A unit coordination control system and method for thermal power units

Abstract

The present invention provides a unit coordinated control system and method for a thermal power unit, and the present invention relates to the technical field of coordinated control of thermal power units. The present invention obtains a prediction model through historical load curves, inputs a real-time load curve into the prediction model to obtain a load prediction curve, obtains a standard power curve and a power increase rate curve of each single power generation equipment in the thermal power unit, sets a power control curve for each single power generation power, and simultaneously sets restriction conditions and solves them in MATLAB, thereby realizing coordinated control of multiple single power generation equipment in the thermal power unit, while meeting the requirements of load increase and increase rate, generating a thermal power unit status score based on the relationship between the power control curve and the load curve of each single power generation equipment, and setting a score threshold to feedback the result, thereby realizing a safe guarantee of the continuous working state of the thermal power unit.

Description

Technical Field

[0001] The present invention relates to the technical field of coordinated control of thermal power units, and in particular to a coordinated control system and method for thermal power units. Background Art

[0002] As an important component of the traditional power system, the operating efficiency and stability of thermal power units directly affect the safety and economy of power supply. In actual operation, thermal power units contain multiple sets of power generation equipment. When supplying power to the power grid, multiple sets of power generation equipment work together. During the construction process of thermal power units, the models and performance of power generation equipment built at different times are not the same, and their power generation capacity and response capabilities are also different. By coordinating and controlling the working conditions of each set of power generation equipment, the thermal power units can meet the power load demand.

[0003] In the existing technology, thermal power units generally adopt the form of variable power supply. When the grid load is low, a lower power is used for power generation. When the grid load increases, the thermal power units also increase the power generation power. However, in actual applications, due to the coordination issues between multiple sets of power generation equipment within the thermal power unit, and each set of power generation equipment has different power generation capacity and response capabilities, there are challenges in the coordinated control of multiple sets of power generation equipment within the thermal power unit when the grid load increases. In particular, when the grid load increases sharply, the performance limitations of the thermal power unit itself can easily cause the power increase rate to be slower than the load increase rate, thereby causing a power supply gap and affecting normal work and production.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The object of the present invention is to provide a unit coordinated control system and method for a thermal power unit to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A coordinated control method for a thermal power unit comprises the following steps:

[0008] Step 1: Obtain the time from startup to maximum power of all individual power generation equipment in the thermal power plant, select the longest time and calibrate it as the prediction window duration, obtain the historical load curve of the thermal power plant, and input the load curve into the LSTM model for training to obtain the prediction model;

[0009] Step 2: Collect the load curve of the thermal power unit in real time during operation and input it into the prediction model for training. The trained prediction model is used to obtain the load prediction curve within the prediction window.

[0010] Step 3: Obtain the standard power curve of each single generating equipment in the thermal power unit. Based on the standard power curve, obtain the power increase rate curve of each single generating equipment. Set the power control curve of each single generating equipment within the forecast window and solve it in MATLAB. Set constraint condition I according to the load forecast curve, set constraint condition II according to the power increase rate curve, and set constraint condition III according to the minimum power requirement, and obtain the solved power control curve.

[0011] Step 4: Obtain the highest load in the load curve and record the time when the highest load occurs. Obtain the highest control power and the time when the highest control power occurs through the power control curve. Construct a status score for the thermal power unit, set a scoring threshold, and feedback the results based on the relationship between the status score of the thermal power unit and the scoring threshold.

[0012] Furthermore, the time from startup to maximum power of all single power generation equipment in the thermal power unit is obtained, and the longest time is selected and calibrated as the prediction window length to obtain the historical load curve of the thermal power unit, where the horizontal axis of the load curve is time and the vertical axis is power.

[0013] Furthermore, the load curve is divided into multiple curve segments of the same length according to the length of the prediction window. All curve segments are numbered in order and summarized to form a training set. The n+1th curve segment is set as the label of the nth curve segment, and the segment is input into the LSTM model for training. The trained model is calibrated as the prediction model.

[0014] Furthermore, the load curve of the thermal power unit during operation is collected in real time and input into the prediction model to obtain the load prediction curve. The horizontal axis of the load prediction curve is time, the time length is the length of the prediction window, and the vertical axis is power. The load curve is updated in real time and the latest load is added to the load curve.

[0015] Furthermore, a standard power curve of each single set of power generation equipment in the thermal power unit is obtained. The standard power curve is a curve of the power increase process of the single set of power generation equipment from startup to maximum power generation. The horizontal axis of the standard power curve is time, and the time span is from time 0 to the time of maximum power generation. According to the standard power curve, a standard power increase rate curve of each single set of power generation equipment is obtained respectively. The horizontal axis of the standard power increase rate curve is power, and the vertical axis is the power increase rate of the single set of power generation equipment in this power state. The standard power increase rate curve represents the power increase rate of the single set of power generation equipment in each power state.

[0016] Furthermore, a power control curve for each power generation device within the prediction window is set. The horizontal axis of the power control curve is time and the vertical axis is power. The power control curve of each single power generation device is solved in MATLAB and constraint condition I is set. The constraint condition I is based on the following formula:

[0017]

[0018] in, For the The power control curve of the power generation equipment is shown here. Set of power generation equipment The power generation at the moment, For the Function of the power control curve of the power generation equipment, is the safety factor, is the load forecast curve, which is represented here in The load of time, Retrieve variables for power generation equipment, , , is the number of sets of power generation equipment;

[0019] Set constraint condition II based on the following formula:

[0020]

[0021] in, is the power increase rate of the power control curve, indicating the The power generation equipment is The power increase rate when is the standard power increase rate curve, which represents the The power generation equipment is Standard power increase rate when

[0022] Set constraint condition III based on the following formula:

[0023]

[0024] in, represents the minimization function, For the The power control curve of the power generation equipment is shown here. Set of power generation equipment The power of the moment, For the current moment, The last moment of the prediction window;

[0025] Input constraint conditions I, II, and III into MATLAB to obtain the solved power control curve of each power generation equipment.

[0026] Furthermore, the maximum load is obtained from the load forecast curve on a daily basis and the time of occurrence of the maximum load is recorded. The maximum load is the numerical average of the maximum load in each period, and the maximum load time is the average of the time when the maximum load occurs in each period. Based on the maximum control power and the time when the maximum control power occurs of the power control curve of each single power generation equipment, the maximum control power is the maximum value in the power control curve, and the time when the maximum control power occurs is the average of the time when the highest value occurs in all power control curves, the status score of the thermal power unit is obtained according to the following formula:

[0027]

[0028] in, Score the status of thermal power units, is the time when the maximum load occurs, is the time when the maximum control power occurs, For the highest load, For the The maximum control power of the power generation equipment, Retrieve variables for power generation equipment, , , is the number of power generation equipment sets, and is the coefficient, , is the indicator coefficient, when hour, is 1, when hour, is 0.

[0029] Furthermore, a scoring threshold is set , compare and analyze the status score of the thermal power unit with the score threshold:

[0030] when When When the police are not called;

[0031] The status score of the thermal power unit is obtained at a fixed frequency with an interval of half the prediction window length and analyzed with the score threshold.

[0032] The present invention also includes a unit coordination control system for a thermal power plant, wherein the system is used to execute the above-mentioned unit coordination control method for a thermal power plant, comprising:

[0033] The model building module is used to obtain the time from startup to maximum power of all individual power generation equipment in the thermal power unit, select the longest time and calibrate it as the prediction window duration, obtain the historical load curve of the thermal power unit, and input the load curve into the LSTM model for training to obtain the prediction model;

[0034] The load acquisition module is used to collect the load curve of the thermal power unit in real time during operation and input it into the prediction model for training. The load prediction curve within the prediction window is obtained through the trained prediction model;

[0035] The power control solution module is used to obtain the standard power curve of each single generating equipment in the thermal power unit, obtain the power increase rate curve of each single generating equipment based on the standard power curve, set the power control curve of each single generating equipment within the prediction window and solve it in MATLAB, set constraint condition I based on the load forecast curve, set constraint condition II based on the power increase rate curve, set constraint condition III based on the minimum power requirement, and obtain the solved power control curve;

[0036] The status evaluation module is used to obtain the maximum load in the load curve and record the time when the maximum load occurs. It obtains the maximum control power and the time when the maximum control power occurs through the power control curve, constructs the status score of the thermal power unit, sets the score threshold, and feedbacks the results based on the relationship between the status score of the thermal power unit and the score threshold.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention obtains a load forecast curve based on a forecast model, obtains a standard power curve and a power increase rate curve for each single set of equipment, and solves the power control curve for each single set of power generation equipment through restriction conditions, thereby realizing coordinated control of multiple single sets of power generation equipment in a thermal power unit, while meeting the requirements of load increase and increase rate, and forming a thermal power unit status score based on the power control curve and the load curve, thereby realizing a safe guarantee of the continuous working status of the thermal power unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Schematic diagram of the overall method flow of the present invention;

[0040] Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0041] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0042] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0043] Example:

[0044] See also Figure 1 , the present invention provides a technical solution:

[0045] A coordinated control method for a thermal power unit comprises the following steps:

[0046] Step 1: Obtain the time from startup to maximum power of all individual power generation equipment in the thermal power plant, select the longest time and calibrate it as the prediction window duration, obtain the historical load curve of the thermal power plant, and input the load curve into the LSTM model for training to obtain the prediction model;

[0047] The step 1 includes the following:

[0048] Step 101:

[0049] The time from startup to maximum power of all single power generation equipment in the thermal power unit is obtained, and the longest time is selected to be calibrated as the prediction window length, and the historical load curve of the thermal power unit is obtained, where the horizontal axis of the load curve is time and the vertical axis is power.

[0050] The core significance of obtaining the forecast window duration is that it physically limits the temporal coverage of the forecast model, ensuring that the load forecast curve output by the forecast model fully covers the maximum time period required for all equipment startup. Furthermore, collecting historical load curves provides a structured time-series data foundation for subsequent model training. Load curves enable the forecast model to capture dynamic demand changes over time, such as daytime and nighttime load differences and seasonal fluctuations, thereby enhancing the global adaptability of the forecast.

[0051] Step 102:

[0052] The load curve is divided into multiple curve segments of the same length according to the prediction window duration. All curve segments are numbered in sequence and aggregated to form a training set. The n+1th curve segment is set as the label of the nth curve segment, and the segment is input into the LSTM model for training. The trained model is calibrated as the prediction model.

[0053] By segmenting the load model into equal-length segments, the training set is expanded. The corresponding load curves for each segmented curve are used as labels to improve model accuracy. Using an LSTM model to train the load curves effectively captures long-term dependencies in time series and strictly limits the prediction window duration, ensuring that the prediction results match the physical capabilities of the actual power generation equipment. For example, if the model predicts a continued increase in demand over the next three hours, but a certain device requires three hours to reach full load, the demand curve output by the model will not include unreachable targets beyond that time window, thus avoiding a disconnect between the control strategy and device performance.

[0054] Step 2: Collect the load curve of the thermal power unit in real time during operation and input it into the prediction model for training. The trained prediction model is used to obtain the load prediction curve within the prediction window.

[0055] The step 2 includes the following:

[0056] The load curve of the thermal power unit during operation is collected in real time and input into the prediction model to obtain the load prediction curve. The horizontal axis of the load prediction curve is time, the time length is the length of the prediction window, and the vertical axis is power. The load curve is updated in real time and the latest load is added to the load curve.

[0057] Dynamically integrate the latest load curves to ensure that the forecast model's input data always reflects current operating conditions. This mechanism addresses the bias caused by data lag in traditional static forecast models. For example, in the event of an emergency, real-time data updates can quickly correct the forecast curve, ensuring the timeliness and accuracy of subsequent control strategies.

[0058] Step 3: Obtain the standard power curve of each single generating equipment in the thermal power unit. Based on the standard power curve, obtain the power increase rate curve of each single generating equipment. Set the power control curve of each single generating equipment within the forecast window and solve it in MATLAB. Set constraint condition I according to the load forecast curve, set constraint condition II according to the power increase rate curve, and set constraint condition III according to the minimum power requirement, and obtain the solved power control curve.

[0059] The step 3 includes the following:

[0060] Step 301: Obtain a standard power curve for each single power generation device in a thermal power unit. The standard power curve is a curve of the power increase process of the single power generation device from startup to maximum power generation. The horizontal axis of the standard power curve is time, and the time span is from time 0 to the time of maximum power generation. According to the standard power curve, obtain a standard power increase rate curve for each single power generation device. The horizontal axis of the standard power increase rate curve is power, and the vertical axis is the power increase rate of the single power generation device in this power state. The standard power increase rate curve represents the power increase rate of the single power generation device in each power state.

[0061] Obtain the standard power curve and power increase rate curve for a single set of equipment to clarify the dynamic performance boundaries of each device. This data provides key constraints for subsequent optimization. For example, the maximum power increase rate of the device limits the physical upper limit of power adjustment in the control strategy, avoiding equipment damage due to overload operation.

[0062] Step 302: Set the power control curve for each power generation device within the prediction window. The horizontal axis of the power control curve is time and the vertical axis is power. Solve the power control curve of each single power generation device in MATLAB and set constraint condition I. The constraint condition I is based on the following formula:

[0063]

[0064] in, For the The power control curve of the power generation equipment is shown here. Set of power generation equipment The power generation at the moment, For the Function of the power control curve of the power generation equipment, is the safety factor, is the load forecast curve, which is represented here in The load of time, Retrieve variables for power generation equipment, , , is the number of sets of power generation equipment;

[0065] The purpose of constraint I is to ensure that the power generation of the thermal power unit can always meet the load within the forecast window, avoiding the situation where the load increases but the power generation of the thermal power unit does not increase. The safety factor provides safety redundancy for the power supply of the thermal power unit.

[0066] Set constraint condition II based on the following formula:

[0067]

[0068] in, is the power increase rate of the power control curve, indicating the The power generation equipment is The power increase rate when is the standard power increase rate curve, which represents the The power generation equipment is Standard power increase rate when

[0069] In the face of increased load, the power control curve must also increase accordingly. However, the increase in the power control curve is constrained by the single set of power generation equipment itself. The power increase rate of the power control curve does not exceed the standard power increase rate of each corresponding power generation equipment, so as to avoid the thermal power unit overloading the single set of power generation equipment in order to rush to increase power. However, as the load increases sharply, every effort must still be made to meet the load. When the load curve shows a sharp increase in power consumption demand after a specific power consumption node, the power increase rate of the thermal power unit at this node cannot be completed in a short time due to the physical limitations of its own equipment, and insufficient power supply will occur. Therefore, it is necessary to increase the power control curve of the thermal power unit in advance so that when the load reaches the peak, the power generation power of the thermal power unit will also rise to meet the load. The "early bird flies first" approach is adopted to ensure the stability and timeliness of power supply.

[0070] Set constraint condition III based on the following formula:

[0071]

[0072] in, represents the minimization function, For the The power control curve of the power generation equipment is shown here. Set of power generation equipment The power of the moment, For the current moment, The last moment of the prediction window;

[0073] On the basis of ensuring that the load curve and load increase are met, the power control curve of the thermal power unit is minimized to avoid the situation where the thermal power unit is always operated at full power in order to simply meet the load, saving energy costs and maximizing benefits.

[0074] Input constraint conditions I, II, and III into MATLAB to obtain the solved power control curve of each power generation equipment.

[0075] The power control curve is set as a linear combination of basis functions, and the constraints I, II, and III are substituted into the coefficient equation group. The coefficients are then solved by the least squares method. The solved power control curve is substituted into the equation, and the residual norm is calculated for verification. If the residual norm condition is met, it is considered to have passed. If it does not pass, the problem is solved again.

[0076] Step 4: Obtain the highest load in the load curve and record the time when the highest load occurs. Obtain the highest control power and the time when the highest control power occurs through the power control curve. Construct a status score for the thermal power unit, set a scoring threshold, and feedback the results based on the relationship between the status score of the thermal power unit and the scoring threshold.

[0077] The step 4 includes the following contents:

[0078] Step 401: Obtain the maximum load from the load forecast curve on a daily basis and record the time when the maximum load occurs. The maximum load is the average value of the maximum load in each cycle, and the maximum load time is the average of the time when the maximum load occurs in each cycle. Based on the maximum control power and the time when the maximum control power occurs of the power control curve of each single power generation equipment, the maximum control power is the maximum value in the power control curve, and the time when the maximum control power occurs is the average of the time when the highest value occurs in all power control curves, obtain the status score of the thermal power unit. The formula is as follows:

[0079]

[0080] in, Score the status of thermal power units, is the time when the maximum load occurs, is the time when the maximum control power occurs, For the highest load, For the The maximum control power of the power generation equipment, Retrieve variables for power generation equipment, , , is the number of power generation equipment sets, and is the coefficient, , is the indicator coefficient, when hour, is 1, when hour, is 0.

[0081] Among them, the state of the thermal power unit in the subsequent load increase process is judged by comparing the maximum load and the maximum control power, the maximum load occurrence time and the maximum control power time respectively. When the maximum load occurs after the maximum control power time, the closer the maximum control power time is to the maximum load occurrence time, the more urgent the window period for handling the power supply not meeting the load condition is, and the higher the thermal power unit status score is. When the maximum load occurs before or at the same time as the maximum control power time, it means that the power generation power of the thermal power unit has met the peak load and there is no need to deal with the problem of power supply not meeting the load. The thermal power unit status score is directly reset to 0. The maximum value of the power control curve of all single power generation equipment within the prediction window period represents the maximum power supply power of the thermal power unit during the prediction window. When the difference between the maximum load and the maximum power supply power of the thermal power unit is smaller, it means that the thermal power unit can almost meet the maximum load, and the possibility of needing to deal with the power supply not meeting the load is smaller, and the thermal power unit status score is smaller.

[0082] Step 402: Set the scoring threshold , compare and analyze the status score of the thermal power unit with the score threshold:

[0083] when When When the police are not called;

[0084] The status score of the thermal power unit is obtained at a fixed frequency with an interval of half the prediction window length and analyzed with the score threshold.

[0085] By comparing scoring thresholds, the system dynamically determines the operating status of generators, providing a predictive basis for handling emergencies where power supply does not meet load requirements. Scores are recalculated periodically at intervals of half the prediction window duration to ensure the system can continuously adapt to changing operating conditions. This mechanism enhances system robustness, preventing long-term misjudgments caused by single scoring errors.

[0086] See also Figure 2 The present invention further provides a unit coordination control system for a thermal power plant, wherein the system is used to execute the above-mentioned unit coordination control method for a thermal power plant, comprising:

[0087] The model building module is used to obtain the time from startup to maximum power of all individual power generation equipment in the thermal power unit, select the longest time and calibrate it as the prediction window duration, obtain the historical load curve of the thermal power unit, and input the load curve into the LSTM model for training to obtain the prediction model;

[0088] The load acquisition module is used to collect the load curve of the thermal power unit in real time during operation and input it into the prediction model for training. The load prediction curve within the prediction window is obtained through the trained prediction model;

[0089] The power control solution module is used to obtain the standard power curve of each single generating equipment in the thermal power unit, obtain the power increase rate curve of each single generating equipment based on the standard power curve, set the power control curve of each single generating equipment within the prediction window and solve it in MATLAB, set constraint condition I based on the load forecast curve, set constraint condition II based on the power increase rate curve, set constraint condition III based on the minimum power requirement, and obtain the solved power control curve;

[0090] The status evaluation module is used to obtain the maximum load in the load curve and record the time when the maximum load occurs. It obtains the maximum control power and the time when the maximum control power occurs through the power control curve, constructs the status score of the thermal power unit, sets the score threshold, and feedbacks the results based on the relationship between the status score of the thermal power unit and the score threshold.

[0091] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0092] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0093] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0094] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for coordinated control of thermal power units, characterized in that: The specific steps include: Step 1: Obtain the time from startup to maximum power of all individual power generation equipment in the thermal power plant, select the longest time and calibrate it as the prediction window duration, obtain the historical load curve of the thermal power plant, and input the load curve into the LSTM model for training to obtain the prediction model; Step 2: Collect the load curve of the thermal power unit in real time during operation and input it into the prediction model for training. The trained prediction model is used to obtain the load prediction curve within the prediction window. Step 3: Obtain the standard power curve of each single generating equipment in the thermal power unit. Based on the standard power curve, obtain the power increase rate curve of each single generating equipment. Set the power control curve of each single generating equipment within the forecast window and solve it in MATLAB. Set constraint condition I according to the load forecast curve, set constraint condition II according to the power increase rate curve, and set constraint condition III according to the minimum power requirement, and obtain the solved power control curve. Set the power control curve of each power generation equipment within the prediction window. The horizontal axis of the power control curve is time and the vertical axis is power. Solve the power control curve of each single power generation equipment in MATLAB and set the constraint condition I. The constraint condition I is based on the following formula: ， in, For the The power control curve of the power generation equipment is shown here. Set of power generation equipment The power generation at the moment, For the Function of the power control curve of the power generation equipment, is the safety factor, is the load forecast curve, which is represented here in The load of time, Retrieve variables for power generation equipment, , , is the number of sets of power generation equipment; Set constraint condition II based on the following formula: ， in, is the power increase rate of the power control curve, indicating the The power generation equipment is The power increase rate when is the standard power increase rate curve, which represents the The power generation equipment is Standard power increase rate when Set constraint condition III based on the following formula: ， in, represents the minimization function, For the The power control curve of the power generation equipment is shown here. Set of power generation equipment The power of the moment, For the current moment, The last moment of the prediction window; Input constraint conditions I, II, and III into MATLAB to obtain the power control curve of each power generation equipment after solution; Step 4: Obtain the highest load in the load curve and record the time when the highest load occurs. Obtain the highest control power and the time when the highest control power occurs through the power control curve. Construct a status score for the thermal power unit, set a scoring threshold, and feedback the results based on the relationship between the status score of the thermal power unit and the scoring threshold.

2. The coordinated control method for a thermal power unit according to claim 1, characterized in that: The time from startup to maximum power of all single power generation equipment in the thermal power unit is obtained, and the longest time is selected to be calibrated as the prediction window length, and the historical load curve of the thermal power unit is obtained, where the horizontal axis of the load curve is time and the vertical axis is power.

3. The coordinated control method for a thermal power unit according to claim 2, characterized in that: The load curve is divided into multiple curve segments of the same length according to the prediction window duration. All curve segments are numbered in sequence and aggregated to form a training set. The n+1th curve segment is set as the label of the nth curve segment, and the segment is input into the LSTM model for training. The trained model is calibrated as the prediction model.

4. The coordinated control method for a thermal power unit according to claim 3, characterized in that: The load curve of the thermal power unit during operation is collected in real time and input into the prediction model to obtain the load prediction curve. The horizontal axis of the load prediction curve is time, the time length is the length of the prediction window, and the vertical axis is power. The load curve is updated in real time and the latest load is added to the load curve.

5. The coordinated control method for thermal power generation units according to claim 4, characterized in that: Obtain a standard power curve for each single power generation device in the thermal power unit. The standard power curve is a curve of the power increase process of the single power generation device from startup to maximum power generation. The abscissa of the standard power curve is time, and the time span is from time 0 to the time of maximum power generation. According to the standard power curve, obtain a standard power increase rate curve for each single power generation device. The abscissa of the standard power increase rate curve is power, and the ordinate is the power increase rate of the single power generation device in this power state. The standard power increase rate curve represents the power increase rate of the single power generation device in each power state.

6. The coordinated control method for thermal power generation units according to claim 5, characterized in that: Setting scoring thresholds , compare and analyze the status score of the thermal power unit with the score threshold: when When When the police are not called; The status score of the thermal power unit is obtained at a fixed frequency with an interval of half the prediction window length and analyzed with the score threshold.

7. The coordinated control method for thermal power generation units according to claim 6, characterized in that: The maximum load is obtained from the load forecast curve on a daily basis and the time of the maximum load occurrence is recorded. The maximum load is the numerical average of the maximum load in each cycle, and the time of the maximum load occurrence is the average of the times when the maximum load occurs in each cycle. Based on the maximum control power and the time of the maximum control power occurrence of the power control curve of each single power generation equipment, the maximum control power is the maximum value in the power control curve, and the time of the maximum control power occurrence is the average of the times when the highest values ​​in all power control curves occur, the status score of the thermal power unit is obtained according to the following formula: ， in, Score the status of thermal power units, is the time when the maximum load occurs, is the time when the maximum control power occurs, For the highest load, For the The maximum control power of the power generation equipment, Retrieve variables for power generation equipment, , , is the number of power generation equipment sets, and is the coefficient, , is the indicator coefficient, when hour, is 1, when hour, is 0.

8. A unit coordination control system for a thermal power plant, the system being used to execute the unit coordination control method for a thermal power plant according to any one of claims 1 to 7, characterized in that: include: The model building module is used to obtain the time from startup to maximum power of all individual power generation equipment in the thermal power unit, select the longest time and calibrate it as the prediction window duration, obtain the historical load curve of the thermal power unit, and input the load curve into the LSTM model for training to obtain the prediction model; The load acquisition module is used to collect the load curve of the thermal power unit in real time during operation and input it into the prediction model for training. The load prediction curve within the prediction window is obtained through the trained prediction model; The power control solution module is used to obtain the standard power curve of each single generating equipment in the thermal power unit, obtain the power increase rate curve of each single generating equipment based on the standard power curve, set the power control curve of each single generating equipment within the prediction window and solve it in MATLAB, set constraint condition I based on the load forecast curve, set constraint condition II based on the power increase rate curve, set constraint condition III based on the minimum power requirement, and obtain the solved power control curve; The status evaluation module is used to obtain the maximum load in the load curve and record the time when the maximum load occurs. It obtains the maximum control power and the time when the maximum control power occurs through the power control curve, constructs the status score of the thermal power unit, sets the score threshold, and feedbacks the results based on the relationship between the status score of the thermal power unit and the score threshold.

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