Method and system for monitoring and controlling a hybrid gas turbine system

Abstract

The present invention discloses a method for monitoring and controlling a hybrid gas turbine system, which is executed by a control logic unit. The method is achieved by performing optimization between operating variables in order to obtain a control signal for controlling the operation of the hybrid gas turbine system. A hybrid gas turbine system is also disclosed, which includes at least one gas turbine to be operated by fuel and at least one electric motor / generator capable of operating as a generator or as a motor. The hybrid gas turbine system includes a control logic unit operatively connected to a fuel controller module and an electric motor / generator controller.

Description

Technical Field

[0001] The present disclosure relates to improvements in methods for controlling hybrid gas turbine systems used in mechanical drive applications, thereby enabling increased efficiency during operation. Specifically but not exclusively, the present disclosure relates to hybrid gas turbine systems for driving a load, such as a compressor for a refrigerant fluid in a liquefied natural gas facility, a compressor for compressing gas in pipeline transportation, a pump, or any other rotating machine.

[0002] The invention also relates to a monitoring and control system capable of implementing the method. Background Art

[0003] Currently, the cooperation between a gas turbine and an electric motor / generator, namely a so-called reversible motor (which can also operate as a generator), such as a variable frequency drive electric motor (VFD electric motor), is now a driving design trend in the field of systems aimed at driving mechanical loads.

[0004] Specifically, there are systems on the market called "powertrains", "drive systems", or the like, in which an electric motor, or more specifically an electric motor / generator, is coupled to a gas turbine to drive a load, such as one or more compressors or pumps. Hereinafter, for the sake of convenience of reference only, a system including a gas turbine, an electric motor / generator, and a load (such as a compressor) typically connected to the electric motor / generator through a clutch or a disconnecting device may generally be referred to as a "powertrain", "drive system", or "drive equipment". As used herein, a system including a gas turbine coupled to an electric motor / generator may be referred to as a "hybrid gas turbine system" or a "hybrid gas turbine powertrain".

[0005] The electric motor / generator can be used to supplement mechanical power to the load, thereby keeping the total mechanical power on the load shaft constant when the power utilization rate of the turbine decreases, and / or to increase the total mechanical power to drive the load. This function of the electric motor / generator is called "auxiliary power". Another electric motor or alternatively a pneumatic motor / generator is also typically used as a starting motor to accelerate the gas turbine from zero to the rated speed.

[0006] Examples of hybrid gas turbine systems designed to drive mechanical loads are those hybrid gas turbine systems applied to liquefied natural gas (LNG) applications. LNG is produced in a liquefaction process, in which natural gas is cooled using one or more cascaded refrigeration cycles until it becomes a liquid. Natural gas is typically liquefied for storage or transportation purposes, especially when pipeline transportation is not possible. The cooling of natural gas is performed using a closed or open refrigeration cycle. The refrigerant is processed in one or more compressors, condensed and expanded. The expanded and cooled refrigerant is used to remove heat from the natural gas flowing through the heat exchanger.

[0007] Some layouts of the drive system are known in the art. One of the most common layouts includes a gas turbine connected to one or more compressors connected in cascade, where the subsequent compressor in the cascade is mechanically connected to an electric motor / generator; alternatively, the gas turbine is connected to an electric motor / generator, which is then connected to the compressor.

[0008] The management of the drive system becomes very complex because several variables must be considered and balanced in order to operate the drive system. As mentioned above, the very complex equipment currently available has multiple sensors and actuators connected to a control computer system, whereby the operation of the drive system is controlled and properly managed.

[0009] However, modern power plants must meet many constraints, not just those of a technical type. Specifically, from an environmental perspective, it is generally required to reduce the emissions of CO, CO 2 and NO x , SO x , which depends on the type of gas turbine being operated and increases almost linearly with the power generated by the gas turbine. Emission control requires the management of several parameters, which can vary according to environmental temperature, humidity, fuel composition, and other parameters and are generally not easily controlled together by the operator.

[0010] In addition, it is generally necessary to program the maintenance of the drive system in order to extend its life. However, in order to extend the life of the drive system, operation management needs to be carried out according to its specific use. In other words, depending on the environment and normal operating conditions, the maintenance plan of the drive system may change significantly.

[0011] In addition, and in combination with what is described above, it is generally required to minimize the capital expenditure and operating expenditure associated with the operation of any hybrid gas turbine system or drive system, and thus maximize the profit obtainable from any equipment, while minimizing gas turbine emissions, or in some cases, keeping the gas turbine emissions below a specific threshold set by law. This means that highly professional operators are needed to manage and control such systems or equipment. Specifically, this requires the operator to have a high level of education and an in-depth understanding of the control and management system.

[0012] It is known that modern devices have a large number of sensors and actuators for operating these devices through a properly programmed computer system. However, sometimes such computer systems cannot control a hybrid gas turbine system to achieve optimal operation, which helps to increase profits. Moreover, generally, an operator may not have all the technical and commercial skills required to evaluate the correct balance and trade-off between the power supply shared between operating a gas turbine (which consumes fuel) and operating an electric motor / generator (which consumes or generates electric power), especially in the case of a multi-unit device as described above.

[0013] Accordingly, there is a growing need for a hybrid gas turbine system or a drive system that can optimize operation, taking into account emissions and several other parameters, not just physical parameters, in order to maximize the profit and efficiency of the hybrid gas turbine. SUMMARY OF THE INVENTION

[0014] In one aspect, the subject matter disclosed herein relates to a method for monitoring and controlling a hybrid gas turbine system. The hybrid gas turbine system includes at least one gas turbine to be operated by fuel, at least one electric motor / generator capable of operating as a generator or as a motor, and a plurality of actuators for controlling them. The method is implemented by a control logic unit. The method includes the following steps: receiving a set of operating variables x for detecting the operating states of the gas turbine and the electric motor / generator; receiving a set of external variables w; selecting a set of control variables u for controlling the operating states of the gas turbine and the electric motor / generator; setting a set of optimization variables y, where the value of the optimization variable y depends on the operating variable x, the control variable u, and the external variable w. The value of the optimization variable y must be adjusted for optimization. Additionally, the method includes the following steps: processing one or more of the operating variable x, the control variable u, and the external variable w by optimizing the value of the optimization variable y, and generating and sending one or more control signals based on the control variable u obtained by the optimization of the optimization variable y to control the actuators of the hybrid gas turbine system.

[0015] In one aspect, one or more control signals allow controlling the fuel supplied to the gas turbine and the power generated or converted by the electric motor / generator.

[0016] In another aspect, a gas turbine is disclosed herein. The gas turbine includes a fuel controller module operably connected to a control logic unit. The electric motor / generator includes an electric motor / generator controller operably connected to the control logic unit. The control logic unit is configured to send the control signals generated in the generating step to the fuel controller module and the electric motor / generator controller to control the operation of the gas turbine and the operation of the electric motor / generator.

[0017] In another aspect, the control logic unit is connected to an energy source or a power generation device. Examples of the type of power generation device may include: a grid energy storage device, a solar panel device, a wind power generation device, a hydrothermal power generation device, and a thermal power generation device.

[0018] In one aspect, a gas turbine is connected to a load, such as a pump or a compressor. Further, the gas turbine is connected to the load through a disconnecting device such as a self-synchronizing clutch or an overspeed clutch. The disconnection or connection performed by the disconnecting device can be manually operated by an operator and / or automatically operated by an actuator.

[0019] In one aspect, the subject matter disclosed herein relates to a hybrid gas turbine system, at least one gas turbine to be operated by fuel, the at least one gas turbine including a fuel controller module capable of operating to control the fuel to be supplied to the gas turbine. The hybrid gas turbine system further includes at least one electric motor / generator capable of operating as a generator or as an electric motor, the at least one electric motor / generator including an electric motor / generator controller and a control logic unit, the electric motor / generator controller being adapted to control and regulate the electric power generated or converted by the electric motor / generator, and the control logic unit being operably connected to the fuel controller module and the electric motor / generator controller. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] When considered in conjunction with the accompanying drawings, a more complete understanding of the disclosed embodiments of the present invention and many of its attendant advantages will be readily obtained by reference to the following detailed description, which will also become better understood, wherein:

[0021] Figure 1 A block diagram showing an embodiment of a system for monitoring and controlling a hybrid gas turbine system is shown;

[0022] Figure 2 A graph showing the operating percentage of the ISO base load of a gas turbine as a function of the ambient temperature is shown;

[0023] Figure 3 A graph showing NO x 、CO and CO 2 emissions as a function of the power percentage of the gas turbine is shown;

[0024] Figure 4 A block diagram showing an embodiment of the control logic unit of a system for monitoring and controlling a hybrid gas turbine system is shown;

[0025] Figure 5 A first embodiment of a transmission system is shown

[0026] Figure 6 Shown is Figure 5The operating scheme of the transmission system in the start / assist mode;

[0027] Figure 7 shows Figure 5 The operating scheme of the transmission system in the start / generation mode;

[0028] Figure 8 shows a second embodiment of the transmission system;

[0029] Figure 9 shows Figure 8 The operating scheme of the transmission system in the assist / full electric mode;

[0030] Figure 10 shows Figure 5 The operating scheme of the transmission system in the generation mode;

[0031] Figure 11 shows a flowchart of a method for monitoring and controlling a hybrid gas turbine system; and

[0032] Figure 12 shows a flowchart of the control of the fuel controller module and / or the electric motor / generator controller. Detailed Description

[0033] Turbomachinery such as gas turbines, electric motors / generators, and compressors / pumps can be connected together in various configurations known in the art as "powertrains" or "kinetic systems". The operation of the transmission system requires the control of a large number of physical parameters, which also depend on environmental parameters such as: ambient temperature and humidity, equipment aging, losses at the inlet / outlet, combustion kinetics, etc. Additionally, the emissions of the gas turbine may need to be optimized to meet the national laws, regulations, or rules for installing and operating the transmission system. Finally, to increase the profit that can be obtained from the equipment, capital expenditures, maintenance expenditures, and operating expenditures must be kept at as low a level as possible. Managing such data loads and constraints becomes complex, so new computer-based optimization methods for achieving all of the above requests are to operate and / or maintain the turbomachinery and / or accessories within the transmission system itself.

[0034] See Figure 1, schematically shows a management and control hybrid gas turbine system generally designated by reference numeral 1, which typically includes a control logic unit 2, a hybrid gas turbine 3, which in turn includes a gas turbine 31 and an electric motor / generator 32, both of which are operatively connected to the control logic unit 2 via a fuel controller module 311 and an electric motor / generator controller 321, respectively. The control logic unit 2 is configured to control the operation of the hybrid gas turbine 3, as better explained below, to meet constraints and optimize resources, i.e., the fuel of the gas turbine 31 and the use of the power absorbed or supplied by the electric motor / generator 32.

[0035] The control logic unit 2 can be implemented in different ways. Specifically, in some embodiments (also see below), the control logic unit 2 is fully embodied in a circuit board directly installed in the management and control hybrid gas turbine system, which has pre-installed processing components, such as microprocessors, PLCs, etc., which are appropriately programmed to perform management and control operations. In other embodiments, the control logic unit 2 can be at least partially implemented as software, running remotely with respect to the management and control hybrid gas turbine system. Specifically, in this case, the control logic unit 2 can be an ordinary personal computer or a general terminal, which is programmed to interface with the hybrid gas turbine system to receive instructions from another personal computer or terminal that may be remotely located relative to it.

[0036] More specifically, the control logic unit 2 is configured to receive data from a number of sources 4 based on an optimization algorithm and thus receive as many types of variables and data as possible, which are used as inputs to the monitoring and control method. This is intended to allow the gas turbine 31 to always preferably operate at 100%, thus maximizing capital expenditure in full automatic mode according to process conditions and financial inputs, increasing revenue streams, and reducing CO 2 and No x emissions. The management and control hybrid gas turbine system 1 also operates in a predictive mode, i.e., for weather forecasting for renewable energy utilization and energy storage in pipelines, and trading of electrical energy or fuel. The control will operate in a dynamic mode such that the maximization always remains at the best possible level.

[0037] The first set of data or variables 41 includes the operator's target rankings, which typically include constraints on emissions of CO, CO 2 and NO x , pipeline energy storage (which includes, for example, the ability of the pipeline to store energy in pipeline management), and requirements for process production, operating expenses, and increasing the life of the gas turbine 31. This data is typically received from the operator.

[0038] The second set of data or variables 42 may include gas turbine operating data and weather forecasts. In fact, it is known that weather significantly affects the operation of gas turbines.

[0039] By way of example only, Figure 2 the operating percentage of the ISO base load of gas turbine 31 as a function of ambient temperature is shown. As can be easily understood, above a specific threshold temperature T th , the power generated by gas turbine 31 decreases significantly as ambient temperature T increases. This occurs regardless of whether the electric motor / generator 32 operates as an auxiliary device. In fact, the operation of the electric motor / generator 32 as an auxiliary device has the sole effect of shifting the threshold temperature T th towards a higher temperature (i.e., to the right of the abscissa of the graph). Also see Figure 3 , it can be seen that the emissions of NO x (curve a), CO (curve b) and CO 2 (curve c) increase at different rates with respect to the power supplied by gas turbine 31 (expressed as a percentage of the total power). Thus, combining the information obtainable from the two graphs of Figure 2 and Figure 3 , it is easy to infer that an increase in ambient temperature T results in a decrease in the performance of gas turbine 31, and thus, this in turn results in emissions of CO, CO 2 and NO x .

[0040] Still referring to Figure 1 , the third set of data 43 may come from one or more energy sources or power generation devices to which the hybrid gas turbine system 3 is connected, such as, by way of example, a grid energy storage device 431, a solar panel power generation device 432, a wind power generation device 433, a hydrothermal power generation device 434 and a thermal power generation device 435. These data specifically relate to the requirements and power supply capabilities of different devices. The data considered will be better discussed below.

[0041] Gas turbine 31 can be of different types, such as, by way of example but not limited to: a heavy-duty gas turbine or an aero-derivative gas turbine. In the case where different types of gas turbines 31 are installed, the relevant control variables or parameters can change. However, the installation of different gas turbines 31 or other possible hybrid architectures (better described below) will not change the scope of protection of the solution disclosed herein.

[0042] The electric motor / generator 32 can also be of a different type. Specifically, one type of electric motor / generator 32 is a variable frequency drive electric motor (VFD electric motor) 32, which in the art is often combined with a gas turbine 31 for several functions and specifically as a generator or a motor, as better explained hereinafter. The use of this type of electric motor / generator 32 is generally particularly suitable for electrical control.

[0043] In some embodiments, and with particular reference to Figure 4 , the control logic unit 2 can include: a processor 21, a bus 22 to which the processor 21 is connected, a database 23 connected to the bus 22 for access and control by the processor 21, a computer-readable memory 24 also connected to the bus 22 for access and control by the processor 21, and a receive-transmit module 25 connected to the bus 22, which is configured to send control signals to the fuel controller module 311 of the gas turbine 31 and to the electric motor / generator controller 321 of the electric motor / generator 32 to control the operation of the hybrid gas turbine system 3 in different available operating modes. In fact, the monitoring and control methods described herein control the gas turbine 31 and the electric motor / generator 32 to optimize the operation of the hybrid gas turbine system 3. The optimization of the hybrid gas turbine system 3 is achieved by appropriately "modulating" the command signals processed by the processor 21 running one or more computer programs and sent to the fuel controller module 311 and the electric motor / generator controller 321. More specifically, the fuel controller module 311 and the electric motor / generator controller 321 respectively control the actuators of the gas turbine 31 and the electric motor / generator 32 to select the operating mode for their operation. In this embodiment, the control logic unit is mounted near the transmission system.

[0044] The control logic unit 2 is configured to execute one or more computer programs for performing an optimization method or algorithm aimed at controlling the hybrid gas turbine system 3. In some embodiments, the control logic unit 2 can be physical hardware, possibly mounted near the hybrid gas turbine 3 or arranged remotely. In some embodiments, the control logic unit 2 can also be based on or run in the cloud. In this embodiment, only the part of the control logic unit 2 that sends control signals needs to be mounted near the transmission system, while the data processing part can be located remotely relative to the transmission system and, as mentioned, in a cloud-based system or near or at the location where the control room (or computer / server / terminal) is located, where the control room / computer / server is connected to the transmission system or the control signal sending part via a wired or wireless device.

[0045] The control and management method based on an optimization algorithm has data and variables from source 4 as inputs, namely the first set 41, the second set 42, and the third set 43 of data and variables. An embodiment of the optimization algorithm will be better explained below. These sets of data 41, 42, and 43 are then organized, for example, into three sets or vectors for processing. The control and management method can process all the data and variables or a part thereof, making the method flexible. In this way, the same algorithm on which the control and management method is based can be used to optimize subsets of data or to adapt to different layouts of a hybrid gas turbine system. This can be achieved, as better explained below, by setting a set of weight parameters to 0 or 1 (or generally to a value different from 0), which are used to select or not select one or more parameters or variables.

[0046] The monitoring and control optimization method / algorithm is based on a set of equations of multivariable regression analysis in a multi-objective optimization problem. The algorithm can be synthesized with the following set of equations

[0047]

[0048] S.t.

[0049] x min ≥x≥x max

[0050] u min ≥u≥u max

[0051] where

[0052] x∈R b := state vector

[0053] u∈R m := control variable vector

[0054] w∈R q := external parameter vector

[0055] Specifically, there are three different variable vectors, where the control function f k(x，u，w) represents the relationship between different variables of the model, as better explained below.

[0056] The variable vector x includes state variables and can include measured values (instruments) and calculated values (through tables and / or known functions) or estimated values (through estimators). Such a state variable vector x describes the operating states of the gas turbine 31 and the electric motor / generator 32. Below, examples of the state variables x organized as vectors are provided, taking into account that different variables or additional variables can be considered.

[0057]

[0058] Another vector u includes control variables that are outputs of the control logic unit 2 for driving the fuel controller module 311 and the electric motor / generator controller 321. The following reports examples of the control variables u, which are arranged as column vectors

[0059]

[0060] The third variable vector w includes external variables or data or parameters, which may also include economic parameters and constraints, such as the fuel cost of operating the system or the taxes to be charged. The control data or variables w include the actual data and constraints usually set by the operator or an external agent. Examples of the external variables are reported in the following vector

[0061]

[0062] The variables processed by the method for monitoring and controlling the hybrid gas turbine system are designed to process three sets of variables, namely, the above state variables x, control variables u, and external variables w. Obviously, all variables that can characterize the operation of the hybrid gas turbine 3 or, in general, the transmission system providing the hybrid gas turbine system 3 may include additional variables or their groupings. The method for controlling the hybrid gas turbine system 3 operates on a set of optimization variables y to be optimized i . These optimization or objective variables y i may be different from the state variables x, control variables u, and external variables w or even their subsets. The optimization variables y i can also be organized in a column vector

[0063] Each optimization variable in the optimization variables y i is expressed by a specific operation function f i(x，u，w) to represent the dependency, which can also be non-linear. Then, the optimized values of the state variables x, control variables u, and external variables w are calculated so as to minimize the norm ‖y i - f i (x, u, w)‖, as follows i (x, u, w)‖, as follows

[0064]

[0065] where

[0066]

[0067] and

[0068]

[0069]

[0070] It can be seen that the optimization method also minimizes the above equation by applying a weight vector α to each summation factor ‖y i - f i (x, u, w)‖. i To minimize the above equation.

[0071] The optimization variables y i collectively have the same metric, i.e., they should in principle be measured by the same metric. For example, in some embodiments, the optimization variables y i can be the maximum capital expenditure cost of the device, so they should be measured in dollars. In other embodiments, the optimization variables y i can be the power consumption of the device, in which case the variables should be measured in MW. For example, if the algorithm is designed to minimize the total operating cost of the device, then each parameter in y i will represent the operating cost of the device, such as fuel cost, maintenance, penalty cost in case of excessive emissions, etc. As another example, the possible optimization variables y in the set i can be the torque / speed ratio of the electric motor / generator 32, which is a known parameter of any electric motor.

[0072] As described above, each control function in the control function f i(x，u，w) represents the relationship between each parameter in y i and the state variable x, the control variable u, and the external parameter w. For example, the fuel cost expressed in dollars, for example, will be a function of the following: the type of operation selected (using the electric motor / generator 32 as a starting / auxiliary mode, power generation mode, etc.), the power generated by the electric motor / generator 32, the current cost of the fuel, and the fuel consumption of the gas turbine 31 as a function of the ambient temperature and humidity. It is clear that the cost of the fuel, although expressed in currency, i.e., dollars, and preferably, all other parameters of the optimization variable y i are functions of several other parameters and variables, both technical (necessary) and non-technical.

[0073] At the end of the optimization process, the model finds the most appropriate set of values for the control variable u, typically including technical variables, to drive the fuel controller module 311 and the electric motor / generator controller 321 at the end of the process, thereby obtaining the desired optimization adjustment.

[0074] As described above, the control function f i(x，u，w) is generally a non-linear function, and the control functions of each gas turbine and each electric motor / generator can be different. Looking only at one example from the above examples, the cost of the fuel used is a function of the power generated by the gas turbine 31 of the hybrid gas turbine 3, which is a specific function of the machine.

[0075] Control function f i(x，u，w) It may also include a combination of Heaviside functions, which is generally represented as follows

[0076]

[0077] To represent possible thresholds, i.e., the constraints (or limitations) of the model. For example, it may be requested to optimize a single optimization variable y (so i = 1), which may be fuel consumption, thus setting a maximum threshold F Max . In this case, this function of y is represented by the state variable x, the external variable u, and the external parameter w

[0078] y = f(x, u, w)

[0079] will take the following form

[0080] y = f(x, u, w)·(1 - Θ(F_Max)).

[0081] Weight parameter α i is applied to set the relative weights between different optimization variables y i . In this way, the algorithm adopted by this monitoring and control method can be flexible. In the case where one or more parameters of the weight α i are set to 0, the corresponding optimization variable y i will be excluded from the optimization process. This also allows simplifying the algorithm, thus adapting the algorithm to a subset of the received or available variables, or even adapting the algorithm and thus making this monitoring and control method applicable to different hybrid gas turbine systems 3 that may have different layouts.

[0082] In addition, when all optimization variables y i are considered, the following normalization

[0083]

[0084] may occur. Of course, the above normalization can vary according to the model and the optimization variable y i .

[0085] In addition, in practice, a subset of the weight parameter α i is preset or selected to achieve specific results according to a specific pattern to drive the optimization method 5 for monitoring and controlling the hybrid gas turbine system, such as maximizing the power to be generated, maximizing the life of the gas turbine 31, minimizing emissions, etc.

[0086] Once the optimization is achieved, the following expression is minimized

[0087]

[0088] The values of the state variable x, the control variable u, and the external parameter w are obtained, and the logic control unit 2 can send one or more command signals to the fuel controller module 311 and the electric motor / generator controller 321 to control their operations and select the hybrid gas turbine system 3, thereby specifically regulating the fuel consumption of the gas turbine 31 and the power generated when the electric motor / generator 32 operates as a motor. Therefore, the power provided when the electric motor / generator 32 operates as a generator assists the gas turbine 31, and the generated power is injected into, for example, the power grid (not shown in the figure).

[0089] The control and monitoring method and the operation of the gas turbine system 1 are as follows.

[0090] As described above, once the control logic unit 2 receives data from several sources 4 connected thereto, the data (or variables) are organized into subsets. Specifically, for the sake of description, they are organized according to the above three sets or vectors, namely the state variable x, the control variable u, and the external variable w, which are subsequently processed by the processor 21. Obviously, the division of variables according to the above three sets or vectors is only formal, and different groupings (or no grouping at all) can be made. Specifically, in Figure 4 the disclosed embodiment, the processor 21 retrieves the computer program to be processed stored in the computer-readable memory 24 to process the received data. Subsequently, the data is processed based on the optimization algorithm, and the implementation of the optimization algorithm has been disclosed above in order to find the optimization of the control of the gas turbine 31 and the electric motor / generator 32.

[0091] Of course, the optimization algorithm and the method for controlling the hybrid gas turbine system 3 must also be adjusted according to the operating mode in which the hybrid gas turbine system 3 must operate in a specific environment and according to the layout of the transmission system in which the hybrid gas turbine system 3 to be controlled is installed. To better explain this, refer to Figure 5 , which shows an embodiment of the hybrid gas turbine 3, including a variable-frequency drive electric motor (VFD electric motor) 32 acting as an electric motor / generator and a gas turbine 31 connected downstream of the VFD electric motor 32. The load L is connected to the gas turbine 31. The VFD electric motor 32 is capable of generating 1 - 3 MW of electric power. It is expected that the VFD electric motor 32 can be of different types and be capable of generating different powers. The VFD electric motor 32 is usually implemented on-site because it is particularly easy to manage and electrically control. The gas turbine 31 is designed to generate a 30 MW power source. Also in this case, the gas turbine 31 is exemplary, and different types of gas turbines 31 can be provided.

[0092] Now refer to Figure 6 and Figure 7, showing two different operating modes of the hybrid gas turbine 2. More specifically, in Figure 6 , the VFD electric motor 32 operates in a start / assist mode, where the VFD electric motor 32 supplies (a maximum of) 2 MW to the gas turbine 31 in order to supply a total of (a maximum of) 32 MW of power to the load L.

[0093] Conversely, in Figure 7 , the gas turbine 31 operates in a start / generation mode such that the gas turbine 31 supplies (a maximum of) 28 MW to the load L and (a maximum of) 2 MW to the VFD electric motor 32. The VFD electric motor 32 can also be connected to the power grid (not shown in the figure) in order to inject electric power into the power grid.

[0094] Both operating modes are available in the above layout. The operation of the hybrid gas turbine system 3 can be operated by the logic control unit 2 so as to select different operating modes and optimize the operation with respect to the environment, i.e., depending on any layout of the hybrid gas turbine 3.

[0095] Specifically, how the processor 21 (or generally the processing device) processes various vectors and the variable part of each vector depends in part on the requirements of the load L and the received constraints for maximizing some optimization variable y i in order to maximize the operating performance of the hybrid gas turbine system 3 or the cost of the power to be supplied.

[0096] Now referring to Figure 8 , showing another layout of the transmission system, where the load L is connected to the 30 MW gas turbine 31 via a disconnecting device 33, and the 30 MW VFD electric motor 32 is also connected to the load L.

[0097] Generally, the disconnecting device 33 installed in the transmission system is of the clutch, self-synchronizing clutch or overrunning clutch type. Such clutches are equipped with devices known as lock-in devices and lock-out devices, which have the function of locking the clutch in the engaged position or disengaged position when activated.

[0098] Figure 9 shows the operation of the hybrid gas turbine 3 in the assist mode, where both the gas turbine 31 and the VFD electric motor 32 generate maximum power for the load L, which is (a maximum of) 60 MW in this case (i.e., for example, the sum of the maximum powers generated by the gas turbine 31 and the VFD electric motor 32); or the operation of the hybrid gas turbine in the all-electric mode, where the gas turbine 31 is shut down and the VFD electric motor 32 supplies (a maximum of) 30 MW of power to the load L. In this case, the disconnecting device 33 is disengaged.

[0099] Figure 10Shows the operation of the hybrid gas turbine 3 in power generation mode, where the gas turbine 31 is capable of generating, for example, (a maximum of) 30 MW of power, a part of which (e.g., 50% of the maximum power generated by the gas turbine 31, i.e., 15 MW) is absorbed by the load L, and the remaining 50% of the generated power, always 15 MW, is absorbed by the VFD electric motor 32 and is thus injected into the power grid (not shown) to which the VFD electric motor 32 is connected.

[0100] As can be understood, in different operating modes, it is a matter of problem or appropriate optimization to appropriately adjust the power generated by the gas turbine 31 or absorbed by the VFD electric motor 32 and / or absorbed by the load L (pump, compressor, etc.) and the VFD electric motor 32, which depends on several variables or data sets 41, 42 and 43 briefly listed above.

[0101] In addition, the monitoring and control method based on the above algorithm is flexible because it can adapt to different layouts. Only by way of example, referring to the state variable x, the state variable "clutch feedback" will not be Figure 5 used in the hybrid gas turbine system 3 shown, but will be used in Figure 8 the layout of the hybrid gas turbine system 3 or in a layout provided with two clutches. Therefore, by making the correct selection of the control function f i(x，u，w) and by setting the appropriate value of the weight parameter α i any possible layout of the hybrid gas turbine system 3 can be achieved.

[0102] As mentioned above, in addition to Figure 5 、 Figure 6 、 Figure 7 、 Figure 8 、 Figure 9 and Figure 10 the layouts shown in, other layouts can be foreseen. More specifically, other embodiments may include a hybrid gas turbine system 3 similar to Figure 8 shown, where an additional clutch is interposed between the load L and the electric motor / generator 32. Specifically, the possible layout will provide a gas turbine, an electric motor / generator 32 (a first clutch is interposed between the gas turbine and the electric motor / generator), a load, and a second clutch interposed between the electric motor / generator 32 and the load L.

[0103] The monitoring and control method 5 disclosed herein can also be understood by looking at Figure 11 the flowchart of, which shows the following steps:

[0104] - Receive 51 the operating variable x for detecting the operating states of the gas turbine 31 and the electric motor / generator 32;

[0105] - Receive the control variable u at 52 for controlling the operating states of the gas turbine 31 and the electric motor / generator 32;

[0106] - Receive a set of external variables w at 53;

[0107] - Receive a set of optimization variables y at 54. The value of the optimization variable y depends on the operating variable x, the control variable u, and the external variable w. In addition, the value of the optimization variable y must be adjusted for optimization;

[0108] - Process one or more of the operating variable x, the control variable u, and the external variable w at 55 by optimizing the value of the optimization variable y; and

[0109] - Generate and send one or more control signals at 56 based on the control variable u obtained by the optimization of the optimization variable y to control the fuel supplied to the gas turbine 31 and the power generated or converted by the electric motor / generator 32.

[0110] These steps can be performed in any suitable order or combination, unless expressly stated otherwise herein.

[0111] The electric motor / generator 32 can be connected to a grid energy storage device 431, a solar panel power generation device 432, a wind power generation device 433, a hydrothermal power generation device 434, or a thermal power generation device 435, or any ordinary grid capable of injecting any excess energy into it.

[0112] In addition, the grid energy storage device 431, the solar panel power generation device 432, the wind power generation device 433, the hydrothermal power generation device 434, or the thermal power generation device 435 can be operably connected to the control logic unit 2 and can collect data, such as a part of the external variable w, from the control logic unit.

[0113] See Figure 12 which specifically shows the control logic unit 2 and specifically how the receive-transmit module 25 sends (step 57) the control signal received by the processor 21 to the fuel controller module 311 of the gas turbine 31 and sends (step 58) it to the electric motor / generator controller 321 of the electric motor / generator 32.

[0114] Although the present invention has been described in accordance with various specific embodiments, those skilled in the art will understand that many modifications, variations, and omissions are possible without departing from the spirit and scope of the claims. In addition, unless otherwise specified herein, the order or sequence of any process or method steps can be changed or reordered according to alternative embodiments.

[0115] Reference has been made in detail to embodiments of the present disclosure, one or more examples of which are shown in the drawings. Each example is provided by way of explanation of the present disclosure and not limitation thereof. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made to the present disclosure without departing from the scope or spirit of the present disclosure. References throughout this specification to "one embodiment" or "an embodiment" or "some embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" or "in some embodiments" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, in one or more embodiments, the particular features, structures, or characteristics may be combined in any suitable manner.

[0116] When introducing elements of various embodiments, the articles "a", "an", "the", and "said" are intended to mean that there is one or more of the elements. The terms "comprising", "including", and "having" are intended to be inclusive and mean that there may be additional elements in addition to the listed elements.

[0117] Barzanò and Zanardo Roma S.p.A.

Claims

1. A method (5) for monitoring and controlling a hybrid gas turbine system (3), wherein the hybrid gas turbine system (3) comprises at least one gas turbine (31) to be operated by fuel, at least one electric motor / generator (32) capable of operating as a generator or as a motor, and a plurality of actuators for controlling them, and wherein the method (5) is implemented by a control logic unit (2), characterized in that, the method (5) comprises the following steps: receiving (51) a set of operating variables x for detecting the operating states of the gas turbine (31) and the electric motor / generator (32); receiving (53) a set of external variables w; selecting (52) a set of control variables u for controlling the operating states of the gas turbine (31) and the electric motor / generator (32); setting (54) a set of optimization variables y, wherein the value of the optimization variable y depends on the operating variable x, the control variable u, and the external variable w, and wherein the value of the optimization variable y must be adjusted for optimization; processing (55) one or more of the operating variable x, the control variable u, and the external variable w by optimizing the value of the optimization variable y; and generating and sending (56) one or more control signals based on the control variable u obtained by the optimization of the optimization variable y to control the actuators of the hybrid gas turbine system (3), wherein the optimization variable y i is represented by a control function f i(x,u,w) that represents the operation variable x and / or the control variable u and / or the external variable w, and wherein the optimization step (55) is performed to minimize the optimization variable y according to the following equation i and the modulus sum of the control function f i(x,u,w) is: where α i is a weighting factor.

2. The method (5) according to claim 1, wherein the one or more control signals allow controlling the fuel supplied to the gas turbine (31) and the power generated or converted by the electric motor / generator (32).

3. The method (5) according to any one of the preceding claims, wherein the optimization variable y i is a subset of the manipulated variable x and / or the control variable u and / or the external variable w.

4. The method (5) according to claim 1, the method comprising the step of selecting a subset of the weight parameter α i to process one or more of the manipulated variable x, the control variable u, and the external variable w by optimizing the value of the optimization variable y according to a specific pattern.

5. The method (5) according to claim 4, wherein optimizing the value of the optimization variable y includes maximizing the power to be generated, maximizing the life of the gas turbine (31), and minimizing emissions.

6. The method (5) according to claim 1, wherein the state variable x includes at least one of the following variables: ambient pressure, ambient humidity, turbine inlet temperature, turbine inlet pressure, turbine compressor exhaust temperature, turbine compressor exhaust pressure, turbine compressor air flow, inlet filter loss, fuel temperature, fuel composition, combustion mode, high-power turbine speed, first nozzle temperature, high-power turbine exhaust temperature, high-power turbine exhaust pressure, high-power turbine flow, booster turbine speed, booster turbine exhaust temperature, booster turbine flow, low-power turbine speed, low-power turbine exhaust temperature, low-power turbine flow, drive compressor inlet pressure, drive compressor inlet temperature, drive compressor inlet flow, drive compressor outlet temperature, drive compressor outlet pressure, electric motor power, emissions, life, clutch, feedback.

7. The method (5) according to claim 1, wherein the control variable u for generating the control signal comprises at least one of the following variables: fuel demand, inlet guide vane demand, variable stator vane demand, variable bleed vane demand, bleed valve demand, outboard bleed vane demand, axial anti-surge valve demand, nozzle guide vane, centrifugal anti-surge valve demand, compressor inlet vane demand, motor setpoint, reactive power setpoint.

8. The method (5) according to claim 1, wherein the external variable w comprises at least one of the following variables: fuel cost, electricity selling price, electricity cost, emission tax, maintenance cost, additional energy availability, reactive power cost / price, power factor.

9. The method (5) according to claim 1, wherein the gas turbine (31) is a heavy-duty or aeroderivative gas turbine.

10. A control logic unit (2), the control logic unit comprising: a processor (21) configured to execute the method according to any one of claims 1 to 9; and a receive-transmit module (25) connectable to the processor (21), the receive-transmit module being configured to transmit (57) the control signal received by the processor (21) to control the actuator of the hybrid gas turbine system (3).

11. The control logic unit (2) according to claim 10, the control logic unit comprising: a bus (22) to which the processor (21) is connected; a database (23) connected to the bus (22) for access and control by the processor (21); and a computer-readable memory (24) connected to the bus (22) for access and control by the processor (21).

12. A hybrid gas turbine system (3), the hybrid gas turbine system comprising: at least one gas turbine (31) to be driven by fuel, including a fuel controller module (311) operable to control the fuel to be supplied to the gas turbine (31); and at least one electric motor / generator (32) capable of operating as a generator or as a motor, the at least one electric motor / generator including an electric motor / generator controller (321) adapted to control and regulate the electric power generated or converted by the electric motor / generator (32); characterized in that the hybrid gas turbine system (3) comprises a control logic unit (2) according to any one of claims 10 or 11, the control logic unit being operably connected to the fuel controller module (311) and the electric motor / generator controller (321).

13. The hybrid gas turbine system (3) according to claim 12, wherein the receiving - transmitting module (25) of the control logic unit (2) is configured to transmit (57) the control signal received by the processor (21) to the fuel controller module (311) of the gas turbine (31), and transmit (58) it to the electric motor / generator controller (321) of the electric motor / generator (32).

14. The hybrid gas turbine system (3) according to claim 12 or claim 13, wherein the electric motor / generator is a variable - frequency drive (VFD) electric motor, and the gas turbine (31) is a heavy - duty or aeroderivative gas turbine.

15. A power generation device, comprising the hybrid gas turbine system (3) according to any one of claims 12 to 14, wherein the electric motor / generator (32) is connected to at least one power generation device (41, 42, 43, 44, 45).

16. The power generation device according to claim 15, wherein the at least one power generation device includes a grid energy storage device (431), a solar panel power generation device (432), a wind power generation device (433), a hydrothermal power generation device (434), and / or a thermal power generation device (435).

17. A load, comprising the hybrid gas turbine system (3) according to any one of claims 12 or 14, wherein the gas turbine (31) is connected to the load (L).

18. The load according to claim 17, wherein, the gas turbine (31) is connected to a pump or a compressor.

19. The load according to claim 17 or 18, wherein the gas turbine (31) is connected to the load (L) through a disconnecting device (33).

20. The load according to claim 19, wherein the gas turbine (31) is connected to the load (L) through a self - synchronizing clutch or an over - speed clutch.

21. The load according to claim 19, wherein the disconnection or connection by the disconnecting device is manually operated by an operator and / or automatically operated by an actuator.

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