CN115270416A - Online judging method, device and equipment for operating state of gas turbine of power station - Google Patents
Online judging method, device and equipment for operating state of gas turbine of power station Download PDFInfo
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Abstract
The application relates to a method, a device and equipment for judging the running state of a gas turbine of a power station on line. The main technical scheme comprises: the method comprises the steps of obtaining gas turbine data of a power station gas turbine in an online operation state, wherein the gas turbine data comprise gas turbine power data and time data, determining a parameter vector of a gas turbine power regression model according to the gas turbine power data and the time data and in combination with the gas turbine power regression model, and the parameter vector comprises the following components: and the slope of the power and time of the combustion engine, and the running state of the gas turbine of the power station is judged on line according to the slope and a predetermined combustion engine power threshold. The method and the device can accurately and quickly judge the running state of the gas turbine on line, and are high in noise resistance and reliability.
Description
Technical Field
The application relates to the technical field of thermodynamic systems of power plants, in particular to a method, a device and equipment for judging the running state of a gas turbine of a power station on line.
Background
The power station gas turbine is used as a current important peak regulation power supply and is often operated under a variable working condition. Generally speaking, the accuracy of modeling the gas turbine system data is high under steady state conditions. Under the working condition of an unstable state, the model relation before the input and the output of the system can not keep strong consistency, and the relation between the input and the output of the system of the combustion engine can not be truly reflected. Therefore, the judgment of the steady state and the unsteady state in the online operation of the gas turbine is the basis of the online process modeling analysis of the gas turbine, and has great significance for the intelligent control optimization, the performance evaluation and the fault detection diagnosis of the gas turbine.
At present, steady state detection methods mainly include mechanism analysis based, statistical theory based, and trend based extraction. Among them, the method based on the mechanism analysis is generally used for the characteristic analysis of the detection object under the assumption of the idealization, and the method based on the statistical theory and the method based on the trend extraction are also mostly applied to the steady-state screening of the off-line historical data. The above steady-state detection methods are difficult to accurately judge the operation state of the gas turbine on line.
Disclosure of Invention
Based on the method, the device and the equipment, the online judging method, the device and the equipment for the operating state of the gas turbine of the power station are provided, so that the operating state of the gas turbine can be accurately judged online.
In a first aspect, a method for online determining an operating state of a gas turbine of a power station is provided, which includes:
acquiring gas turbine data of a power station in an online operation state, wherein the gas turbine data comprises gas turbine power data and time data;
determining a parameter vector of a combustion engine power regression model according to combustion engine power data and time data and in combination with the combustion engine power regression model, wherein the parameter vector comprises: slope of combustion engine power and time;
and judging the running state of the gas turbine of the power station on line according to the slope and a predetermined power threshold of the gas turbine.
According to an implementation manner in the embodiment of the present application, determining a parameter vector of a combustion engine power regression model according to combustion engine power data and time data and in combination with the combustion engine power regression model includes:
generating a gas turbine power vector of a gas turbine power regression model according to the gas turbine power data;
generating a regression vector of a combustion engine power regression model according to the time data;
and determining a parameter vector of the combustion engine power regression model according to the combustion engine power vector and the regression vector.
According to an implementation manner of the embodiment of the application, determining the parameter vector of the combustion engine power regression model according to the combustion engine power vector and the regression vector comprises the following steps:
constructing a time matrix according to the regression vector;
and determining a parameter vector of the combustion engine power regression model according to the combustion engine power vector and the time matrix.
According to an implementable manner in an embodiment of the present application, the online operating state includes a steady state; judging the online operation state of the gas turbine of the power station according to the slope and a predetermined threshold value of the power of the gas turbine, wherein the method comprises the following steps:
and when the absolute value of the slope is smaller than the power threshold of the combustion engine, judging that the online running state of the gas turbine of the power station is a stable state.
According to an implementable manner in an embodiment of the present application, the online operating state includes an unstable state; judging the online operation state of the gas turbine of the power station according to the slope and a predetermined threshold value of the power of the gas turbine, wherein the method comprises the following steps:
and when the absolute value of the slope is greater than or equal to the power threshold of the combustion engine, judging that the online running state of the gas turbine of the power station is an unstable state.
According to one achievable approach in an embodiment of the present application, the threshold value of the power of the combustion engine is predetermined in the following manner:
acquiring historical gas turbine data of a gas turbine of a power station in a preset time period, wherein the historical gas turbine data comprises steady-state gas turbine power data in a steady state;
determining the standard deviation of the power data of the steady-state combustion engine according to the power data of the steady-state combustion engine and the number of the power data of the steady-state combustion engine;
and determining the power threshold of the combustion engine according to the standard deviation and a preset observation window value.
According to an implementation manner of the embodiment of the present application, before determining the standard deviation of the steady-state combustion engine data according to the steady-state combustion engine data and the number of the steady-state combustion engine data, the method further includes:
and filtering historical gas turbine data by adopting a smoothing noise reduction algorithm.
In a second aspect, an apparatus for online determination of the operating state of a gas turbine of a power station is provided, the apparatus comprising:
the acquisition module is used for acquiring gas turbine data of the power station in an online operation state, wherein the gas turbine data comprises gas turbine power data and time data;
the determining module is used for determining a parameter vector of the combustion engine power regression model according to the combustion engine power data and the time data and by combining the combustion engine power regression model, wherein the parameter vector comprises: the slope of the power and time of the combustion engine;
and the judging module is used for judging the online running state of the gas turbine of the power station on line according to the slope and the predetermined power threshold of the gas turbine.
In a third aspect, a computer device is provided, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores computer instructions executable by the at least one processor to enable the at least one processor to perform the method referred to in the first aspect above.
In a fourth aspect, a computer-readable storage medium is provided, on which computer instructions are stored, wherein the computer instructions are configured to cause a computer to perform the method according to the first aspect.
According to the technical content provided by the embodiment of the application, the gas turbine data of the power station in the online operation state are obtained, wherein the gas turbine data comprise gas turbine power data and time data, and the parameter vector of the gas turbine power regression model is determined according to the gas turbine power data and the time data and by combining with the gas turbine power regression model, and comprises the following steps: the method has the advantages that the slope of the power and time of the gas turbine is used for judging the operating state of the gas turbine of the power station on line according to the slope and the predetermined power threshold of the gas turbine, the operating state of the gas turbine can be accurately and quickly judged on line, the noise resistance is high, and the reliability is high.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for online determination of the operating status of a gas turbine of a power plant according to an embodiment;
FIG. 2 is a diagram illustrating an on-line determination of the operating status of the gas turbine of the power plant in accordance with an embodiment;
FIG. 3 is a block diagram showing an arrangement of an online determination device of an operating state of a gas turbine of a power plant according to an embodiment;
FIG. 4 is a schematic block diagram of a computer apparatus in one embodiment.
Detailed Description
The present application will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
When the gas turbine of the power station is in an unstable state working condition, dynamic modeling is needed to ensure the modeling accuracy of the unit in the 'stable state-unstable state-stable state' alternate operation. And selecting a corresponding model (a steady-state model or a dynamic model) for computational analysis according to the steady-state judgment result of the online process. If the steady-state process original model is still adopted for the unsteady-state process, false alarm of normal parameters can be caused, and maintainers need to be added to troubleshoot faults, so that fault diagnosis and maintenance decisions are seriously even caused, and the operation and maintenance cost is increased.
The existing mechanism analysis method generally performs characteristic analysis of a detection object on the premise of an ideal assumption, but the actual combustion engine operation process cannot meet the assumption easily, and the calculation precision and accuracy of a steady-state index are reduced to a certain degree. The steady-state detection method based on the statistical theory and the trend extraction is also mostly applied to steady-state screening of offline historical data, the effective online steady-state discrimination capability is lacked, and the determination of part of relevant steady-state threshold parameters is lacked of reliable theory or data support. In addition, the noise resistance is poor, and the requirement of online steady-state judgment of the actual production field data of the complex combustion engine cannot be met.
In order to solve the prior art problems, the embodiment of the application provides a method, a device and equipment for judging the running state of a gas turbine of a power station on line. First, the method for online determining the operating state of the gas turbine of the power plant provided in the embodiment of the present application will be described.
FIG. 1 is a flow chart illustrating a method for online determination of the operating status of a gas turbine of a power plant according to an embodiment of the present application. As shown in fig. 1, the method may include the steps of:
s110, obtaining gas turbine data of the power station gas turbine in an online operation state, wherein the gas turbine data comprises gas turbine power data and time data.
According to the operation mechanism of the power station gas turbine, the power signal of the power station gas turbine is selected as the only parameter variable for online judgment of the operation state of the power station gas turbine in consideration of the fact that the power signal of the power station gas turbine can reflect the actual dynamic state of the unit most objectively.
Therefore, the power data and the time data of the gas turbine data power station in the online running state are obtained in real time, and the running state of the gas turbine of the power station can be accurately and quickly judged online. The gas turbine power data comprises gas turbine power meter measurement values, and the time data comprises time corresponding to the gas turbine power.
S120, determining a parameter vector of the combustion engine power regression model according to the combustion engine power data and the time data and by combining the combustion engine power regression model, wherein the parameter vector comprises: slope of engine power and time.
And fitting the power data and the time data of the gas turbine based on the power regression model of the gas turbine to obtain the slope of the power and the time of the gas turbine. The slope obtained through fitting is used as an important parameter for judging the running state of the gas turbine of the power station, the operation speed is high, calculation resources are saved, and the running state of the gas turbine of the power station can be accurately and quickly judged on line.
And S130, judging the running state of the gas turbine of the power station on line according to the slope and a predetermined gas turbine power threshold.
The threshold value of the power of the combustion engine is calculated according to historical data of the gas turbine of the power station, has a large amount of data support, and can accurately reflect the critical states of the stable state and the unstable state of the gas turbine of the power station.
According to the relation between the slope and the power threshold of the gas turbine, the running state of the gas turbine of the power station is judged on line, the noise resistance is high, the reliability is high, and a reliable data basis is provided for intelligent control optimization, performance evaluation and fault monitoring and diagnosis of the gas turbine.
It can be seen that, in the embodiment of the present application, the gas turbine data in the online operation state of the gas turbine of the power station is obtained, where the gas turbine data includes gas turbine power data and time data, and a parameter vector of a gas turbine power regression model is determined according to the gas turbine power data and the time data and by combining with the gas turbine power regression model, where the parameter vector includes: the method has the advantages that the slope of the power and time of the gas turbine is used for judging the running state of the gas turbine of the power station on line according to the slope and the predetermined power threshold of the gas turbine, the running state of the gas turbine can be accurately and quickly judged on line, the noise resistance is high, and the reliability is high.
The steps in the above-described process flow are described in detail below. First, the above-mentioned S120, i.e., "determining the parameter vector of the combustion engine power regression model according to the combustion engine power data and the time data and in combination with the combustion engine power regression model", will be described in detail with reference to the embodiment.
As an achievable mode, generating a combustion engine power vector of a combustion engine power regression model according to the combustion engine power data;
generating a regression vector of a combustion engine power regression model according to the time data;
and determining a parameter vector of the combustion engine power regression model according to the combustion engine power vector and the regression vector.
The combustion engine power regression model is a polynomial regression function of pre-established combustion engine power in an observation window N. The observation window N may be determined according to the data acquisition frequency, and the observation window N may be expressed as a ratio of the window time length to the data acquisition frequency. For example, the window time length is 5min, the data acquisition frequency is 1min, and then N is 5.
Generally, the measured value of the combustion engine power meter is the sum of the true value of the combustion engine power and the random error of the combustion engine power, because of the influence of the boundary where the unit is located and the operation condition. However, in the embodiment of the application, a large amount of collected combustion engine data are fitted, and the influence caused by random errors can be ignored, so that the measurement value of the combustion engine power meter is approximately equal to the true value of the combustion engine power. Based on this, the established polynomial regression function can be expressed as the following formula:
C(t)＝k0+k1t+k2t2+…+kmtm (1)
where C (t) represents the engine power at time t, k0Mean value, k, representing the power of the internal combustion engine in the observation window N1Slope, k, representing power and time of combustion engine2，…，kmRepresenting the coefficient and t the time.
A gas turbine power vector of a gas turbine power regression model is generated from the gas turbine power data, and the gas turbine power vector can be represented as C = [ C (1), C (2), C (3), \8230;, C (n) ].
Generating a regression vector of a combustion engine power regression model from the time data, the regression vector being representable as b (t) = [ t ]0,t1,t2，…，tm]T。
Let K = [ K =0，k1，k2，…，km]TAs a coefficient vector of the combustion engine power regression model, equation (1) can be expressed as:
C(t)＝KTb(t) (2)
obtaining coefficient k by least square method0，k1，k2，…，kmThe optimal solution of (a), namely:
K＝(BT·B)-1·BTC (3)
specifically, a time matrix is constructed from the regression vector. The time matrix can be expressed as:
determining a parameter vector of a gas turbine power regression model according to the gas turbine power vector and the time matrix, namely substituting the gas turbine power vector and the time matrix into formula (3) to obtain a coefficient vector K = [ K ]0，k1，k2，…，km]TTo obtain the slope k of the power and time of the combustion engine2。
In order to improve the processing speed and the robustness of the combustion engine power regression model, the combustion engine power regression model can be simplified by changing the value of the model order m. For example, if the model order is 2, i.e., m =2, the combustion engine power regression model may be represented as:
C(t)＝k0+k1t+k2t2 (4)
next, the above-mentioned "determining the online operation state of the power plant gas turbine based on the slope and the predetermined threshold value of the engine power" at S130 will be described in detail with reference to the embodiment.
As an achievable way, the combustion engine power threshold is predetermined in the following way:
acquiring historical gas turbine power data of a gas turbine of a power station in a preset time period, wherein the historical gas turbine data comprises steady-state gas turbine power data in a steady state;
determining the standard deviation of the power data of the steady-state combustion engine according to the power data of the steady-state combustion engine and the number of the power data of the steady-state combustion engine;
and determining the power threshold of the combustion engine according to the standard deviation and a preset observation window value.
Historical combustion engine power data of a power station gas turbine operating under the full working condition in a preset time period can be obtained from a Distributed Control System (DCS) or a plant Information monitoring System (SIS) database of a power station unit, and the shutdown working condition is eliminated. Wherein, the collection frequency can be 5s-2min. Full regime refers to all operating conditions including the lowest load, which may be 61.00MW, and the highest load, which may be 314.66MW. The predetermined period of time may be set relatively long, say a month, to avoid acquiring less historical data that would make the determined engine power threshold contingent.
The combustion engine power threshold is mainly determined according to steady-state combustion engine power data in historical combustion engine power data, and the combustion engine power threshold is actually a threshold of the combustion engine in steady-state operation. Steady state engine power data can contain significant errors in view of conditions such as sudden fluctuations in engine power meters, or sudden changes in plant conditions. Therefore, before the standard deviation of the steady-state combustion engine power data is determined according to the steady-state combustion engine power data and the number of the steady-state combustion engine power data, the steady-state combustion engine power data is filtered by adopting a smooth noise reduction algorithm so as to reduce the influence of random errors and gross errors. The Gaussian filtering method of the smoothing noise reduction algorithm has the mathematical form of probability density as follows:
wherein,x represents steady-state engine power data, mu represents the mean of steady-state engine power data, sigma2Represents the variance of the steady-state engine power data.
After the power data of the steady-state combustion engine are filtered, the power threshold of the combustion engine is determined according to a relational expression between the standard deviation of the power threshold of the combustion engine and the power data of the steady-state combustion engine and a preset observation window value. Wherein, the relational expression may be:
where beta denotes the engine power threshold, sigma1And the standard deviation of the steady-state combustion engine power data is shown, and N is a preset observation window value.
The standard deviation of the power data of the steady-state combustion engine is calculated according to the following formula:
where M represents the number of steady-state engine data, and C (t) represents steady-state engine power data at time t.
The on-line operating conditions of the power plant gas turbine include steady state and unsteady state. And when the absolute value of the slope is smaller than the power threshold of the combustion engine, judging that the online running state of the gas turbine of the power station is a stable state.
When the absolute value of the slope is greater than or equal to the power threshold of the combustion engine, the online operation state of the gas turbine of the power station is judged to be an unstable state, which can also be called dynamic state.
Because the combustion engine power threshold is calculated based on a large amount of historical data, the critical state of the online operation of the gas turbine of the power station can be reflected more truly. Therefore, the steady state and the unsteady state of the power plant gas turbine can be accurately determined based on the magnitude relationship between the absolute value of the slope and the engine power threshold.
The online judgment result of the operating state of the gas turbine of the power station obtained by executing the method is shown in fig. 2, wherein a gray dotted line is the dynamic working condition of the gas turbine, and a black dotted line is the steady-state working condition of the gas turbine. From fig. 2, it can be clearly and intuitively seen which time period of the power station gas turbine is in the steady-state working condition and which time period is in the dynamic working condition, and how much power the power station gas turbine operates stably, so that a reliable data basis is provided for intelligent control optimization, performance evaluation and fault monitoring and diagnosis of the gas turbine.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least some of the sub-steps or stages of other steps.
Fig. 3 is a schematic structural diagram of an online determination device for an operating state of a gas turbine of a power plant according to an embodiment of the present application, configured to execute the method flow shown in fig. 1. As shown in fig. 3, the apparatus may include: the obtaining module 310, the determining module 320, and the determining module 330 may further include: and a filtering module. The main functions of each component module are as follows:
the obtaining module 310 is configured to obtain gas turbine data of the power station in an online operation state, where the gas turbine data includes gas turbine power data and time data;
a determining module 320, configured to determine a parameter vector of a combustion engine power regression model according to the combustion engine power data and the time data, and by combining with the combustion engine power regression model, where the parameter vector includes: slope of combustion engine power and time;
and the judging module 330 is configured to judge an online operating state of the gas turbine of the power station online according to the slope and a predetermined threshold of the power of the combustion engine.
In the embodiment of the application, the running state of the gas turbine can be accurately and quickly judged on line, the noise resistance is high, and the reliability is high.
As an achievable way, the determining module 320 is specifically configured to generate a combustion engine power vector of a combustion engine power regression model according to the combustion engine power data;
generating a regression vector of a combustion engine power regression model according to the time data;
and determining a parameter vector of the combustion engine power regression model according to the combustion engine power vector and the regression vector.
As an implementation manner, the determining module 320 is specifically configured to construct a time matrix according to the regression vector;
and determining a parameter vector of the combustion engine power regression model according to the combustion engine power vector and the time matrix.
As one way of accomplishing this, the online operating state comprises a steady state; the determining module 330 is specifically configured to determine that the online operating state of the gas turbine of the power station is a stable state when the absolute value of the slope is smaller than the power threshold of the combustion engine.
As one implementable way, the online running state includes an unsteady state; the determining module 330 is specifically configured to determine that the online operating state of the gas turbine of the power plant is an unstable state when the absolute value of the slope is greater than or equal to the threshold of the power of the combustion engine.
As an implementation manner, the determining module 320 is further configured to obtain historical combustion engine data of the gas turbine of the power station in a preset time period, where the historical combustion engine data includes steady-state combustion engine power data in a steady state;
determining the standard deviation of the power data of the steady-state gas turbine according to the power data of the steady-state gas turbine and the number of the power data of the steady-state gas turbine;
and determining the power threshold of the combustion engine according to the standard deviation and a preset observation window value.
As an implementable manner, the apparatus further comprises: and the filtering module is used for filtering the historical gas turbine data by adopting a smooth noise reduction algorithm.
The same and similar parts among the various embodiments are referred to each other, and each embodiment focuses on differences from other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should be noted that, in the embodiments of the present application, the use of user data may be involved, and in practical applications, the user-specific personal data may be used in the scheme described herein within the scope permitted by applicable laws and regulations, under the condition of meeting the requirements of applicable laws and regulations in the country (for example, the user explicitly agrees, the user is informed, the user explicitly authorizes, etc.).
According to an embodiment of the present application, a computer device and a computer-readable storage medium are also provided.
As shown in fig. 4, a block diagram of a computer device according to an embodiment of the present application is shown. Computer apparatus is intended to represent various forms of digital computers or mobile devices. Which may include desktop computers, laptop computers, workstations, personal digital assistants, servers, mainframe computers, and other suitable computers. The mobile device may include a tablet, smartphone, wearable device, and the like.
As shown in fig. 4, the apparatus 400 includes a computing unit 401, a ROM 402, a RAM 403, a bus 404, and an input/output (I/O) interface 405, the computing unit 401, the ROM 402, and the RAM 403 being connected to each other via the bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The calculation unit 401 may execute various processes in the method embodiments of the present application according to computer instructions stored in a Read Only Memory (ROM) 402 or computer instructions loaded from a storage unit 408 into a Random Access Memory (RAM) 403. Computing unit 401 may be a variety of general and/or special purpose processing components with processing and computing capabilities. The computing unit 401 may include, but is not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. In some embodiments, the methods provided by embodiments of the present application may be implemented as a computer software program tangibly embodied in a computer-readable storage medium, such as storage unit 408.
The RAM 403 may also store various programs and data required for the operation of the device 400. Part or all of the computer program may be loaded and/or installed onto the device 400 via the ROM 802 and/or the communication unit 409.
An input unit 406, an output unit 407, a storage unit 408 and a communication unit 409 in the device 400 may be connected to the I/O interface 405. The input unit 406 may be, for example, a keyboard, a mouse, a touch screen, a microphone, or the like; the output unit 407 may be, for example, a display, a speaker, an indicator light, or the like. The device 400 is capable of exchanging information, data, etc. with other devices via the communication unit 409.
It should be noted that the device may also include other components necessary to achieve proper operation. It may also contain only the components necessary to implement the solution of the present application and not necessarily all of the components shown in the figures.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof.
Computer instructions for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer instructions may be provided to the computing unit 401 such that the computer instructions, when executed by the computing unit 401 such as a processor, cause the steps involved in embodiments of the method of the present application to be performed.
The computer-readable storage medium provided herein may be a tangible medium that may contain, or store, computer instructions for performing the steps involved in the method embodiments of the present application. The computer readable storage medium may include, but is not limited to, storage media in the form of electronic, magnetic, optical, electromagnetic, and the like.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. An on-line judging method for the operating state of a gas turbine of a power station is characterized by comprising the following steps:
the method comprises the steps of obtaining gas turbine data of a power station gas turbine in an online operation state, wherein the gas turbine data comprise gas turbine power data and time data;
determining a parameter vector of a combustion engine power regression model according to the combustion engine power data and the time data and by combining a combustion engine power regression model, wherein the parameter vector comprises: slope of combustion engine power and time;
and judging the running state of the gas turbine of the power station on line according to the slope and a predetermined power threshold of the gas turbine.
2. The method of claim 1, wherein determining a parameter vector for a combustion engine power regression model from the combustion engine power data and the time data in conjunction with a combustion engine power regression model comprises:
generating a gas turbine power vector of the gas turbine power regression model according to the gas turbine power data;
generating a regression vector of the combustion engine power regression model according to the time data;
and determining a parameter vector of the combustion engine power regression model according to the combustion engine power vector and the regression vector.
3. The method of claim 2, wherein determining the parameter vector of the combustion engine power regression model from the combustion engine power vector and the regression vector comprises:
constructing a time matrix according to the regression vector;
and determining a parameter vector of the combustion engine power regression model according to the combustion engine power vector and the time matrix.
4. The method of claim 1, wherein the online operating state comprises a steady state; the step of judging the online running state of the power station gas turbine according to the slope and the predetermined gas turbine power threshold comprises the following steps:
and when the absolute value of the slope is smaller than the power threshold of the combustion engine, judging that the online running state of the gas turbine of the power station is a stable state.
5. The method of claim 1 or 4, wherein the online operating state comprises an unsteady state; the step of judging the online running state of the power station gas turbine according to the slope and the predetermined gas turbine power threshold comprises the following steps:
and when the absolute value of the slope is greater than or equal to the power threshold of the combustion engine, judging that the online running state of the gas turbine of the power station is an unstable state.
6. The method of claim 1, wherein the engine power threshold is predetermined by:
acquiring historical gas turbine data of a gas turbine of a power station in a preset time period, wherein the historical gas turbine data comprises steady-state gas turbine power data in a steady state;
determining the standard deviation of the steady-state gas turbine power data according to the steady-state gas turbine power data and the number of the steady-state gas turbine power data;
and determining the power threshold of the combustion engine according to the standard deviation and a preset observation window value.
7. The method of claim 6, wherein prior to determining the standard deviation of the steady state combustion engine data based on the steady state combustion engine data and the number of steady state combustion engine data, the method further comprises:
and filtering the historical gas turbine data by adopting a smooth noise reduction algorithm.
8. An on-line judging device for the operating state of a gas turbine of a power station, characterized in that the device comprises:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring gas turbine data of a power station in an online operation state, and the gas turbine data comprises gas turbine power data and time data;
the determining module is used for determining a parameter vector of the combustion engine power regression model according to the combustion engine power data and the time data and by combining a combustion engine power regression model, wherein the parameter vector comprises: the slope of the power and time of the combustion engine;
and the judging module is used for judging the online running state of the power station gas turbine on line according to the slope and a predetermined gas turbine power threshold.
9. A computer device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores computer instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
10. A computer-readable storage medium having computer instructions stored thereon for causing a computer to perform the method of any one of claims 1 to 7.
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