阅读说明：本技术 燃气轮机压气机叶片积垢状态监测方法及装置 (Method and device for monitoring accumulated dirt state of gas turbine compressor blade ) 是由 赵瑜 王金建 郭庆 于 2021-09-26 设计创作，主要内容包括：本申请提出一种燃气轮机压气机叶片积垢状态监测方法及装置,涉及燃气轮机技术领域,其中燃气轮机压气机叶片积垢状态监测方法包括：确定燃气轮机当前的功率相关参数,根据功率相关参数以及预设的功率仿真模型,确定燃气轮机的仿真功率；根据仿真功率以及燃气轮机当前的实际功率,确定燃气轮机的压气机叶片是否处于积垢状态,提高压气机叶片积垢程度判断的准确性,压气机水洗由隐性判断转为显性判断,提高水洗判断的准确度和效率,提高燃气轮机的工作效率。(The application provides a method and a device for monitoring the accumulated scale state of a gas turbine compressor blade, which relate to the technical field of gas turbines, wherein the method for monitoring the accumulated scale state of the gas turbine compressor blade comprises the following steps: determining the current power related parameters of the gas turbine, and determining the simulation power of the gas turbine according to the power related parameters and a preset power simulation model; according to the simulation power and the current actual power of the gas turbine, whether the compressor blades of the gas turbine are in the scaling state or not is determined, the accuracy of judging the scaling degree of the compressor blades is improved, the compressor washing is converted from recessive judgment to explicit judgment, the accuracy and the efficiency of washing judgment are improved, and the working efficiency of the gas turbine is improved.)

1. A method for monitoring the fouling state of a gas turbine compressor blade, comprising:

determining the current power related parameters of the gas turbine;

determining the simulation power of the gas turbine according to the power related parameters and a preset power simulation model;

and determining whether the compressor blade of the gas turbine is in a fouling state or not according to the simulated power and the current actual power of the gas turbine.

2. The method of claim 1, further comprising, prior to determining the simulated power of the gas turbine based on the power-related parameter and a preset power simulation model:

obtaining training data, wherein the training data comprises: sample power related parameters and corresponding sample powers;

and training an initial power simulation model by taking the sample power related parameters as input and the sample power as output to obtain the preset power simulation model.

3. The method of claim 1, further comprising, prior to determining the simulated power of the gas turbine based on the power-related parameter and a preset power simulation model:

determining whether a screen of a filter in an intake air filtering system corresponding to the gas turbine is available;

correspondingly, the determining the simulation power of the gas turbine according to the power-related parameter and a preset power simulation model includes:

and when the filter screen is available, determining the simulation power of the gas turbine according to the power related parameters and a preset power simulation model.

4. The method of claim 1, wherein determining whether a compressor blade of the gas turbine is in a fouling state based on the simulated power and a current actual power of the gas turbine comprises:

determining a difference between the simulated power and the actual power;

when the difference is larger than or equal to a preset difference threshold value, determining that a compressor blade of the gas turbine is in a scaling state;

determining that a compressor blade of the gas turbine is not in a fouling state when the difference is less than the difference threshold.

5. The method of claim 1, further comprising:

and when the compressor blade of the gas turbine is in a scaling state, performing water washing reminding.

6. The method of claim 1, wherein the power-related parameter is an operating parameter that satisfies a preset condition;

wherein the preset condition comprises at least one of the following conditions: the correlation degree of the power-related parameter and the power is greater than or equal to a preset correlation degree threshold value, the signal-to-noise ratio of the power-related parameter is greater than or equal to a preset signal-to-noise ratio threshold value, and the total simulation error of the power-related parameter is a minimum value.

7. The method according to claim 1 or 6, wherein the power-related parameter comprises at least one of the following parameters: compressor inlet temperature, compressor inlet pressure, IGV opening, exhaust temperature, atmospheric pressure, and atmospheric humidity.

8. A gas turbine compressor blade fouling status monitoring device, comprising:

a first determination module for determining a current power related parameter of the gas turbine;

the second determination module is used for determining the simulation power of the gas turbine according to the power related parameters and a preset power simulation model;

and the third determining module is used for determining whether the compressor blade of the gas turbine is in a fouling state or not according to the simulated power and the current actual power of the gas turbine.

9. The apparatus of claim 8, further comprising an acquisition module and a training module;

the obtaining module is configured to obtain training data, where the training data includes: sample power related parameters and corresponding sample powers;

and the training module is used for training an initial power simulation model by taking the sample power related parameters as input and the sample power as output to obtain the preset power simulation model.

10. The apparatus of claim 8, further comprising a fourth determination module;

the fourth determination module is used for determining whether a filter screen of a filter in an inlet air filtering system corresponding to the gas turbine is available;

correspondingly, the second determining module is specifically configured to determine the simulation power of the gas turbine according to the power-related parameter and a preset power simulation model when the filter screen is available.

11. The apparatus of claim 8, wherein the third determination module is specifically configured to,

determining a difference between the simulated power and the actual power;

when the difference is larger than or equal to a preset difference threshold value, determining that a compressor blade of the gas turbine is in a scaling state;

determining that a compressor blade of the gas turbine is not in a fouling state when the difference is less than the difference threshold.

12. The apparatus of claim 8, further comprising a reminder module;

and the reminding module is used for carrying out water washing reminding when the compressor blade of the gas turbine is in a scaling state.

13. The apparatus of claim 8, wherein the power-related parameter is an operating parameter that satisfies a preset condition;

wherein the preset condition comprises at least one of the following conditions: the correlation degree of the power-related parameter and the power is greater than or equal to a preset correlation degree threshold value, the signal-to-noise ratio of the power-related parameter is greater than or equal to a preset signal-to-noise ratio threshold value, and the total simulation error of the power-related parameter is a minimum value.

14. The apparatus according to claim 8 or 13, wherein the power related parameter comprises at least one of: compressor inlet temperature, compressor inlet pressure, IGV opening, exhaust temperature, atmospheric pressure, and atmospheric humidity.

15. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to any of claims 1-7 when executing the program.

16. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method of any one of claims 1-7.

17. A computer program product, characterized in that instructions in the computer program product, when executed by a processor, perform the method according to any of claims 1-7.

Technical Field

The application relates to the technical field of gas turbines, in particular to a method and a device for monitoring the accumulated dirt state of a gas turbine compressor blade.

Background

The air used by the gas turbine is sourced from the atmosphere, although most of impurities are filtered by the air inlet system filter, a part of tiny impurities enter the unit, and the impurities are gradually deposited on the surface of a compressor blade after long-time accumulation and gradually deposited with scale or salt, so that the compression ratio and the efficiency of the compressor are reduced. According to statistics, 70% -85% of the unit output loss is caused by blade dust accumulation. In order to recover the running performance of the unit, the compressor needs to be washed by water to maintain the cleanness of the compressor channel.

At present, the conventional method is to carry out washing regularly according to experience, and the washing is difficult to carry out in time according to the dirt state, so that the working efficiency of the gas turbine is influenced.

Disclosure of Invention

The present application is directed to solving, at least to some extent, one of the technical problems in the related art.

To this end, a first object of the present application is to propose a method for monitoring the fouling status of a compressor blade of a gas turbine.

A second object of the present application is to provide a device for monitoring the fouling status of gas turbine compressor blades.

A third object of the present application is to provide an electronic device.

A fourth object of the present application is to propose a non-transitory computer-readable storage medium.

A fifth object of the present application is to propose a computer program product.

In order to achieve the above object, a first embodiment of the present application provides a method for monitoring a fouling state of a compressor blade of a gas turbine, including: determining the current power related parameters of the gas turbine; determining the simulation power of the gas turbine according to the power related parameters and a preset power simulation model; and determining whether the compressor blade of the gas turbine is in a fouling state or not according to the simulated power and the current actual power of the gas turbine.

Optionally, before determining the simulated power of the gas turbine according to the power-related parameter and a preset power simulation model, the method further includes:

obtaining training data, wherein the training data comprises: sample power related parameters and corresponding sample powers;

and training an initial power simulation model by taking the sample power related parameters as input and the sample power as output to obtain the preset power simulation model.

Optionally, before determining the simulated power of the gas turbine according to the power-related parameter and a preset power simulation model, the method further includes:

determining whether a screen of a filter in an intake air filtering system corresponding to the gas turbine is available;

correspondingly, the determining the simulation power of the gas turbine according to the power-related parameter and a preset power simulation model includes:

and when the filter screen is available, determining the simulation power of the gas turbine according to the power related parameters and a preset power simulation model.

Optionally, the determining whether a compressor blade of the gas turbine is in a fouling state according to the simulated power and the current actual power of the gas turbine includes:

determining a difference between the simulated power and the actual power;

when the difference is larger than or equal to a preset difference threshold value, determining that a compressor blade of the gas turbine is in a scaling state;

determining that a compressor blade of the gas turbine is not in a fouling state when the difference is less than the difference threshold.

Optionally, the method further comprises:

and when the compressor blade of the gas turbine is in a scaling state, performing water washing reminding.

Optionally, the power-related parameter is an operating parameter meeting a preset condition;

wherein the preset condition comprises at least one of the following conditions: the correlation degree of the power-related parameter and the power is greater than or equal to a preset correlation degree threshold value, the signal-to-noise ratio of the power-related parameter is greater than or equal to a preset signal-to-noise ratio threshold value, and the total simulation error of the power-related parameter is a minimum value.

Optionally, the power-related parameter comprises at least one of: compressor inlet temperature, compressor inlet pressure, IGV opening, exhaust temperature, atmospheric pressure, and atmospheric humidity.

In order to achieve the above object, a second aspect of the present application provides a device for monitoring fouling status of compressor blades of a gas turbine, comprising: a first determination module for determining a current power related parameter of the gas turbine; the second determination module is used for determining the simulation power of the gas turbine according to the power related parameters and a preset power simulation model; and the third determining module is used for determining whether the compressor blade of the gas turbine is in a fouling state or not according to the simulated power and the current actual power of the gas turbine.

Optionally, the apparatus further comprises an acquisition module and a training module;

the obtaining module is configured to obtain training data, where the training data includes: sample power related parameters and corresponding sample powers;

and the training module is used for training an initial power simulation model by taking the sample power related parameters as input and the sample power as output to obtain the preset power simulation model.

Optionally, the apparatus further comprises a fourth determining module;

the fourth determination module is used for determining whether a filter screen of a filter in an inlet air filtering system corresponding to the gas turbine is available;

correspondingly, the second determining module is specifically configured to determine the simulation power of the gas turbine according to the power-related parameter and a preset power simulation model when the filter screen is available.

Optionally, the third determining module is specifically configured to,

determining a difference between the simulated power and the actual power;

when the difference is larger than or equal to a preset difference threshold value, determining that a compressor blade of the gas turbine is in a scaling state;

determining that a compressor blade of the gas turbine is not in a fouling state when the difference is less than the difference threshold.

Optionally, the device further comprises a reminder module;

and the reminding module is used for carrying out water washing reminding when the compressor blade of the gas turbine is in a scaling state.

Optionally, the power-related parameter is an operating parameter meeting a preset condition;

wherein the preset condition comprises at least one of the following conditions: the correlation degree of the power-related parameter and the power is greater than or equal to a preset correlation degree threshold value, the signal-to-noise ratio of the power-related parameter is greater than or equal to a preset signal-to-noise ratio threshold value, and the total simulation error of the power-related parameter is a minimum value.

Optionally, the power-related parameter comprises at least one of: compressor inlet temperature, compressor inlet pressure, IGV opening, exhaust temperature, atmospheric pressure, and atmospheric humidity.

To achieve the above object, a third aspect of the present application provides an electronic device, including: a processor; a memory for storing the processor-executable instructions; wherein the processor is configured to execute the instructions to implement the gas turbine compressor blade fouling status monitoring method as described above.

In order to achieve the above object, a fourth aspect of the present application provides a non-transitory computer-readable storage medium, wherein the computer instructions are configured to cause the computer to perform the method for monitoring fouling status of a compressor blade of a gas turbine according to the above aspect of the present application.

In order to achieve the above object, according to a fifth aspect of the present application, a computer program product is provided, which includes a computer program, when being executed by a processor, the computer program implementing the method for monitoring fouling state of compressor blades of a gas turbine compressor according to the above aspect of the present application.

Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.

Drawings

The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

FIG. 1 is a schematic flow chart of a method for monitoring a fouling state of a compressor blade of a gas turbine according to an embodiment of the present disclosure;

FIG. 2 is a schematic flow chart of another method for monitoring fouling status of compressor blades of a gas turbine according to an embodiment of the present disclosure;

FIG. 3 is a block diagram of a genetic algorithm routine;

FIG. 4 is a block diagram of a radial basis function neural network;

FIG. 5 is a schematic structural diagram of a device for monitoring fouling status of compressor blades of a gas turbine according to an embodiment of the present application;

FIG. 6 is a schematic structural view of another gas turbine compressor blade fouling state monitoring device;

FIG. 7 is a schematic structural view of another gas turbine compressor blade fouling state monitoring device;

FIG. 8 is a schematic structural view of another gas turbine compressor blade fouling condition monitoring device;

FIG. 9 is a block diagram of an exemplary computer device used to implement embodiments of the present application.

Detailed Description

Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.

The air used by the gas turbine is sourced from the atmosphere, although most of impurities are filtered by the air inlet system filter, a part of tiny impurities enter the unit, and the impurities are gradually deposited on the surface of a compressor blade after long-time accumulation and gradually deposited with scale or salt, so that the compression ratio and the efficiency of the compressor are reduced. According to statistics, 70% -85% of the unit output loss is caused by blade dust accumulation. In order to recover the running performance of the unit, the compressor needs to be washed by water to maintain the cleanness of the compressor channel.

At present, the method is more implemented by periodically washing according to experience, and explicit washing reminding and judgment are lacked. GE company gives another compressor water wash scheme: and under the fully-opened working condition of the IGV valve of the gas turbine, on-line washing is adopted when the rated efficiency of the gas compressor is lower than about 3%, and off-line washing is adopted when the rated efficiency of the gas compressor is lower than about 10%. Although the scheme can improve the efficiency of the compressor compared with a regular online cleaning mode, in the actual production process, the fully-opened working condition of the IGV valve of the gas turbine is basically rare, most of the IGV valves of the gas turbine are in certain opening degrees (50%, 60% and the like), and the fully-opened working condition of the IGV valve of the fully-opened 100% gas turbine is less, so that the washing scheme of the compressor has large use limitation and cannot meet the actual production requirement.

In order to solve the problems, the application provides a method and a device for monitoring the accumulated dirt state of the blades of the gas turbine compressor.

The method and the device for monitoring the fouling state of the compressor blade of the gas turbine according to the embodiment of the application are described below with reference to the attached drawings.

The method for monitoring the fouling state of the compressor blade of the gas turbine provided by the application is described in detail below with reference to fig. 1. FIG. 1 is a schematic flow chart of a method for monitoring a fouling state of a compressor blade of a gas turbine according to an embodiment of the present application.

The execution main body of the gas turbine compressor blade fouling state monitoring method is the gas turbine compressor blade fouling state monitoring device provided by the application, and the gas turbine compressor blade fouling state monitoring device provided by the embodiment of the application can be configured in electronic equipment, so that the electronic equipment can execute a gas turbine compressor blade fouling state monitoring function; alternatively, the gas turbine compressor blade fouling state monitoring device can be configured in an application of electronic equipment, so that the application can execute the gas turbine compressor blade fouling state monitoring method.

As shown in fig. 1, the method for monitoring the fouling state of the compressor blade of the gas turbine comprises the following steps:

in step 101, the current power related parameters of the gas turbine are determined.

In the embodiment of the application, the power related parameter is an operation parameter meeting a preset condition; wherein the preset condition comprises at least one of the following conditions: the correlation degree of the power-related parameter and the power is greater than or equal to a preset correlation degree threshold value, the signal-to-noise ratio of the power-related parameter is greater than or equal to a preset signal-to-noise ratio threshold value, and the total simulation error of the power-related parameter is a minimum value.

The reason why the correlation degree between the power-related parameter and the power is greater than or equal to the preset correlation degree threshold is taken as a preset condition is to eliminate the operating parameter with the smaller correlation degree threshold, reduce the complexity of the power simulation model, and reduce the interference caused by the operating parameter with the smaller correlation degree threshold.

The reason for taking the signal-to-noise ratio of the power-related parameter greater than or equal to the preset signal-to-noise ratio threshold as the popular river condition is to eliminate the operating parameter with smaller signal-to-noise ratio, namely the operating parameter with larger noise, so that the interference of the noise on the training of the power simulation model is avoided, and the power simulation model is difficult to converge due to the larger noise.

The preset condition that the total simulation error of the power-related parameters is a minimum value is used for selecting a group of power-related parameters with the total simulation error as the minimum value, so that the accuracy of the trained power simulation model is improved.

Wherein the power-related parameter comprises at least one of: compressor inlet temperature, compressor inlet pressure, IGV opening, exhaust temperature, atmospheric pressure, and atmospheric humidity.

In the embodiment of the application, the selection of the power related parameters meets two principles of stronger relevance and stronger reliability. The correlation is strong, namely the correlation with the power related parameters is strong, and the change of the power related parameters has obvious follow-up characteristics under the full working condition of the gas turbine. If some power related parameters with weak correlation are used as an input parameter set, not only the interference on the network is easily generated, but also the complexity of network training is increased; the reliability is strong, namely the power related parameters require strong reliability, if the power related parameters are unreliable, large noise exists, the training samples have no accuracy, and the established simulation model also loses significance.

In the embodiment of the present application, it should be noted that the power related parameters selected empirically have subjective judgment, and therefore, the power related parameters need to be optimized according to a genetic algorithm to determine optimal input parameters, and a program block diagram of the genetic algorithm is shown in fig. 2.

It can be understood that, the initial power related parameters are randomly generated, a group of power related parameters are selected according to a certain cross probability and a certain variation probability respectively, the cross and variation operations are implemented, the power related parameters which are not selected to be cross and varied are kept unchanged, the fitness of the selected power related parameters is evaluated, whether the termination criterion is met or not is judged through the selection process, and if the termination criterion is met, the selected power related parameters are the optimal input parameters; if the termination criterion is not satisfied, judging whether the selected power-related parameter is the last group, if so, performing exception processing, and if not, performing the above loop process again. And searching a group of parameter combinations with the minimum simulation error in the global space to determine the optimal input parameters of the simulation model.

And 102, determining the simulation power of the gas turbine according to the power related parameters and a preset power simulation model.

In the embodiment of the application, in order to eliminate the influence of the unavailable filter screen on the power of the gas turbine, eliminate the reduction of the power of the gas turbine caused by the unavailable filter screen and improve the monitoring accuracy of the fouling state, before the simulation power of the gas turbine is determined according to the power related parameters and a preset power simulation model, whether the filter screen of a filter in an air inlet filtering system corresponding to the gas turbine is available or not is determined.

Correspondingly, when the filter screen is available, the simulation power of the gas turbine is determined according to the power related parameters and the preset power simulation model. And when the filter screen is unavailable, replacing the filter screen in time, and then determining the simulation power of the gas turbine according to the power related parameters and a preset power simulation model.

In general, the filter is replaced at a fixed operation time interval or when the pressure difference reaches a critical value, and the efficiency and the dust holding rate of the filter cannot be actually reflected. If the filter screen is replaced but not replaced, the impurities entering the compressor are increased, the blade is seriously scaled or the salt is seriously accumulated, the output of the unit is reduced, and the efficiency of the unit is reduced.

And 103, determining whether the compressor blade of the gas turbine is in a fouling state or not according to the simulation power and the current actual power of the gas turbine.

In the embodiment of the present application, the process of step 103 executed by the device for monitoring the fouling state of the compressor blade of the gas turbine may be, for example, determining a difference between the simulated power and the actual power; when the difference value is larger than or equal to a preset difference value threshold value, determining that a compressor blade of the gas turbine is in a scaling state; and when the difference value is smaller than the difference value threshold value, determining that the compressor blade of the gas turbine is not in a fouling state.

In the embodiment of the application, when the compressor blade of the gas turbine is in a scaling state, the water washing reminding is carried out. Wherein the water wash reminder may be given at a human-machine interface, for example.

In conclusion, by determining the current power related parameters of the gas turbine; determining the simulation power of the gas turbine according to the power related parameters and a preset power simulation model; according to the simulation power and the current actual power of the gas turbine, whether the compressor blades of the gas turbine are in the scaling state or not is determined, the accuracy of judging the scaling degree of the compressor blades is improved, the compressor washing is converted from recessive judgment to explicit judgment, the accuracy and the efficiency of washing judgment are improved, and the working efficiency of the gas turbine is improved.

For clarity of the above embodiment, another method for monitoring the fouling state of the compressor blade of the gas turbine is provided in this embodiment, and fig. 3 is a schematic flow chart of another method for monitoring the fouling state of the compressor blade of the gas turbine provided in this embodiment.

As shown in fig. 3, the method for monitoring the fouling state of the compressor blade of the gas turbine can comprise the following steps:

step 301, obtaining training data, wherein the training data includes: a sample power related parameter and a corresponding sample power.

In the embodiment of the application, when the filter screen of the air inlet filtering system corresponding to the gas turbine is good, the operation data of the compressor under the whole working condition after washing is collected, and the parameter strongly related to the power of the gas turbine is selected as the power related parameter, so that the training data is determined.

And 302, training the initial power simulation model by taking the sample power related parameters as input and the sample power as output to obtain a preset power simulation model.

In the embodiment of the present application, the power simulation model is obtained according to a radial basis function neural network, where fig. 4 is a calculation block diagram of the radial basis function neural network, as shown in fig. 4.

It is understood that, assuming that the output of the hidden layer is a {1}, the output formula of the hidden layer is a {1} — radbas (netprod (net. iw {1,1}, p), convur (net. b {1}, x))).

In the formula: p is a transposed matrix of the input matrix; iw {1,1 }: the weight of the hidden layer; net.b {1 }: a threshold of hidden layer; x: when the matrix p after the transposition is determined by p and is an n × m matrix, x is m.

Assuming that the output of the output layer is a {2}, the final output is a {2} ═ lw {2,1} × a {1} + net.b {2 }.

In the formula: lw {2,1 }: outputting the weight of the layer; net.b {2 }: and outputting the threshold value of the layer.

The training process of the initial power simulation model may be, for example, inputting sample power related parameters into the initial power simulation model to obtain simulation power output by the power simulation model; and constructing a loss function and determining the value of the loss function according to the simulation power and the sample power corresponding to the sample power related parameter, and adjusting the coefficient of the power simulation model according to the value of the loss function to realize training until training of training data is completed, or the training times reach a preset time threshold, or the simulation accuracy of the power simulation model reaches a preset accuracy threshold.

In summary, by obtaining training data, wherein the training data comprises: sample power related parameters and corresponding sample powers; and training the initial power simulation model by taking the sample power related parameters as input and the sample power as output to obtain a preset power simulation model. The establishment of the online power simulation model has universality and can be used for different types of gas turbines and various situations.

In order to realize the embodiment, the application also provides a device for monitoring the fouling state of the compressor blade of the gas turbine.

FIG. 5 is a schematic structural diagram of a device for monitoring a fouling state of a compressor blade of a gas turbine according to an embodiment of the present application.

As shown in fig. 5, the gas turbine compressor blade fouling status monitoring device 500 includes: a first determination module 510, a second determination module 520, and a third determination module 530.

The first determination module 510 is configured to determine a current power-related parameter of the gas turbine; a second determining module 520, configured to determine a simulation power of the gas turbine according to the power-related parameter and a preset power simulation model; a third determining module 530, configured to determine whether a compressor blade of the gas turbine is in a fouling state according to the simulated power and a current actual power of the gas turbine.

In a possible implementation manner of the embodiment of the application, fig. 6 is a schematic structural diagram of another gas turbine compressor blade fouling state monitoring device, as shown in fig. 6. On the basis of the embodiment shown in fig. 5, the gas turbine compressor blade fouling status monitoring device 500 further comprises an acquisition module 540 and a training module 550.

The obtaining module 540 is configured to obtain training data, where the training data includes: sample power related parameters and corresponding sample powers;

the training module 550 is configured to train the initial power simulation model with the sample power related parameter as input and the sample power as output, so as to obtain a preset power simulation model.

In a possible implementation manner of the embodiment of the application, fig. 7 is a schematic structural diagram of another gas turbine compressor blade fouling state monitoring device, as shown in fig. 7. In addition to the embodiment shown in fig. 5, the gas turbine compressor blade fouling status monitoring apparatus 500 further comprises a fourth determination module 560.

The fourth determination module 560 is configured to determine whether a filter screen of a filter in an intake air filtering system corresponding to the gas turbine is available;

correspondingly, the second determining module 520 is specifically configured to determine the simulation power of the gas turbine according to the power-related parameter and the preset power simulation model when the filter screen is available.

In a possible implementation manner of the embodiment of the present application, the third determining module 530 is specifically configured to determine a difference between the simulated power and the actual power;

when the difference value is larger than or equal to a preset difference value threshold value, determining that a compressor blade of the gas turbine is in a scaling state;

and when the difference value is smaller than the difference value threshold value, determining that the compressor blade of the gas turbine is not in a fouling state.

In a possible implementation manner of the embodiment of the application, fig. 8 is a schematic structural diagram of another gas turbine compressor blade fouling state monitoring device, as shown in fig. 8. In addition to the embodiment shown in fig. 5, the gas turbine compressor blade fouling status monitoring device 500 further comprises a reminder module 570.

The reminding module 570 is used for reminding water washing when the compressor blades of the gas turbine are in a scaling state.

In a possible implementation manner of the embodiment of the present application, the power-related parameter is an operation parameter that satisfies a preset condition.

Wherein the preset condition comprises at least one of the following conditions: the correlation degree of the power-related parameter and the power is greater than or equal to a preset correlation degree threshold value, the signal-to-noise ratio of the power-related parameter is greater than or equal to a preset signal-to-noise ratio threshold value, and the total simulation error of the power-related parameter is a minimum value.

In a possible implementation manner of the embodiment of the present application, the power-related parameter includes at least one of the following parameters: compressor inlet temperature, compressor inlet pressure, IGV opening, exhaust temperature, atmospheric pressure, and atmospheric humidity.

It should be noted that the explanation of the foregoing embodiment of the method for monitoring the fouling state of the compressor blade of the gas turbine is also applicable to the device for monitoring the fouling state of the compressor blade of the gas turbine of the embodiment, and details are not repeated here.

FIG. 9 illustrates a block diagram of an exemplary computer device suitable for use to implement embodiments of the present application. The computer device 12 shown in fig. 9 is only an example, and should not bring any limitation to the function and the scope of use of the embodiments of the present application.

As shown in FIG. 9, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.

Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. These architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, to name a few.

Computer device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.

Memory 28 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 30 and/or cache Memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 9, and commonly referred to as a "hard drive"). Although not shown in FIG. 9, a disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk read Only Memory (CD-ROM), a Digital versatile disk read Only Memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.

A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described herein.

The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with the computer system/server 12, and/or with any devices (e.g., network card, modem, etc.) that enable the computer system/server 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Moreover, computer device 12 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network such as the Internet) via Network adapter 20. As shown, network adapter 20 communicates with the other modules of computer device 12 via bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The processing unit 16 executes various functional applications and data processing, for example, implementing the methods mentioned in the foregoing embodiments, by executing programs stored in the system memory 28.

In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.

The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.

In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.

The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.