preset average humidity coefficient; H represents the average humidity; a s This represents the preset salt spray concentration coefficient; S represents the salt spray concentration; a w This indicates the preset wave level coefficient; W represents the wave level.

[0056] S4. Analyze each component using the Analytic Hierarchy Process (AHP) to obtain fault weights. Then, score the importance of each pair of components according to an expert scoring table to obtain the fault score matrix A = {a...} iz}, where a izThis represents the element value of the fault score matrix in the i-th row and z-th column, i.e., the score for the importance of every two components, where i represents the index of the i-th row and z represents the index of the z-th column. a ii =a zz =1;

[0057] The fault weights are calculated using the following formula:

[0058] In the formula, M i ω represents the product of the scores in the fault score matrix of the i-th row; i This represents the fault weight of the i-th component; This represents the fault weight of the i-th component after normalization; N represents the number of component categories.

[0059] S5. Combining the fault weights and the corrected component failure rates, the overall failure rate of the offshore converter station is obtained, expressed by the formula:

[0060] S6. Based on the overall failure rate, the expected mean time between failures (MTTF) is obtained, expressed by the formula:

[0061] The expected annual average downtime can intuitively and accurately present the proportion of downtime of offshore converter station equipment within a year, providing a clear and quantitative time scale reference for operation and maintenance planning, and helping to scientifically and rationally arrange key operation and maintenance tasks such as inspection cycles and spare parts reserves.

[0062] Example 2

[0063] This embodiment provides a system for calculating the equipment failure rate of an offshore converter station. The system includes a data acquisition module, a failure calculation module, a failure correction module, an expected annual average failure time module, and a result output module, wherein:

[0064] The data acquisition module is used to acquire fault data of the offshore converter station equipment within a preset time period, and divide the offshore converter station equipment into components according to function; and transmit the fault data to the initial fault calculation module;

[0065] The fault calculation module is used to perform correlation analysis on the fault time series using the autocorrelation function to obtain the fault type; if it is a static fault, it uses the law of large numbers and normal distribution to construct confidence intervals to obtain the initial fault rate of each component; if it is a dynamic fault, it uses a time series analysis model to obtain the initial fault rate of each component.

[0066] The fault correction module is used to construct an environmental correction coefficient model to correct the initial failure rate of each component and obtain the corrected component failure rate.

[0067] The annual average failure time expectation calculation module is used to analyze each component using the analytic hierarchy process to obtain failure weights, and combine the failure weights with the corrected component failure rates to obtain the overall failure rate of the entire offshore converter station equipment; and obtain the annual average failure time expectation based on the overall failure rate.

[0068] The results output module is used to display the expected value of the average annual failure time.

[0069] Example 3

[0070] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a method for calculating the equipment failure rate of an offshore converter station as described in any embodiment of the present invention.

[0071] Example 4

[0072] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for calculating the equipment failure rate of an offshore converter station as described in any embodiment of the present invention.

[0073] It is worth noting that the system, electronic device, and computer-readable storage medium described in this invention are all based on the same principle as the method described in Embodiment 1, and will not be repeated here.

[0074] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for calculating the equipment failure rate of an offshore converter station, characterized in that, The method includes: Fault data of offshore converter station equipment within a preset time period is obtained, and the offshore converter station equipment is divided into components according to function; the autocorrelation function is used to perform correlation analysis on the fault time series to obtain the fault type; if it is a static fault, the confidence interval is constructed using the law of large numbers and normal distribution to obtain the initial fault rate of each component; if it is a dynamic fault, the initial fault rate of each component is obtained using a time series analysis model. An environmental correction coefficient model is constructed to correct the initial failure rate of each component, resulting in the corrected component failure rate. The fault weights of each component are obtained by analyzing the analytic hierarchy process (AHP). The overall fault rate of the offshore converter station is obtained by combining the fault weights with the corrected component fault rates. The expected value of the annual average failure time is obtained based on the overall fault rate.

2. The method for calculating the equipment failure rate of an offshore converter station according to claim 1, characterized in that, The method also includes data cleaning of the fault data, which includes processing missing values, outliers, and standardizing data formats.

3. The method for calculating the equipment failure rate of an offshore converter station according to claim 1, characterized in that, The fault data includes the number of faults, the average humidity of the offshore converter station, the salt spray concentration of the offshore converter station, the wind and wave level of the offshore converter station, and the component type.

4. The method for calculating the equipment failure rate of an offshore converter station according to claim 1, characterized in that, Correlation analysis of fault time series using the autocorrelation function yields the fault type, expressed by the formula: In the formula, R(k) represents the value of the autocorrelation function; t i Let represent the failure time series of the i-th component; t represent the mean of the failure time series; k represent the lag order; n i This represents the number of failures of the i-th component; Based on hypothesis testing, different autocorrelation function values ​​are analyzed. If the autocorrelation function value corresponding to the current k value is not zero, then there is a correlation between the fault times, and the fault type is a dynamic fault; otherwise, the fault type is a static fault. The dynamic fault is specifically determined by using a time series analysis model to obtain the initial failure rate of each component, expressed by the formula: f p =1-φ1B-φ2B 2 -...f j B j -...-f p B p ,j=1,2,...,p; In the formula, λ i y represents the initial failure rate of the i-th component; t Indicates the predicted time interval; c represents the preset constant term; φ p B represents the autoregressive function of the p-th order autoregressive term; j Let represent the j-th order lag operator; B represents the lag operator; P represents the order of the autoregressive term; p represents the index value of the p-th order autoregressive term; θ q Let represent the moving average coefficient of the q-th order moving average term; Q represent the order of the moving average term; q represent the index value of the order of the q-th order moving average term; j represent the index value of the j-th order lag operator; ε t The noise figure is represented by h; the preset time step is represented by h. The static failure is specifically determined by constructing confidence intervals using the law of large numbers and the normal distribution to obtain the initial failure rate of each component, expressed by the formula: In the formula, T represents the preset failure time; α represents the preset significance level of the normal distribution; z α / 2 α represents the critical value when the significance level of the normal distribution is α / 2; i represents the index value of the i-th component.

5. The method for calculating the equipment failure rate of an offshore converter station according to claim 1, characterized in that, An environmental correction coefficient model is constructed to correct the initial failure rate of each component, expressed by the formula: K = a0 + a h H+a s S+a w W; kλ i =λ i ×K; In the formula, kλ i Represents the corrected failure rate of the i-th component; K represents the environmental correction coefficient model; a0 represents the initial coefficients of the preset environmental correction coefficient model; a h Indicates the preset average humidity coefficient; H represents the average humidity; a s This represents the preset salt spray concentration coefficient; S represents the salt spray concentration; a w This indicates the preset wind and wave level coefficient; W represents the wind and wave level.

6. The method for calculating the equipment failure rate of an offshore converter station according to claim 5, characterized in that, The overall failure rate of the entire offshore converter station is obtained by combining the fault weights and the corrected component failure rates, as follows: The analytic hierarchy process (AHP) is used to analyze each component to obtain fault weights. Based on an expert scoring table, the importance of each pair of components is scored, resulting in a fault score matrix A = {a...} iz }, where a iz This represents the element value of the fault score matrix in the i-th row and z-th column, i.e., the score for the importance of every two components, where i represents the index of the i-th row and z represents the index of the z-th column. a ii =a zz =1; The fault weights are calculated using the following formula: In the formula, M i ω represents the product of the scores in the fault score matrix of the i-th row; i This represents the fault weight of the i-th component; This represents the fault weight of the i-th component after normalization; N represents the number of component categories. The overall failure rate of the offshore converter station equipment is expressed by the formula:

7. The method for calculating the equipment failure rate of an offshore converter station according to claim 6, characterized in that, The expected mean time to failure (MTTF) is obtained based on the overall failure rate and expressed by the formula:

8. A system for calculating the equipment failure rate of an offshore converter station, characterized in that, The system includes a data acquisition module, a fault calculation module, a fault correction module, an expected annual average failure time module, and a result output module, wherein: The data acquisition module is used to acquire fault data of the offshore converter station equipment within a preset time period, and divide the offshore converter station equipment into components according to function; and transmit the fault data to the initial fault calculation module; The fault calculation module is used to perform correlation analysis on the fault time series using the autocorrelation function to obtain the fault type; if it is a static fault, it uses the law of large numbers and normal distribution to construct confidence intervals to obtain the initial fault rate of each component; if it is a dynamic fault, it uses a time series analysis model to obtain the initial fault rate of each component. The fault correction module is used to construct an environmental correction coefficient model to correct the initial failure rate of each component and obtain the corrected component failure rate. The annual average failure time expectation calculation module is used to analyze each component using the analytic hierarchy process to obtain failure weights, and combine the failure weights with the corrected component failure rates to obtain the overall failure rate of the entire offshore converter station equipment; and obtain the annual average failure time expectation based on the overall failure rate. The results output module is used to display the expected value of the average annual failure time.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a method for calculating the equipment failure rate of an offshore converter station as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a method for calculating the equipment failure rate of an offshore converter station as described in any one of claims 1 to 7.

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