Cruise control method for transient state peak load regulation of thermal power unit based on intelligent control algorithm

Abstract

The application discloses a thermal power unit transfer state peak load cruise control method based on an intelligent control algorithm and relates to the technical field of load coordination control. The thermal power unit transfer state peak load cruise control method based on the intelligent control algorithm determines the load demand change condition by classifying and processing data, realizes internal coordination of the thermal power unit based on the load demand change condition, realizes control operation on various factors in the boiler, analyzes the time for generating and implementing the instruction and the setting of the threshold value in the boiler, completes compensation and optimization of the coordination strategy through a compensation algorithm, completes the prediction operation when the load changes, analyzes the safety condition by using historical big data, obtains the desired prediction time, traces the corresponding load demand according to the prediction time, gives enough time to complete the coordination operation in the load supply process, and guarantees the operation safety of the thermal power unit.

Description

Technical Field

[0001] This invention relates to the field of load coordination control technology, specifically to a method for cruise control of shifting peak load in thermal power units based on intelligent control algorithms. Background Technology

[0002] With the widespread integration of new energy sources and the continuous changes in the load characteristics of the power system, thermal power units need to undertake more peak-shaving tasks. Traditional control methods often suffer from slow response speed and low control accuracy when facing rapidly changing load demands, making it difficult to meet the needs of modern power systems.

[0003] The reference patent is titled: "A Flexible and Deep Peak Shaving Intelligent Control Method for Thermal Power Units" (Patent Publication No.: CN115327910A, Patent Publication Date: 2022-11-11). It includes: conducting disturbance tests at typical operating points; establishing a gain scheduling model for the coordinated system through fitting and identification methods; establishing a fuzzy rule base; inferring the dynamic mathematical model of the coordinated system corresponding to the grid load command in real time; and using this model as a prediction model; predicting the output trajectory based on the prediction equation, estimating the system state using a Kalman filter, and calculating the optimal control quantity based on performance indicators. This method is suitable for coordinated control systems of thermal power units with frequently changing operating conditions. It is simple, reliable, and easy to implement, and can be configured and implemented through the power plant's DCS system, possessing broad engineering application value.

[0004] Based on the description in the above documents, existing thermal power units, when undertaking peak-shaving tasks, do not experience a stable increase in load demand changes. As a result, by the time personnel notice the data anomaly during the response process, the actual coordinated operation of the thermal power units is insufficient to meet the demand, leading to increased boiler pressure or equipment damage and safety issues. Consequently, timely coordination and handling are not carried out, and subsequent load demand cannot be predicted. Therefore, this invention provides a method for cruise control of transitional peak-shaving load in thermal power units based on intelligent control algorithms. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a cruise control method for shifting peak loads in thermal power units based on intelligent control algorithms. This method solves the problem that, in the process of undertaking peak load tasks, changes in load demand do not necessarily lead to a stable increase. As a result, by the time personnel notice the abnormal data, the actual coordinated operation of the thermal power unit is insufficient to meet the demand, leading to increased boiler pressure or equipment damage and safety issues. This also results in the failure to conduct timely coordination and the inability to predict subsequent load demand.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for cruise control of shifting peak load in thermal power units based on intelligent control algorithms, specifically including the following steps:

[0007] S1. Collect real-time and historical operating data of thermal power units, and collect historical load demand data.

[0008] S2. Classify and process the data. First, determine the load demand changes. Based on the load demand changes, realize the internal coordination of the thermal power unit and realize the control operation of various factors in the boiler. Then, combine the time from instruction generation to implementation and the setting of threshold warnings in the boiler for analysis. Through the compensation algorithm, complete the compensation and optimization of the coordination strategy and complete the prediction operation when the load changes.

[0009] S3. Introduce current real-time operating data to predict subsequent load changes and generate corresponding control strategies, which are then displayed through a visual interface.

[0010] Preferably, the data classification process in S2 is as follows:

[0011] s21. Perform initial classification of the collected data into real-time data and historical data, and set classification templates to implement the data filling operation;

[0012] s22, and the classification template completes the separate screening of real-time data and historical data based on the search criteria, and performs repair operations on abnormal data in the populated data;

[0013] s23. After completing the selection, retain the classification template and classify them according to time order to form datasets for each individual category.

[0014] Preferably, the operation in s22 is as follows:

[0015] The selection criteria for the classification template are: first, the type of the generator unit; second, the data category corresponding to the generator unit type; and third, the data collection time.

[0016] Furthermore, the system iterates through the collected data according to the conditions, and fills the classification template with the numerical results of the data that match the conditions.

[0017] The system iterates through the numerical result columns of the classification template, verifies blank result columns, and deletes and repairs result columns containing garbled characters.

[0018] Preferably, the operation in S2 to determine the change in load demand is as follows:

[0019] A1. After extracting the load demand data, determine the period with the largest total load demand according to the date changes, and extract the load demand data for the current period.

[0020] A2. Establish load change curves according to time sequence and corresponding load demand values, and derive the average load demand value based on the load demand value at each time, marking the nodes that are higher than the average load demand.

[0021] The formula for calculating the average load demand is:

[0022] P 平 = (P1+P2+…+P) n ) / n;

[0023] P 平 P represents the average load demand value. n This represents the load demand value at the nth time point.

[0024] A3. Determine the specific coordination operation of the corresponding thermal power unit based on the marked node section.

[0025] Preferably, the internal coordination operation of the thermal power unit based on load demand changes in S2 is as follows:

[0026] B1. Based on the increase in load demand, the command is transmitted to the thermal power unit to realize the coordinated control of the turbine and the boiler. After the turbine main control responds, it generates a command and transmits it to the boiler main control for coordinated control operation.

[0027] B2. The time stamp for transmitting the instruction to the boiler main controller after recording the load demand change is T1. Then, the boiler's fuel quantity and feedwater quantity are controlled according to the instruction to complete the regulation of the thermal power unit and generate the corresponding load supply.

[0028] Preferably, the specific operation of the compensation algorithm in S2 is as follows:

[0029] C1. When extracting the corresponding marked node portion, the total fuel quantity of the boiler at the cycle node is labeled as G. x Let x represent the nth cycle node, and x∈{1,2,3,…}. The time interval between adjacent cycle nodes is the same. When extracting the corresponding marked node portion, the real-time pressure value in the boiler corresponding to the cycle node is labeled as U. x The total water supply at the corresponding cycle node is the total H. x ;

[0030] C2. Set the pressure safety threshold range in the boiler as [W1, W2] and the water-coal ratio safety threshold range in the boiler as [K1, K2]. Extract multiple processing times after exceeding the safety threshold range during the operation of the thermal power unit. After removing outliers, select the maximum value as the reaction time of personnel handling, labeled as T2.

[0031] C3. Establish curves for fuel quantity and boiler pressure at cycle nodes, as well as curves for cycle nodes and water-coal ratio, and derive corresponding optimized values ​​that can provide early warnings through analysis of the curves.

[0032] Preferably, the curve analysis operation for fuel quantity and boiler pressure at the periodic node in C3 is as follows:

[0033] d01. Establish a curve coordinate system, form a horizontal axis according to the periodic node sequence, and start from the starting point of the horizontal axis, taking the fuel quantity and boiler pressure as the vertical axis;

[0034] d02. Then, fill the fuel quantity and boiler pressure values ​​under the corresponding period node into the curve coordinate system, and complete the establishment of the fuel quantity and boiler pressure curve with a smooth curve. The segmentation operation is realized according to the rise and fall of the fuel quantity and boiler pressure curve.

[0035] d03. Introduce the pressure safety threshold range as the pressure threshold constant function, and determine the first hidden danger node by the intersection of the pressure threshold maximum value constant function with the rise curve in the fuel quantity and boiler pressure curves. Then determine the second hidden danger node by the intersection of the pressure threshold minimum value constant function with the fall curve in the fuel quantity and boiler pressure curves. Finally, compensate the personnel's reaction time T2 and the time T1 for completing the coordinated operation and transmission to the point before the hidden danger node to determine the corresponding fuel quantity safety node.

[0036] Preferably, the curve analysis operation of the water-coal ratio versus boiler pressure at the periodic node in C3 is as follows:

[0037] d11. Establish a curve coordinate system, form a horizontal axis according to the periodic node sequence, and take the water-coal ratio as the vertical axis starting from the starting point of the horizontal axis.

[0038] d12. Then fill the corresponding period nodes and water-coal ratio values ​​into the curve coordinate system, and complete the establishment of the period nodes and water-coal ratio curves with a smooth curve. The segmentation operation is realized according to the rise and fall of the period nodes and water-coal ratio curves.

[0039] d13. Introduce the safe threshold range of water-coal ratio as the threshold constant function of water-coal ratio. Then, determine the third hidden danger node by the intersection of the maximum value constant function of water-coal ratio threshold with the periodic node and the rise curve in the water-coal ratio curve. Then, determine the fourth hidden danger node by the intersection of the minimum value constant function of water-coal ratio threshold with the periodic node and the fall curve in the water-coal ratio curve. Finally, compensate the reaction time T2 of personnel handling and the time T1 for completing the coordinated operation and transmission to the point before the hidden danger node to determine the corresponding safe water-coal ratio node.

[0040] Preferably, the formula for determining the safe node in the operations of d03 and d13 is:

[0041] Formula for determining the safe fuel level: G Z1 =F(Z1-T1-T2), when U x =W1 or U x =W2, and the corresponding cycle node at this time is Z1, (Z1-T1-T2) represents the time node after the compensation time of the corresponding cycle node, F(Z1-T1-T2) represents the fuel quantity value at the corresponding time node, G Z1 This is the fuel quantity value when the period node is Z1, i.e., the fuel quantity safety node;

[0042] Formula for determining the safe threshold of the water-coal ratio: (H) x / G x ) Z2 =f(Z2-T1-T2), when (H x / G x ) = K1 or (H x / G x When ) = K2, and the corresponding period node is Z2, (Z2-T1-T2) represents the time node after the compensation time of the corresponding period node, f(Z1-T1-T2) represents the water-coal ratio value at the corresponding time node, (H x / G x ) Z2 This is the water-to-coal ratio value when the period node is Z2, i.e., the safe water-to-coal ratio node.

[0043] Preferably, the operation of introducing real-time running data for prediction in S3 is as follows:

[0044] s31. By collecting real-time operating data and tracing back to the safe points of fuel quantity and water-coal ratio, the historical load demand is determined.

[0045] s32. Compare the load demand corresponding to the safety node with the load demand in the real-time data. Once the load demand in the real-time data reaches the load demand corresponding to the safety node, generate a control command and transmit it to the thermal power unit for coordinated control.

[0046] This invention provides a method for cruise control of shifting peak load in thermal power units based on intelligent control algorithms. Compared with existing technologies, it has the following advantages:

[0047] 1. This intelligent control algorithm-based method for cruise control of peak load in thermal power units first determines the load demand changes by classifying and processing the data. Based on the load demand changes, it achieves internal coordination of the thermal power unit and controls various factors within the boiler. Then, it analyzes the time from command generation to implementation and the setting of early warning thresholds within the boiler. Through compensation algorithms, it completes the compensation and optimization of the coordination strategy and completes the predictive operation when the load changes. By utilizing historical big data, it achieves safety analysis, obtains the desired prediction time, and traces the corresponding load demand based on the prediction time. This provides sufficient time to complete the coordination operation during the load supply process and ensures the safe operation of the thermal power unit.

[0048] 2. This intelligent control algorithm-based method for cruise control of shifting peak load in thermal power units establishes a load change curve according to the time sequence and the corresponding load demand value. Based on the load demand value at each time, the average load demand value is derived, and nodes with load demand values ​​higher than the average load demand are marked. This allows for the extraction of high-demand data nodes from historical data, enabling faster identification of target data during data analysis. This ensures that the data is more representative during subsequent coordinated control of thermal power units, thereby maximizing time compensation and better ensuring the safety of thermal power unit control.

[0049] 3. This intelligent control algorithm-based method for the shifting peak load cruise control of thermal power units establishes curves for fuel quantity and boiler pressure at cycle nodes, as well as curves for cycle nodes and water-coal ratio. Through analysis of these curves, it derives corresponding optimized values ​​that can provide early warnings. Furthermore, when the load demand in the real-time data meets the load demand corresponding to any safety node, it is necessary to determine whether it corresponds to a specific safety hazard. This method completes the load cruise control operation while maintaining fault tolerance, enabling thermal power units to achieve better load supply. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating the operation of the load cruise control method of the present invention.

[0051] Figure 2 This is a flowchart illustrating the operation of the compensation algorithm of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Please see Figures 1-2 This invention provides two technical solutions:

[0054] Example 1: A method for cruise control of shifting peak load in thermal power units based on intelligent control algorithms, specifically including the following steps:

[0055] S1. Collect real-time and historical operating data of thermal power units, and collect historical load demand data.

[0056] S2. Classify and process the data. First, determine the load demand changes. Based on the load demand changes, realize the internal coordination of the thermal power unit and realize the control operation of various factors in the boiler. Then, combine the time from instruction generation to implementation and the setting of threshold warnings in the boiler for analysis. Through the compensation algorithm, complete the compensation and optimization of the coordination strategy and complete the prediction operation when the load changes.

[0057] S3. Introduce current real-time operating data to predict subsequent load changes and generate corresponding control strategies, which are then displayed through a visual interface.

[0058] By classifying and processing the data, the changes in load demand are first determined. Based on these changes, internal coordination of the thermal power unit is achieved, and control operations on various factors within the boiler are implemented. Then, the time from command generation to implementation and the setting of early warning thresholds within the boiler are analyzed. Compensation algorithms are used to compensate and optimize the coordination strategy, and predictive operations are performed when load changes occur. By utilizing historical big data, safety conditions are analyzed to determine the desired prediction time. Based on the prediction time, the corresponding load demand is traced, thus providing sufficient time to complete the coordination operations during load supply and ensuring the safe operation of the thermal power unit.

[0059] In this embodiment of the invention, the data classification process in S2 is as follows:

[0060] s21. Perform initial classification of the collected data into real-time data and historical data, and set classification templates to implement the data filling operation;

[0061] s22, and the classification template completes the separate screening of real-time data and historical data based on the search criteria, and performs repair operations on abnormal data in the populated data;

[0062] s23. After completing the selection, retain the classification template and classify them according to time order to form datasets for each individual category.

[0063] In this embodiment of the invention, the operation in s22 is as follows:

[0064] The selection criteria for the classification template are: first, the type of the generator unit; second, the data category corresponding to the generator unit type; and third, the data collection time.

[0065] Furthermore, the system iterates through the collected data according to the conditions, and fills the classification template with the numerical results of the data that match the conditions.

[0066] The system iterates through the numerical result columns of the classification template, verifies blank result columns, and deletes and repairs result columns containing garbled characters.

[0067] In this embodiment of the invention, the operation of determining the change in load demand in S2 is as follows:

[0068] A1. After extracting the load demand data, determine the period with the largest total load demand according to the date changes, and extract the load demand data for the current period.

[0069] A2. Establish load change curves according to time sequence and corresponding load demand values, and derive the average load demand value based on the load demand value at each time, marking the nodes that are higher than the average load demand.

[0070] The formula for calculating the average load demand is:

[0071] P 平 = (P1+P2+…+P) n ) / n;

[0072] P 平 P represents the average load demand value. n This represents the load demand value at the nth time point.

[0073] A3. Determine the specific coordination operation of the corresponding thermal power unit based on the marked node section.

[0074] In this embodiment of the invention, the internal coordination operation of the thermal power unit based on load demand changes in S2 is as follows:

[0075] B1. Based on the increase in load demand, the command is transmitted to the thermal power unit to realize the coordinated control of the turbine and the boiler. After the turbine main control responds, it generates a command and transmits it to the boiler main control for coordinated control operation.

[0076] B2. The time stamp for transmitting the instruction to the boiler main controller after recording the load demand change is T1. Then, the boiler's fuel quantity and feedwater quantity are controlled according to the instruction to complete the regulation of the thermal power unit and generate the corresponding load supply.

[0077] By establishing load change curves according to time sequence and corresponding load demand values, and deriving average load demand values ​​based on the load demand values ​​at each time, nodes with load demand values ​​higher than the average load demand are marked. This allows for the extraction of high-demand data nodes from historical data, enabling faster identification of target data during data analysis. This ensures that the data is more representative during subsequent coordinated control of thermal power units, thereby maximizing time compensation and better guaranteeing the safety of thermal power unit control.

[0078] In this embodiment of the invention, the specific operation of the compensation algorithm in S2 is as follows:

[0079] C1. When extracting the corresponding marked node portion, the total fuel quantity of the boiler at the cycle node is labeled as G. x Let x represent the nth cycle node, and x∈{1,2,3,…}. The time interval between adjacent cycle nodes is the same. When extracting the corresponding marked node portion, the real-time pressure value in the boiler corresponding to the cycle node is labeled as U. x The total water supply at the corresponding cycle node is the total H. x ;

[0080] C2. Set the pressure safety threshold range in the boiler as [W1, W2] and the water-coal ratio safety threshold range in the boiler as [K1, K2]. Extract multiple processing times after exceeding the safety threshold range during the operation of the thermal power unit. After removing outliers, select the maximum value as the reaction time of personnel handling, labeled as T2.

[0081] C3. Establish curves for fuel quantity and boiler pressure at cycle nodes, as well as curves for cycle nodes and water-coal ratio, and derive corresponding optimized values ​​that can provide early warnings through analysis of the curves.

[0082] In this embodiment of the invention, the curve analysis operation for fuel quantity and boiler pressure at the periodic node C3 is as follows:

[0083] d01. Establish a curve coordinate system, form a horizontal axis according to the periodic node sequence, and start from the starting point of the horizontal axis, taking the fuel quantity and boiler pressure as the vertical axis;

[0084] d02. Then, fill the fuel quantity and boiler pressure values ​​under the corresponding period node into the curve coordinate system, and complete the establishment of the fuel quantity and boiler pressure curve with a smooth curve. The segmentation operation is realized according to the rise and fall of the fuel quantity and boiler pressure curve.

[0085] d03. Introduce the pressure safety threshold range as the pressure threshold constant function, and determine the first hidden danger node by the intersection of the pressure threshold maximum value constant function with the rise curve in the fuel quantity and boiler pressure curves. Then determine the second hidden danger node by the intersection of the pressure threshold minimum value constant function with the fall curve in the fuel quantity and boiler pressure curves. Finally, compensate the personnel's reaction time T2 and the time T1 for completing the coordinated operation and transmission to the point before the hidden danger node to determine the corresponding fuel quantity safety node.

[0086] In this embodiment of the invention, the curve analysis operation of water-coal ratio versus boiler pressure at the periodic node in C3 is as follows:

[0087] d11. Establish a curve coordinate system, form a horizontal axis according to the periodic node sequence, and take the water-coal ratio as the vertical axis starting from the starting point of the horizontal axis.

[0088] d12. Then fill the corresponding period nodes and water-coal ratio values ​​into the curve coordinate system, and complete the establishment of the period nodes and water-coal ratio curves with a smooth curve. The segmentation operation is realized according to the rise and fall of the period nodes and water-coal ratio curves.

[0089] d13. Introduce the safe threshold range of water-coal ratio as the threshold constant function of water-coal ratio. Then, determine the third hidden danger node by the intersection of the maximum value constant function of water-coal ratio threshold with the periodic node and the rise curve in the water-coal ratio curve. Then, determine the fourth hidden danger node by the intersection of the minimum value constant function of water-coal ratio threshold with the periodic node and the fall curve in the water-coal ratio curve. Finally, compensate the reaction time T2 of personnel handling and the time T1 for completing the coordinated operation and transmission to the point before the hidden danger node to determine the corresponding safe water-coal ratio node.

[0090] In this embodiment of the invention, the formula for determining the safe node in the operations of d03 and d13 is as follows:

[0091] Formula for determining the safe fuel level: G Z1 =F(Z1-T1-T2), when U x =W1 or U x =W2, and the corresponding cycle node at this time is Z1, (Z1-T1-T2) represents the time node after the compensation time of the corresponding cycle node, F(Z1-T1-T2) represents the fuel quantity value at the corresponding time node, G Z1 This is the fuel quantity value when the period node is Z1, i.e., the fuel quantity safety node;

[0092] Formula for determining the safe threshold of the water-coal ratio: (H) x / G x ) Z2 =f(Z2-T1-T2), when (H x / G x ) = K1 or (H x / Gx When ) = K2, and the corresponding period node is Z2, (Z2-T1-T2) represents the time node after the compensation time of the corresponding period node, f(Z1-T1-T2) represents the water-coal ratio value at the corresponding time node, (H x / G x ) Z2 This is the water-to-coal ratio value when the period node is Z2, i.e., the safe water-to-coal ratio node.

[0093] In this embodiment of the invention, the operation of introducing real-time running data for prediction in S3 is as follows:

[0094] s31. By collecting real-time operating data and tracing back to the safe points of fuel quantity and water-coal ratio, the historical load demand is determined.

[0095] s32. Compare the load demand corresponding to the safety node with the load demand in the real-time data. Once the load demand in the real-time data reaches the load demand corresponding to the safety node, generate a control command and transmit it to the thermal power unit for coordinated control.

[0096] By establishing curves for fuel quantity and boiler pressure at cycle nodes, as well as curves for cycle nodes and water-coal ratio, and through analysis of these curves, optimized values ​​that can provide early warnings are derived. Furthermore, when the load demand in the real-time data meets the load demand corresponding to any safety node, it is necessary to determine whether it corresponds to a specific safety hazard. This allows for the completion of load cruise control operations while maintaining fault tolerance, enabling thermal power units to achieve better load supply.

[0097] Example 2 differs from Example 1 in that: the present invention also uses existing load control methods and the load cruise control method of the present invention to control the simulated thermal power unit with problems, and records the number of problems predicted in advance, as well as the time to complete the supply. The specific results are shown in Table 1.

[0098] Table 1 Control Results

[0099] Problem prediction rate Supply processing time Existing load control methods 76％ 6h The load control method of the present invention 98％ 4.5h

[0100] In summary, the load cruise control method of the present invention can predict problems in advance when controlling changes in load demand, and can find most of the existing problems. At the same time, the supply completion time is greatly reduced after load cruise control, so it can be better realized in practical applications.

[0101] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0102] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0103] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for cruise control of a thermal power unit during transition state peak load regulation based on an intelligent control algorithm, characterized in that: Specifically comprising the following steps: S1, collecting real-time operation data and historical operation data of the thermal power generating unit, and collecting historical load demand data; S2, classifying the data, first determining the load demand change condition, realizing internal coordination of the thermal power generating unit based on the load demand change condition, and realizing control operation of various factors in the boiler, and then analyzing the time taken from instruction generation to implementation and the setting of the boiler threshold warning, completing compensation and optimization of the coordination strategy through a compensation algorithm, and completing prediction operation during load change; S3, introducing the current real-time operation data, completing prediction of subsequent load change conditions, and generating corresponding regulation and control strategies, which are displayed through a visual interface; The specific operation of the compensation algorithm in S2 is: C1, the total fuel amount of the boiler at the period node when the corresponding marked node part is extracted is marked as G x , x represents the period node, and x∈{1, 2, 3, …} The time interval between adjacent period nodes is the same, and the real-time pressure value of the boiler at the corresponding period node is extracted and marked as U x , and the total water supply amount at the corresponding period node is total H x ; C2, set the pressure safety threshold interval in the boiler as [W1, W2], and set the water-coal ratio safety threshold interval in the boiler as [K1, K2], extract the processing time after exceeding the safety threshold interval during the operation of the thermal power generating unit, select the maximum value after removing outliers as the personnel processing reaction time T2; C3, establish a curve about the fuel quantity and the boiler pressure at the cycle node, and a curve about the cycle node and the water-coal ratio, and through the analysis of the curve, obtain the optimized value that can be warned in advance; The curve analysis operation of the fuel quantity and the boiler pressure at the cycle node in C3 is: d01, establish a curve coordinate system, form a horizontal axis according to the cycle node order, and take the starting point of the horizontal axis as the vertical axis of the fuel quantity and the boiler pressure; d02, then fill the fuel quantity and the boiler pressure values at the corresponding cycle node into the curve coordinate system, and complete the establishment of the fuel quantity and the boiler pressure curve with a smooth curve, and realize the segmentation operation according to the rise and fall of the fuel quantity and the boiler pressure curve; d03, introduce the pressure safety threshold interval as a pressure threshold constant function, and determine the first hidden danger node by the intersection of the pressure threshold maximum constant function and the rising curve in the fuel quantity and the boiler pressure curve, and then determine the second hidden danger node according to the intersection of the pressure threshold minimum constant function and the falling curve in the fuel quantity and the boiler pressure curve, and then compensate the personnel processing reaction time T2 and the coordination operation transmission time T1 to the corresponding fuel quantity safety node before the hidden danger node.

2. The method of claim 1, wherein the method is characterized by: The operation of classifying the data in S2 is: s21, classify the collected data according to real-time data and historical data, and set a classification template to realize the filling operation of the collected data; s22, and the classification template completes the separate screening of real-time data and historical data with search conditions, and repairs the abnormal data in the filled data; s23, after searching, retain the classification template and classify the data set of each single category according to time sequence.

3. The method of claim 2, wherein the method further comprises: The operation in s22 is: The search condition of the classification template takes the unit type as the first condition, the data category corresponding to the unit type as the second condition, and the data collection time as the third condition; And according to the condition content and the data collected to traverse operation, to fill the data content in the classification template with the value of the condition content; The numerical result column of the classification template is traversed to verify the blank result column and delete and repair the result column with garbled code.

4. The intelligent control algorithm-based transient state peak load cruise control method for thermal power generating units according to claim 1, characterized in that: The operation of determining the load demand change in S2 is: A1, after extracting the load demand data, determining the period with the maximum total load demand according to the date change, and extracting the load demand data of the current period; A2, establishing a load change curve according to the time sequence and the corresponding load demand value, and deriving the average load demand value based on the load demand value of each time, and marking the part of the node higher than the average load demand; The calculation formula of the average load demand value is: P 平 =(P1+P2+…+P n ) / n; P 平 is the average load demand value, and P n is the load demand value at the nth time node. A3, determining the specific coordination operation of the corresponding thermal power unit according to the marked node part.

5. The intelligent control algorithm based cruise control method for transient state load peaking of thermal power generating units according to claim 1, characterized in that: The internal coordination operation of the thermal power unit based on the load demand change in S2 is: B1, according to the increase of the load demand value, instructing transmission to the thermal power unit to realize the coordinated control of the steam turbine and the boiler, and after the steam turbine main control responds, instructing transmission to the boiler main control to realize the coordinated control operation; B2, recording the transmission time T1 of the instruction generated after the load demand change to the boiler main control to complete the coordination operation, and then controlling the fuel quantity and the water supply quantity of the boiler according to the instruction to complete the regulation and control of the thermal power unit and generate the corresponding load supply.

6. The intelligent control algorithm based transients state peak load cruise control method for thermal power generating units according to claim 1, characterized in that: The curve analysis operation of the period node and the water-coal ratio in C3 is: d11, establishing a curve coordinate system, forming a horizontal axis according to the order of the period node, and taking the starting point of the horizontal axis as the vertical axis of the water-coal ratio; d12, then fill the corresponding period node and water-coal ratio value into the curve coordinate system, and complete the establishment of the period node and water-coal ratio curve with a smooth curve, and realize the segmentation according to the rise and fall of the period node and water-coal ratio curve; d13, introduce the water-coal ratio safety threshold interval as a water-coal ratio threshold constant function, and determine the third hidden node by the intersection of the water-coal ratio threshold maximum constant function and the rising curve of the period node and water-coal ratio curve, and then determine the fourth hidden node according to the intersection of the water-coal ratio threshold minimum constant function and the falling curve of the period node and water-coal ratio curve, and then compensate the reaction time T2 of personnel processing and the transmission time T1 of coordination operation to the corresponding water-coal ratio safety node before the hidden node.

7. The intelligent control algorithm-based transient state load cruise control method for a thermal power generating unit according to claim 6, characterized in that: The formula for determining the safety node in the operation of d03 and d13 is: The determination formula of the fuel quantity safety node is G Z1 =F (Z1-T1-T2) when U x =W1 or U x =W2, and the corresponding period node at this time is Z1, (Z1-T1-T2) represents a time node after time compensation of the corresponding period node, F (Z1-T1-T2) represents a fuel quantity value under the corresponding time node, and G Z1 is the fuel quantity value when the period node is Z1, that is, the fuel quantity safety node. Water coal ratio safety node determination formula: (H x / G x ) Z2 = f (Z2-T1-T2) when (H x / G x )=K1 or (H x / G x )=K2, and the corresponding period node at this time is Z2, (Z2-T1-T2) represents the time node after the time compensation of the corresponding period node, f (Z1-T1-T2) represents the water coal ratio value under the corresponding time node, (H x / G x ) Z2 is the water coal ratio value when the period node is Z2, that is, the water coal ratio safety node.

8. The intelligent control algorithm-based transient state peak load cruise control method for thermal power generating units according to claim 7, characterized in that: The operation of introducing real-time running data for prediction in S3 is: s31, by collecting real-time running data, and tracing back to determine the historical load demand according to the fuel quantity safety node and the water-coal ratio safety node; s32, compare the corresponding load demand under the safety node with the load demand in the real-time data, and when the load demand in the real-time data reaches the corresponding load demand under the safety node, generate the regulation and control instruction to transmit to the thermal power unit for coordinated control.

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