Heavy-duty gas turbine safety protection monitoring method and device fused with fuzzy evaluation

Abstract

The invention relates to the technical field of gas turbines, in particular to a heavy gas turbine safety protection monitoring method and device fused with fuzzy evaluation. According to the heavy gas turbine safety protection monitoring method, on the basis of establishing a heavy gas turbine risk matrix, a multi-factor evaluation model is constructed by fusing with fuzzy comprehensive evaluation, and a heavy gas turbine multi-safety protection strategy is formed by quantifying multi-factor coupling influence and uncertainty and determining risk factor levels; through the heavy gas turbine control platform and the heavy gas turbine protection system platform, strategy-to-logic conversion is achieved, through debugging and iteration, protection and monitoring of the whole gas turbine system are achieved, and operation safety is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of gas turbines, and in particular to a heavy-duty gas turbine safety protection monitoring method and device integrating fuzzy evaluation. Background Art

[0002] As a highly coupled, nonlinear, and complex system, heavy-duty gas turbines have highly variable operating conditions that must be strictly controlled within a narrow safety range. Currently, the safety protection strategies of mainstream international gas turbine manufacturers rely primarily on design experience and are subject to strong technical confidentiality, leading to the following challenges in domestic gas turbine development: Strong reliance on experience: Existing protection strategies lack scientific quantitative methods and find it difficult to balance safety, economy, and reliability.

[0003] Insufficient processing of multi-factor coupling: Traditional methods find it difficult to quantify the dynamic coupling impact and uncertainty of risk factors.

[0004] Weak verification process: The transformation from strategy to implementation lacks systematic verification, and there may be deviations in threshold setting and logical response. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the problems in the prior art of strong dependence on experience, insufficient multi-factor coupling processing and weak verification links.

[0006] To solve the above technical problems, the present invention provides a heavy-duty gas turbine safety protection monitoring method integrating fuzzy evaluation, comprising: Based on the decomposition and failure mode analysis of the gas turbine subsystem, a risk matrix is ​​constructed, and the risk matrix is ​​split into multiple risk factor matrices; Summarize the risk factors for each group of risk factor matrices, construct multiple fuzzy evaluation factor sets, and determine the weight of each factor in each fuzzy evaluation factor set; Based on the historical fault data of gas turbine operation and maintenance with pre-calibrated event comment categories, the membership degree of each factor in each fuzzy evaluation factor set to each comment category is calculated to construct a fuzzy relationship matrix; Calculating the fuzzy comprehensive evaluation result based on the fuzzy relationship matrix and the weights, calculating the risk judgment level of each factor in combination with the comment category, and associating corresponding protection measures with each risk judgment level to generate a multiple safety protection strategy for the gas turbine; The target gas turbine is monitored for safety protection based on the gas turbine multiple safety protection strategy.

[0007] Preferably, the construction of a risk matrix based on the decomposition and failure mode analysis of the gas turbine subsystems and the fission of the risk matrix into multiple groups of risk factor matrices includes: Decompose the gas turbine subsystem, identify the key monitoring nodes of each subsystem, and determine the key monitoring parameters of each key monitoring node; Define the failure mode and failure threshold of each key monitoring parameter and analyze the consequences of each failure mode; Establish a subsystem-node-parameter-failure mode-consequence association table to obtain the risk matrix; According to subsystems, key monitoring nodes and working conditions, the risk matrix is ​​split into multiple groups of risk factor matrices.

[0008] Preferably, the gas turbine subsystem includes at least one or more of a gas turbine main engine, auxiliary engine, rotor, cylinder, related instruments, sensors and waste heat boiler related systems.

[0009] Preferably, determining the weight of each factor in each fuzzy evaluation factor set includes: According to the number of failures of each factor in each fuzzy evaluation factor set, the original data matrix is ​​constructed; Normalizing the original data matrix according to the range method; Based on the normalized data matrix, the entropy value of each factor is calculated using the probability matrix; The weight of each factor is calculated based on the entropy value.

[0010] Preferably, the calculating of the fuzzy comprehensive evaluation result based on the fuzzy relationship matrix and the weight, and the calculating of the risk determination level of each factor in combination with the comment category include: Calculating a fuzzy comprehensive evaluation result based on the fuzzy relationship matrix and the weight according to matrix multiplication; Based on the numerical results of the elements and corresponding comment categories in the fuzzy comprehensive evaluation results, the risk judgment level of each element is calculated.

[0011] Preferably, the protection measures include at least one or more of alarm, prohibition of startup, normal shutdown, load reduction shutdown, load reduction tripping, and load rejection tripping.

[0012] Preferably, the multiple safety protection strategies of the gas turbine include at least one or more of overspeed protection, bearing vibration protection, bearing temperature protection, compressor-related protection, turbine-related protection, combustion chamber-related protection, secondary air protection, turbine inlet temperature protection, cranking system protection, air intake system protection, fuel system protection, purge system protection, lubricating oil system protection, top shaft oil system protection, control oil system protection, fire monitoring and fire protection system protection, hazardous gas detection system protection, casing ventilation system protection, compressor water washing system protection, drying system protection, variable frequency starting device protection, drain and sewage system protection, instrument compressed air system protection, generator-transformer group protection, UPS protection, main transformer protection, and generator hydrogen, oil and water protection.

[0013] Preferably, the performing safety protection monitoring on the target gas turbine based on the gas turbine multiple safety protection strategy includes: Based on the multiple safety protection strategy for the gas turbine, the target gas turbine is monitored for safety protection through a SAMA diagram digital model platform and / or a gas turbine control and protection system platform that can run on controller hardware.

[0014] Preferably, the SAMA diagram digital model platform and / or the gas turbine control and protection system platform that can run on the controller hardware includes a gas turbine monitoring interface, and the gas turbine monitoring interface includes at least one or more of a system diagnosis interface, a first-out interface for a trip, an alarm interface, a power supply monitoring interface, a network monitoring interface, and a controller operation status monitoring interface.

[0015] The present invention also provides a heavy-duty gas turbine safety protection monitoring device integrating fuzzy evaluation, comprising: A risk factor matrix construction module is used to construct a risk matrix based on the decomposition of the gas turbine subsystem and the failure mode analysis, and to split the risk matrix into multiple groups of risk factor matrices; A fuzzy evaluation factor set construction module is used to summarize the risk factors of each group of risk factor matrices, construct multiple fuzzy evaluation factor sets, and determine the weight of each factor in each fuzzy evaluation factor set; The fuzzy relationship matrix construction module is used to calculate the membership degree of each factor in each fuzzy evaluation factor set to each comment category based on the historical fault data of the gas turbine operation and maintenance with pre-calibrated event comment categories, and construct the fuzzy relationship matrix; A multiple safety protection strategy construction module is used to calculate the fuzzy comprehensive evaluation result based on the fuzzy relationship matrix and the weight, calculate the risk judgment level of each factor in combination with the comment category, and associate corresponding protection measures with each risk judgment level to generate a multiple safety protection strategy for the gas turbine; The safety protection monitoring module is used to perform safety protection monitoring on the target gas turbine based on the gas turbine multiple safety protection strategy.

[0016] The present invention also provides a heavy-duty gas turbine safety protection monitoring device, comprising: memory for storing computer programs; The processor is used to implement the above-mentioned steps of the heavy-duty gas turbine safety protection monitoring method when executing the computer program.

[0017] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned heavy-duty gas turbine safety protection monitoring method are implemented.

[0018] The above technical solution of the present invention has the following advantages over the prior art: The heavy-duty gas turbine safety protection monitoring method described in the present invention, based on the establishment of a heavy-duty gas turbine risk matrix, constructs a multi-factor evaluation model by integrating it with fuzzy comprehensive evaluation, determines the risk factor level by quantifying the multi-factor coupling influence and uncertainty, and forms a multiple safety protection strategy for heavy-duty gas turbines. Through the two major platforms of heavy-duty gas turbine control and protection system, the transformation from strategy to logic is realized. After debugging and iteration, the protection monitoring of the entire gas turbine system is realized to ensure safe operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings, wherein: Figure 1 This is a flow chart of a heavy-duty gas turbine safety protection monitoring method provided by the present invention; Figure 2 This is a structural block diagram of a heavy-duty gas turbine safety protection monitoring device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The core of the present invention is to provide a heavy-duty gas turbine safety protection monitoring method and device. On the basis of establishing a heavy-duty gas turbine risk matrix, a multi-factor evaluation model is constructed by integrating it with fuzzy comprehensive evaluation. By quantifying the multi-factor coupling influence and uncertainty, the risk factor level is determined, and a multiple safety protection strategy for heavy-duty gas turbines is formed. Through the two major platforms of heavy-duty gas turbine control and protection system, the transformation from strategy to logic is realized. After debugging and iteration, the protection monitoring of the entire gas turbine system is effectively realized, ensuring operational safety.

[0021] In order to enable those skilled in the art to better understand the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0022] Please refer to Figure 1. Figure 1 This is a flowchart of a heavy-duty gas turbine safety protection monitoring method provided by the present invention; the specific operating steps are as follows: S101: Based on the decomposition and failure mode analysis of the gas turbine subsystem, a risk matrix is ​​constructed, and the risk matrix is ​​split into multiple risk factor matrices; S102: Summarize the risk factors for each group of risk factor matrices, construct multiple fuzzy evaluation factor sets, and determine the weight of each factor in each fuzzy evaluation factor set; S103: Based on the gas turbine operation and maintenance historical fault data with pre-calibrated event comment categories, calculate the membership degree of each factor in each fuzzy evaluation factor set to each comment category, and construct a fuzzy relationship matrix; S104: Calculating a fuzzy comprehensive evaluation result based on the fuzzy relationship matrix and the weights, calculating the risk judgment level of each factor in combination with the comment category, and associating corresponding protection measures with each risk judgment level to generate a multiple safety protection strategy for the gas turbine; S105: Perform safety protection monitoring on the target gas turbine based on the gas turbine multiple safety protection strategy.

[0023] Based on the above embodiment, this embodiment describes step S101 in detail: The heavy-duty gas turbine system is the safety protection monitoring object provided by the present invention, and the gas turbine subsystem includes at least one or more of the gas turbine main engine, auxiliary engine, rotor, cylinder, related instruments, sensors and waste heat boiler related systems; among them, the gas turbine main engine includes three major components: compressor, combustion chamber, and turbine, and the auxiliary engine includes lubricating oil system, control oil system, top shaft oil system, fuel system, cover ventilation system, air intake system, compressor drying / water washing system, frequency conversion starting system, fire monitoring protection and fire fighting system, turning device, etc.

[0024] In some embodiments, based on the decomposition and failure mode analysis of the combustion engine subsystems, a risk matrix is ​​constructed, and the risk matrix is ​​split into multiple risk factor matrices including: Decompose the gas turbine subsystem, identify the key monitoring nodes of each subsystem, and determine the key monitoring parameters of each key monitoring node; Define the failure mode and failure threshold of each key monitoring parameter and analyze the consequences of each failure mode; Establish a subsystem-node-parameter-failure mode-consequence association table to obtain the risk matrix.

[0025] In a specific embodiment, a heavy-duty gas turbine risk matrix is ​​constructed, which refers to various failures that may occur during the operation of the gas turbine and the resulting consequences, involving various subsystems and components of the heavy-duty gas turbine system, including but not limited to those shown in Table 1: Table 1 Risk Matrix

[0026] In some embodiments, the risk matrix is ​​split into multiple groups of risk factor matrices according to subsystems, key monitoring nodes, and operating conditions.

[0027] In some specific embodiments, the risk matrix in Table 1 is split into multiple risk factor matrices according to subsystems (such as main engine / auxiliary engine / electrical system, etc.), their key monitoring nodes (such as component inlet / component outlet / main pipe, etc.), and different operating conditions, as shown in Tables 2 to 10: Table 2 Compressor outlet risk factor sub-matrix

[0028] Table 3 Turbine export risk factor sub-matrix

[0029] Table 4 Combustion chamber combustion oscillation risk factor sub-matrix

[0030] Table 5 Sub-matrix of jacking oil risk factors

[0031] Table 6 Lubricant risk factor sub-matrix

[0032] Table 7 Electrical system risk factor sub-matrix

[0033] Table 8 Bearing temperature risk factor sub-matrix

[0034] Table 9 Bearing vibration risk factor sub-matrix

[0035] Table 10 Speeding risk factor sub-matrix

[0036] Note: The specific threshold is determined based on the turbine manufacturer's design specifications, operating procedures, and historical data of the power plant. It is entered into the risk factor sub-table during implementation and continuously optimized during S105.

[0037] The above risk factor table only represents part of the realization and is not a complete list.

[0038] Based on the above embodiment, this embodiment describes step S102 in detail: Based on the above multiple risk factor matrices, fuzzy evaluation factor sets are constructed one by one:

[0039] Where n represents the number of risk factors (i.e., key monitoring parameters) in the risk factor matrix; In some embodiments, determining the weight of each factor in each fuzzy evaluation factor set includes: According to the number of failures of each factor in each fuzzy evaluation factor set, the original data matrix X is constructed. The number of failures of various factors in the gas turbine is derived from the actual failure rate (failures / operating hours) of a type XX gas turbine from a mature international gas turbine manufacturer during operation and maintenance. Preferably, historical operation and maintenance data of the target gas turbine power plant is used; if unavailable, typical failure rate data for the same type of gas turbine can be used. In order to eliminate the dimension effect, the original data matrix is ​​normalized according to the range method ,in, Indicates the Year Data on risk factors, and Respectively represent The maximum and minimum values ​​of the number of failures occurring for each risk factor; m represents the number of samples, i.e., the failure data of multiple XX-type gas turbines over many years; n represents the number of risks; Based on the normalized data matrix, the entropy value of each factor is calculated using the probability matrix , ,in, , ; Calculate each factor according to the entropy value Weight , , weight It reflects the The importance of each risk factor in the entire risk assessment system. The greater the weight, the greater the impact of the factor on the risk of heavy-duty gas turbines.

[0040] Determine the weight of each factor. , determine its weight A through the above entropy weight method ,in, ,Matrix A mainly reflects the failure frequency of each risk, and its dimension is 1 xn.

[0041] Based on the above embodiment, this embodiment describes step S103 in detail: In some embodiments, based on the gas turbine safety standards and operation requirements, a set of comments including “very low risk, low risk, medium risk, high risk, very high risk, and ultra-high risk” is set:

[0042] In some specific embodiments, historical operation and maintenance data of a type XX gas turbine is obtained, mainly including failure modes (factors), maintenance costs, and interruption time. The comment category (v1 to v6) of the event is determined according to the standards in Table 11, and historical gas turbine operation and maintenance fault data with pre-calibrated event comment categories is obtained: Table 11 Definition of economic loss severity levels (Maintenance costs due to gas turbine maintenance and economic losses caused by unplanned downtime and inability to generate electricity)

[0043] *Category C overhaul: This is a planned medium-sized overhaul, between routine maintenance (Category A / B) and overhaul (Category D), and focuses on inspecting key components, replacing worn parts, and restoring performance.

[0044] Based on the historical fault data of gas turbine operation and maintenance with pre-calibrated event comment categories, the frequency statistics method is used to calculate the membership value of each factor relative to each comment, namely: Count the number of times each factor i (failure mode) leads to each comment j , and the total number of times factor i appears By calculating the membership of each factor to each comment, a fuzzy relationship matrix is ​​constructed. :

[0045] : The number of times factor i leads to comment j; : The number of times factor i appears; K: total number of comment categories, here K=6; α: Smoothing coefficient (usually 1 to avoid zero value problems).

[0046] Based on the above embodiment, this embodiment describes step S104 in detail: In some embodiments, calculating the fuzzy comprehensive evaluation result based on the fuzzy relationship matrix and the weights, and calculating the risk determination level of each factor in combination with the comment category includes: Based on the fuzzy relationship matrix and the weight, the fuzzy comprehensive evaluation result is calculated according to matrix multiplication. ; (A is 1 x n; R is n x 6; B is 1 x 6. Therefore, B takes both failure rate and economy into consideration.) Based on the numerical results of the elements and corresponding comment categories in the fuzzy comprehensive evaluation results, the risk judgment level of each element is calculated ,in, is an element in B, = is the result of digitizing the elements in comment set V. Furthermore, because gas turbine safety protection is extremely sensitive to high-risk events, non-uniformly spaced values ​​are designed to amplify the impact of high-risk factors and ensure timely handling of high-risk events: v1' = 1, v2' = 2, v3' = 4, v4' = 8, v5' = 16, v6' = 32. Based on the final D value and its proximity to the aforementioned values, the risk level in comment set V is determined and corresponding protective measures are formulated: "alarm, prohibit start, normal shutdown, load reduction shutdown, load reduction trip, and trip (including load rejection trip)." Based on all risk factor sub-tables, the above fuzzy evaluation calculations are performed one by one to obtain the risk levels and protection strategies of all risk items under the risk matrix.

[0047] In some embodiments, the protection measures include at least one or more of alarm, startup prohibition, normal shutdown, load reduction shutdown, load reduction tripping, and load rejection tripping.

[0048] In some embodiments, the multiple safety protection strategies of the gas turbine include at least one or more of overspeed protection, bearing vibration protection, bearing temperature protection, compressor-related protection, turbine-related protection, combustion chamber-related protection, secondary air protection, turbine inlet temperature protection, cranking system protection, intake system protection, fuel system protection, purge system protection, lubricating oil system protection, top shaft oil system protection, control oil system protection, fire monitoring and fire protection system protection, hazardous gas detection system protection, casing ventilation system protection, compressor water washing system protection, drying system protection, variable frequency starting device protection, drain and sewage system protection, instrument compressed air system protection, generator-transformer group protection, UPS protection, main transformer protection, and generator hydrogen, oil and water protection.

[0049] For example, Table 12: Table 12 Gas turbine multiple safety protection strategies

[0050] Protection items: compressor outlet temperature is too high, turbine outlet temperature is too high, turbine outlet pressure is too high, etc.

[0051] Based on the above embodiment, this embodiment describes step S105 in detail: In some embodiments, performing safety protection monitoring on a target gas turbine based on the gas turbine multiple safety protection strategy includes: The multiple safety protection strategies of the gas turbine are transformed and embedded into the gas turbine control and protection system to realize real-time monitoring of gas turbine fault safety. The present invention provides two strategy transformation platforms, namely the SAMA diagram digital model platform and the gas turbine control and protection system platform that can run on the controller hardware.

[0052] To better adapt to the platform's logical transformation, the protection items are replaced according to the sensor signals at the system / component location. At the same time, to further multiplex the protection strategy, the sensor adopts a 3-redundancy design and increases the redundancy requirement design, such as 2oo3, to form a multiple safety protection strategy as shown in Table 13. Among them, 3 redundancy means that three sensors are used to monitor the same measurement point at the same time, and 2oo3 means that if two or more measurement points in the monitoring data of these three sensors are in a valid state (exceeding the protection threshold), the corresponding action is triggered.

[0053] Table 13 Multiple security protection strategies for the adaptive platform

[0054] The heavy-duty gas turbine control and protection system, which implements the safety protection monitoring provided by the present invention, comprises a SAMA diagram digital model platform and a gas turbine control and protection system platform that runs on controller hardware. In some embodiments, the SAMA diagram digital model platform can be a platform such as Matlab or Simulink, and the gas turbine control and protection system platform can be a platform such as Ovation or NT6000. Both platforms integrate multiple safety protection strategies to ensure that when abnormal conditions occur, unit protection actions are promptly implemented, including warnings, tripping, or automatic shutdowns, ensuring safe unit operation.

[0055] In some embodiments, the SAMA diagram digital model platform or the gas turbine control and protection system platform that can run on the controller hardware includes a gas turbine monitoring interface, that is, a human-machine interface, and the gas turbine monitoring interface includes at least one or more of a system diagnosis interface, a trip first-out interface, an alarm interface, a power supply monitoring interface, a network monitoring interface, and a controller operation status monitoring interface; wherein, the trip first-out interface includes the gas turbine trip first-out caused by various risk factors, the gas turbine load shedding first-out, the gas turbine rapid load reduction shutdown first-out, the gas turbine rapid load reduction trip first-out, the gas turbine shutdown first-out, etc., and the alarm interface includes the main machine alarm, auxiliary machine alarm, electrical alarm and system alarm, etc.; the above-mentioned first-out refers to the first cause used to determine the tripping of the gas turbine or other critical faults.

[0056] In some embodiments, the SAMA diagram digital model platform can be a platform such as MATALAB\SIMULINK, which mainly includes a digital model of the gas turbine, a control system and a protection system embedded with multiple safety protection strategies. The digital model of the gas turbine is mainly built through the relevant thermodynamic equations of various components of the gas turbine and model libraries under platforms such as MATALAB\SIMULINK (including self-developed special model libraries), etc. The control and protection system is implemented through logical operation modules, analog operation modules, PID operation modules, HMI manual operation modules, equipment switch drive blocks, step blocks, etc. under platforms such as MATALAB\SIMULINK, and the system realizes automatic control of different stages of all working conditions such as gas turbine start-up, ignition, warm-up, acceleration to full speed and no-load, grid connection, load addition, etc.

[0057] In some embodiments, the gas turbine control and protection system platform that can run on the controller hardware can be an Ovation system, NT6000 system, or other platforms, and mainly includes a control system and a protection system embedded with multiple safety protection strategies. The control and monitoring object is the actual gas turbine system. Optionally, to further enhance the simulation verification process, a gas turbine digital model can be added before connecting the gas turbine control and protection system platform to the actual gas turbine system. In some instances, the digital model can be a MUSE platform gas turbine simulation model, which can more accurately reflect the gas turbine operating rules and approximate the gas turbine operating curve. The control and protection system platform mainly includes a hardware platform and a software platform. The hardware platform consists of a controller, analog / digital input / output board, speed card, servo card, Ethernet card, Profibus communication card, sensor, operator station, etc. The software platform mainly implements related control logic and multiple safety protection strategy logic through logic operation modules, analog operation modules, PID operation modules, HMI manual operation modules, equipment switch driver blocks, step blocks, etc.

[0058] In some embodiments, the SAMA diagram digital model platform serves as a pre-verification link for the gas turbine control and protection system platform that can run on controller hardware. It can be used to preliminarily determine the fault failure threshold and gradually correct the threshold in the event of fault injection. On the hardware platform, hardware-in-the-loop testing is used to further verify the feasibility and effectiveness of the protection strategy in the actual hardware system, and to discover and solve problems that are difficult to detect in the digital model, such as hardware response delay and signal interference, so that the multiple safety protection strategies can be further approached to the gas turbine operating limit and safety red line.

[0059] The present invention establishes a set of heavy-duty gas turbine safety protection monitoring methods that integrate fuzzy evaluation; by integrating the gas turbine risk matrix with fuzzy evaluation, a multiple safety protection strategy for the gas turbine is constructed in a more scientific manner in a qualitative and quantitative manner; the multiple safety protection strategies for the gas turbine are verified through a SAMA diagram digital model platform and a gas turbine control and protection system platform that can run on controller hardware, with two-fold front-end and back-end testing, to achieve the transformation from strategy to logic and then to application, so that the multiple safety protection strategies are closer to the gas turbine operating limits and safety red lines. This method runs through the entire process of heavy-duty gas turbine demand demonstration, design and development, production and manufacturing, testing and verification, and safety implementation, covering the protection requirements of the entire life cycle of heavy-duty gas turbine operation and maintenance, thereby ensuring the integrity and consistency of the gas turbine design to ensure the reliability and safety of the gas turbine design.

[0060] Please refer to Figure 2 , Figure 2 This is a structural block diagram of a heavy-duty gas turbine safety protection monitoring device provided by an embodiment of the present invention; the specific device may include: The risk factor matrix construction module 100 is used to construct a risk matrix based on the decomposition of the gas turbine subsystem and the failure mode analysis, and to split the risk matrix into multiple groups of risk factor matrices; A fuzzy evaluation factor set construction module 200 is used to summarize the risk factors in each group of risk factor matrices, construct multiple fuzzy evaluation factor sets, and determine the weight of each factor in each fuzzy evaluation factor set; The fuzzy relationship matrix construction module 300 is used to calculate the membership degree of each factor in each fuzzy evaluation factor set to each comment category based on the historical fault data of the gas turbine operation and maintenance with pre-calibrated event comment categories, and to construct the fuzzy relationship matrix; A multiple safety protection strategy construction module 400 is used to calculate a fuzzy comprehensive evaluation result based on the fuzzy relationship matrix and the weights, calculate the risk judgment level of each factor in combination with the comment category, and associate corresponding protection measures with each risk judgment level to generate a multiple safety protection strategy for the gas turbine; The safety protection monitoring module 500 is used to perform safety protection monitoring on the target gas turbine based on the gas turbine multiple safety protection strategy.

[0061] The heavy-duty gas turbine safety protection monitoring device of this embodiment is used to implement the aforementioned heavy-duty gas turbine safety protection monitoring method. Therefore, the specific implementation methods of the heavy-duty gas turbine safety protection monitoring device can be seen in the embodiment part of the heavy-duty gas turbine safety protection monitoring method mentioned above. For example, the risk factor matrix construction module 100, the fuzzy evaluation factor set construction module 200, the fuzzy relationship matrix construction module 300, the multiple safety protection strategy construction module 400, and the safety protection monitoring module 500 are respectively used to implement steps S101, S102, S103, S104 and S105 in the above-mentioned heavy-duty gas turbine safety protection monitoring method. Therefore, its specific implementation methods can refer to the description of the corresponding embodiments of each part, and will not be repeated here.

[0062] A specific embodiment of the present invention further provides a heavy-duty gas turbine safety protection monitoring device, comprising: a memory for storing a computer program; and a processor for implementing the steps of the above-mentioned heavy-duty gas turbine safety protection monitoring method when executing the computer program.

[0063] A specific embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned heavy-duty gas turbine safety protection monitoring method are implemented.

[0064] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0066] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0067] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0068] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A heavy-duty gas turbine safety protection monitoring method integrating fuzzy evaluation, characterized in that: include: Based on the decomposition and failure mode analysis of the gas turbine subsystem, a risk matrix is ​​constructed, and the risk matrix is ​​split into multiple risk factor matrices; Summarize the risk factors for each group of risk factor matrices, construct multiple fuzzy evaluation factor sets, and determine the weight of each factor in each fuzzy evaluation factor set; Based on the historical fault data of gas turbine operation and maintenance with pre-calibrated event comment categories, the membership degree of each factor in each fuzzy evaluation factor set to each comment category is calculated to construct a fuzzy relationship matrix; Calculating the fuzzy comprehensive evaluation result based on the fuzzy relationship matrix and the weights, calculating the risk judgment level of each factor in combination with the comment category, and associating corresponding protection measures with each risk judgment level to generate a multiple safety protection strategy for the gas turbine; The target gas turbine is monitored for safety protection based on the gas turbine multiple safety protection strategy.

2. The heavy-duty gas turbine safety protection monitoring method according to claim 1, characterized in that: The risk matrix is ​​constructed based on the decomposition and failure mode analysis of the gas turbine subsystem, and the risk matrix is ​​split into multiple groups of risk factor matrices including: Decompose the gas turbine subsystem, identify the key monitoring nodes of each subsystem, and determine the key monitoring parameters of each key monitoring node; Define the failure mode and failure threshold of each key monitoring parameter and analyze the consequences of each failure mode; Establish a subsystem-node-parameter-failure mode-consequence association table to obtain the risk matrix; According to subsystems, key monitoring nodes and working conditions, the risk matrix is ​​split into multiple groups of risk factor matrices.

3. The heavy-duty gas turbine safety protection monitoring method according to claim 1 or 2, characterized in that: The gas turbine subsystem includes at least one or more of the gas turbine main engine, auxiliary engine, rotor, cylinder, related instruments, sensors and waste heat boiler related systems.

4. The heavy-duty gas turbine safety protection monitoring method according to claim 1, characterized in that: Determining the weight of each factor in each fuzzy evaluation factor set includes: According to the number of failures of each factor in each fuzzy evaluation factor set, the original data matrix is ​​constructed; Normalizing the original data matrix according to the range method; Based on the normalized data matrix, the entropy value of each factor is calculated using the probability matrix; The weight of each factor is calculated based on the entropy value.

5. The heavy-duty gas turbine safety protection monitoring method according to claim 1, characterized in that: The calculation of the fuzzy comprehensive evaluation result based on the fuzzy relationship matrix and the weight, and the calculation of the risk determination level of each factor in combination with the comment category include: Calculating a fuzzy comprehensive evaluation result based on the fuzzy relationship matrix and the weight according to matrix multiplication; Based on the numerical results of the elements and corresponding comment categories in the fuzzy comprehensive evaluation results, the risk judgment level of each element is calculated.

6. The heavy-duty gas turbine safety protection monitoring method according to claim 1, characterized in that: The protection measures include at least one or more of alarm, prohibition of startup, normal shutdown, load reduction shutdown, load reduction tripping, and load rejection tripping.

7. The heavy-duty gas turbine safety protection monitoring method according to claim 6, characterized in that: The multiple safety protection strategies for the gas turbine include at least one or more of overspeed protection, bearing vibration protection, bearing temperature protection, compressor-related protection, turbine-related protection, combustion chamber-related protection, secondary air protection, turbine inlet temperature protection, cranking system protection, air intake system protection, fuel system protection, purge system protection, lubricating oil system protection, top shaft oil system protection, control oil system protection, fire monitoring and fire protection system protection, hazardous gas detection system protection, casing ventilation system protection, compressor water washing system protection, drying system protection, variable frequency starting device protection, drain and sewage system protection, instrument compressed air system protection, generator-transformer group protection, UPS protection, main transformer protection, and generator hydrogen, oil, and water protection.

8. The heavy-duty gas turbine safety protection monitoring method according to claim 1, characterized in that: The safety protection monitoring of the target gas turbine based on the gas turbine multiple safety protection strategy includes: Based on the multiple safety protection strategy for the gas turbine, the target gas turbine is monitored for safety protection through a SAMA diagram digital model platform and / or a gas turbine control and protection system platform that can run on controller hardware.

9. The heavy-duty gas turbine safety protection monitoring method according to claim 8, characterized in that: The SAMA diagram digital model platform and / or the gas turbine control and protection system platform that can run on the controller hardware includes a gas turbine monitoring interface, and the gas turbine monitoring interface includes at least one or more of a system diagnosis interface, a first-out interface for a trip, an alarm interface, a power supply monitoring interface, a network monitoring interface, and a controller operation status monitoring interface.

10. A heavy-duty gas turbine safety protection monitoring device integrating fuzzy evaluation, characterized in that: include: A risk factor matrix construction module is used to construct a risk matrix based on the decomposition of the gas turbine subsystem and the failure mode analysis, and to split the risk matrix into multiple groups of risk factor matrices; A fuzzy evaluation factor set construction module is used to summarize the risk factors of each group of risk factor matrices, construct multiple fuzzy evaluation factor sets, and determine the weight of each factor in each fuzzy evaluation factor set; The fuzzy relationship matrix construction module is used to calculate the membership degree of each factor in each fuzzy evaluation factor set to each comment category based on the historical fault data of the gas turbine operation and maintenance with pre-calibrated event comment categories, and construct the fuzzy relationship matrix; A multiple safety protection strategy construction module is used to calculate the fuzzy comprehensive evaluation result based on the fuzzy relationship matrix and the weight, calculate the risk judgment level of each factor in combination with the comment category, and associate corresponding protection measures with each risk judgment level to generate a multiple safety protection strategy for the gas turbine; The safety protection monitoring module is used to perform safety protection monitoring on the target gas turbine based on the gas turbine multiple safety protection strategy.

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