Method and system for monitoring and controlling a hybrid gas turbine system - Patents.com

Abstract

A method for monitoring and controlling a hybrid gas turbine system is disclosed, performed by a control logic unit. The method is implemented by performing optimization among operating variables to derive control signals and control operation of the hybrid gas turbine system. Also disclosed is a hybrid gas turbine system including at least one gas turbine operated by a fuel and at least one electric motor / generator that can operate as a generator or a motor. The hybrid gas turbine system includes a control logic unit operably connected to a fuel controller module and an electric motor / generator controller.

Description

[Technical field]

[0001] The present disclosure relates to an improved method for controlling a hybrid gas turbine system used in mechanical drive applications that can improve efficiency during operation. In particular, but not exclusively, the present disclosure relates to a hybrid gas turbine system 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 making it possible to implement the method. [Background technology]

[0003] Recently, the synergy between gas turbines and electric motors / generators, such as variable frequency drive electric motors (VFD electric motors), i.e. so-called electric reversible machines (which can also operate as generators), is the current drive design trend in the field of systems intended to drive mechanical loads.

[0004] Specifically, there are available in the market systems referred to as "trains", "train systems", or the like, in which an electric machine, 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 ease of reference only, a system including a gas turbine, an electric motor / generator, and a load, e.g., a compressor, typically connected to the electric motor / generator by a clutch or disconnection device, may be generally referred to as a "train", "train system", or "train plant". 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 train".

[0005] An electric motor / generator may be used to keep the overall mechanical power on the load shaft constant by supplementing the mechanical power to the load when turbine power availability is reduced, and / or to increase the total mechanical power used to drive the load. This function of the electric motor / generator is referred to as "helper duty". Another electric motor, or alternatively, a pneumatic motor / generator, is also typically used as a starter motor to accelerate the gas turbine from zero to rated speed.

[0006] An example of a hybrid gas turbine system designed to drive a mechanical load is one applied in Liquefied Natural Gas (LNG) applications. LNG comes from a liquefaction process in which natural gas is cooled to a liquid using one or more refrigeration cycles in a cascaded arrangement. Natural gas is often liquefied for storage or transportation purposes, especially when pipeline transportation is not possible. Cooling of the natural gas is achieved using a closed or open refrigeration cycle. The refrigerant is processed, condensed, and expanded in one or more compressors. The expanded and cooled refrigerant is used to remove heat from the natural gas flowing in a heat exchanger.

[0007] Several layouts of train systems are known in the art, one of the most common involves a gas turbine connected to one or more cascaded compressors, with the latter compressor in the cascade connected to a mechanical motor / generator, or alternatively, the gas turbine is connected to an electric motor / generator, which is in turn connected to the compressor.

[0008] Managing a train system proves to be extremely difficult as there are several variables that must be considered and balanced in order to operate the train system. As mentioned above, currently available plants, which are very sophisticated, have several sensors and actuators that are connected to a control computer system so that the operation of the train system can be controlled and appropriately managed.

[0009] However, modern power plants must meet several constraints beyond their technical type: from an environmental point of view, in particular, the emissions of CO, CO2 and NO, which usually depend on the type of gas turbine in operation and increase almost linearly with the power generated by the gas turbine, are also important. x , S.O. x There is a demand to reduce emissions of diesel. Emissions control requires management of several parameters that can vary depending on ambient temperature, humidity, fuel composition, and other parameters, and are usually difficult to collectively control by the operator.

[0010] In addition, it is usually necessary to program the maintenance of the train system to extend the life of the train system. However, to extend the life of the train system, it is necessary to perform operation management according to a specific application. In other words, depending on the environment and normal operating conditions, the maintenance plan of the train system may vary greatly.

[0011] In addition, and in relation to the above, it is usually necessary to minimize the capital and operational expenses associated with the operation of any hybrid gas turbine system or train system, and therefore to maximize the profits that can be obtained from any plant, while at the same time minimizing the gas turbine emissions at all times, or in some circumstances, to keep the gas turbine emissions below certain thresholds, often set by law. This means that highly specialized operators are required to manage and control such types of systems or plants. In particular, this requires a high level of education and in-depth knowledge by the operators to control and manage the system.

[0012] Modern plants are known to be equipped with numerous sensors and actuators for operating the plant through appropriately programmed computer systems. However, sometimes such computer systems are unable to control the hybrid gas turbine system for optimal operation that promotes increased profits. Also, as explained above, especially in the case of multi-unit plants, a typical operator may not have all the technical and business skills required to evaluate the correct balance and trade-off between the operation of the gas turbine (which consumes fuel) and the sharing of the power supply between the electric motors / generators (which consume or generate).

[0013] Therefore, there is an increasing demand for a hybrid gas turbine system or train system that can optimize operation and also take into account emissions and several other parameters, not necessarily just physical parameters, to maximize the benefits and efficiency of the hybrid gas turbine. Summary of the Invention

[0014] In one aspect, the subject matter disclosed herein is directed to a method for monitoring and controlling a hybrid gas turbine system. The hybrid gas turbine system includes at least one gas turbine operated by a fuel, at least one electric motor / generator that can be operated as a generator or a motor, and a number 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 conditions 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 conditions of the gas turbine and the electric motor / generator, and setting a set of optimization variables y, the values ​​of which depend on the operating variables x, the control variables u, and the external variables w. The values ​​of the optimization variables y must be adjusted to be optimized. The method also includes processing one or more of the operating variables x, the control variables u, and the external variables w by optimizing a value of the optimization variable y, and generating and sending one or more control signals based on the control variable u obtained by optimizing the optimization variable y to control actuators of the hybrid gas turbine system.

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

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

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

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

[0019] In one aspect, the subject matter disclosed herein is directed to a hybrid gas turbine system, including at least one gas turbine operated by a fuel and including a fuel controller module operable to control a fuel supplied to the gas turbine. The hybrid gas turbine system also includes at least one electric motor / generator capable of operating as a generator or a motor and including an electric motor / generator controller adapted to control and regulate electrical power generated or converted by the electric motor / generator, and a control logic unit operably connected to the fuel controller module and the electric motor / generator controller. [Brief description of the drawings]

[0020] A more complete understanding of the disclosed embodiments of the present invention and many of the attendant advantages thereof will be readily obtained by reference to the following detailed description when considered in connection with the accompanying drawings, in which: [Figure 1] 1 illustrates a block diagram of an embodiment of a system for monitoring and controlling a hybrid gas turbine system. [Diagram 2] 1 illustrates a graph showing a graph of percentage of ISO base load operation of a gas turbine as a function of ambient temperature. [Diagram 3]1 illustrates a graph of NOx, CO, and CO2 emissions as a function of power percentage for a gas turbine. [Figure 4] 1 illustrates a block diagram of an embodiment of a control logic unit of a system for monitoring and controlling a hybrid gas turbine system. [Diagram 5] 1 illustrates a first embodiment of a train system. [Figure 6] 6 illustrates a scheme of operation of the train system of FIG. 5 in starter / helper mode. [Figure 7] 6 illustrates a scheme of operation of the train system of FIG. 5 in starter / generator mode. [Figure 8] 2 illustrates a second embodiment of a train system. [Figure 9] 10 illustrates a scheme of operation of the train system of FIG. 8 in helper / full electric mode. [Figure 10] 6 illustrates a scheme of operation of the train system of FIG. 5 in generator mode. [Figure 11] 1 illustrates a flow chart of a method for monitoring and controlling a hybrid gas turbine system. [Figure 12] 1 illustrates a flow chart of the control of a fuel controller module and / or an electric / motor generator controller. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] Turbomachinery devices such as gas turbines, electric motors / generators, and compressors / pumps may be coupled together in various configurations, referred to in the art as "trains" or "train systems". The operation of a train system requires the control of numerous physical parameters and also depends on ambient parameters such as ambient temperature and humidity, age of the equipment, inlet / outlet losses, combustion dynamics, and others. In addition, the exhaust gases of the gas turbines may need to be optimized to meet the laws, rules, or regulations of the country in which the train system is installed and operated. Finally, capital expenditures, maintenance costs, and recurring costs must be kept as low as possible to increase the profits that can be obtained from the plant. Managing this large amount of data and constraints has proven difficult, and therefore a novel computer-based optimization method to achieve all the above requirements is presented, which is the turbomachinery devices and / or attachments within the train system itself to be operated and / or maintained.

[0022] 1, there is illustrated generally the management and control of a hybrid gas turbine system, generally designated by reference numeral 1, which generally includes a control logic unit 2 and a hybrid gas turbine 3 comprising a gas turbine 31 and an electric motor / generator 32, which are operatively connected to the control logic unit 2 through 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 to meet constraints and optimize the use of resources, i.e. fuel for the gas turbine 31 and power absorbed or provided by the electric motor / generator 32, as will be better explained below.

[0023] The control logic unit 2 can be realized in different ways. In particular, in some embodiments (see also below), the control logic unit 2 is fully embodied in a circuit board installed directly in the management and control of the hybrid gas turbine system, with previously installed processing components such as microprocessors, PLCs, etc., suitably programmed to carry out the management and control of the operation. In other embodiments, the control logic unit 2 can be realized, at least in part, as software executed remotely with respect to the management and control of the hybrid gas turbine system. In particular, in this case, the control logic unit 2 can be a general personal computer or terminal, generally programmed to interface with the hybrid gas turbine system and to receive instructions from others, possibly remote to it.

[0024] More specifically, the control logic unit 2 is configured to receive data from several sources 4, and thus many types of variables and data, which are used as inputs for the monitoring and control methods based on optimization algorithms. This allows the gas turbine 31 to operate at preferably 100% all the time, thus maximizing capital expenditure, adding revenue streams, and regulating CO2 and NOx in a fully automatic mode based on process conditions and financial inputs. x The aim is to reduce the emissions of renewable energies. The management and control of the hybrid gas turbine system 1 also operates in predictive mode, i.e. it makes weather forecasts regarding the availability of renewable energies and energy storage using pipelines as containers, trading of electrical energy or fuel. The control works in dynamic mode so that the maximization is always kept at the highest possible level.

[0025] The first group of data or variables 41 includes the operator's goal rankings, which generally include CO, CO2, and NO xConstraints on emissions, for example pipeline energy storage including the ability of the pipeline to store energy in pipeline management, and process production, operating costs, and requirements to extend the life of the gas turbine 31. These data are typically received from the operator.

[0026] The second group of data or variables 42 may include gas turbine operational data and weather forecasts. Indeed, weather is known to significantly affect the operation of gas turbines.

[0027] By way of example only, FIG. 2 illustrates the percentage of ISO base load operation of the gas turbine 31 as a function of ambient temperature. As can be easily understood, a particular threshold temperature T th Above the threshold temperature T, the power generated by the gas turbine 31 decreases significantly with increasing ambient temperature T. This occurs regardless of whether the electric motor / generator 32 is operated as a helper or not. In fact, the operation of the electric motor / generator 32 as a helper is not possible beyond the threshold temperature T th 3. The effect of NO is only to shift the NO x It can be seen that the emissions of CO (curve a), CO (curve b) and CO2 (curve c) increase at different rates (expressed as a percentage of total power) with respect to the power delivered by the gas turbine 31. Thus, by combining the information that can be obtained from the two graphs of Figures 2 and 3, it can be seen that an increase in the ambient temperature T causes a decrease in the performance of the gas turbine 31, which in turn leads to an increase in the emissions of CO, CO2 and NO x It can be easily estimated that this will increase emissions.

[0028] 1, a third group of data 43 may originate from one or more energy sources or power plants to which the hybrid gas turbine system 3 is connected, such as, by way of example, a grid energy storage 431, a solar panel power plant 432, a wind power plant 433, a hydrothermal power plant 434, and a thermal power plant 435. These data relate in particular to the demand and power supply capacity of the different power plants. In the following, the data considered will be better discussed.

[0029] The gas turbine 31 may be of different types, such as, by way of example and not by way of limitation, a heavy duty, gas turbine, or aeroderivative gas turbine. When a different type of gas turbine 31 is installed, the associated control variables or parameters may change. However, as will be better specified below, the installation of a different gas turbine 31 or other possible hybrid architectures does not change the scope of protection of the solutions disclosed herein.

[0030] The electric motor / generator 32 can also be of different types. In particular, the type of electric motor / generator 32 is a variable frequency drive electric motor (VFD electric motor) 32, which is frequently used as a generator or motor, particularly in the field in combination with a gas turbine 31 for several functions, as better explained below. The use of this type of electric motor / generator 32 is often motivated by the fact that it is particularly useful for electrical control.

[0031] In some embodiments, and with particular reference to Figure 4, the control logic unit 2 may 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 receiver-transmitter module 25 connected to the bus 22 and configured to transmit control signals to a fuel controller module 311 of the gas turbine 31 and an electric motor / generator controller 321 of the electric motor / generator 32 for controlling the operation of the hybrid gas turbine system 3 in different available operating modes. In effect, the monitoring and control method described herein controls 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 through appropriate "modulation" of command signals processed by the processor 21 executing one or more computer programs transmitted 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 control the actuators of the gas turbine 31 and the electric motor / generator 32, respectively, to select the operating pattern of their operation. In this embodiment, the control logic unit is installed near the train system.

[0032] The control logic unit 2 is configured to execute one or more computer programs for executing optimization methods or algorithms aimed at controlling the hybrid gas turbine system 3. In some embodiments, the control logic unit 2 can be a physical hardware, possibly installed near the installation site of the hybrid gas turbine 3 or located remotely. In some embodiments, the control logic unit 2 can also be cloud-based or run in the cloud. In this embodiment, only the part of the control logic unit 2 that needs to transmit control signals needs to be installed near the train system, while the data processing unit can be located remotely with respect to the train system and, as mentioned, in a cloud-based system, near it or where the control room (or computer / server / terminal) is located, and the control room / computer / server is coupled to the train system or the control signal transmission unit via wired or wireless means.

[0033] The control and management method based on an optimization algorithm, an embodiment of which will be better explained below, has as input data and variables coming from sources 4, namely a first group 41, a second group 42 and a third group 43 of data and variables. These groups 41, 42 and 43 of data are then organized, for example in three sets or vectors, for processing. The control and management method can process all data and variables or a part of them, so that it can be flexible. In this way, the same algorithm on which the control and management method is based can be used to optimize a subset of data or to adapt to different layouts of the hybrid gas turbine system. This is done by setting to zero or one (or, in general, to a non-zero value) a set of weighting parameters used to select or not select one or more parameters or variables, as will be better explained below.

[0034] The monitoring and control optimization method / algorithm is based on a set of equations for multivariate regression analysis in multi-objective optimization problems. The algorithm can be synthesized with the following set of equations:

[0035]

number

[0036] Specifically, as better explained below, there are three different variable vectors and a control function f k (x,u,w) represent the relationships between the different variables of the model.

[0037] The variable vector x includes state variables and may include both measured (instruments) and calculated (through tables and / or known functions) or estimated (through estimators) values. Such state variable vector x describes the operating conditions of the gas turbine 31 and the electric motor / generator 32. Below, an example of state variables x organized as a vector is provided, taking into account that different or additional variables may be considered.

[0038]

number

[0039] Another vector u is the output of the control logic unit 2, which contains the control variables for driving the fuel controller module 311 and the electric motor / generator controller 321. An example of the control variables u arranged as a column vector is reported below.

[0040]

number

[0041] The third variable vector w includes external variables or data or parameters, which may also include economic parameters and constraints, such as fuel costs or taxes imposed to operate the system. The control data or variables w actually include data and constraints that are usually set by either an operator or an external agent. Examples of external variables are reported in the following vectors:

[0042]

number

[0043] The variables processed by the method for monitoring and controlling a hybrid gas turbine system are designed to process three sets of variables, namely the state variables x, the control variables u and the external variables w mentioned above. It is clear that all variables capable of characterizing the operation of the hybrid gas turbine 3, or in general the train system in which the hybrid gas turbine system 3 is provided, may include additional variables or groups thereof. The method for controlling the hybrid gas turbine system 3 is designed to process a set of optimization variables y i These optimization variables or goal variables y i can be different from the state variables x, the control variables u, and the external variables w, or even a subset thereof. i can also be organized into a column vector.

[0044] Optimization variable y i Each of these has a specific operational function f i (x,u,w), whereby the dependencies, which may also be nonlinear, are represented. The optimized values ​​of the state variables x, the control variables u, and the external variables w are then expressed as i For each norm ||y i -f i It is calculated to minimize (x,u,w)||.

[0045]

number

[0046] Thus, the optimization method converts the above equation into a weight vector α i Through the application of i -f i Minimize for each (x,u,w)||.

[0047] Optimization variable y i have the same measurement in common, i.e., they should in principle be measured by the same metric. For example, in some embodiments, the optimization variables y i may be the capital expenditure costs of the plant, and therefore should be measured in US dollars. In another embodiment, the optimization variables y i can be the power consumption of the plant, in which case the variable should be measured in MW. As an example, if the algorithm aims to minimize the total training cost of the plant, the parameter y i Each of the variables y represents a cost of operating the plant, such as fuel costs, maintenance, and costs of penalties for excessive emissions. As another example, a set of possible optimization variables y i may be the torque / speed ratio of the electric motor / generator 32, which is a known parameter of any electric machine.

[0048] As mentioned above, the control function f i Each of (x,u,w) has a parameter y i represents the relationship between each of the state variables x, the control variables u, and the external parameters w. For example, the cost of fuel, for example expressed in US dollars, is a function of the type of operation selected (use of the electric motor / generator 32 as a starter / helper mode, generator mode, etc.), the power generated by the electric motor / generator 32, the current cost of fuel, and the consumption of fuel by the gas turbine 31 as a function of the environmental temperature and humidity. As will be apparent, the cost of fuel, also expressed in a currency, i.e. US dollars, as well as the optimization variables y, preferably iEvery other parameter in is a function of several other parameters and variables, both technical (necessary) and non-technical.

[0049] The model finds an optimal set of values ​​of the control variables u, which typically include technological variables, at the end of the optimization process to drive the fuel controller module 311 and the electric motor / generator controller 321 to obtain the required optimized tuning.

[0050] As mentioned above, the control function f i (x,u,w) are typically non-linear functions and may vary, for example, from gas turbine to gas turbine and from electric motor / generator to electric motor / generator. Simply continuing with one of the examples above, the cost of fuel employed is a function of the power generated by the gas turbine 31 of the hybrid gas turbine 3, which is a machine specific function.

[0051] Control function f i (x,u,w) can also include a combination of Heaviside functions, generally expressed as:

[0052]

number

[0053] Weight parameter α i are the different optimization variables y iIn this way, the algorithm underlying the monitoring and control method can be flexible. i If one or more parameters in are set to zero, the corresponding optimization variable y i are excluded by the optimization process. This also makes it possible to simplify the algorithm and adapt it to a subset of the received or available variables, and even to adapt the same algorithm, and therefore the monitoring and control method, to different hybrid gas turbine systems 3 that may have different layouts.

[0054] Also, for all optimization variables y i If we consider the following normalization,

[0055]

number

[0056] In practice, the optimization method 5 for monitoring and controlling a hybrid gas turbine system may be driven according to a particular pattern to achieve a particular result, such as maximizing power production, maximizing the life of the gas turbine 31, minimizing emissions, etc., by adjusting the weighting parameter α. i A subset of is preset or selected.

[0057] When optimization is achieved, the following expression is minimized:

[0058]

number

[0059] The method of control and monitoring and operation of the gas turbine system 1 is as follows.

[0060] As mentioned above, when the control logic unit 2 receives data from several sources 4 connected thereto, the data (or variables) are organized into subsets. In particular, for ease of explanation, the data are organized according to the tree set or vectors mentioned above (i.e. state variables x, control variables u and external variables w) and then processed by the processor 21. It is clear that the division of the variables according to the above three sets or vectors is merely formal and different groups can be arranged (or there are no groups at all). The processor 21 processes the received data by searching, in particular, in the embodiment disclosed in FIG. 4, for a computer program to be processed, stored in the computer readable memory 24. The data are then processed based on the optimization algorithm, one embodiment of which is disclosed above, to find the optimization of the control of the gas turbine 31 and the electric motor / generator 32.

[0061] Naturally, the optimization algorithm for controlling the hybrid gas turbine system 3, and also the method, must be adapted to operate the hybrid gas turbine system 3 under certain circumstances depending on the operating mode, as well as depending on the layout of the train system in which the controlled hybrid gas turbine system 3 is installed. To better explain this point, reference is made to FIG. 5, which illustrates an embodiment of a hybrid gas turbine 3 with a variable frequency drive electric motor (VFD electric motor) 32 as an electric motor / generator and a gas turbine 31 connected downstream of the VFD electric motor 32. A load L is connected to the gas turbine 31. The VFD electric motor 32 can generate a power of 1 to 3 MW. It is intended that the VFD electric motor 32 can be of different types and can generate different powers. The VFD electric motor 32 is usually implemented on site, since it has been found to be particularly easy to electrically manage and control. The gas turbine 31 is designed to generate a power supply of 30 MW. Also, in this case, the gas turbine 31 is exemplary and different types of gas turbines 31 can be provided.

[0062] 6 and 7, there are shown two different operating modes of the hybrid gas turbine 2. More specifically, in FIG 6, the VFD electric motor 32 is operated as a starter / helper mode, where the VFD electric motor 32 provides (maximum) 2 MW to the gas turbine 31 to provide a total power of (maximum) 32 MW to the load L.

[0063] 7, gas turbine 31 is operated in starter / generator mode, where gas turbine 31 supplies 28 MW (maximum) to load L and 2 MW (maximum) to VFD electric motor 32. VFD electric motor 32 can also be connected to a power grid (not shown) to inject power into the power grid.

[0064] Either operating mode can be utilized with the layouts described above. The operation of the hybrid gas turbine system 3 can be manipulated by the logic control unit 2 to select different operating modes and to optimize the operation for the conditions, i.e., for any layout of the hybrid gas turbine 3.

[0065] Specifically, how the processor 21 (or general processing means) processes each of the various vectors and variables depends, in part, on the optimization variable y i Depending on the load L requirements and constraints received, some of which may be maximized to maximize either the operating performance of the hybrid gas turbine system 3 or the cost of the power delivered.

[0066] Referring now to FIG. 8, another layout of a train system is shown in which a load L is connected to a 30 MW gas turbine 31 through a disconnect device 33, and similarly a 30 MW VFD electric motor 32 is connected to the load L.

[0067] Typically, the disconnecting devices 33 installed in the train system are self-synchronizing or overrunning type clutches, which are equipped with devices known as lock-in and lock-out devices, which when activated, function to lock the clutch in an engaged or disengaged position.

[0068] 9 illustrates operation of the hybrid gas turbine 3 in a helper mode, when the gas turbine 31 and the VFD electric motor 32 both generate maximum power to the load L, in this case (up to) 60 MW (i.e., e.g., the sum of the maximum power generated by the gas turbine 31 and the VFD electric motor 32), or in a full electric mode, when the gas turbine 31 is shut off and the VFD electric motor 32 provides (up to) 30 MW of power to the load L. In this case, the disconnection device 33 is disconnected.

[0069] FIG. 10 shows a hybrid gas turbine system 3 operating in generator mode, where the gas turbine 31 can generate, for example, (maximum) 30 MW of power, a portion 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 power generated, always 15 MW, is absorbed by the VFD electric motor 32 and thus injected into the power grid (not shown) to which the VFD electric motor 32 is connected.

[0070] As can be appreciated, proper coordination of the power generated by the gas turbine 31 or absorbed by the VFD electric motor 32 and / or the load L (pump, compressor, etc.) and the power absorbed by the VFD electric motor 32 in different operating modes is a problem or suitable optimization depending on several variables or data groups 41, 42, and 43 briefly listed above.

[0071] In addition, the monitoring and control method based on the above algorithm is flexible since it can be adapted to different layouts. As an example only, with reference to the state variable x, the state variable "clutch feedback" is not used in the hybrid gas turbine system 3 shown in Figure 5, but is used in the layout of the hybrid gas turbine system 3 of Figure 8, or in a layout with two clutches. Thus, the control function f i By choosing the right (x,u,w), the weight parameter α i Through setting appropriate values ​​of , any possible layout of the hybrid gas turbine system 3 can be realized.

[0072] As mentioned above, other layouts can be envisaged in addition to the layouts shown in Figures 5, 6, 7, 8, 9 and 10. More specifically, other embodiments can include a hybrid gas turbine system 3 similar to that shown in Figure 8, with an additional clutch interposed between the load L and the electric motor / generator 32. In particular, a possible layout provides a gas turbine, an electric motor / generator 32, a first clutch interposed therebetween, a load, and a second clutch interposed between the electric motor / generator 32 and the load L.

[0073] The monitoring and control method 5 disclosed herein can also be understood by looking at the flow chart in FIG. 11, which shows the following steps: - receiving (51) operating variables x for detecting the operating conditions of the gas turbine 31 and the electric motor / generator 32; - receiving a control variable u for controlling the operating state of the gas turbine 31 and the electric motor / generator 32 (52); - receiving a set of external variables w (53). receiving (54) a set of optimization variables y, the values ​​of which depend on the operating variables x, the control variables u, and the external variables w, and which have to be adjusted to be optimized; - manipulating one or more of the operating variables x, the control variables u, and the external variables w by optimizing the value of an optimization variable y (55); - Based on the control variables u obtained by optimizing the optimization variables y, one or more control signals are generated and sent to control the fuel supplied to the gas turbine 31 and the power generated or converted by the electric motor / generator 32 (56).

[0074] These steps may be performed in any suitable order or combination unless expressly stated otherwise herein.

[0075] The electric motor / generator 32 can be connected to a grid energy storage 431, to a solar panel power plant 432, to a wind power plant 433, to a hydrothermal power plant 434, or to a thermal power plant 435, or to any public power grid where any excess energy can be injected.

[0076] Also, the grid energy storage facility 431, the solar panel power plant 432, the wind power plant 433, the hydrothermal power plant 434, or the thermal power plant 435 may be operatively connected to the control logic unit 2 from which the latter may collect data, such as some of the external variables w.

[0077] Referring to FIG. 12, there is illustrated in detail how the control logic unit 2, and specifically the receiver-transmitter module 25, transmits the control signals received by the processor 21 to the fuel controller module 311 of the gas turbine 31 (step 57) and to the electric motor / generator controller 321 of the electric motor / generator 32 (step 58).

[0078] While the present invention has been described in terms of various specific embodiments, it will be apparent to those skilled in the art 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 may be changed or rearranged according to alternative embodiments.

[0079] Reference will now be made in detail to embodiments of the present disclosure, one or more examples of which are illustrated in the drawings. Each example is provided as an explanation of the present disclosure, not as a limitation of the present disclosure. Indeed, 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 "an embodiment" or "one embodiment" or "some embodiments" mean that a particular feature, structure, or characteristic described with respect to an embodiment is included in at least one embodiment of the disclosed subject matter. Thus, the appearance of the phrases "in an embodiment" or "in one embodiment" or "in some embodiments" in various places throughout this specification are not necessarily referring to the same embodiment. Moreover, particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0080] When presenting elements of various embodiments, the articles "a," "an," "the," and "said" are intended to mean that there are one or more of the elements. The terms "comprising," "including," and "having" are intended to be non-exclusive and mean that there may be additional elements other than the listed elements.

[0081] Barzano & Zanardo Roma SpA

Claims

1. A method for monitoring and controlling a hybrid gas turbine system (3), the hybrid gas turbine system (3) including at least one gas turbine (31) operated by a fuel, at least one electric motor / generator (32) capable of operating as a generator or a motor, and a number of actuators for controlling them, the method being implemented by a control logic unit (2), the method comprising: receiving (51) a set of operating variables x representative of operating conditions 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 conditions of the gas turbine (31) and the electric motor / generator (32); establishing (54) a set of optimization variables y, where the value of each optimization variable y is expressed as a function of the operating variables x, the control variables u, and the external variables w; optimizing (55) the values ​​of the optimization variables y to determine the optimized set of control variables u; generating and transmitting (56) one or more control signals based on the optimized set of control variables u to control the actuators of the hybrid gas turbine system (3); The method, wherein the one or more control signals dynamically control the fuel supplied to the gas turbine (31) and the power generated or converted by the electric motor / generator (32).

2. sending one or more control signals based on the optimized set of control variables u to a fuel controller module (311) configured to control the fuel supplied to the gas turbine (31); and / or 2. The method of claim 1, comprising sending one or more control signals based on the optimized set of control variables u to an electric motor / generator controller (321) adapted to control and regulate power generated or converted by the electric motor / generator (32).

3. The optimization variable y ｉ 3. The method of claim 1, wherein x, u, and w are a subset of the operating variables x and / or the control variables u and / or the external variables w.

4. The optimization variable y ｉ is the control function f ｉ (x, u, w) is a function of the operating variable x and / or the control variable u and / or the external variable w, The optimizing step (55) optimizes the optimization variable y according to the following formula: ｉ and the control function f ｉ It is performed to minimize the modulus of (x, u, w), [0010] In the formula, α ｉ The method according to any one of claims 1 to 3, wherein is a weighting factor.

5. The weighting parameter α is a weighting factor that maximizes the power generated, maximizes the life of the gas turbine (31), or minimizes exhaust gases. ｉ The method of claim 4 , further comprising the step of selecting a subset of

6. 6. The method of claim 1, wherein the operating variables x include at least one variable selected from ambient pressure, ambient temperature, ambient humidity, turbine inlet temperature, turbine inlet pressure, turbine compressor discharge temperature, turbine compressor discharge pressure, turbine compressor air flow, inlet filter losses, fuel temperature, fuel composition, combustion mode, high power turbine speed, first nozzle temperature, high power turbine discharge temperature, high power turbine discharge pressure, high power turbine flow, boost turbine speed, boost turbine discharge temperature, boost turbine flow, low power turbine speed, low power turbine discharge temperature, low power turbine flow, driven compressor inlet pressure, driven compressor inlet temperature, driven compressor inlet flow, driven compressor outlet temperature, driven compressor outlet pressure, electric motor power, exhaust gas volume, lifetime, clutch feedback.

7. 7. The method of any one of claims 1 to 6, wherein the control variables u include at least one variable selected from fuel demand, inlet guide vane demand, variable stator vane demand, variable bleed vane demand, bleed valve demand, overboard bleed vane demand, axial anti-surge valve demand, nozzle guide vanes, centrifugal anti-surge valve demand, compressor inlet vane demand, motor set point, reactive power set point for generating the control signal.

8. 8. The method according to claim 1, wherein the external variables w include at least one variable selected from fuel cost, electric energy selling price, electric energy cost, exhaust gas tax, maintenance cost, additional energy availability, reactive power cost / price, power factor.

9. The method according to any one of claims 1 to 8, wherein the gas turbine (31) is a heavy duty or aeroderivative gas turbine.

10. A method according to any one of claims 1 to 9, wherein the set of optimization variables y represents capital expenditure costs of the hybrid gas turbine system (3), power consumption of the hybrid gas turbine system (3), or operating costs of the hybrid gas turbine system (3).

11. A control logic unit (2), A processor (21) configured to perform the method according to any one of claims 1 to 10; a receiver-transmitter module (25) connectable to the processor (21) and configured to transmit (57) the control signal received by the processor (21) to control the actuators of the hybrid gas turbine system (3).

12. A bus (22) to which the processor (21) is connected; a database (23) connected to the bus (22) so as to be accessed and controlled by the processor (21); A control logic unit (2) as claimed in claim 11, further comprising: a computer readable memory (24) connected to said bus (22) for being accessed and controlled by said processor (21).

13. A hybrid gas turbine system (3), comprising: At least one gas turbine (31) operated by a fuel and comprising a fuel controller module (311) operable to control the fuel supplied to the gas turbine (31); at least one electric motor / generator (32), capable of operating as a generator or a motor, and comprising an electric motor / generator controller (321) adapted to control and regulate the power generated or converted by said electric motor / generator (32); 13. A hybrid gas turbine system (3), comprising a control logic unit (2) according to claim 11 or 12, operatively connected to said fuel controller module (311) and to said electric motor / generator controller (321).

14. 14. The hybrid gas turbine system (3) of claim 13, wherein the receiver-transmitter 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 to transmit (58) the control signal to the electric motor / generator controller (321) of the electric motor / generator (32).

15. 15. The hybrid gas turbine system (3) according to claim 13 or 14, wherein the electric motor / generator (32) is connected to at least one power plant (41, 42, 43, 44, 45).

16. 16. The hybrid gas turbine system (3) of claim 15, wherein the power plant comprises a grid energy storage (431), a solar panel power plant (432), a wind power plant (433), a hydrothermal power plant (434), and / or a thermal power plant (435).

17. The hybrid gas turbine system (3) according to any one of claims 13 to 16, wherein the gas turbine (31) is connected to a load (L) comprising a pump or a compressor.

18. 18. The hybrid gas turbine system (3) of claim 17, wherein the gas turbine (31) is connected to a load (L) through a disconnecting device (33) that is a self-synchronizing clutch or an overrunning.

19. 20. The hybrid gas turbine system (3) according to claim 18, wherein the disconnection or connection effected by the disconnection device is operated manually by an operator and / or automatically by an actuator.

20. The hybrid gas turbine system (3) of any one of claims 13 to 19, wherein the electric motor / generator (32) is a variable frequency drive electric motor (VFD electric motor).

21. The hybrid gas turbine system (3) according to any one of claims 13 to 20, wherein the gas turbine (31) is a heavy duty or aero-derivative gas turbine.

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