Method, device and equipment for monitoring starting process of gas turbine and medium
By constructing the Jacobian coefficient matrix and Kalman filtering algorithm, the accuracy of component performance monitoring during the start-up of the gas turbine is solved, and the accurate status monitoring and fault warning of the start-up of the gas turbine is achieved, which improves the safety and stability of the system.
Patent Information
- Application Number
- CN202510407468.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-02
- Publication Date
- 2025-07-22
AI Technical Summary
The prior art is difficult to accurately monitor the real-time performance changes of components during the start-up of gas turbines, especially in the initial complexity of transient changes, and traditional methods rely on external observations to reflect internal states.
The Jacobian coefficient matrix is constructed based on the state parameter equations of the gas turbine and the observed parameter equations, and the optimal estimate is calculated through the Kalman filtering algorithm to reflect the actual operating state of the gas turbine.
It realizes accurate monitoring of parameters during the start-up of the gas turbine, improves the accuracy of the state estimation calculation method, and can timely identify potential faults and performance decays to ensure the safe and stable operation of the system.
Smart Images
Figure CN120354040A_ABST
Abstract
Description
Technical Field
[0001] The embodiments of the present application relate to the technical field of gas turbines, and in particular, to a method, device, equipment and medium for monitoring the starting process of a gas turbine. Background Art
[0002] With the continuous growth of modern energy demand, gas turbines, as core equipment for efficient power generation and power systems, have been widely used in the fields of aviation, energy, and industry. Especially during the starting process, various components of the gas turbine, such as compressors, combustion chambers, turbines, etc., face complex dynamic loads and high-temperature and high-pressure environments, and the performance of the components directly affects the starting effect, efficiency, and long-term operation stability of the gas turbine. Therefore, effectively monitoring the component performance during the starting process of the gas turbine can timely identify potential faults and performance degradation, and ensure the safe operation of the system.
[0003] In the process of implementing the present application, the applicant found that during the starting process of the gas turbine, especially in the initial stage, the transient changes of the gas turbine parameters are complex, and the traditional monitoring methods rely on external observations to obtain monitoring data, thus it is difficult to accurately reflect the real-time performance changes of the internal components of the gas turbine. Therefore, a monitoring method needs to be proposed to at least solve the above technical problems. Summary of the Invention
[0004] The present application provides a method, device, equipment and medium for monitoring the starting process of a gas turbine, which can accurately monitor the parameters of the gas turbine during the starting process.
[0005] In a first aspect, an embodiment of the present application provides a method for monitoring the starting process of a gas turbine, the method comprising:
[0006] Constructing a first Jacobian coefficient matrix based on a gas turbine state parameter equation model, where the gas turbine state parameter equation model is a calculation model for calculating the state parameters at the k + 1 moment based on the state parameters at the k moment;
[0007] Constructing a second Jacobian coefficient matrix based on a gas turbine observation parameter equation model, where the gas turbine observation parameter equation model is a calculation model for calculating the observation parameters at the k + 1 moment based on the state parameters at the k + 1 moment;
[0008] Calculating an optimal estimated value of the state parameters of the gas turbine based on the first Jacobian coefficient matrix and the second Jacobian coefficient matrix, where the optimal estimated value is used to reflect the actual operating state of the gas turbine.
[0009] In some embodiments, the state parameters include the compressor outlet temperature, the combustion chamber outlet pressure, the combustion chamber outlet temperature, and the turbine outlet temperature;
[0010] The observation parameters include the compressor outlet temperature, the compressor outlet pressure, the turbine outlet temperature, and the net power during speed increase.
[0011] In some embodiments, it further includes:
[0012] Respectively construct state equations corresponding to the compressor outlet temperature, the combustion chamber outlet pressure, the combustion chamber outlet temperature, and the turbine outlet temperature to obtain the gas turbine state parameter equation model;
[0013] Based on the state equations of each of the state parameters at the k + 1 moment, respectively construct observation equations corresponding to the compressor outlet temperature, the compressor outlet pressure, the turbine outlet temperature, and the net power during speed increase;
[0014] Obtain the gas turbine observation parameter equation model based on the observation equations of each of the observation parameters.
[0015] In some embodiments, the construction of the first Jacobian coefficient matrix based on the gas turbine state parameter equation model includes:
[0016] Take the partial derivatives of the state equations of each of the state parameters to obtain the corresponding first Jacobian coefficient matrix components;
[0017] Obtain the first Jacobian coefficient matrix based on the first Jacobian coefficient matrix components of each of the state parameters.
[0018] In some embodiments, the construction of the second Jacobian coefficient matrix based on the gas turbine observation parameter equation model includes:
[0019] Take the partial derivatives of the observation equations of each of the observation parameters to obtain the corresponding second Jacobian coefficient matrix components;
[0020] Obtain the second Jacobian coefficient matrix based on the second Jacobian coefficient matrix components of each of the state parameters.
[0021] In some embodiments, the calculation of the optimal estimated value of the state parameters of the gas turbine based on the first Jacobian coefficient matrix and the second Jacobian coefficient matrix includes:
[0022] Based on the first Jacobian coefficient matrix and the second Jacobian coefficient matrix, calculate the optimal estimated value of the state parameters of the gas turbine through the Kalman filter algorithm.
[0023] In some embodiments, it further includes:
[0024] Based on the optimal estimated value, use the thermodynamic method to calculate the component performance parameters during the startup process of the gas turbine.
[0025] Second aspect, an embodiment of the present application further provides a monitoring device for the starting process of a gas turbine. The monitoring device includes:
[0026] A first construction unit, configured to construct a first Jacobian coefficient matrix based on a gas turbine state parameter equation model, where the gas turbine state parameter equation model is a calculation model for calculating state parameters at the (k + 1)-th moment based on state parameters at the k-th moment;
[0027] A second construction unit, configured to construct a second Jacobian coefficient matrix based on a gas turbine observation parameter equation model, where the gas turbine observation parameter equation model is a calculation model for calculating observation parameters at the (k + 1)-th moment based on the state parameters at the (k + 1)-th moment;
[0028] A calculation unit, configured to calculate an optimal estimated value of the state parameters of the gas turbine based on the first Jacobian coefficient matrix and the second Jacobian coefficient matrix, where the optimal estimated value is used to reflect the actual operating state of the gas turbine.
[0029] Third aspect, an embodiment of the present application provides an electronic device, including:
[0030] One or more processors;
[0031] A memory, configured to store one or more programs,
[0032] When the one or more programs are executed by the one or more processors, the one or more processors implement the monitoring method for the starting process of the gas turbine according to any embodiment of the present application.
[0033] Fourth aspect, an embodiment of the present application provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the monitoring method for the starting process of the gas turbine according to any embodiment of the present application.
[0034] An embodiment of the present application provides a monitoring method, device, equipment and medium for the starting process of a gas turbine. A first Jacobian coefficient matrix is constructed based on the gas turbine state parameter equation model, and the gas turbine state parameter equation model is a calculation model for calculating the state parameters at the k+1 moment based on the state parameters at the k moment; based on the gas turbine observation parameter equation model, a second Jacobian coefficient matrix is constructed, and the gas turbine observation parameter equation model is a calculation model for calculating the observation parameters at the k+1 moment based on the state parameters at the k+1 moment; the optimal estimated value of the state parameters of the gas turbine is calculated based on the first Jacobian coefficient matrix and the second Jacobian coefficient matrix, and the optimal estimated value is used to reflect the actual operating state of the gas turbine. Compared with the prior art that directly obtains the parameter changes in the starting process of the gas turbine through sensors, the monitoring method for the starting process of the gas turbine proposed in the embodiment of the present application can accurately describe the dynamic behavior and observation behavior of the gas turbine through the state equation and the observation equation. The Jacobian coefficient matrix established based on the above state equation and observation equation can linearize the nonlinear system, improve the accuracy of the state estimation algorithm, and obtain the optimal estimated value, so as to accurately monitor the parameters of the gas turbine during the starting process. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] Figure 1 It is a schematic flowchart of the monitoring method for the starting process of the gas turbine provided by an embodiment of the present application;
[0036] Figure 2 It is a schematic flowchart of the monitoring method for the starting process of the gas turbine provided by another embodiment of the present application;
[0037] Figure 3 It is a schematic flowchart of the monitoring method for the starting process of the gas turbine provided by another embodiment of the present application;
[0038] Figure 4 It is a schematic structural diagram of the monitoring device for the starting process of the gas turbine provided by an embodiment of the present application;
[0039] Figure 5 It is a schematic structural diagram of the electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0040] The following further describes the present application in detail with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present application, rather than limiting the present application. In addition, it should be noted that, for the sake of convenience of description, only parts related to the present application are shown in the drawings instead of all structures.
[0041] Figure 1The flowchart shows a monitoring method for the starting process of a gas turbine provided by an embodiment of the present application. This method can be executed by a monitoring device or an electronic device for the starting process of the gas turbine. The device or the electronic device can be implemented in a software and / or hardware manner and can be integrated into any intelligent device with network communication functions. As Figure 1 shown, the monitoring method for the starting process of the gas turbine may include the following steps:
[0042] S101. Construct a first Jacobian coefficient matrix based on the gas turbine state parameter equation model.
[0043] In this step, the above-mentioned gas turbine state parameter equation model is a calculation model for calculating the state parameters at the k+1 moment based on the state parameters at the k moment.
[0044] S102. Construct a second Jacobian coefficient matrix based on the gas turbine observation parameter equation model.
[0045] In this step, the above-mentioned gas turbine observation parameter equation model is a calculation model for calculating the observation parameters at the k+1 moment based on the state parameters at the k+1 moment.
[0046] It should be noted that the above-mentioned state parameter equation mentioned in the embodiment of the present application is an equation describing the time variation of the internal state parameters of the gas turbine, that is, from the k moment to the k+1 moment; the above-mentioned observation parameter equation is a calculation model for determining the observation parameters at the k+1 moment through the state parameters at the k+1 moment.
[0047] S103. Calculate the optimal estimated value of the state parameters of the gas turbine based on the first Jacobian coefficient matrix and the second Jacobian coefficient matrix.
[0048] In this step, the above-mentioned optimal estimated value is used to reflect the actual operating state of the gas turbine.
[0049] Compared with the prior art that directly obtains the parameter changes in the starting process of the gas turbine through sensors, the monitoring method for the starting process of the gas turbine proposed in the embodiment of the present application can accurately describe the dynamic behavior and observation behavior of the gas turbine through the state equation and the observation equation. Based on the Jacobian coefficient matrix established by the above-mentioned state equation and observation equation, the nonlinear system can be linearized, the accuracy of the state estimation algorithm can be improved, and the optimal estimated value can be obtained, so that the parameters of the gas turbine during the starting process can be accurately monitored.
[0050] In some examples, the state parameters include the compressor outlet temperature, the combustor outlet pressure, the combustor outlet temperature, and the turbine outlet temperature.
[0051] It should be noted that the selection of the gas turbine state parameters should be able to reflect the internal operating state of the gas turbine during the startup process. In the embodiments of the present application, the compressor outlet temperature, the combustor outlet pressure, the combustor outlet temperature, and the turbine outlet temperature are selected.
[0052] In some examples, if the gas turbine is of the type with cooling air, the above-mentioned turbine outlet temperature should be the turbine mainstream temperature.
[0053] In some examples, the observed parameters include the compressor outlet temperature, the compressor outlet pressure, the turbine outlet temperature, and the net power during speed increase.
[0054] It should be noted that among the above-mentioned observed parameters, the compressor outlet temperature, the compressor outlet pressure, and the turbine outlet temperature can be directly or indirectly measured by sensors. The net power during speed increase can be indirectly calculated through the rotational speed, and the rotational speed is a parameter that can be directly measured.
[0055] Figure 2 It is a schematic flow chart of the monitoring method for the gas turbine startup process provided by another embodiment of the present application. Based on the above technical solutions, it is further optimized and extended, and can be combined with the above various optional implementation manners. As Figure 2 shown, this method may further include the following steps on the basis of the foregoing embodiments:
[0056] S104. Respectively construct state equations corresponding to the compressor outlet temperature, the combustor outlet pressure, the combustor outlet temperature, and the turbine outlet temperature to obtain a gas turbine state parameter equation model.
[0057] In this step, the gas turbine state equation model is to establish a state equation model based on the selected state parameters described above, based on the gas turbine mechanism and the basic principles of thermodynamics. That is, a calculation model for calculating the compressor outlet temperature, the combustor outlet pressure, the combustor outlet temperature, and the turbine outlet temperature at the k + 1 moment by using the compressor outlet temperature, the combustor outlet pressure, the combustor outlet temperature, and the turbine outlet temperature at the k moment.
[0058] In some embodiments, the state equation of the compressor outlet temperature is:
[0059]
[0060] Among them, T1 is the compressor inlet temperature; CPR is the compressor pressure ratio; Tk2 is the compressor outlet temperature; η is the compressor efficiency; k cg is the air adiabatic index; N 0_Cis the compressor reduced speed; Pk2 is the compressor outlet pressure; Pk1 is the compressor outlet pressure; Pk3 is the combustor outlet pressure; σ is the combustor pressure loss; f1 is the compressor efficiency characteristic function.
[0061] In some embodiments, taking a gas turbine without cooling air extraction as an example, the state equation of the combustor outlet pressure is:
[0062]
[0063]
[0064] where Δt is the calculation period; G3 is the combustor outlet flow rate; G4 is the turbine inlet flow rate; C1 is the calculation coefficient of the process balance equation, which can be taken as 0.0196; T3 is the combustor outlet temperature; G2 is the combustor inlet air flow rate; G f is the fuel flow rate; N 0_T is the turbine reduced speed; TPR is the turbine expansion ratio; Pk4 is the turbine outlet pressure; f2 and f3 are the compressor flow characteristic function and the turbine flow characteristic function respectively.
[0065] It should be noted that the function of the compressor outlet temperature and the combustor outlet pressure is usually difficult to accurately characterize during the startup process, and this function does not need to be precise in this example.
[0066] In some embodiments, the state equation of the combustor outlet temperature is:
[0067]
[0068] where h2 is the compressor outlet specific enthalpy, which can be obtained by looking up the physical property table according to the temperature; h f is the fuel specific enthalpy; η CB is the combustor efficiency, which can be taken as 0.999, and the optimal estimated value will also be calculated later; LHV is the lower heating value of the fuel; h3 is the combustor outlet specific enthalpy; C2 is the calculation coefficient of the combustor capacity balance equation, which can be taken as 0.0187.
[0069] In some embodiments, for a turbine without cooling air, the state equation of the turbine outlet temperature is:
[0070]
[0071] where T4 is the turbine outlet temperature; η T is the turbine efficiency; k tg is the gas adiabatic index; f4 is the turbine efficiency characteristic function.
[0072] It should be noted that this function is usually difficult to accurately characterize during the startup process, and this function does not need to be precise in this embodiment.
[0073] In some examples, for a turbine with cooling air, the above T4 uses the mainstream outlet temperature of the turbine and can be expressed as T 4m .
[0074] Through the state equations corresponding to the above compressor outlet temperature, combustor outlet pressure, combustor outlet temperature, and turbine outlet temperature, a gas turbine state parameter equation model is obtained.
[0075] S105. Based on the state parameters at the k+1 moment, observation equations corresponding to the compressor outlet temperature, compressor outlet pressure, turbine outlet temperature, and net accelerating power are respectively constructed.
[0076] In this step, by using the gas turbine mechanism and the basic principles of thermodynamics, and using the state parameters at the k+1 moment, namely the compressor outlet temperature, combustor outlet pressure, combustor outlet temperature, and turbine outlet temperature, corresponding observation equations are constructed to calculate the observation parameters at the k+1 moment, that is, the calculation models of the compressor outlet temperature, compressor outlet pressure, turbine outlet temperature, and net accelerating power.
[0077] In some embodiments, the observation equation of the compressor outlet temperature is as follows:
[0078]
[0079] In some embodiments, the observation equation of the compressor outlet pressure is as follows:
[0080]
[0081] In some embodiments, for a turbine without cooling air, the observation equation of the turbine outlet temperature is as follows:
[0082]
[0083] It should be noted that for a turbine with cooling air, after the mainstream outlet temperature of the turbine and the cooling air outlet temperature of the turbine are cooled and mixed, the turbine outlet temperature can be calculated.
[0084] In some embodiments, the observation equation model of the net accelerating power is as follows:
[0085]
[0086] Among them, P is the net accelerating power; P T is the turbine power; P C is the compressor work consumption; P L is the resistance work consumption of the gas turbine; h4 is the specific enthalpy of the turbine outlet temperature.
[0087] S106. Based on the observation equations of the respective observation parameters, a gas turbine observation parameter equation model is obtained.
[0088] In this step, an observation parameter equation model of the gas turbine is obtained through the above equations of the compressor outlet temperature, the compressor outlet pressure, the turbine outlet temperature, and the net power during speed increase.
[0089] The monitoring method for the gas turbine startup process proposed in the embodiment of the present application constructs a Jacobian coefficient matrix through the state equation and the observation equation, quantitatively describes the parameter changes during the gas turbine startup process in a model manner, and thus helps to improve the real-time prediction accuracy of the component performance during the gas turbine startup process.
[0090] In some examples, the above step S101 may include:
[0091] Taking the partial derivatives of the state equations of each state parameter to obtain the corresponding components of the first Jacobian coefficient matrix.
[0092] Based on the components of the first Jacobian coefficient matrix of each state parameter, the first Jacobian coefficient matrix is obtained.
[0093] Exemplarily, taking the partial derivatives of the state equations of the above state parameters respectively to obtain the corresponding components of the first Jacobian coefficient matrix, and then combining the components of each first Jacobian coefficient matrix into the first Jacobian coefficient matrix. The following is an example of obtaining the corresponding components of the first Jacobian coefficient matrix for each state parameter:
[0094] The state equation of the compressor outlet temperature of the above state parameter can be characterized as:
[0095] Tk2 = F1(Tk2, Pk3, T3, T4, u1, u2,... u n )
[0096] Wherein, F1 represents the foregoing specific calculation function; u is an external input, such as fuel quantity, ambient temperature, etc.
[0097] Taking the partial derivatives of F1 with respect to Tk2, Pk3, T3, and T4 respectively, the components of the Jacobian coefficient matrix can be obtained
[0098] The state equation of the combustion chamber outlet pressure can be characterized as follows:
[0099] Pk3 = F2(Tk2, Pk3, T3, T4, u1, u2,... u n )
[0100] Wherein, F2 represents the foregoing specific calculation function.
[0101] Taking the partial derivatives of F2 with respect to Tk2, Pk3, T3, and T4 respectively, the components of the Jacobian coefficient matrix can be obtained
[0102] The state equation of the above combustor outlet temperature can be characterized as follows:
[0103] T3 = F3(Tk2, Pk3, T3, T4, u1, u2,... u n )
[0104] Wherein, F3 represents the aforementioned specific calculation function.
[0105] By taking the partial derivatives of F3 with respect to Tk2, Pk3, T3, and T4 respectively, the components of the Jacobian coefficient matrix can be obtained
[0106] The state equation of the above turbine outlet temperature can be characterized as follows:
[0107] T4 = F4(Tk2, Pk3, T3, T4, u1, u2,... u n )
[0108] Wherein, F4 represents the aforementioned specific calculation function.
[0109] By taking the partial derivatives of F4 with respect to Tk2, Pk3, T3, and T4 respectively, the components of the Jacobian coefficient matrix can be obtained
[0110] Combining the matrix components of the above respective state parameters, the above first Jacobian coefficient matrix is obtained as follows:
[0111]
[0112] In some examples, the above step S102 may include:
[0113] Taking the partial derivatives of the observation equations of the respective observation parameters to obtain the corresponding second Jacobian coefficient matrix components;
[0114] Based on the second Jacobian coefficient matrix components of the respective state parameters, a second Jacobian coefficient matrix is obtained. Exemplarily, by taking the partial derivatives of the state equations of the above respective observation parameters respectively to obtain the corresponding second Jacobian coefficient matrix components, and then combining the respective second Jacobian coefficient matrix components into a second Jacobian coefficient matrix. The following gives an example of obtaining the corresponding second Jacobian coefficient matrix components for each observation parameter:
[0115] The observation equation of the above compressor outlet temperature can be characterized as follows:
[0116] Tk2 = E1(Tk2, Pk3, T3, T4, u1, u2,... u n )
[0117] Among them, E1 represents the aforementioned specific calculation function.
[0118] Respectively perform the partial derivatives of E1 with respect to Tk2, Pk3, T3, and T4 to obtain the components of the Jacobian coefficient matrix
[0119] The observation equation of the compressor outlet pressure can be characterized as follows:
[0120] Pk2 = E2(Tk2, Pk3, T3, T4, u1, u2,...u n )
[0121] Among them, E2 represents the aforementioned specific calculation function.
[0122] Respectively perform the partial derivatives of E2 with respect to Tk2, Pk3, T3, and T4 to obtain the components of the Jacobian coefficient matrix
[0123] The observation equation of the turbine outlet temperature can be characterized as follows:
[0124] T4 = E3(Tk2, Pk3, T3, T4, u1, u2,...u n )
[0125] Among them, E3 represents the aforementioned specific calculation function.
[0126] Respectively perform the partial derivatives of E3 with respect to Tk2, Pk3, T3, and T4 to obtain the components of the Jacobian coefficient matrix
[0127] The observation equation of the net power during speed increase can be characterized as follows:
[0128] P = E4(Tk2, Pk3, T3, T4, u1, u2,...u n )
[0129] Among them, E4 represents the aforementioned specific calculation function.
[0130] Respectively perform the partial derivatives of E4 with respect to Tk2, Pk3, T3, and T4 to obtain the components of the Jacobian coefficient matrix
[0131] Combine the matrix components of the above respective observation parameters to obtain the aforementioned second Jacobian coefficient matrix as follows:
[0132]
[0133] According to some embodiments, the above step S103 may include:
[0134] Based on the first Jacobian coefficient matrix and the second Jacobian coefficient matrix, calculate the optimal estimated value of the state parameters of the gas turbine through the Kalman filtering algorithm.
[0135] Exemplarily, Figure 3 is a schematic flowchart of a monitoring method for the starting process of a gas turbine provided by another embodiment of the present application. As Figure 3 shown, the optimal estimated value of the state parameters of the gas turbine can be calculated through the Kalman filtering algorithm in the following manner:
[0136] S201. Real-time collect the on-site operation data of the gas turbine.
[0137] Real-time collect the on-site operation data through the gas turbine control system, including the compressor outlet pressure, compressor outlet temperature, turbine outlet temperature, rotational speed, and static frequency converter power.
[0138] S202. Real-time calculate the net power of the gas turbine during speed increase.
[0139] Since the gas turbine is not connected to the grid during the speed increase stage and the measured value of power cannot be directly obtained, the net power during speed increase is indirectly calculated through the measured value of rotational speed, and the calculation is as follows:
[0140]
[0141]
[0142] where N is the rotational speed; P SFC is the static frequency converter power; J is the moment of inertia; Δt can take the sampling period of the gas turbine control system.
[0143] In some examples, since the measurement noise of the rotational speed has a great influence on the calculation, the rotational speed signal can be filtered by a first-order inertial filter before calculation.
[0144] S203. Adopt the Kalman filtering algorithm and use the Jacobian coefficient matrices of the state equation and the observation equation and the real-time measured values to calculate the optimal estimated value of the state parameters during the starting process of the gas turbine.
[0145] The calculation method is as follows:
[0146] X(k|k - 1) = AX(k - 1|k - 1)
[0147] R(k|k - 1) = AR(k - 1|k - 1)A T +Q
[0148] X(k|k) = X(k|k - 1) + K(k)[Y(k) - HX(k|k - 1)]
[0149]
[0150] R(k|k) = [I - K(k)H]R(k|k - 1)
[0151] Wherein, X is the state variable vector, which is composed of the aforementioned state variables; Y is the observation variable vector, which is composed of the aforementioned observation variables; Q and M are adjustment matrix constants, and diagonal matrices can be selected; A is the first Jacobian coefficient matrix above, and H is the second Jacobian coefficient matrix above.
[0152] The embodiments of the present application can accurately estimate the key state parameters during the start-up process of the gas turbine based on the Jacobian coefficient matrix and real-time data. The optimally estimated value updated in real time can reflect the actual operating state, improve the accuracy of gas turbine state prediction, and especially solve the problem of unable to directly measure the power by indirectly calculating the net power during the speed-up stage.
[0153] In some examples, the monitoring method provided by the embodiments of the present application further includes:
[0154] Based on the optimally estimated value, calculate the component performance parameters during the start-up process of the gas turbine by using the thermodynamic method.
[0155] Exemplarily, the gas turbine component performance parameters mainly include compressor efficiency, compressor flow rate, combustion chamber efficiency, and turbine efficiency. The mechanism relationship between the state parameters and the gas turbine component performance parameters is reflected in the aforementioned state equation and observation equation models, which will not be elaborated here. Using this mechanism relationship, the component performance parameters during the start-up process of the gas turbine can be calculated through the optimally estimated value of the state parameters during the start-up process of the gas turbine.
[0156] In some examples, the component performance parameters can also be monitored based on rule judgment.
[0157] Exemplarily, based on the predicted values of the gas turbine component performance parameters calculated by the aforementioned Jacobian coefficient matrix, use the rule judgment method to monitor them. Set the curve of the rotational speed and the designed value of the performance parameters during the start-up process, and judge the deviation between the predicted value of the performance parameter and the designed value in real time. When the deviation exceeds the threshold, an alarm for abnormal component performance is issued.
[0158] In some examples, the component performance parameters can also be monitored based on artificial intelligence algorithms.
[0159] Exemplarily, an artificial intelligence algorithm is used to monitor it. Using the obtained data set of component performance parameters during the historical startup process of the gas turbine, a calculation model of performance parameters under historical conditions is trained and established. Then, the calculated value of this model is compared with the predicted value of the component performance parameters during the real-time startup process of the gas turbine. When the deviation exceeds the threshold, an alarm for abnormal component performance is issued.
[0160] Based on the optimal estimated values of the key components during the startup process of the gas turbine obtained from the Jacobian coefficient matrix, the embodiments of the present application are monitored through rule judgment or artificial intelligence algorithms. When the deviation between the predicted value of the performance parameters (i.e., the optimal estimated value) and the design value or historical data exceeds the threshold, an alarm for abnormal component performance can be issued in a timely manner, improving the real-time monitoring ability during the startup process of the gas turbine, ensuring that the component performance is always within the expected range, and enhancing the safety, stability, and efficiency of the system.
[0161] In summary, the technical solutions provided by the embodiments of the present application at least bring the following beneficial effects:
[0162] During the startup process of the gas turbine, by using the real-time monitoring method for the component performance during the startup process of the gas turbine, the operating states of the key components during the startup process of the gas turbine can be accurately and real-time monitored, such as important parameters such as the compressor flow rate and efficiency, combustion chamber efficiency, and turbine efficiency during the startup process. This method can effectively identify potential problems during the startup process and timely detect abnormal or fault omens during the startup process, thereby improving the safety and reliability of the startup process. For example, the control system can adjust the startup program in a timely manner according to the real-time monitored working state during the startup process of the gas turbine to avoid equipment damage caused by unstable states during the startup process; the health management system can evaluate the performance changes of key components based on the monitored data, perform fault diagnosis, and provide data support for subsequent maintenance to ensure that the gas turbine is always in the best operating state.
[0163] The above-mentioned monitoring device for the startup process of the gas turbine can execute the methods provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects for executing the methods. For the technical details not described in detail in this embodiment, reference can be made to the monitoring method for the startup process of the gas turbine provided in any embodiment of the present application.
[0164] Figure 4 It is a schematic structural diagram of a monitoring device for the startup process of a gas turbine provided by an embodiment of the present application, as Figure 4 shown, the above-mentioned monitoring device includes:
[0165] A first construction unit 401, configured to construct a first Jacobian coefficient matrix based on the gas turbine state parameter equation model, where the gas turbine state parameter equation model is a calculation model for calculating the state parameters at the k + 1 moment based on the state parameters at the k moment;
[0166] The second construction unit 402 is configured to construct a second Jacobian coefficient matrix based on a gas turbine observation parameter equation model, where the gas turbine observation parameter equation model is a calculation model for calculating the observation parameters at the k+1 moment based on the state parameters at the k+1 moment;
[0167] The calculation unit 403 is configured to calculate an optimal estimated value of the state parameters of the gas turbine based on the first Jacobian coefficient matrix and the second Jacobian coefficient matrix, and the optimal estimated value is used to reflect the actual operating state of the gas turbine.
[0168] Figure 5 It is a schematic structural diagram of an electronic device provided in an embodiment of the present application. Figure 5 It shows a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present application. Figure 5 The displayed electronic device 12 is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.
[0169] As Figure 5 shown, the electronic device 12 is presented in the form of a general-purpose computing device. The components of the electronic 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 connecting different system components (including the system memory 28 and the processing unit 16).
[0170] The bus 18 represents one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the multiple bus structures. For example, 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.
[0171] The electronic device 12 typically includes a variety of computer system-readable media. These media can be any available media accessible by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.
[0172] The system 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. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 can be used to read and write non-removable, non-volatile magnetic media ( Figure 5 not shown, commonly referred to as a "hard disk drive"). Although Figure 5Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (such as a "floppy disk") and an optical disk drive for reading and writing a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM or other optical medium) can be provided. In these cases, each drive can be connected to the bus 18 through one or more data medium interfaces. The memory 28 may include at least one program product having a set (such as at least one) of program modules configured to perform the functions of the embodiments of the present application.
[0173] A program / utility 40 having a set (at least one) of program modules 42 can be stored, for example, in the memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. The implementation of a network environment may be included in each or some combination of these examples. The program modules 42 generally perform the functions and / or methods in the embodiments described in the present application.
[0174] The electronic device 12 can also communicate with one or more external devices 14 (such as a keyboard, a pointing device, a display 24, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 12, and / or communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 22. Moreover, the electronic device 12 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 20. As shown in the figure, the network adapter 20 communicates with other modules of the electronic device 12 through the bus 18. It should be understood that although Figure 5 not shown in the figure, other hardware and / or software modules can be used in combination with the electronic 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, etc.
[0175] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing the monitoring method for the gas turbine startup process provided by the embodiments of the present application.
[0176] The embodiments of the present application also provide a computer storage medium.
[0177] The computer-readable storage medium of the embodiments of the present application may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: an electrical connection having 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 document, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0178] The computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.
[0179] The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the above.
[0180] The computer program code for performing the operations of the present application may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone 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., through the Internet using an Internet service provider).
[0181] The embodiments of the present application also provide a computer program product.
[0182] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer program products, the one or more computer program products can include one or more computer programs, the one or more computer programs can be executed and / or interpreted on a programmable system including at least one programmable processor, the programmable processor can be a dedicated or general-purpose programmable processor, can receive data and instructions from a storage system, at least one input device, and at least one output device, and can transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.
[0183] Note that the above is only the preferred embodiment of the present application and the applied technical principle. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments. Without departing from the concept of the present application, more other equivalent embodiments can be included, and the scope of the present application is determined by the scope of the appended claims.
Claims
1. A monitoring method for the starting process of a gas turbine, characterized in that The method includes: Constructing a first Jacobian coefficient matrix based on a gas turbine state parameter equation model, where the gas turbine state parameter equation model is a calculation model for calculating the state parameters at the (k + 1)-th moment based on the state parameters at the k-th moment; Constructing a second Jacobian coefficient matrix based on a gas turbine observation parameter equation model, where the gas turbine observation parameter equation model is a calculation model for calculating the observation parameters at the (k + 1)-th moment based on the state parameters at the (k + 1)-th moment; Calculating an optimal estimated value of the state parameters of the gas turbine based on the first Jacobian coefficient matrix and the second Jacobian coefficient matrix, where the optimal estimated value is used to reflect the actual operating state of the gas turbine.
2. The monitoring method according to claim 1, wherein The state parameters include the compressor outlet temperature, the combustor outlet pressure, the combustor outlet temperature, and the turbine outlet temperature; The observation parameters include the compressor outlet temperature, the compressor outlet pressure, the turbine outlet temperature, and the net power during speed increase.
3. The monitoring method according to claim 2, wherein It further includes: Respectively constructing state equations corresponding to the compressor outlet temperature, the combustor outlet pressure, the combustor outlet temperature, and the turbine outlet temperature to obtain the gas turbine state parameter equation model; Based on each of the state parameters at the (k + 1)-th moment, respectively constructing observation equations corresponding to the compressor outlet temperature, the compressor outlet pressure, the turbine outlet temperature, and the net power during speed increase; Obtaining the gas turbine observation parameter equation model based on the observation equations of each of the observation parameters.
4. The monitoring method according to claim 3, characterized in that, The constructing the first Jacobian coefficient matrix based on the gas turbine state parameter equation model includes: Taking partial derivatives of the state equations of each of the state parameters to obtain corresponding first Jacobian coefficient matrix components; Obtaining the first Jacobian coefficient matrix based on the first Jacobian coefficient matrix components of each of the state parameters.
5. The monitoring method according to claim 3, characterized in that, The constructing the second Jacobian coefficient matrix based on the gas turbine observation parameter equation model includes: Taking partial derivatives of the observation equations of each of the observation parameters to obtain corresponding second Jacobian coefficient matrix components; Obtaining the second Jacobian coefficient matrix based on the second Jacobian coefficient matrix components of each of the state parameters.
6. The monitoring method according to any one of claims 1 to 5, characterized in that The calculating the optimal estimated value of the state parameters of the gas turbine based on the first Jacobian coefficient matrix and the second Jacobian coefficient matrix includes: Calculating the optimal estimated value of the state parameters of the gas turbine through a Kalman filtering algorithm based on the first Jacobian coefficient matrix and the second Jacobian coefficient matrix.
7. The monitoring method according to claim 6, wherein It further includes: Calculating the component performance parameters during the startup process of the gas turbine by using a thermodynamic method based on the optimal estimated value.
8. A monitoring device for the starting process of a gas turbine, characterized in that, The monitoring device includes: A first construction unit for constructing a first Jacobian coefficient matrix based on a gas turbine state parameter equation model, where the gas turbine state parameter equation model is a calculation model for calculating the state parameters at the (k + 1)-th moment based on the state parameters at the k-th moment; A second construction unit for constructing a second Jacobian coefficient matrix based on a gas turbine observation parameter equation model, where the gas turbine observation parameter equation model is a calculation model for calculating the observation parameters at the (k + 1)-th moment based on the state parameters at the (k + 1)-th moment; A calculation unit is configured to calculate an optimal estimated value of the state parameters of the gas turbine based on the first Jacobian coefficient matrix and the second Jacobian coefficient matrix, and the optimal estimated value is used to reflect the actual operating state of the gas turbine.
9. An electronic device, characterized in that, Comprising: One or more processors; A memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the monitoring method according to any one of claims 1 to 7.
10. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the monitoring method according to any one of claims 1 to 7.