Power plant control system, power plant control method, and power plant control program

Abstract

By controlling the power plant based on operating conditions suitable for its operating state, plant performance is improved. [Solution] This application relates to a power plant control system for controlling a power plant capable of generating electricity by driving a turbine connected to a generator with steam generated by a boiler device. This system uses a predictive model constructed by machine learning with the power plant's operating data as training data to predict process values ​​of the power plant corresponding to the operating data, and determines optimized operating conditions by optimizing the operating conditions of the power plant based on the process values. The optimized operating conditions are stored in a memory unit in association with the operating state of the power plant. If it is determined that past optimized operating conditions corresponding to the operating state of the power plant stored in the memory unit are applicable as operating conditions, the past optimized operating conditions are set as the operating conditions.

Description

Technical Field

[0001] The present disclosure relates to a power generation plant control system, a power generation plant control method, and a power generation plant control program.

Background Art

[0002] There is known a power generation plant that generates steam by burning fossil fuels such as coal with a boiler device and generates electricity by driving a turbine connected to a generator with the steam. This type of power generation plant is operated by controlling each component device with a control system. In the control system, control is performed by a control signal generated based on operation data acquired from the power generation plant under operating conditions set according to the operating state of the power generation plant.

[0003] The operating conditions set in the control system are, for example, calculated by calculating an index related to plant performance from the process values of the power generation plant and optimized so that the index becomes optimal. When the process value of the power generation plant used for such optimization of the operating conditions is a parameter that cannot be actually measured, it may be obtained as a calculated predicted value using a prediction model that simulates the behavior of the power generation plant. For example, in Patent Document 1, it is proposed to improve the prediction accuracy of the process value under each operating condition by making the operating data used for constructing the prediction model switchable for each operating condition in a system that targets a coal-fired power plant.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Patent Document 1, mentioned above, allows switching of the operating data used to construct the prediction model for each operating condition. When this technology is applied to the control system of the aforementioned power plant, each time the operating state of the power plant changes, the prediction model is reconstructed using the operating data, and the operating conditions are optimized using the process values ​​predicted by the prediction model. Such reconstruction and optimization processes of the prediction model are computationally intensive and require a considerable amount of time to complete. Therefore, when the operating state of the power plant changes, while the system is calculating and optimizing the operating conditions to correspond to the changed operating state, the operating conditions optimized for the previous operating state continue to be applied. During this time, the power plant is controlled under operating conditions that are not optimized for the operating state, which may lead to a decrease in plant performance.

[0006] Furthermore, in recent years, renewable energy power generation facilities capable of generating electricity using renewable energy have become increasingly widespread. Since the amount of electricity generated by such renewable energy power generation facilities is greatly affected by environmental conditions, other power plants, such as thermal power plants connected to the same power grid, are required to function as buffers to absorb the changes in the amount of electricity generated by renewable energy power generation facilities. In this case, other power plants are required to respond flexibly to power generation commands from external sources, such as a central control room that manages the common power grid, by performing partial load operation in addition to conventional rated load operation. In this case, as described above, if optimization processing of operating conditions is performed every time the operating state of the power plant changes, there is a risk that the plant performance will deteriorate because the power plant will be controlled under operating conditions that are not optimized for the operating state until the optimization processing is completed.

[0007] At least one embodiment of this disclosure has been made in view of the above circumstances and aims to provide a power plant control system, a power plant control method, and a power plant control program that can improve plant performance by controlling the power plant based on operating conditions suitable for the operating state. [Means for solving the problem]

[0008] A power plant control system according to at least one embodiment of this disclosure solves the above problems. A power plant control system for controlling a power plant capable of generating electricity by driving a turbine connected to a generator with steam generated by a boiler device, An operating data acquisition unit for acquiring operating data of the aforementioned power plant, An optimization unit for determining optimized operating conditions by using a predictive model constructed by machine learning with the aforementioned operating data as training data to predict the process values ​​of the power plant corresponding to the aforementioned operating data, and optimizing the operating conditions of the power plant based on the aforementioned process values, A storage unit for storing the optimization operating conditions in association with the operating state of the power plant, An operating condition setting unit for setting the operating conditions of the power plant, Equipped with, If the operation condition setting unit determines that past optimization operation conditions corresponding to the operating state of the power plant stored in the memory unit are applicable as operation conditions, it sets the past optimization operation conditions as operation conditions.

[0009] A power plant control method according to at least one embodiment of this disclosure solves the above problems. A power plant control method for controlling a power plant capable of generating electricity by driving a turbine connected to a generator with steam generated by a boiler device, A process for acquiring operating data of the aforementioned power plant, A step of determining optimized operating conditions by using a predictive model constructed by machine learning with the aforementioned operating data as training data to predict the process values ​​of the power plant corresponding to the aforementioned operating data, and optimizing the operating conditions of the power plant based on the aforementioned process values, A step of storing the optimization operating conditions in a memory unit in association with the operating state of the power plant, The process of setting the operating conditions of the power plant, Equipped with, In the step of setting the operating conditions, if it is determined that past optimization operating conditions corresponding to the operating state of the power plant stored in the memory unit are applicable as the operating conditions, the past optimization operating conditions are set as the operating conditions.

[0010] A power plant control program according to at least one embodiment of this disclosure solves the above problems. A power plant control program for controlling a power plant capable of generating electricity by driving a turbine connected to a generator with steam generated by a boiler device, In a computer device, A process for acquiring operating data of the aforementioned power plant, A step of determining optimized operating conditions by using a predictive model constructed by machine learning with the aforementioned operating data as training data to predict the process values ​​of the power plant corresponding to the aforementioned operating data, and optimizing the operating conditions of the power plant based on the aforementioned process values, A step of storing the optimization operating conditions in a memory unit in association with the operating state of the power plant, The process of setting the operating conditions of the power plant, It is possible to do this, In the step of setting the operating conditions, if it is determined that past optimization operating conditions corresponding to the operating state of the power plant stored in the memory unit are applicable as the operating conditions, the past optimization operating conditions are set as the operating conditions. [Effects of the Invention]

[0011] According to at least one embodiment of this disclosure, a power plant control system, a power plant control method, and a power plant control program can be provided that can improve plant performance by controlling the power plant based on operating conditions suitable for the operating state.

Brief Description of the Drawings

[0012] [Figure 1] It is a schematic configuration diagram of a power plant control system according to an embodiment. [Figure 2] It is a flowchart showing a power plant control method according to the related art. [Figure 3] It is an example of a change pattern of the operating state of a power plant. [Figure 4A] It is a flowchart showing a control method according to an embodiment. [Figure 4B] It is a flowchart showing a control method according to an embodiment together with FIG. 4A. [Figure 4C] It is a modification of FIG. 4B. [Figure 5] It is another example of a change pattern of the operating state of a power plant. [Figure 6] It is a modification of FIG. 4B. [Figure 7A] It is an explanatory diagram regarding a prediction method of past optimization operation conditions corresponding to an operating state. [Figure 7B] It is an explanatory diagram regarding a prediction method of past optimization operation conditions corresponding to an operating state. [Figure 8] It is a modification of FIG. 4A. [Figure 9] It is an example of an operation schedule specified in step S222 of FIG. 8.

Modes for Carrying Out the Invention

[0013] Hereinafter, some embodiments of the present disclosure will be described with reference to the accompanying drawings. However, the dimensions, materials, shapes, relative arrangements, etc. of the configurations described as embodiments or shown in the drawings are not intended to limit the scope of the present disclosure thereto, but are merely illustrative examples.

[0014] Figure 1 is a schematic diagram of a power plant control system 1 according to one embodiment. The power plant control system 1 comprises a power plant 2 to be controlled, a control device 100 for controlling the power plant 2, and a control support device 200 for assisting the control of the control device 100.

[0015] The power plant 2 comprises a boiler 4, a steam turbine 6, and a generator 8. The boiler 4 is configured to generate steam S by heating feedwater supplied from the feedwater system using combustion gas produced by burning fuel F supplied from the fuel supply system 10 and air A supplied from the air supply system 12. The steam S, superheated to a high temperature, is sent to the steam turbine 6 as a working fluid through a water wall and superheater, which constitute the boiler (not shown).

[0016] A generator 8 is connected to the output shaft of the steam turbine 6. The generator 8 generates electricity by being driven by the power output from the steam turbine 6. The electricity generated by the generator 8 is supplied to a power grid (not shown). The steam that has finished its work in the steam turbine 6 is returned to condensate by the condenser 16. The condensate produced in the condenser 16 is sent to the boiler 4 via the feedwater pump 18 and used for steam generation.

[0017] Furthermore, the power plant 2 is equipped with sensors to monitor the status of each component of the power plant 2. Specifically, a fuel temperature sensor 20 is located at the outlet of the fuel supply system 10 to detect the temperature Tf of the fuel F supplied by the fuel supply system 10. An air temperature sensor 22 is located at the outlet of the air supply system 12 to detect the temperature Ta of the air A supplied by the air supply system 12. A steam temperature sensor 24 and a steam pressure sensor 26 are installed at the inlet of the steam turbine 6 to detect the temperature Ts and pressure Ps of the steam S (steam superheated in the superheater) introduced into the steam turbine 6, respectively. A condensate temperature sensor 28 is installed at the outlet of the condenser 16 to detect the temperature Tk of the condensate produced in the condenser 16.

[0018] The control device 100 is a control unit for controlling the power plant 2 having the above configuration. The control device 100 acquires parameters related to the operating state of the power plant 2 (hereinafter referred to as "operating parameters" as appropriate) and controls the power plant 2 by transmitting control signals generated by calculation processing using the acquired operating parameters to the power plant 2. The operating parameters may include calculation intermediate values ​​and control signals generated by the control device 100 based on the results detected by the aforementioned sensors installed in the power plant 2, as well as power generation command values ​​D that can be received from an external source (e.g., a central control room).

[0019] The control device 100 generates control signals corresponding to the operating conditions of the power plant 2 based on operating conditions that can be set by the control support device 200 described later. These operating conditions are set by the control support device 200 for each operating state of the power plant 2, so that the control of the power plant 2 by the control device 100 can be optimized according to the operating state of the power plant 2. In this embodiment, as an example of the control performed by the control device 100, we will describe a case in which a control signal is generated to control the feedwater pump 18 based on operating parameters so that the amount of power generated in the generator 8 becomes the power generation command value D input to the control device 100. The detailed configuration of the control device 100 will follow known examples and will therefore be omitted here.

[0020] The control support device 200 is configured to support the control of the control device 100, and in particular has the function of setting operating conditions in the control device 100. Specifically, the control support device 200 includes an operation data acquisition unit 202, a prediction model construction unit 204, an optimization unit 206, a storage unit 208, and an operating condition setting unit 210.

[0021] The operation data acquisition unit 202 is configured to acquire operation data of the power plant 2. As mentioned above, the operation data includes the results detected by the aforementioned sensors installed in the power plant 2, the calculation intermediate values ​​and control signals generated by the control device 100, and the power generation command value D that can be received from an external source (e.g., a central control room). In this embodiment, the operation data acquisition unit 202 can acquire operation data via the control device 100, but it may also acquire operation data directly from the power plant 2.

[0022] The prediction model construction unit 204 is configured to construct a prediction model for predicting process values ​​of the power plant 2 corresponding to the operating data, using machine learning with the operating data as training data. This machine learning is carried out according to a machine learning algorithm. While linear regression, neural networks, or random forests are possible, they are not limited to these machine learning algorithms.

[0023] The optimization unit 206 is configured to use the prediction model constructed by the prediction model construction unit 204 to predict process values ​​of the power plant 2 corresponding to the operating data, and to optimize the operating conditions of the power plant 2 based on those process values. The optimization of the operating conditions is performed, for example, by calculating an index corresponding to the performance of the power plant 2 based on the process values ​​predicted using the prediction model M, and optimizing the index to be the best possible.

[0024] Furthermore, the index used in the optimization process can be defined from various perspectives. For example, if the performance of power plant 2 is considered to be the nitrogen oxide concentration contained in the exhaust gas of boiler 4, it may be an estimated nitrogen oxide concentration calculated from process values ​​predicted using a prediction model. Alternatively, if the performance of power plant 2 is considered to be the operating cost, it may be a cost evaluation index calculated from process values ​​predicted using a prediction model.

[0025] The memory unit 208 is configured to store the operation data acquired by the operation data acquisition unit 202 and the operation conditions optimized by the optimization unit 206 (hereinafter referred to as "optimized operation conditions") in association with the operating state of the power plant 2. In this embodiment, the operating state of the power plant 2 is defined by a power generation command value D, which is a type of operation parameter, and each optimized operation condition is stored in the memory unit 208 in association with the corresponding power generation command value D. The memory unit 208 stores the optimized operation conditions for each operating state, and the corresponding optimized operation conditions can be retrieved based on the operating state by external access. In the following explanation, the optimization operation conditions stored in the memory unit 208 will be referred to as "past optimization operation conditions" as appropriate.

[0026] The operation condition setting unit 210 is configured for setting operation conditions for the control device 100. The operation condition setting unit 210 may set the optimized operation conditions calculated by the optimization unit 206 as the operation conditions, or it may set past optimized operation conditions stored in the storage unit 208. In the latter case, the operation condition setting unit 210 searches the storage unit 208 for past optimized operation conditions corresponding to the operating state of the power plant 2, and sets the past optimized operation conditions obtained as a search result as the operation conditions for the control device 100.

[0027] Next, a power plant control method implemented using the power plant control system 1 having the above configuration will be described. Here, we will first describe a power plant control method relating to reference technology with reference to Figure 2. Figure 2 is a flowchart of a power plant control method relating to reference technology.

[0028] First, the operation data acquisition unit 202 acquires operation data of the power plant 2 from the control device 100 (step S100). The operation data acquired in step S100 includes, as described above, the operation parameters that can be obtained from the various sensors installed in the power plant 2, the calculation intermediate values ​​and control signals generated by the control device 100, and the power generation command value D that can be received by the control device 100 from an external source (e.g., a central control room).

[0029] Next, preprocessing is performed on the operating data acquired in step S100 (step S101). This preprocessing is a calculation process that uses the operating data to calculate parameters for determining the success or failure of the operating data setting conditions in the following step S102. Examples of such parameters include the amount of change related to parameters included in the operating data over a predetermined period in step S100 (for example, the output of the generator 8 or the temperature of the steam S).

[0030] Next, the success or failure of the operation data setting conditions is determined using the parameters obtained in the preprocessing step S101 (step S102). The operation data setting conditions are conditions for determining whether the operation data acquired from the power plant 2 is in a set state, and are determined based on the parameters obtained in the preprocessing step S102. For example, if the parameters obtained in the preprocessing step S101 are fluctuations related to the output of the generator 8 and the temperature of the steam S over a predetermined period, then if these fluctuations are below a preset reference value, the operation data is considered to be in a set state, and the operation data setting conditions are determined to be met. If the operation data setting conditions are met (step S102: YES), the operation data acquired by the operation data acquisition unit 102 is stored in the storage unit 208 (step S103). If the conditions for setting the operating data are not met (step S102: NO), the process returns to step S100, and the acquisition of operating data is restarted.

[0031] Next, the success or failure of the prediction model construction conditions is determined (step S104). The prediction model construction conditions are the conditions for determining whether or not it is necessary to construct a new prediction model, and the success or failure of these conditions can be determined based on several criteria.

[0032] Here, we will exemplify some criteria for determining whether the prediction model construction conditions are met. First, if a prediction model corresponding to the operating state of the power plant 2 is not stored in the memory unit 208, it is necessary to construct a new prediction model, and therefore the prediction model construction condition is met. Also, even if a prediction model corresponding to the operating state of the power plant 2 is stored in the memory unit 208, if a sufficiently long period of time has passed since the construction of that prediction model, it is necessary to construct a new prediction model to ensure the reliability of the prediction model, and therefore the prediction model construction condition is met. Furthermore, even if a prediction model corresponding to the operating state of the power plant 2 is stored in the memory unit 208, if the prediction accuracy of that prediction model falls below a predetermined value, it is necessary to rebuild the prediction model, and therefore the prediction model construction condition is met.

[0033] If the conditions for constructing a prediction model are met (step S104), the prediction model construction unit 204 constructs a prediction model using the operating data stored in the storage unit 108 in step S103 (step S105). In step S105, machine learning is performed using the operating data stored in step S103 as training data to construct a prediction model corresponding to the operating state of the power plant 2. The prediction model constructed in step S105 is stored in the storage unit 108 in a readable format in association with the operating state of the power plant 2.

[0034] Next, the optimization unit 206 reads a prediction model corresponding to the operating state of the power plant 2 from the storage unit 108 and performs an optimization process using the prediction model to determine the optimization operation conditions (step S106). In the optimization process of step S106, the process values ​​of the power plant 2 are predicted by inputting operating data obtained from the power plant 2 to the prediction model, and the optimization operation conditions are determined so that the index corresponding to the performance of the power plant 2, calculated using the process values, is the best possible.

[0035] Next, the operation condition setting unit 210 sets the optimized operation conditions calculated in step S106 as operation conditions in the control device 100 (step S107). As a result, the control device 100 generates control signals based on the operation data acquired from the power plant 2 according to the optimized operation conditions corresponding to the operating state, thereby enabling optimized plant control according to the operating state.

[0036] Here, Figure 3 shows an example of a pattern of changes in the operating state of power plant 2. In this example, the operating state of power plant 2 is initially in operating state A, which corresponds to the power generation command value D1. At time t1, it begins to change and at time t2, it transitions to operating state B, which corresponds to the power generation command value D2. Subsequently, at time t3, it begins to change again and at time t4, it transitions back to the original operating state A (hereinafter, when distinguishing it from operating state A before time t1, it will be appropriately referred to as "operating state A'").

[0037] Furthermore, the periods between times t1 and t2, and between times t3 and t4, are transient periods during which the operating state changes. In the example in Figure 3, the rate of change of the operating state during the transient period is set to a constant value, but this is not particularly limited.

[0038] In the reference technology shown in Figure 2, whenever the operating state changes, the optimization operating conditions are calculated in step S106 using an optimization calculation with a prediction model. However, since the calculation of the optimization operating conditions is a computationally intensive process, it takes time for the optimization operating conditions to be calculated. Therefore, when the operating state changes, the control device 100 has the operating conditions set for the time before the operating state changed, which may lead to a decrease in plant performance during that period. This problem can be suitably resolved by the control method described below.

[0039] Figures 4A and 4B are flowcharts showing a control method according to one embodiment. First, steps S200 to S203 shown in Figure 4A of the control method in this embodiment are the same as steps S100 to S103 of the reference technology shown in Figure 2.

[0040] Next, the operating status of power plant 2 is identified by analyzing the operating data accumulated in step S203 (step S204). In step S204, the operating status of power plant 2 is identified by analyzing each operating parameter (e.g., power generation command value D) included in the operating data.

[0041] Next, similar to step S104 described above, the success or failure of the prediction model construction conditions is determined (step S205). If the prediction model construction conditions are met (step S205: YES), a new prediction model is constructed, similar to step S105 described above (step S206). On the other hand, if the prediction model construction conditions are not met (step S205: NO), the prediction model is not constructed.

[0042] Next, we move to Figure 4B, where it is determined whether or not past optimization operation conditions stored in the memory unit 208 can be applied (step S207). The determination in step S207 can be made based on several criteria. For example, if past optimization operation conditions cannot be applied, such as when there are no past optimization operation conditions in the memory unit 208 (step S207: NO), the process proceeds to step S211. While the optimization operation conditions are being calculated in step S211, the base operation conditions are applied.

[0043] Here, we will illustrate some of the criteria for making a decision in step S207. First, by accessing the memory unit 208, if past optimization operation conditions corresponding to the operating state identified in step S204 are not stored in the memory unit 208, it is determined that past optimization operation conditions cannot be applied.

[0044] Furthermore, even if past optimization operating conditions corresponding to the operating state identified in step S204 are stored in the memory unit 208, if a sufficiently long period has elapsed since the calculation of those past optimization operating conditions, it is determined that the past optimization operating conditions are old and cannot be applied to setting the operating conditions. This effectively prevents a decrease in plant performance due to the setting of unreliable, old past optimization operating conditions as operating conditions.

[0045] Furthermore, even if the optimization operating conditions corresponding to the operating state identified in step S204 are stored in the memory unit 208, if the operating conditions of the power plant 2 (for example, the properties of the fuel (calorific value, moisture content, ash content, and various other components contained in the fuel (carbon, hydrogen, oxygen, etc.)) have changed compared to when the past optimization operating conditions were calculated, it is determined that the past optimization operating conditions cannot be applied to setting the operating conditions. This effectively prevents the plant performance from degrading due to past optimization operating conditions calculated under different operating conditions.

[0046] If it is determined that past optimization operation conditions can be applied as setting conditions based on these criteria (step S207: YES), the operation condition setting unit 210 reads the past optimization operation conditions from the storage unit 208 (step S208), and the operation condition setting unit 210 sets the past optimization operation conditions read in step S208 as operation conditions in the control device 100 (step S209).

[0047] Next, in step S209, it is determined whether the operating state of the power plant 2, controlled by the control device 100 with previously set optimized operating conditions, is normal (step S210). The determination in step S210 can be made from several perspectives. For example, if an alarm is generated because the operating parameters of the power plant 2, controlled by the control device 100 with previously set optimized operating conditions, deviate from the acceptable range, it is determined that the operating state of the power plant 2 is not normal. Also, if the evaluation index (e.g., an index related to cost or performance) calculated based on the operating parameters of the power plant 2, controlled by the control device 100 with previously set optimized operating conditions, deviates from the acceptable range, it is determined that the operating state is not normal.

[0048] As a result, if the operating state of the power plant 2 is normal (step S210: YES), the control device 100 is set with past optimization operating conditions, and the optimization operating conditions corresponding to the current operating state are calculated (step S211). Since this calculation of optimization operating conditions is a computationally intensive process, it takes a considerable amount of time to complete the calculation of the optimization operating conditions in step S211. If the previous operating conditions remain set in the control device 100 at this time, there is a risk that the plant performance will deteriorate, as in the reference technology described above. In contrast, in this embodiment, past optimization operating conditions corresponding to the operating state of the power plant 2 are set in step S209, so the deterioration of plant performance as in the reference technology described above can be effectively suppressed.

[0049] Once the calculation of the optimization operating conditions is completed in step S211, the operation condition setting unit 210 sets the optimization operating conditions calculated in step S211 to the control device 100 (step S212). As a result, the previously set optimization operating conditions in the control device 100 are overwritten with the newly calculated optimization operating conditions.

[0050] Then, similar to step S210 described above, it is determined again whether the operating state of the power plant 2 controlled by the control device 100 with the optimized operating conditions set is normal (step S213). If it is determined that the operating state of the power plant 2 is normal (step S213: YES), the operating state of the power plant 2 becomes optimized by the control device 100 with the optimized operating conditions set.

[0051] On the other hand, if the operating state of the power plant 2 is not normal (step S210: NO), the operating condition setting unit 210 sets the control device 100 with base operating conditions, which are pre-prepared as standard operating conditions, instead of the past optimized operating conditions set in step S209 (step S215). In this way, if normal operation control of the power plant 2 cannot be achieved with the past optimized operating conditions, the control state of the power plant 2 can be maintained normally by switching to operation control based on the base operating conditions (even in this case, the optimized operating conditions are calculated in the subsequent step S211, but until the calculation of the optimized operating conditions is completed, the plant performance can be improved to some extent by controlling based on the base operating conditions).

[0052] Furthermore, if it is determined that the operating state of the power plant 2 based on the optimized operating conditions set in step S212 is not normal (step S213: NO), base operating conditions are set in the control device 100, similar to step S215 described above (step S216). As a result, if normal operation control of the power plant 2 cannot be achieved with the optimized operating conditions set in step S212, the control state of the power plant 2 can be maintained normally by switching to operation control based on the base operating conditions.

[0053] In the operating state change pattern illustrated in Figure 3, operating condition A' is the same as the past operating state A. Therefore, at time t4 when the operating state of the power plant 2 becomes operating condition A', the memory unit 208 contains past optimization operation conditions corresponding to operating state A. Consequently, in this case, while the optimization process using the prediction model calculates new optimization operation conditions, the past optimization operation conditions obtained from the memory unit 208 are temporarily set in the control device 100, thereby effectively suppressing the deterioration of plant performance during that period.

[0054] Figure 4C is a modified version of Figure 4B. In this modified version, once the calculation of the optimized operating conditions is completed in step S211, in step S212', the optimized operating conditions calculated in step S211 are set in the control device 100 by the operating condition setting unit 210, and the set values ​​are stored in the storage unit 208. Since it may take time from setting the optimized operating conditions calculated in step S211 in the control device 100 until it is determined whether the operating state is normal or not (for example, it may take time from setting new optimized operating conditions until the operating state stabilizes), it is not easy to determine in real time and with accuracy whether the optimized operating conditions are appropriate.

[0055] In this modified example, the optimization operation conditions calculated in step S211 are temporarily stored in the storage unit 208 in step S212, allowing for subsequent scrutiny of whether the optimization operation conditions stored in the storage unit 208 are appropriate. For example, if the scrutiny reveals that the optimization operation conditions are inappropriate, they can be deleted as appropriate.

[0056] In the modified example shown in Figure 4C, the optimization operation conditions are stored in the memory unit 208 in step S212', so step S214 in Figure 4B is unnecessary.

[0057] Figure 5 shows another example of a change pattern in the operating state of the power plant 2. In this example, the change pattern is the same as shown in Figure 3 until time t3, but at time t4, it transitions to operating state C, which corresponds to the power generation command value D3. Operating state C is different from operating state A, which corresponds to the power generation command value D1, and operating state B, which corresponds to the power generation command value D2. At time t4, the memory unit 208 stores the optimization operation conditions corresponding to operating states A and B as optimization operation conditions corresponding to the previous operating states of the power plant 2, but it does not store the optimization operation conditions corresponding to the new operating state C. Therefore, in the above embodiment, in step S207, it is determined that the past optimization operation conditions cannot be applied as the optimization operation conditions corresponding to operating state C.

[0058] Here, Figure 6 is a modified version of Figure 4B. In this modified control method, the optimization unit 206 may calculate past optimization operating conditions corresponding to the operating state of the power plant 2 based on at least one past optimization operating condition corresponding to another operating state. In this case, even if the storage unit 208 does not store past optimization operating conditions corresponding to operating state C, it is possible to predict past optimization operating conditions corresponding to operating state C based on past optimization operating conditions corresponding to at least one of operating states A or B.

[0059] In this modified example, compared to Figure 4B, if it is determined that past optimization operating conditions cannot be applied (step S207: NO), it is further determined whether past optimization operating conditions corresponding to the operating state of power plant 2 can be predicted based on past optimization operating conditions corresponding to other operating states (step S220). In the example shown in Figure 5, when power plant 2 is in operating state C, the memory unit 208 stores at least past optimization operating conditions corresponding to the previous operating states A or B. In step S220, it is determined whether past optimization operating conditions corresponding to operating state C can be predicted based on past optimization operating conditions corresponding to at least one of the other operating states A or B.

[0060] The prediction of past optimization operating conditions corresponding to operating state C can be performed, for example, by the following method. Figures 7A and 7B are explanatory diagrams regarding the prediction method for past optimization operating conditions corresponding to operating state C.

[0061] In Figure 7A, past optimization operating conditions corresponding to other operating conditions A and B are known, and by linear interpolation of these, past optimization operating conditions corresponding to the unknown operating condition C can be predicted. In Figure 7B, a function FX is shown that defines the relationship between the generator output, boiler load, etc., and the parameter P included in the operating conditions in the base operating conditions which are prepared in advance as standard operating conditions for power plant 2. By applying this function FX to the parameter Pb corresponding to the generator output and boiler load in other operating conditions B (for example, by translating the function FX so that it passes through operating condition B), the parameter Pc corresponding to the generator output and boiler load in operating condition C can be predicted, and the optimization operating conditions corresponding to operating condition C can be predicted.

[0062] Furthermore, as described above, it is preferable that the past optimization operating conditions under other operating conditions used to predict the past optimization operating conditions under the operating conditions of power plant 2 are also data from within a predetermined time period from the time of calculation.

[0063] In step S220, it is determined whether or not it is possible to predict past optimization operating conditions using this method. If it is determined that it is possible to predict past optimization operating conditions (step S220: YES), the prediction of past optimization operating conditions is performed using the above method (step S221), and these are set as operating conditions in the control device 100 (step S209). Thus, in this embodiment, even if it is determined in step S207 that past optimization operating conditions cannot be applied, if it is possible to predict those past optimization operating conditions, the predicted past optimization operating conditions can be set as operating conditions.

[0064] On the other hand, if past optimization operation conditions cannot be predicted (step S220: NO), the process is advanced to step S211, which is the same as the embodiment described above with reference to Figures 4A and 4B.

[0065] In the optimization unit 206, as described above, the process values ​​of the power plant 2 are predicted using the prediction model constructed in the prediction model construction unit 204, and optimization calculations are performed so that the index calculated using these process values ​​is the best possible. At this time, the optimization calculations determine the optimization operation conditions by searching within a range in which a predetermined likelihood can be ensured for the process values ​​within the acceptable range.

[0066] In some embodiments, the optimization unit 206 may determine, for each operating state of the power plant 2, a first optimization operating condition having a relatively small first likelihood relative to the allowable range, and a second optimization operating condition having a second likelihood greater than the first likelihood. The first optimization operating condition is an optimization operating condition that prioritizes economic efficiency under stable operating conditions because it has a small likelihood relative to the allowable range, while the second optimization operating condition is an optimization operating condition that prioritizes controllability under transient conditions because it has a large likelihood relative to the allowable range.

[0067] The first and second optimization operating conditions are stored in the memory unit 208 in association with the corresponding operating state. In other words, the memory unit 208 stores two types of optimization operating conditions (first and second optimization operating conditions) for each operating state. The operation condition setting unit 210 can then read either the first or second optimization operating conditions from the memory unit 208 as past optimization operating conditions corresponding to the operating state, and set them as operating conditions in the control device 100.

[0068] Here, we will specifically describe a control method that uses either the first or second optimization operating condition as the optimization operating condition. Figure 8 is a modified version of Figure 4A (the latter half of the control method related to this modified version is the same as that of Figure 4B, so the explanation is omitted).

[0069] In this modified example, after performing steps up to S204, as in the previously described embodiment, the operating schedule for the power plant 2 is determined (step S222). In the operating schedule, for example, the time-series changes of the power generation command value D input to the control device 100 from an external source are defined as the operating schedule, and in step S222, the future pattern of changes in the operating state is determined based on the operating schedule.

[0070] Next, based on the operating schedule identified in step S222, it is determined whether the future operating state of the power plant 2 is in a stable state (step S223). In step S223, for example, it is possible to determine whether the state is stable based on whether the amount of change in the future operating state exceeds a preset threshold, based on the operating schedule identified in step S222.

[0071] If it is determined that the future operating state is a stable state (step S223: YES), the process proceeds to step S205, as in the embodiment described above. In this case, the past optimization operating conditions read out in step S208 (see Figure 4B) are the first optimization operating conditions which have low likelihood and excellent economic performance. As a result, when the operating state is a stable state, operation prioritizing economic efficiency becomes possible.

[0072] On the other hand, if it is determined that the system is not in a stable state (step S223: NO), it is determined whether a second optimization operation condition with high likelihood and excellent controllability can be applied as a past optimization operation condition (step S224). In step S224, the determination can be made based on several criteria. For example, if a second optimization operation condition corresponding to the operating state is stored in the memory unit 208, it is determined that the second optimization operation condition can be applied. Even if a second optimization operation condition corresponding to the operating state is not stored in the memory unit 208, if it is possible to predict the second optimization operation condition corresponding to the transient state based on the optimization operation conditions corresponding to the stable states before and after the transient state, it is determined that the second optimization operation condition can be applied (for example, the second optimization operation condition corresponding to the transient state can be predicted by apportioning the second optimization operation conditions corresponding to the stable states before and after the transient state).

[0073] As a result, if it is determined that the second optimization operating condition is applicable (step S224: YES), the process returns to step S208. This ensures the stability of the control state of the power plant 2 by ensuring a high likelihood when the operating state is in a transient state, as the operating state is not stable.

[0074] Here, we will refer to Figure 9 and explain using power plant 2, which operates according to a specific operating schedule, as an example. Figure 9 is an example of an operating schedule identified in step S222 of Figure 8.

[0075] In this example, the operating state is constant before time t1, but changes from time t1 to time t2, and then settles back into a constant state at time t2. In step S223, based on the behavior of the operating state in the operating schedule identified in step S222, a stable state where the operating state is constant and a transient state where the operating state changes are identified.

[0076] In the embodiment shown in Figure 9, in particular, when identifying transient states from the operating schedule, a predetermined margin is taken into consideration. In the operating schedule, the operating state actually changes from time t1 to time t2, but when identifying the operating state, the transient state is identified as the period from time ta, which is a predetermined margin T1 before time t1, to time tb, which is a predetermined margin T2 after time t2. By identifying the transient state broadly by considering this margin, controllability is prioritized during periods when the operating state is prone to instability, including immediately before and after the transient state, while economic efficiency is prioritized during stable settling states. As a result, overall, it is possible to improve the plant performance while considering the balance between the controllability and economic efficiency of the power plant.

[0077] For example, in power plant 2, during transient states where the load on boiler 4 changes, an imbalance between fuel F from fuel supply system 10 and feedwater from feedwater system can cause parameters such as metal temperature to deviate from acceptable ranges, potentially triggering various alarms. In recent years, the use of renewable energy power generation facilities has become widespread, but the amount of electricity generated from renewable energy is easily affected by weather conditions. Therefore, power plants 2 connected to a common power grid are increasingly required to have a buffer function to absorb these fluctuations. In this case, power plant 2 may operate by increasing the load change rate of boiler 4 or lowering the minimum load value. When such operations are performed using power plant 2, which is primarily designed for steady-state operation, the possibility of control parameters deviating from acceptable ranges during transient states increases. In this embodiment, as described above, by selecting a second optimization operating condition with a high likelihood during transient states where the possibility of control parameters deviating from acceptable ranges increases, the risk of such deviations from acceptable ranges can be effectively suppressed. On the other hand, in stable state settings, by selecting a first optimization operating condition with a low likelihood, the economic efficiency of operation can be improved.

[0078] In each of the above embodiments, the storage unit 208 stores the optimization operation conditions for each operating state, and the operation condition setting unit 210 reads the optimization operation conditions corresponding to the operating state of the power plant 2 from the storage unit 208 and sets them in the control device 100. The optimization operation conditions may be prepared in the storage unit 208 to correspond to each operating state, but they may also be prepared for each range of parameters that define the operating state.

[0079] For example, if the boiler load is used as a parameter to define the operating state, the memory unit 208 may store optimized operating conditions for each load range with a certain width. For example, if the memory unit 208 stores optimized operating conditions for each numerical value indicating the load, such as 100%, 99%, 98%, etc., the number of holding circuits in the memory unit 208 becomes enormous, and even if the change in the operating state is gradual, the setting of the operating conditions may continue to change during operation, potentially impairing the stability of the operation. Therefore, in this embodiment, by defining the operating state for each load range with a certain width (for example, 30-40%, 40-65%, 65-85%, 85-100%, etc.), the scale of the holding circuits in the memory unit 208 can be reduced while ensuring the stability of the operation. Furthermore, such load zone settings can be efficiently implemented, for example, by dividing the load zones into those with high usage frequency based on the operational performance of power plant 2.

[0080] Furthermore, the parameters defining the operating state can broadly include parameters that affect the operating state of the power plant 2. For example, the operating state may be defined by the power generation command value D and the load as described above, but in addition to these, parameters related to the type of fuel handled in the fuel supply system 10 and the operating state of the fuel supply system 10 may also be included.

[0081] For example, the fuel supply system 10 handles coal fuel, biomass fuel, or pulverized fuel produced by crushing a mixture of coal and biomass fuel as fuel F supplied to the boiler 4. In addition, ammonia may be used as a liquid (or gaseous) fuel and may be supplied to the boiler 4 together with coal fuel or biomass fuel. The following example describes the case where only coal is used. The fuel supply system 10 includes a coal fuel source for supplying coal fuel and multiple mills for producing pulverized coal fuel by crushing the coal fuel from the coal fuel source. In this case, parameters that define the operating state may include the type of coal fuel supplied from the coal fuel source and the number of operating mills for crushing the coal fuel. In this case, depending on the coal type, the number of operating mills may differ even at the same boiler load depending on the calorie level. Also, when raising or lowering the boiler load, each mill is turned on / off, so the number of operating mills may not be the same even at the same boiler load. Therefore, by defining the operating state based on the type of coal and the number of operating mills, the scale of the storage circuit in the memory unit 208 can be reduced while ensuring operational stability.

[0082] Furthermore, it is possible to replace the components in the above-described embodiments with well-known components as appropriate, without departing from the spirit of this disclosure, and the above-described embodiments may also be combined as appropriate.

[0083] The contents described in each of the above embodiments can be understood, for example, as follows:

[0084] (1) A power plant control system according to one embodiment is: A power plant control system for controlling a power plant capable of generating electricity by driving a turbine connected to a generator with steam generated by a boiler device, An operating data acquisition unit for acquiring operating data of the aforementioned power plant, An optimization unit for determining optimized operating conditions by using a predictive model constructed by machine learning with the aforementioned operating data as training data to predict the process values ​​of the power plant corresponding to the aforementioned operating data, and optimizing the operating conditions of the power plant based on the aforementioned process values, A storage unit for storing the optimization operating conditions in association with the operating state of the power plant, An operating condition setting unit for setting the operating conditions of the power plant, Equipped with, If the operation condition setting unit determines that past optimization operation conditions corresponding to the operating state of the power plant stored in the memory unit are applicable as operation conditions, it sets the past optimization operation conditions as operation conditions.

[0085] According to the embodiment of (1) above, as operating conditions for controlling the power plant, optimized operating conditions are set based on the process values ​​of the power plant predicted using a predictive model constructed with operating data corresponding to the operating state of the power plant as learning data. Therefore, in this embodiment, by setting past optimized operating conditions stored in the memory as operating conditions, the deterioration of plant performance in the changed operating state can be effectively suppressed.

[0086] (2) In other embodiments, in the embodiment of (1) above, If the operating condition setting unit determines that past optimization operating conditions corresponding to the operating state of the power plant stored in the memory unit are applicable as operating conditions, it sets the past optimization operating conditions as operating conditions while the optimization unit is calculating the optimization operating conditions.

[0087] According to the embodiment of (2) above, when the operating state of the power plant changes, calculating the optimization operating conditions using a predictive model for the changed operating state takes a considerable amount of time. However, by setting past optimization operating conditions stored in the memory unit as operating conditions during that period, the deterioration of the plant performance in the changed operating state can be effectively suppressed.

[0088] (3) In other embodiments, in the embodiment of (1) or (2) above, If the storage unit does not contain the optimized operating conditions corresponding to the operating state, the operating condition setting unit sets the optimized operating conditions obtained by the optimization unit as the operating conditions.

[0089] According to the embodiment of (3) above, if no optimization operating conditions corresponding to the operating state of the power plant exist in the memory unit, the optimization unit can perform calculations again to suitably determine the optimization operating conditions corresponding to the new operating state.

[0090] (4) In other embodiments, in any one embodiment of (1) to (3) above, If the control state of the power plant is not normal when the past optimized operating conditions are set as the operating conditions, the operating condition setting unit sets a pre-prepared base operating condition as the operating condition instead of the past optimized operating conditions.

[0091] According to the embodiment of (4) above, if the control state of the power plant is determined to be abnormal as a result of setting past optimized operating conditions as operating conditions, then, instead of the past optimized operating conditions, base operating conditions, which are pre-prepared as standard operating conditions corresponding to the operating state, are set as operating conditions. In this way, if the past optimized operating conditions are not suitable for the current operating state, the deterioration of plant performance can be effectively suppressed by setting base operating conditions as operating conditions.

[0092] (5) In other embodiments, in the embodiment of (4) above, If the operating parameters of the power plant, or the evaluation index calculated based on the operating parameters, deviate from the acceptable range, the control state is determined to be abnormal.

[0093] According to the embodiment of (5) above, whether or not the control state is normal when past optimization operating conditions are set as operating conditions can be determined by comparing the operating parameters of the power plant, or an evaluation index calculated based on the operating parameters, with an acceptable range.

[0094] (6) In other embodiments, in any one embodiment of (1) to (5) above, If a predetermined period has elapsed since the calculation of the aforementioned past optimization operating conditions, it is determined that the aforementioned past optimization operating conditions cannot be applied as operating conditions.

[0095] According to the embodiment of (6) above, if a sufficiently long time has passed since the calculation of past optimization operating conditions, the past optimization operating conditions are deemed outdated and unsuitable for setting as operating conditions. This effectively prevents a decrease in plant performance that would result from setting unreliable, outdated past optimization operating conditions as operating conditions.

[0096] (7) In other embodiments, in any one embodiment of (1) to (6) above, If the operating conditions of the power plant have changed since the calculation of the past optimized operating conditions, it is determined that the past optimized operating conditions cannot be applied as the operating conditions.

[0097] According to the embodiment of (7) above, if the operating conditions of the power plant have changed since the calculation of past optimized operating conditions, those past optimized operating conditions are determined to be unsuitable for setting as operating conditions. This effectively prevents a decrease in plant performance due to past optimized operating conditions calculated under different operating conditions.

[0098] (8) In other embodiments, in any one embodiment of (1) to (7) above, If the past optimization operation conditions are not stored in the memory unit, the operation condition setting unit sets the past optimization operation conditions corresponding to the predicted operating state, based on other past optimization operation conditions corresponding to other operating states, as the operation conditions.

[0099] According to the embodiment of (8) above, even if past optimization operation conditions corresponding to a specific operating state are not stored in the memory unit, it is possible to predict such past optimization operation conditions based on past optimization operation conditions corresponding to other operating states.

[0100] (9) In other embodiments, in any one embodiment of (1) to (8) above, The optimization unit calculates, as the optimization operation conditions corresponding to the operating state, a first optimization operation condition and a second optimization operation condition which has a greater likelihood than the first optimization operation condition for the allowable range of the process value. The operation condition setting unit sets the first optimized operation condition as the operation condition when the operating state is in a stable state. If the aforementioned operating state is in a transient state, the second optimization operating condition is set as the operating condition.

[0101] According to the embodiment described in (9) above, the first and second optimized operating conditions are calculated by an optimization process using process values ​​predicted using a prediction model for the operating state of the power plant. The optimization operating conditions used to calculate the second optimized operating conditions are set to have a higher likelihood of the process values ​​remaining within the acceptable range compared to the optimization operating conditions used to calculate the first optimized operating conditions. As a result, the second optimized operating conditions are calculated as optimized operating conditions with a higher likelihood of the process values ​​remaining within the acceptable range compared to the first optimized operating conditions. The first optimized operating conditions are set as operating conditions when the operating state is in a stable state, and the second optimized operating conditions are set as operating conditions when the operating state is in a transient state. In this way, controllability can be improved by setting the second optimized operating conditions, which have a high likelihood, as operating conditions in transient states where process values ​​and indicators calculated from process values ​​are likely to deviate from the acceptable range. On the other hand, in stable states where process values ​​and indicators calculated from process values ​​are unlikely to deviate from the acceptable range, economic efficiency can be improved by setting the first optimized operating conditions, which have a low likelihood, as operating conditions.

[0102] (10) In other embodiments, in any one embodiment of (1) to (9) above, The optimization unit calculates the optimization operating conditions for each load zone of the boiler system as the operating state.

[0103] According to the embodiment of (10) above, the optimization operating conditions are calculated for each load range of the boiler system, which reduces the computational load compared to when they are calculated for each load, while improving control stability when the operating conditions change.

[0104] (11) In other embodiments, in any one embodiment of (1) to (10) above, The aforementioned operating conditions are defined by parameters including at least one of the type of fuel supplied to the boiler system or the number of operating fuel supply devices for supplying the fuel.

[0105] According to the embodiment of (11) above, the operating state of the power plant is preferably defined by a parameter that includes at least one of the type of fuel supplied to the boiler equipment or the number of operating fuel supply devices (for example, a mill for crushing fuel, or a source of liquid or gaseous fuel).

[0106] Furthermore, the generator may be electrically connected to a power grid supplied with electricity from renewable energy power generation equipment.

[0107] In this case, the power plant being controlled is one that has a generator connected to a common power grid with renewable energy power generation equipment, the amount of power generated being greatly affected by environmental conditions. In this case, when the amount of power generated by the renewable energy power generation equipment changes, the power supply command to the power plant changes, and consequently, the operating state of the power plant changes. Even when the operating state changes in this way, as described in each of the embodiments above, when the operating state of the power plant changes, while the optimization process using the predictive model is performed for the changed operating state, past optimization operation conditions stored in the memory unit can be set as operating conditions, thereby effectively suppressing the deterioration of the plant performance in the changed operating state.

[0108] (12) A power plant control method according to one embodiment is: A power plant control method for controlling a power plant capable of generating electricity by driving a turbine connected to a generator with steam generated by a boiler device, A process for acquiring operating data of the aforementioned power plant, A step of determining optimized operating conditions by using a predictive model constructed by machine learning with the aforementioned operating data as training data to predict the process values ​​of the power plant corresponding to the aforementioned operating data, and optimizing the operating conditions of the power plant based on the aforementioned process values, A step of storing the optimization operating conditions in a memory unit in association with the operating state of the power plant, The process of setting the operating conditions of the power plant, Equipped with, In the step of setting the operating conditions, if it is determined that past optimization operating conditions corresponding to the operating state of the power plant stored in the memory unit are applicable as the operating conditions, the past optimization operating conditions are set as the operating conditions.

[0109] According to the embodiment of (12) above, as operating conditions for controlling the power plant, optimized operating conditions are set based on the process values ​​of the power plant predicted using a predictive model constructed with operating data corresponding to the operating state of the power plant as learning data. Therefore, in this embodiment, by setting past optimized operating conditions stored in the memory as operating conditions, the deterioration of plant performance in the changed operating state can be effectively suppressed.

[0110] (13) A power plant control program according to one embodiment is: A power plant control program for controlling a power plant capable of generating electricity by driving a turbine connected to a generator with steam generated by a boiler device, In a computer device, A process for acquiring operating data of the aforementioned power plant, A step of determining optimized operating conditions by using a predictive model constructed by machine learning with the aforementioned operating data as training data to predict the process values ​​of the power plant corresponding to the aforementioned operating data, and optimizing the operating conditions of the power plant based on the aforementioned process values, A step of storing the optimization operating conditions in a memory unit in association with the operating state of the power plant, The process of setting the operating conditions of the power plant, It is possible to do this, In the step of setting the operating conditions, if it is determined that past optimization operating conditions corresponding to the operating state of the power plant stored in the memory unit are applicable as the operating conditions, the past optimization operating conditions are set as the operating conditions.

[0111] According to the embodiment of (13) above, as operating conditions for controlling the power plant, optimized operating conditions are set based on the process values ​​of the power plant predicted using a predictive model constructed with operating data corresponding to the operating state of the power plant as learning data. Therefore, in this embodiment, by setting past optimized operating conditions stored in the memory as operating conditions, the deterioration of plant performance in the changed operating state can be effectively suppressed. [Explanation of Symbols]

[0112] 1. Power plant control system 2 Power plant 4 Boiler 6. Steam Turbine 8 Generators 10 Fuel supply system 12 Air supply system 16 Condenser 18. Water supply pump 20 Fuel temperature sensor 22 Air temperature sensor 24 Steam temperature sensor 26. Steam pressure sensor 28 Condensate temperature sensor 100 Control device 200 Control support device 202 Operation Data Acquisition Unit 204 Predictive Model Construction Department 206 Optimization Unit 208 Storage section 210 Operation Condition Setting Unit

Claims

1. A power plant control system for controlling a power plant capable of generating electricity by driving a turbine connected to a generator with steam generated by a boiler device, An operating data acquisition unit for acquiring operating data of the aforementioned power plant, An optimization unit for determining optimized operating conditions by using a predictive model constructed by machine learning with the aforementioned operating data as training data to predict the process values ​​of the power plant corresponding to the aforementioned operating data, and optimizing the operating conditions of the power plant based on the aforementioned process values, A storage unit for storing the optimization operating conditions in association with the operating state of the power plant, An operating condition setting unit for setting the operating conditions of the power plant, Equipped with, A power plant control system in which the operation condition setting unit sets past optimization operation conditions corresponding to the operating state of the power plant stored in the memory unit as operation conditions when it is determined that such past optimization operation conditions are applicable as operation conditions.

2. The power plant control system according to claim 1, wherein the operation condition setting unit determines that past optimization operation conditions corresponding to the operating state of the power plant stored in the memory unit are applicable as operation conditions, and sets the past optimization operation conditions as operation conditions while the optimization unit is calculating the optimization operation conditions.

3. The power plant control system according to claim 1 or 2, wherein if the optimization operating conditions corresponding to the operating state do not exist in the storage unit, the optimization operating conditions obtained by the optimization unit are set as the operating conditions.

4. The power plant control system according to claim 1 or 2, wherein if the control state of the power plant is not normal when the past optimized operating conditions are set as the operating conditions, the operating condition setting unit sets a pre-prepared base operating condition as the operating condition instead of the past optimized operating conditions.

5. The power plant control system according to claim 4, wherein if the operating parameters of the power plant, or an evaluation index calculated based on the operating parameters, deviate from an acceptable range, the control state is determined to be abnormal.

6. The power plant control system according to claim 1 or 2, wherein if a predetermined period has elapsed since the calculation of the past optimization operation conditions, it is determined that the past optimization operation conditions cannot be applied as the operation conditions.

7. The power plant control system according to claim 1 or 2, which determines that the past optimized operating conditions cannot be applied as operating conditions if the operating conditions of the power plant have changed since the calculation of the past optimized operating conditions.

8. The power plant control system according to claim 1 or 2, wherein if the past optimization operation conditions are not stored in the memory unit, the operation condition setting unit sets the past optimization operation conditions corresponding to the operating state predicted based on other past optimization operation conditions corresponding to other operating states as the operation conditions.

9. The optimization unit calculates, as the optimization operation conditions corresponding to the operating state, a first optimization operation condition and a second optimization operation condition which has a greater likelihood than the first optimization operation condition for the allowable range of the process value. The operation condition setting unit sets the first optimized operation condition as the operation condition when the operating state is in a stable state. The power plant control system according to claim 1 or 2, wherein the second optimized operating condition is set as the operating condition when the aforementioned operating state is in a transient state.

10. The power plant control system according to claim 1 or 2, wherein the optimization unit calculates the optimization operating conditions for each load zone of the boiler equipment as the operating state.

11. The power plant control system according to claim 1 or 2, wherein the operating state is defined by a parameter including at least one of the type of fuel supplied to the boiler system or the number of operating fuel supply devices for supplying the fuel.

12. A power plant control method for controlling a power plant capable of generating electricity by driving a turbine connected to a generator with steam generated by a boiler device, A process for acquiring operating data of the aforementioned power plant, A step of determining optimized operating conditions by using a predictive model constructed by machine learning with the aforementioned operating data as training data to predict the process values ​​of the power plant corresponding to the aforementioned operating data, and optimizing the operating conditions of the power plant based on the aforementioned process values, A step of storing the optimization operating conditions in a memory unit in association with the operating state of the power plant, The process of setting the operating conditions of the power plant, Equipped with, A power plant control method comprising the step of setting the operating conditions, wherein if it is determined that past optimization operating conditions corresponding to the operating state of the power plant stored in the memory unit are applicable as the operating conditions, the past optimization operating conditions are set as the operating conditions.

13. A power plant control program for controlling a power plant capable of generating electricity by driving a turbine connected to a generator with steam generated by a boiler device, In a computer device, A process for acquiring operating data of the aforementioned power plant, A step of determining optimized operating conditions by using a predictive model constructed by machine learning with the aforementioned operating data as training data to predict the process values ​​of the power plant corresponding to the aforementioned operating data, and optimizing the operating conditions of the power plant based on the aforementioned process values, A step of storing the optimization operating conditions in a memory unit in association with the operating state of the power plant, The process of setting the operating conditions of the power plant, It is possible to do this, In the step of setting the operating conditions, a power plant control program sets the past optimized operating conditions, which correspond to the operating state of the power plant and are stored in the memory unit, as the operating conditions if it is determined that such conditions are applicable.

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