Control method, device and equipment of gas turbine and storage medium

Abstract

The invention relates to the technical field of gas turbines, in particular to a control method, device and equipment of a gas turbine and a storage medium. Predicting to-be-adjusted working parameters of the gas turbine through a trained neural network model based on the working parameter set; determining an adjustment priority of each to-be-adjusted working parameter; and adjusting the operation state of the gas turbine based on the adjustment priority and the to-be-adjusted working parameters until the to-be-adjusted working parameters are reduced to be within a preset safe working parameter interval, and predicting the to-be-adjusted working parameters of the gas turbine through a trained neural network model. And then the operation state of the gas turbine is adjusted based on the adjustment priority of each to-be-adjusted working parameter, so that the combustion performance of the gas turbine is interfered in advance, the combustion performance of the gas turbine is improved, and the technical problem that in the prior art, the combustion performance of the gas turbine fluctuates, and consequently the pollutant emission amount of the gas turbine is too high is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of gas turbines, and in particular to a control method, device, equipment and storage medium for a gas turbine. Background Art

[0002] During the operation of the gas turbine, the combustion performance of the gas turbine may be reduced due to external environment, changes in fuel composition or structural blockage, resulting in a large amount of pollutant emissions, violating local environmental protection standards. At the same time, the combustion performance of the gas turbine under different loads will also affect the combustion stability in the combustion chamber. More seriously, it will cause the gas turbine to malfunction and reduce the working efficiency of the gas turbine.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present invention is to provide a control method, device, equipment and storage medium for a gas turbine, aiming to solve the technical problem in the prior art that the combustion performance of the gas turbine fluctuates, resulting in excessive pollutant emissions from the gas turbine.

[0005] To achieve the above object, the present invention provides a gas turbine control method, the method comprising the following steps:

[0006] Obtain the gas turbine operating parameter set in real time;

[0007] Predicting the to-be-adjusted operating parameters of the gas turbine through a trained neural network model based on the operating parameter set;

[0008] Determine the adjustment priority of each working parameter to be adjusted;

[0009] The operating state of the gas turbine is adjusted based on the adjustment priority and the operating parameter to be adjusted until the operating parameter to be adjusted is reduced to within a preset safe operating parameter range.

[0010] Optionally, predicting the to-be-adjusted operating parameter of the gas turbine through a trained neural network model based on the operating parameter set includes:

[0011] Dividing the acquired operating parameters of the gas turbine to obtain a first operating parameter outside a preset safe operating parameter range and a second operating parameter within the preset safe operating parameter range;

[0012] Determine a target operating parameter through a trained neural network model based on the second operating parameter;

[0013] The operating parameter to be adjusted of the gas turbine is determined according to the first operating parameter and the target operating parameter.

[0014] Optionally, determining the target operating parameter through a trained neural network model based on the second operating parameter includes:

[0015] Performing data preprocessing on the second working parameter, wherein the data preprocessing at least includes moving arithmetic average, exponentially weighted moving average, extreme value processing, elimination of abnormal data, and smoothing processing;

[0016] The trained neural network model is used to perform an out-of-bounds prediction on the second working parameter after data preprocessing to obtain a target working parameter, where the target working parameter is a second working parameter that has a risk of exceeding a corresponding preset working parameter interval.

[0017] Optionally, performing an out-of-bounds prediction on the second working parameter after data preprocessing by using a trained neural network model includes:

[0018] Acquiring a historical change trend of the second operating parameter and an equipment parameter of the gas turbine;

[0019] Predicting the working parameters of the next adjustment cycle through the trained neural network model according to the historical change trend and the preprocessed second working parameters;

[0020] Calculating the confidence of the working parameters of the next adjustment cycle according to the equipment parameters;

[0021] According to the confidence level, it is detected whether the working parameter of the next adjustment cycle exceeds the corresponding preset safe working parameter range.

[0022] Optionally, the adjusting the operating state of the gas turbine based on the adjustment priority and the operating parameter to be adjusted includes:

[0023] When the priority of the first operating parameter is greater than the target operating parameter, the operating state of the gas turbine is adjusted to reduce the first operating parameter to a corresponding preset safe operating parameter range.

[0024] Optionally, the gas turbine control method further includes:

[0025] When the priority of the first operating parameter is less than or equal to the target operating parameter, the operating state of the gas turbine is adjusted to reduce the target operating parameter to a corresponding preset safe operating parameter range.

[0026] Optionally, the adjusting the operating state of the gas turbine includes at least one of the following methods:

[0027] controlling the combustion temperature of each zone of the combustion chamber in the gas turbine;

[0028] adjusting a valve opening of at least one fuel inlet in communication with the gas turbine;

[0029] The pressure within the combustion chamber is adjusted.

[0030] In addition, to achieve the above-mentioned object, the present invention further provides a control device for a gas turbine, the control device for a gas turbine comprising:

[0031] An acquisition module, used for acquiring a set of working parameters of a gas turbine in real time;

[0032] A prediction module, used for predicting the to-be-adjusted operating parameters of the gas turbine through a trained neural network model based on the operating parameter set;

[0033] A determination module, used to determine the adjustment priority of each working parameter to be adjusted;

[0034] An adjustment module is used to adjust the operating state of the gas turbine based on the adjustment priority and the operating parameter to be adjusted until the operating parameter to be adjusted is reduced to within a preset safe operating parameter range.

[0035] In addition, to achieve the above-mentioned purpose, the present invention also proposes a control device of a gas turbine, which includes: a memory, a processor, and a control program of the gas turbine stored in the memory and executable on the processor, and the control program of the gas turbine is configured to implement the steps of the control method of the gas turbine as described above.

[0036] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a control program of a gas turbine is stored. When the control program of the gas turbine is executed by a processor, the steps of the gas turbine control method as described above are implemented.

[0037] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the gas turbine control method as described above are implemented.

[0038] One or more technical solutions proposed in the present invention have at least the following technical effects: the present invention obtains a working parameter set of the gas turbine in real time; predicts the working parameters to be adjusted of the gas turbine through a trained neural network model based on the working parameter set; determines the adjustment priority of each working parameter to be adjusted; adjusts the operating state of the gas turbine based on the adjustment priority and the working parameter to be adjusted until the working parameter to be adjusted is reduced to within a preset safe working parameter range, predicts the working parameters to be adjusted of the gas turbine through a trained neural network model, and then adjusts the operating state of the gas turbine based on the adjustment priority of each working parameter to be adjusted, thereby realizing pre-intervention of the combustion performance of the gas turbine, improving the combustion performance of the gas turbine, and avoiding the technical problem in the prior art that the combustion performance of the gas turbine fluctuates, resulting in excessive pollutant emissions from the gas turbine. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] Figure 1 It is a flow chart of a first embodiment of a control method for a gas turbine according to the present invention;

[0042] Figure 2 It is a flow chart of a second embodiment of a gas turbine control method of the present invention;

[0043] Figure 3 It is a flow chart of a third embodiment of a gas turbine control method of the present invention;

[0044] Figure 4 It is a structural block diagram of a first embodiment of a control device for a gas turbine according to the present invention;

[0045] Figure 5 It is a schematic diagram of the structure of a control device of a gas turbine in a hardware operating environment involved in an embodiment of the present invention.

[0046] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0048] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0049] Based on this, an embodiment of the present invention provides a control method for a gas turbine, referring to Figure 1 , Figure 1 The figure is a flow chart of a first embodiment of a gas turbine control method according to the present invention.

[0050] In this embodiment, the gas turbine control method includes:

[0051] Step S10: Acquire the working parameter set of the gas turbine in real time.

[0052] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a control computer electrically connected to a gas turbine, etc. The following takes a control computer electrically connected to a gas turbine as an example to illustrate this embodiment and the following embodiments.

[0053] The working parameter set of the gas turbine includes the current working parameters and the historical working parameters, wherein the working parameters of the gas turbine include but are not limited to the gas emissions and combustion chamber pressure pulsation of the gas turbine during operation, wherein the gas emissions include but are not limited to various NOx gases and CO, etc. A large amount of polluting gases will be emitted during the operation of the gas turbine, and excessive polluting gases will cause environmental pollution, and incomplete combustion will occur. The combustion residue will subsequently affect the service life of the equipment, and the combustion chamber pressure pulsation data will affect the normal operation of the gas turbine. Too low combustion chamber pressure may cause the gas turbine to stall, and frequent shutdown and restart will seriously damage the service life of the equipment.

[0054] Furthermore, after the real-time acquisition of the gas turbine operating parameter set, the method further includes:

[0055] The working parameter set is preprocessed, and the preprocessing at least includes: moving arithmetic average, exponentially weighted moving average and extreme value processing.

[0056] Since it is difficult to collect the working parameters of the gas turbine during operation, especially the emission of pollutant gases, it is difficult to directly use it for subsequent combustion adjustment judgment and single adjustment step calculation. For example, when obtaining the pressure pulsation data of the combustion chamber, a combustion chamber pressure probe or a high-speed pressure sensor and other devices can be used for collection. In order to improve the accuracy of the combustion chamber pressure pulsation data, the arithmetic average of multiple collected pressure pulsation data can be performed to improve the accuracy of the pressure pulsation data within a time period. When collecting the emission of pollutant gases, a continuous emission monitoring system (CEMS) can be used to monitor and record the concentration and emission of industrial pollutants in real time, or a risk evaluation and mitigation strategy system (REMS). Other devices or equipment that can realize gas emission collection can also be used. This embodiment does not impose specific restrictions on this.

[0057] Step S20: predicting the operating parameters to be adjusted of the gas turbine through the trained neural network model based on the operating parameter set.

[0058] It should be understood that the trained neural network model in this embodiment is used to predict the operating parameters of the gas turbine in the next cycle that may exceed the preset safe operating parameter range, so that the operating parameters in the next cycle that may exceed the preset safe operating parameter range can be intervened in advance to avoid fluctuations in combustion performance of the gas turbine in the next cycle.

[0059] Step S30: determining the adjustment priority of each working parameter to be adjusted.

[0060] The working parameters to be adjusted refer to working parameters that partially exceed the corresponding preset safe working parameter range and working parameters that may exceed the preset safe working parameter range in the next cycle predicted by the trained neural network model. Different gas turbines and current environmental emission standards will directly affect the range of the corresponding preset safe working parameter range. A priority will be pre-set between different working parameters. In this embodiment, the priorities of pressure pulsation parameters, NOx exhaust concentration and CO exhaust concentration decrease in sequence.

[0061] Step S40: adjusting the operating state of the gas turbine based on the adjustment priority and the operating parameter to be adjusted until the operating parameter to be adjusted is reduced to within a preset safe operating parameter range.

[0062] It should be noted that the control of the gas turbine in this embodiment is different for different operating parameters to be adjusted, because in different environments, adjusting a certain operating parameter of the gas turbine may cause changes in another operating parameter. For example, flue gas circulation technology can be used to reduce the generation of NOx, but it may lead to increased CO emissions and poor low-load stability.

[0063] By setting the priority between various working parameters, working parameters with higher priorities can be adjusted first, thereby improving the working efficiency of the gas turbine and increasing the production capacity. For example: when the working parameters to be adjusted are pressure pulsation parameters and NOx exhaust concentration, since the pressure pulse parameters will directly affect the working efficiency of the gas turbine, under normal load conditions, the larger the pressure pulse parameters are within the preset safe working parameter range, the higher the working efficiency of the gas turbine and the better the combustion performance. Therefore, the pressure pulse parameters can be adjusted first. Even if the pressure pulse parameters do not exceed the preset safe working parameter range in the current cycle, the pressure pulse parameters will be predictively adjusted to avoid the pressure pulse parameter range exceeding the preset safe working parameter range in the next cycle.

[0064] This embodiment obtains the working parameter set of the gas turbine in real time; predicts the working parameters to be adjusted of the gas turbine through a trained neural network model based on the working parameter set; determines the adjustment priority of each working parameter to be adjusted; adjusts the operating state of the gas turbine based on the adjustment priority and the working parameter to be adjusted until the working parameter to be adjusted is reduced to within a preset safe working parameter range, predicts the working parameters to be adjusted of the gas turbine through a trained neural network model, and then adjusts the operating state of the gas turbine based on the adjustment priority of each working parameter to be adjusted, thereby achieving pre-intervention of the combustion performance of the gas turbine, improving the combustion performance of the gas turbine, and avoiding the technical problem in the prior art that the combustion performance of the gas turbine fluctuates and causes excessive pollutant emissions from the gas turbine.

[0065] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 , step S20, comprising:

[0066] Step S201: dividing the acquired operating parameters of the gas turbine to obtain a first operating parameter outside a preset safe operating parameter range and a second operating parameter within the preset safe operating parameter range.

[0067] Step S202: determining a target operating parameter through a trained neural network model based on the second operating parameter.

[0068] Step S203: determining the operating parameter to be adjusted of the gas turbine according to the first operating parameter and the target operating parameter.

[0069] It should be noted that when dividing the working parameters of the gas turbine, the various working parameters of the current cycle can be compared with their corresponding preset safe working parameter ranges to determine whether the various working parameters in the current cycle are within the corresponding preset safe working parameter ranges, and those outside the preset safe working parameter ranges are recorded as first working parameters, and those within the preset safe working parameter ranges are recorded as second working parameters.

[0070] The target operating parameters are those that are within the preset safe operating parameter range, but are judged by the trained neural network model to have a risk of exceeding the boundary of the preset safe operating parameter range. For example, the local environmental protection standard requires that the NOx exhaust concentration should not exceed 35mg / m 3 , then when the NOx exhaust concentration of the gas turbine is 34mg / m 3 When the NOx exhaust concentration is within the preset safe working parameter range, the trained neural network model combined with the historical change trend of the NOx exhaust concentration determines that the NOx exhaust concentration has a risk of exceeding the preset safe working parameter range, that is, exceeding 35mg / m3. In this case, the NOx exhaust concentration can be recorded as the target working parameter.

[0071] The trained neural network model may be a recurrent neural network (RNN). In this embodiment, the trained neural network model includes an input layer, a hidden layer, and an output layer. In this embodiment, the hidden layer state is:

[0072] ht=σ(W hh *h t-1 +W ih *x t +b h )h t =σ(W hh *ht-1+W ih *x t +b h )

[0073] The output layer status is:

[0074] yt=W ho *h t +b o

[0075] Among them, h t is the hidden state of the current time step, W hh is the weight matrix from hidden layer to hidden layer, which represents the recursive connection inside the hidden layer, Wih is the weight matrix from the input layer to the hidden layer, which represents the influence of the input on the hidden state, σ is the activation function, xt is the current input, and yt is the current output.

[0076] Further, determining the target operating parameter through a trained neural network model based on the second operating parameter includes:

[0077] Performing data preprocessing on the second working parameter, wherein the data preprocessing at least includes moving arithmetic average, exponentially weighted moving average, extreme value processing, elimination of abnormal data, and smoothing processing;

[0078] The trained neural network model is used to perform an out-of-bounds prediction on the second working parameter after data preprocessing to obtain a target working parameter, where the target working parameter is a second working parameter that has a risk of exceeding a corresponding preset working parameter interval.

[0079] In a specific implementation, since it is difficult to collect the working parameters of the gas turbine during operation, especially the emission of pollutant gases, it is difficult to directly use it for subsequent combustion adjustment judgment and single adjustment step calculation. For example, when obtaining the pressure pulsation data of the combustion chamber, a combustion chamber pressure probe or a high-speed pressure sensor or other device can be used for collection. In order to improve the accuracy of the combustion chamber pressure pulsation data, arithmetic averaging can be performed on multiple collected pressure pulsation data to improve the accuracy of the pressure pulsation data within a time period. When collecting pollutant gas emissions, a continuous emission monitoring system (CEMS) can be used, which can be used to monitor and record the concentration and emission of industrial pollutants in real time, or a risk assessment and mitigation strategy system (REMS). Other devices or equipment that can realize gas emission collection can also be used. This embodiment does not impose specific restrictions on this.

[0080] Furthermore, the performing of an over-boundary prediction on the second working parameter after data preprocessing by using the trained neural network model includes:

[0081] Acquiring a historical change trend of the second operating parameter and an equipment parameter of the gas turbine;

[0082] Predicting the working parameters of the next adjustment cycle through the trained neural network model according to the historical change trend and the preprocessed second working parameters;

[0083] Calculating the confidence of the working parameters of the next adjustment cycle according to the equipment parameters;

[0084] According to the confidence level, it is detected whether the working parameter of the next adjustment cycle exceeds the corresponding preset safe working parameter range.

[0085] The historical change trend of the second working parameter refers to the data change trend of the second working parameter within the historical adjustment period, as well as the change trend of the working parameters of the same type of gas turbines under similar operating conditions. If the confidence of the predicted working parameters for the next adjustment period is greater than the set confidence threshold, it can be determined that the working parameters for the next adjustment period are credible and can be directly used for the determination of the target working parameters. If the confidence of the predicted working parameters for the next adjustment period is less than the set confidence threshold, it can be re-predicted.

[0086] This embodiment divides the acquired working parameters of the gas turbine to obtain a first working parameter within a preset safe working parameter range and a second working parameter outside the preset safe working parameter range; determines a target working parameter based on the second working parameter through a trained neural network model; determines the working parameter to be adjusted of the gas turbine according to the first working parameter and the target working parameter, and interferes with the combustion efficiency of the gas turbine in the next cycle in advance by taking the working parameters that have exceeded the boundary and the working parameters that are about to exceed the boundary as the working parameters to be adjusted, so as to improve the working efficiency of the gas turbine in the next cycle.

[0087] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3 , step S40, comprising:

[0088] Step S401: When the priority of the first operating parameter is greater than the target operating parameter, the operating state of the gas turbine is adjusted to reduce the first operating parameter to a corresponding preset safe operating parameter range.

[0089] Step S402: When the priority of the first operating parameter is less than or equal to the target operating parameter, the operating state of the gas turbine is adjusted to reduce the target operating parameter to a corresponding preset safe operating parameter range.

[0090] It should be noted that when adjusting the operating state of the gas turbine based on the working parameters to be adjusted, the working parameters with higher priority can be adjusted first. Even if the working parameters with higher priority do not exceed the corresponding preset safety working parameter boundaries, they can be adjusted first, thereby improving the combustion performance of the gas turbine in the next cycle.

[0091] The priority setting is related to the degree of influence of various operating parameters on the combustion performance of the gas turbine. For example, the pressure pulsation data of the combustion chamber has a greater impact on the combustion performance, and the priority setting is higher. Specifically, in this embodiment, the priorities of the pressure pulsation parameters, NOx exhaust concentration, and CO exhaust concentration decrease in sequence.

[0092] Further, the adjusting the operating state of the gas turbine includes at least one of the following methods:

[0093] controlling the combustion temperature of each zone of the combustion chamber in the gas turbine;

[0094] adjusting a valve opening of at least one fuel inlet in communication with the gas turbine;

[0095] The pressure within the combustion chamber is adjusted.

[0096] In a specific implementation, controlling the combustion temperature of each area of ​​the combustion chamber in the gas turbine can reduce the generation of thermal NOx. For example, the combustion temperature and residence time can be controlled by homogeneous premixing of lean fuel to achieve the purpose of reducing NOx emissions; or supercharged combustion can be achieved through high-frequency or continuous detonation waves to improve cycle efficiency, and the flow rate of the fuel involved in the combustion can be adjusted to control the fuel content in the combustion chamber, thereby controlling the combustion chamber to be in a suitable combustion condition and reducing the CO exhaust concentration.

[0097] In this embodiment, when the priority of the first operating parameter is greater than the target operating parameter, the operating state of the gas turbine is adjusted to reduce the first operating parameter to a corresponding preset safe operating parameter range; when the priority of the first operating parameter is less than or equal to the target operating parameter, the operating state of the gas turbine is adjusted to reduce the target operating parameter to a corresponding preset safe operating parameter range, thereby giving priority to adjusting the parameters to be adjusted with higher priorities, preventing more serious combustion performance degradation of the gas turbine in the next cycle, and improving combustion efficiency.

[0098] The present application also provides a control device for a gas turbine, please refer to Figure 4 , the control device of the gas turbine comprises:

[0099] The acquisition module 10 is used to acquire the working parameter set of the gas turbine in real time.

[0100] The prediction module 20 is used to predict the operating parameters to be adjusted of the gas turbine based on the operating parameter set through a trained neural network model.

[0101] The determination module 30 is used to determine the adjustment priority of each working parameter to be adjusted.

[0102] The adjustment module 40 is used to adjust the operating state of the gas turbine based on the adjustment priority and the operating parameter to be adjusted until the operating parameter to be adjusted is reduced to within a preset safe operating parameter range.

[0103] This embodiment obtains the working parameter set of the gas turbine in real time; predicts the working parameters to be adjusted of the gas turbine through a trained neural network model based on the working parameter set; determines the adjustment priority of each working parameter to be adjusted; adjusts the operating state of the gas turbine based on the adjustment priority and the working parameter to be adjusted until the working parameter to be adjusted is reduced to within a preset safe working parameter range, predicts the working parameters to be adjusted of the gas turbine through a trained neural network model, and then adjusts the operating state of the gas turbine based on the adjustment priority of each working parameter to be adjusted, thereby achieving pre-intervention of the combustion performance of the gas turbine, improving the combustion performance of the gas turbine, and avoiding the technical problem in the prior art that the combustion performance of the gas turbine fluctuates and causes excessive pollutant emissions from the gas turbine.

[0104] In one embodiment, the prediction module 20 is further used to divide the acquired operating parameters of the gas turbine to obtain a first operating parameter within a preset safe operating parameter range and a second operating parameter outside the preset safe operating parameter range; determine a target operating parameter based on the second operating parameter through a trained neural network model; and determine the operating parameter to be adjusted of the gas turbine according to the first operating parameter and the target operating parameter.

[0105] In one embodiment, the prediction module 20 is also used to perform data preprocessing on the second working parameter, and the data preprocessing at least includes moving arithmetic average, exponentially weighted moving average, extreme value processing, elimination of abnormal data and smoothing processing; the second working parameter after data preprocessing is predicted beyond the boundary through a trained neural network model to obtain a target working parameter, and the target working parameter is a second working parameter that has a risk of exceeding the corresponding preset working parameter interval.

[0106] In one embodiment, the prediction module 20 is also used to obtain the historical change trend of the second operating parameter and the equipment parameters of the gas turbine; predict the operating parameters of the next adjustment cycle through a trained neural network model based on the historical change trend and the preprocessed second operating parameter; calculate the confidence level of the operating parameters of the next adjustment cycle based on the equipment parameters; and detect whether the operating parameters of the next adjustment cycle exceed the corresponding preset safe operating parameter range based on the confidence level.

[0107] In one embodiment, the adjustment module 40 is further used to adjust the operating state of the gas turbine when the priority of the first operating parameter is greater than the target operating parameter, so as to reduce the first operating parameter to a corresponding preset safe operating parameter range.

[0108] In one embodiment, the adjustment module 40 is further used to adjust the operating state of the gas turbine when the priority of the first operating parameter is less than or equal to the target operating parameter, so as to reduce the target operating parameter to a corresponding preset safe operating parameter range.

[0109] In one embodiment, the adjustment module 40 is further used to control the combustion temperature of each area of ​​the combustion chamber in the gas turbine; adjust the valve opening of at least one fuel inlet connected to the gas turbine; and adjust the pressure in the combustion chamber.

[0110] The present application provides a control device for a gas turbine, the control device for the gas turbine comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the control method for the gas turbine in the above-mentioned embodiment one.

[0111] Reference below Figure 5 , which shows a schematic diagram of the structure of a control device for a gas turbine suitable for implementing an embodiment of the present application. The control device for a gas turbine in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The control device of the gas turbine shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0112] like Figure 5As shown, the control device of the gas turbine may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the control device of the gas turbine are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the control device of the gas turbine to communicate with other devices wirelessly or by wire to exchange data. Although the control device of the gas turbine with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0113] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0114] The control device of the gas turbine provided in the present application adopts the control method of the gas turbine in the above embodiment, which can solve the technical problem of gas turbine control. Compared with the prior art, the beneficial effects of the control device of the gas turbine provided in the present application are the same as the beneficial effects of the control method of the gas turbine provided in the above embodiment, and other technical features in the control device of the gas turbine are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0115] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0116] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0117] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the control method of the gas turbine in the above-mentioned embodiment.

[0118] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0119] The computer-readable storage medium may be included in the control device of the gas turbine; or may exist independently without being installed in the control device of the gas turbine.

[0120] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the control device of the gas turbine, the control device of the gas turbine controls the gas turbine.

[0121] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0122] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0123] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0124] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned gas turbine control method, and can solve the technical problems of gas turbine control. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the gas turbine control method provided in the above-mentioned embodiment, and will not be repeated here.

[0125] The present application also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the gas turbine control method as described above are implemented.

[0126] The computer program product provided in this application can solve the technical problem of gas turbine control. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the gas turbine control method provided in the above embodiment, which will not be repeated here.

[0127] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A gas turbine control method, characterized in that: The gas turbine control method comprises: Obtain the gas turbine operating parameter set in real time; Predicting the to-be-adjusted operating parameters of the gas turbine through a trained neural network model based on the operating parameter set; Determine the adjustment priority of each working parameter to be adjusted; The operating state of the gas turbine is adjusted based on the adjustment priority and the operating parameter to be adjusted until the operating parameter to be adjusted is reduced to within a preset safe operating parameter range.

2. The method according to claim 1, characterized in that The predicting the to-be-adjusted operating parameter of the gas turbine by using a trained neural network model based on the operating parameter set includes: Dividing the acquired operating parameters of the gas turbine to obtain a first operating parameter outside a preset safe operating parameter range and a second operating parameter within the preset safe operating parameter range; Determine a target operating parameter through a trained neural network model based on the second operating parameter; The operating parameter to be adjusted of the gas turbine is determined according to the first operating parameter and the target operating parameter.

3. The method according to claim 2, characterized in that The determining the target operating parameter through the trained neural network model based on the second operating parameter includes: Performing data preprocessing on the second working parameter, wherein the data preprocessing at least includes moving arithmetic average, exponentially weighted moving average, extreme value processing, elimination of abnormal data, and smoothing processing; The trained neural network model is used to perform an out-of-bounds prediction on the second working parameter after data preprocessing to obtain a target working parameter, where the target working parameter is a second working parameter that has a risk of exceeding a corresponding preset working parameter interval.

4. The method according to claim 3, characterized in that The performing of an over-boundary prediction on the second working parameter after data preprocessing by using the trained neural network model comprises: Acquiring a historical change trend of the second operating parameter and an equipment parameter of the gas turbine; Predicting the working parameters of the next adjustment cycle through the trained neural network model according to the historical change trend and the preprocessed second working parameters; Calculating the confidence of the working parameters of the next adjustment cycle according to the equipment parameters; According to the confidence level, it is detected whether the working parameter of the next adjustment cycle exceeds the corresponding preset safe working parameter range.

5. The method according to claim 1, characterized in that The adjusting the operating state of the gas turbine based on the adjustment priority and the operating parameter to be adjusted includes: When the priority of the first operating parameter is greater than the target operating parameter, the operating state of the gas turbine is adjusted to reduce the first operating parameter to a corresponding preset safe operating parameter range.

6. The method according to claim 5, characterized in that The gas turbine control method further includes: When the priority of the first operating parameter is less than or equal to the target operating parameter, the operating state of the gas turbine is adjusted to reduce the target operating parameter to a corresponding preset safe operating parameter range.

7. The method according to any one of claims 5 or 6, characterized in that The adjusting the operating state of the gas turbine includes at least one of the following methods: controlling the combustion temperature of each zone of the combustion chamber in the gas turbine; adjusting a valve opening of at least one fuel inlet in communication with the gas turbine; The pressure within the combustion chamber is adjusted.

8. A control device for a gas turbine, characterized in that: The control device of the gas turbine comprises: An acquisition module, used for acquiring a set of working parameters of a gas turbine in real time; A prediction module, used for predicting the to-be-adjusted operating parameters of the gas turbine through a trained neural network model based on the operating parameter set; A determination module, used to determine the adjustment priority of each working parameter to be adjusted; An adjustment module is used to adjust the operating state of the gas turbine based on the adjustment priority and the operating parameter to be adjusted until the operating parameter to be adjusted is reduced to within a preset safe operating parameter range.

9. A gas turbine control device, characterized in that: The gas turbine control device comprises: a memory, a processor and a gas turbine control program stored in the memory and executable on the processor, wherein the gas turbine control program is configured to implement the steps of the gas turbine control method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a control program for a gas turbine, and when the control program for the gas turbine is executed by a processor, the steps of the method for controlling a gas turbine according to any one of claims 1 to 7 are implemented.

Images