Deep peak regulation control method and system for heat supply unit

Abstract

The invention relates to the technical field of peak regulation control, in particular to a heat supply unit deep peak regulation control method and system.The heat supply unit deep peak regulation control method comprises the following steps that on the basis of a cold end draught fan cluster, cycle parameters are collected, the disturbance trend is judged, the air pressure and temperature of draught fans at the tail gas exhaust section of a condenser are monitored, the interference degree is sequenced, and key propagation nodes are recognized; and monitoring wind pressure and temperature rise of a main steam outlet and a cold end negative pressure area, classifying risk grades, adjusting the rotating speed and the wind direction of a fan for a high-risk area, and executing partition regulation and control response. According to the method, dynamic screening of disturbance transmission paths and key fan nodes is achieved through deep judgment of operation details and disturbance trends of the cold-end fans, hot-end parameter linkage and dynamic cooperation of partition fans and response priorities are combined, so that the heat supply system has agile recognition and layered response capabilities, local disturbance influences are effectively buffered, and the reliability of the heat supply system is improved. The whole system shows continuous response flexibility and risk controllability, and efficient cooperative operation of all links is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of peak shaving control technology, and in particular to a deep peak shaving control method and system for heating units. Background Technology

[0002] Peak shaving control involves the management and control of the load regulation capacity on the generation side of a power system. It mainly includes operational control strategies for cogeneration units, coal-fired units, gas turbines, and new energy power generation equipment under different load conditions. The core objective is to improve the load response capability of the power system, optimize the economic efficiency of unit operation, and ensure system safety and stability. Among these, the traditional deep peak shaving control method for cogeneration units refers to an operational strategy in cogeneration systems that addresses the insufficient regulation capacity of cogeneration units under complex conditions such as ultra-low loads or rapid load changes. This strategy achieves deep load regulation of the cogeneration unit by controlling the turbine's thermal parameters and the operating status of the cold-end system fans.

[0003] Existing technologies use static or single-variable methods to regulate fans and hot ends, lacking real-time zoning and dynamic intervention mechanisms. Inter-zone coordination relies on static parameter configuration, making it difficult to capture changes in disturbance paths in a timely manner. When encountering load fluctuations or atypical disturbances, the response of fans in some areas is delayed, which can easily lead to the accumulation of risks. The flow field structure is not adjusted in a timely manner, resulting in frequent regional imbalances and local hot end fluctuations. The operation process is constrained by static control logic, making it difficult to adapt to the requirements of system response agility and safety under dynamic operating conditions. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a method and system for deep peak-shaving control of heating units. The technical solution is as follows: On the one hand, a deep peak-shaving control method for heating units is provided, including the following steps: S1: Based on the cold-end fan cluster, collect the axial velocity gradient of each fan, continuously track the changes in fan outlet shear velocity and wind pressure, identify shear disturbance trends, screen key nodes with the same direction of change, and obtain a set of disturbance trend features. S2: Based on the set of disturbance trend characteristics, select the target fan in the condenser exhaust section, continuously monitor the wind pressure change trend, flow field shear velocity and end heat exchange zone temperature, perform fan disturbance degree ranking, and obtain the dominant influence ranking result; S3: Based on the ranking results of the dominant influences, collect the wind direction change trend, measure the main axis direction angle, combine wind direction change and periodic data to determine the continuity of wake interference, screen key propagation nodes, and obtain the criteria for key propagation nodes. S4: Based on the key propagation node criteria, locate the main steam outlet area and the cold end negative pressure section, monitor the wind pressure changes in each area, compare the correlation between the two simultaneously, classify the risk level, and obtain the linkage risk classification results. S5: Based on the aforementioned linkage risk classification results, adjust the fan speed for high-risk areas, set time delay and wind direction deflection, and use conventional rate and direction parameters for coordinated control in medium and low-risk areas. Determine the zone operation status, execute response control actions, and obtain zone control response indicators.

[0005] On the other hand, the disturbance trend feature set includes disturbance origin number, trend duration period, and shear consistency category; the dominant influence ranking result includes priority ranking level, key affected wind turbines, and response thermal section; the key propagation node criterion includes propagation node number, influence path identifier, and propagation sequence level; the linkage risk classification result includes high-risk area label, medium-risk area label, and low-risk area label; and the zonal control response index includes adjustment speed parameter, response timing parameter, and control command number.

[0006] On the other hand, the specific steps for obtaining the set of disturbance trend features are as follows: S101: Based on the cold-end fan cluster, analyze the axial velocity data frames of the fans in a fixed period, compare the velocity data of each monitoring position along the fan axis, calculate the velocity change rate between adjacent monitoring points, determine the spatial difference of velocity distribution, and obtain the velocity distribution feature set. S102: Based on the velocity distribution feature set, continuously compare the outlet shear velocity and wind pressure data of the corresponding fan in multiple cycles, calculate the difference in shear velocity between layers in the cycle, identify the wind pressure change trend, combine the shear velocity fluctuation and wind pressure trend, determine the fan operation characteristics and associate them with the number to obtain the operation disturbance mapping group. S103: Based on the operational disturbance mapping group, determine the periodic shear direction sequence of each wind turbine, identify wind turbine nodes whose shear direction remains consistent within a continuous period, and organize the shear direction change trajectory, trend duration period, and number of the nodes to obtain a disturbance trend feature set.

[0007] On the other hand, the specific steps for obtaining the dominant influence on the sorting results are as follows: S201: Based on the set of disturbance trend features, identify the fans marked as trend deviations in the exhaust section of the condenser tail gas, monitor the change process of the outlet air pressure of the target fan, determine the consistency of the direction and magnitude of the air pressure change in adjacent cycles, and obtain the air pressure trend index group. S202: Based on the wind pressure trend index group, compare the outlet flow field shear velocity sequence of the corresponding fan, analyze the changes in shear velocity distribution during each period, identify the temperature change process of the terminal heat exchange zone, calculate the correspondence between temperature trend and wind pressure trend in time stage, and obtain the linkage interference feature group. S203: Based on the aforementioned linkage interference feature group, determine the proportion of the wind pressure change trend of each fan in the joint feature combination, adjust the priority of temperature change and shear rate in the interference classification, and analyze the order and level of interference influence of each target fan to obtain the ranking result of dominant influence.

[0008] On the other hand, the specific steps for obtaining the key propagation node criterion are as follows: S301: Based on the ranking results of the dominant influence, select the wind turbine nodes with the highest ranking as targets, monitor the outlet wind direction sequence of the target wind turbine in each operating cycle, calculate the angle change between wind direction vectors in adjacent cycles, determine the continuous deflection trend and transfer trajectory of wind direction, and obtain the wind direction deflection feature group. S302: Based on the wind direction deflection feature group, compare the angle deviation between the main axis direction of each wind turbine and the wind path, analyze the relationship between the wind trajectory and the main axis, determine the continuous deviation state of the wind direction during the operating cycle, and combine the wind turbine interference level data for group comparison to obtain the wake interference impact sequence. S303: Based on the wake interference effect sequence, determine the consistency between the wind direction continuity of each wind turbine and the disturbance level response, identify wind turbine nodes with continuous propagation trend and high response level, and obtain the key propagation node criterion.

[0009] On the other hand, the specific steps for obtaining the linked risk classification results are as follows: S401: Based on the key propagation node criteria, locate the main steam outlet area and the cold end negative pressure section, monitor the wind pressure change process in each area, compare the periodic trends of wind pressure data between each monitoring point, determine the synchronicity and amplitude of wind pressure fluctuations in each area, and obtain the regional wind pressure synchronization characteristics. S402: Based on the regional wind pressure synchronization characteristics, synchronously detect the temperature rise change process at the main steam terminal, analyze the time correspondence between wind pressure change and temperature rise change in each time period, identify the time periods when wind pressure and temperature rise changes are synchronous or have similar trends, and obtain temperature rise and wind pressure correlation data. S403: Based on the temperature rise and wind pressure correlation data, determine the degree of coupling between wind pressure and temperature rise changes in each region, classify regional risks according to the linkage standard, compare the region numbers and characteristic performance under each risk level, and obtain the linkage risk classification results.

[0010] On the other hand, the specific steps for obtaining the zoning control response indicators are as follows: S501: Based on the aforementioned linkage risk classification results, determine the combination of wind pressure and temperature rise parameters of the fans in high-risk areas, optimize the operating status of the target fans in high-risk areas, adjust the speed according to the risk level of each fan, set the response time delay and wind direction deflection, and obtain a set of high-risk control parameters. S502: Based on the high-risk control parameter set, monitor the current operating status of wind turbines in medium- and low-risk areas, identify the differences between the conventional speed and direction parameters and the actual operation, adjust the wind turbine speed and wind direction in medium- and low-risk areas, execute intra-zone collaborative control actions, and obtain the intra-zone collaborative control configuration group. S503: Based on the aforementioned zoned coordinated control configuration group, collect disturbance information from each zone, analyze the operating conditions of wind turbines within the region, allocate control actions according to the risk level, and obtain zoned control response indicators.

[0011] On the other hand, the axial velocity gradient refers to the rate of change of airflow velocity with spatial position along the fan axis, the shear velocity refers to the velocity difference of airflow at different levels in the tangential direction, and the wind pressure change refers to the change of static pressure at the fan outlet.

[0012] On the other hand, the condenser exhaust section refers to the cold-end fan section of the heating unit responsible for discharging the waste heat gas from the condenser outlet to the atmosphere, and the wind pressure change trend refers to the direction and rate of change of the fan outlet wind pressure over time.

[0013] On the other hand, a deep peak-shaving control system for heating units is provided. This system is applied to a deep peak-shaving control method for heating units, including: The disturbance trend identification module is based on the cold end fan cluster. It collects the axial velocity gradient of each fan, continuously tracks the changes in shear velocity and wind pressure at the fan outlet, identifies the shear disturbance trend, screens key nodes that change in the same direction, and obtains a set of disturbance trend features. Based on the set of disturbance trend features, the interference ranking monitoring module selects the target fan in the condenser exhaust section, continuously monitors the wind pressure change trend, flow field shear velocity and terminal heat exchange zone temperature, performs fan interference degree ranking, and obtains the dominant influence ranking result. Based on the ranking results of the dominant influences, the propagation node screening module collects the wind direction change trend, measures the main axis direction angle, and combines wind direction change and periodic data to determine the continuity of wake interference, screens key propagation nodes, and obtains the key propagation node criteria. Based on the key propagation node criteria, the risk classification and determination module locates the main steam outlet area and the cold end negative pressure section, monitors the wind pressure changes in each area, compares the correlation between the two simultaneously, classifies the risk level, and obtains the linkage risk classification result. Based on the linkage risk classification results, the zone response control module adjusts the fan speed for high-risk areas, sets time delay and wind direction deflection, and uses conventional rate and direction parameters for coordinated control in medium and low-risk areas. It judges the zone operation status, executes response control actions, and obtains the zone control response index.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By deeply analyzing the operational details and disturbance trends of the cold-end fans, dynamic screening of disturbance transmission paths and key fan nodes is achieved. Combined with the linkage of hot-end parameters, the fans in each zone are dynamically coordinated according to response priorities. The control logic adjusts the fan control actions according to the risk level, enabling the heating system to have agile identification and hierarchical response capabilities. Each zone can accurately execute multi-dimensional adjustments according to actual operating conditions, effectively buffering the impact of local disturbances. The system as a whole exhibits continuous response flexibility and risk controllability under complex load fluctuations and unsteady conditions, ensuring efficient and coordinated operation of all links. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of the main steps of the present invention; Figure 2 This is a flowchart of steps S1 of the present invention; Figure 3 This is a flowchart of steps S2 of the present invention; Figure 4 This is a flowchart of steps S3 of the present invention; Figure 5 This is a flowchart of step S4 of the present invention; Figure 6 This is a flowchart of steps S5 of the present invention; Figure 7 This is a system block diagram of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] This invention provides a deep peak-shaving control method for heating units, such as... Figure 1 As shown, it includes the following steps: S1: Based on the cold-end fan cluster, the axial velocity gradient of each fan is collected in a fixed period. The changes in shear velocity and air pressure at the fan outlet are collected in a continuous period. The fan operating status is detected based on the period difference changes, the fan shear disturbance trend is identified, and the changes in the fan shear direction in subsequent periods are tracked. Trend nodes are screened according to the same direction change rule to obtain the disturbance trend feature set. S2: Based on the disturbance trend feature set, the target fan of the condenser exhaust section is selected, the wind pressure change trend at the outlet of the target fan is continuously monitored, the flow field shear velocity and the temperature change of the terminal heat exchange zone are detected, and the wind pressure trend is determined as the main cause of disturbance by specifying the dominant weight principle. The fan disturbance degree is ranked by auxiliary flow field and temperature changes to obtain the dominant influence ranking result. S3: Based on the ranking results of the dominant influence, for the wind turbine nodes with high ranking, collect the wind direction change trend of the target node, measure the change of its main axis direction angle, and judge the wake interference persistence capability by combining the wind direction change with the operating cycle. Then, compare it with the wind turbine disturbance level. Through the dual judgment of propagation trend and response capability, identify representative propagation nodes and obtain the key propagation node criteria. S4: Based on the key propagation node criteria, locate the main steam outlet area and the cold end negative pressure section, monitor the wind pressure change in each area, combine the terminal steam temperature rise change trend, and simultaneously compare the correlation between wind pressure and temperature rise changes. Through the linkage standard, each area is divided into multiple risk levels to obtain the linkage risk classification result. S5: Based on the results of the linkage risk classification, the wind turbine speed is adjusted in an orderly manner in high-risk areas, and response time delay and wind direction deflection are set. In medium and low-risk areas, conventional rate and direction parameters are used for coordinated control. The operating status of the zone is judged in real time according to the disturbance information, and then the control action of the zone is executed to obtain the zone control response index.

[0023] The disturbance trend feature set includes the disturbance origin number, trend duration period, and shear consistency category. The dominant impact ranking results include priority ranking level, key affected wind turbines, and response thermal sections. The key propagation node criteria include propagation node number, impact path identifier, and propagation sequence level. The linkage risk classification results include high-risk area label, medium-risk area label, and low-risk area label. The zone control response indicators include adjustment speed parameters, response timing parameters, and control command number.

[0024] In S1, axial velocity gradient refers to the rate of change of airflow velocity along the fan axis with spatial position, used to determine the distribution characteristics of airflow at different locations. Shear velocity refers to the velocity difference of airflow at different levels in the tangential direction. Air pressure change refers to the change of static pressure at the fan outlet. Both are used to analyze the strength of local disturbances. Periodic difference change refers to the comparison of the changes of the above parameters (such as velocity and air pressure) between different detection cycles, used to determine the operating trend. Operating status refers to the actual operating condition of the fan in the cycle, including speed, flow field stability, load, etc. Fan shear disturbance trend refers to the analysis of shear velocity fluctuations to determine whether the fan is in a state of flow field disturbance or instability. Shear direction change refers to the change of the shear direction of the fluid at the fan outlet over time, used to determine whether the disturbance is persistent or trending. Same-direction change rule means that if the shear direction shifts in the same direction for multiple consecutive cycles within a certain period of time, it is considered to have a "same-direction" trend and is marked as a disturbance source. Trend node refers to the fan selected based on continuous same-direction changes, which is considered a key disturbance node.

[0025] In S2, the condenser exhaust section refers to the cold-end fan section of the heating unit responsible for discharging the waste heat gas from the condenser outlet to the atmosphere. It is a key area for identifying fan disturbances. The air pressure change trend refers to the direction and rate of change of the fan outlet air pressure over time, reflecting the changes in fan operating load and disturbance characteristics. The flow field shear velocity refers to the velocity difference between different layers in the airflow in the cold-end region, revealing changes in the flow field structure. The terminal heat exchange zone refers to the final section of the cold-end system that exchanges heat with the main steam, directly affecting the condenser outlet temperature and energy transfer. The dominant weight principle refers to setting the main influence weight of air pressure change on system disturbances based on engineering experience or data analysis. Its parameters are auxiliary factors. The air pressure trend refers to the judgment conclusion that the air pressure change trend is the main source of disturbance. The main cause of disturbance refers to the confirmation that the air pressure trend is the main controlling factor of the fan disturbance through ranking analysis. The flow field and temperature changes refer to the changes in airflow characteristics and heat transfer characteristics that assist in determining the degree of disturbance. The degree of fan disturbance refers to the comprehensive factors that rank the influence level of the fan in the disturbance.

[0026] In S3, the wind direction change trend refers to the trajectory of the wind turbine outlet airflow direction over time, reflecting the disturbance source and its propagation direction. The main axis direction angle change refers to the change in the angle between the wind direction and the main axis of the wind turbine, used to assess the degree of deviation in disturbance propagation. The wake interference persistence capability refers to whether the disturbance can continue to propagate with the wind direction, reflecting the duration and intensity of the disturbance's impact on downstream wind turbines. The wind turbine disturbance level refers to the impact level label assigned to each wind turbine in the ranking. The dual discrimination of propagation trend and response capability refers to analyzing both the spatial propagation trend of the disturbance and examining the wind turbine's response speed and amplitude to the disturbance, in order to comprehensively identify key propagation nodes. The representative propagation node refers to the wind turbine node that meets the criteria and has the greatest impact in the disturbance chain.

[0027] In S4, the main steam outlet area refers to the critical heat exchange area after the main steam is discharged from the heating unit. Temperature rise changes are sensitive and affect the system's thermal efficiency. The cold end negative pressure section refers to the area in the cold end system where the air pressure is lower than atmospheric pressure. It is an important monitoring point between the fan and the condenser. Air pressure change monitoring refers to continuously recording the changes in airflow pressure in each section for dynamic risk assessment. Terminal steam temperature rise change trend refers to monitoring the change trajectory of the main steam terminal temperature over time, reflecting the operating status of the hot end. Correlation refers to the synchronous or related relationship between air pressure change and temperature rise change, which serves as the basis for judging regional risks. Linkage standard refers to classifying the risk categories of each zone based on the correlation rules of air pressure and temperature rise changes. Risk level refers to the different safety levels assigned to each zone according to the risk assessment.

[0028] In S5, high-risk areas refer to cold-end or hot-end zones with a high safety level in risk assessment, requiring key control. Response lag and wind direction deflection refer to appropriately delaying the fan adjustment response time in high-risk zones while adjusting the wind direction to balance disturbance propagation. Medium- and low-risk areas refer to areas with a general or low risk in risk assessment, using conventional control strategies. Conventional speed refers to the fan operating at the standard speed according to design or empirical parameters. Directional parameters refer to the adjustment parameters set according to the operation plan for the fan's outlet air direction. Disturbance information refers to real-time collected monitoring data such as wind speed, wind pressure, and temperature, used for dynamic feedback control. Zone operation status refers to the current operating status of the fans and flow field within the zone. Control actions refer to active control operations such as speed adjustment and wind direction adjustment based on risk assessment.

[0029] like Figure 2 As shown, the specific steps for obtaining the perturbation trend feature set are as follows: S101: Based on the cold-end fan cluster, analyze the axial velocity data frames of the fans in a fixed period, compare the velocity data of each monitoring position along the fan axis, calculate the velocity change rate between adjacent monitoring points, determine the spatial difference of velocity distribution, and obtain the velocity distribution feature set. Multiple monitoring points, such as five fixed positions, are set on the axis of each wind turbine. Wind speed data frames are collected from each monitoring point within the current cycle. In practice, the wind speed values ​​of each point are extracted sequentially, and the difference between the wind speeds of adjacent positions is calculated to obtain the wind speed change between each pair of adjacent points. Then, the rate of change of speed between adjacent positions is calculated. This operation traverses the wind speed data between all monitoring points on the wind turbine axis to obtain the overall axial distribution. Subsequently, the wind turbine axis is divided into several segments, and the fluctuation of the rate of change of wind speed in each segment is statistically analyzed. If the wind speed change in a certain segment deviates significantly from other segments, for example, a sudden drop from 4.5 m / s to 3.2 m / s, and such a jump occurs in the middle of the wind turbine main shaft or near the outlet end, then it is marked. This location is a velocity anomaly point. At this point, it is necessary to further determine whether the distribution curve of the wind speed change rate along the entire axis shows abrupt change or inflection point. If the distribution is uneven or the local change amplitude exceeds twice the average change amplitude, the flow velocity distribution of the fan in this cycle can be preliminarily identified as abnormal. After performing multi-cycle monitoring, the location of the flow velocity change anomaly point, the wind speed change amplitude, and the number of the abnormal continuous section for each cycle of all fans are classified and summarized, and a periodic flow velocity characteristic file corresponding to each fan is established. For example, if the wind speed of fan 1 drops sharply from 5.0 to 3.6 in the 3rd cycle at the axial position of section 3-4, and this change occurs for two consecutive cycles, it is recorded as a repetitive flow velocity distribution anomaly. Fan 1 is marked into the flow velocity distribution characteristic set, and the characteristic data classification of all fans is gradually completed.

[0030] S102: Based on the velocity distribution feature set, continuously compare the outlet shear velocity and wind pressure data of the corresponding fan in multiple cycles, calculate the difference in shear velocity between layers in the cycle, identify the wind pressure change trend, combine the shear velocity fluctuation and wind pressure trend, determine the fan operation characteristics and associate them with the number to obtain the operation disturbance mapping group. The outlet shear velocity and wind pressure data of each wind turbine are analyzed synchronously over multiple consecutive cycles. Specifically, two representative measuring points are selected at the top and bottom of the wind turbine outlet section. The wind speed at these two locations is extracted for each cycle, and the difference between the two values ​​is compared to determine the change in shear velocity. This difference is then arranged into a sequence over multiple cycles and paired with the wind pressure data collected in each cycle for analysis. Simultaneously, the degree of wind pressure change between two adjacent cycles is calculated. During the process, if it is found that the shear velocity difference of a certain wind turbine increases cycle by cycle, and the corresponding wind pressure also rises or falls synchronously, for example, if the shear velocity of wind turbine B gradually increases from 0.5, 0.8, 1.1 to 1.5 in four cycles, and the wind pressure is 1200, 1285, 1370, and 1450 respectively, then it is judged that… Fan B exhibits coordinated shear and wind pressure disturbance characteristics. If the shear velocity changes by more than 0.3 per cycle and the wind pressure changes by more than 70, an operational disturbance is confirmed. By checking whether the direction of change shows a continuous monotonic trend or regular oscillations, the fan number is further organized with its shear and wind pressure trend characteristics. The shear velocity change category is recorded, such as "stable enhancement type" or "oscillating repetitive type". This is then matched with the wind pressure change trend to form a pair of fan operational disturbance association entries. The association data is used to form a mapping group. For example, fan B is recorded as number B, type "continuous enhancement", shear change sequence number is 3, and wind pressure trend number is 2. This is stored in the operational disturbance mapping group. After the execution process is completed, the disturbance classification and number of all fans in the continuous cycle are obtained.

[0031] S103: Based on the operational disturbance mapping group, determine the periodic shear direction sequence of each wind turbine, identify wind turbine nodes with consistent shear direction within continuous periods, organize the shear direction change trajectory, trend duration period and number of the nodes, and obtain the disturbance trend feature set. Extract the shear direction change data for each fan in each cycle. In specific operation, within each cycle, determine whether the wind speed above the fan outlet is greater than the wind speed below. If so, record it as "positive"; otherwise, record it as "reverse". Then, check the shear direction record of each fan in consecutive cycles. If a fan is found to have a consistent shear direction in three or more cycles, such as consecutive "positive" or "reverse", it is determined that its disturbance direction is continuous, and the fan is marked as a node with consistent shear direction. Subsequently, the total number of cycles in which its direction is consistent is counted, and the trajectory of the direction change in each cycle is recorded. For example, if the direction of fan C is positive, positive, positive, positive, positive in five cycles, then its continuous cycle is 5. Further determine whether the increase or decrease of its shear velocity is stable. For example, if the shear velocity changes continuously to 0. If the values ​​are 6, 0.7, 0.8, 0.9, and 1.0, then the trend is considered clear. Data such as wind turbine number, shear direction category, duration, and shear amplitude changes are collected to generate a shear evolution path. Then, all wind turbines are sorted by duration, and the average change in shear velocity is used as a secondary sorting condition. Several wind turbines with the highest values ​​are selected as representative nodes. For example, the top 30% of nodes with duration ≥4 and average shear velocity increase of more than 0.2 are selected. The wind turbine numbers and corresponding evolution information are uniformly summarized into a disturbance trend feature set, and a node sequence is established to record its shear direction path and disturbance intensity. For example, if wind turbine C is positive for five consecutive periods and the shear velocity increases by more than 0.1 per period, it is classified as a stable positive enhancement type of shear direction, and the number C is added to the disturbance trend feature set.

[0032] like Figure 3 As shown, the specific steps that dominate the sorting results are as follows: S201: Based on the disturbance trend feature set, identify the fans marked as trend deviation in the exhaust section of the condenser tail gas, monitor the change process of the outlet air pressure of the target fan, judge the consistency of the direction and magnitude of the air pressure change in adjacent cycles, and obtain the air pressure trend index group. From the identified disturbance trend set, the fan number, shear direction, duration, and disturbance level information are extracted. Fan nodes located in the condenser exhaust section and marked as trend deviations are selected. During the selection process, the fan location information is matched with the functional section. Once confirmed to be located in the exhaust section, the monitoring process begins. For each target fan, three or more cycles of outlet air pressure data sequences are continuously collected, and the collection time and value for each cycle are recorded. The difference in air pressure values ​​between adjacent cycles is calculated according to the recording order. Then, the direction of change is determined, i.e., whether the air pressure is increasing, decreasing, or oscillating cycle by cycle. A criterion for consistent air pressure change direction is established; for example, if the air pressure increases for three consecutive cycles, it is marked as an "upward trend," and if it decreases continuously, it is marked as a "downward trend." If the direction of fluctuation varies during the week, it is marked as "fluctuating trend". In addition, it is necessary to further calculate the change range of wind pressure in each cycle, compare the wind pressure difference with the set benchmark value, which is initially referenced at 100Pa based on the field operation data. If the cycle difference is greater than this value, it is defined as a significant change in amplitude. Record the wind pressure trend direction and amplitude characteristic number of all wind turbines as trend labels, and summarize them according to the wind turbine number to form a wind pressure trend index group. For example, if the wind pressure of wind turbine A in the 4th, 5th and 6th cycles is 1220Pa, 1320Pa and 1405Pa respectively, the direction is rising, and the amplitude is 100Pa and 85Pa respectively, with a clear significant change trend, it is marked as "rising-significant". Store the wind turbine A number, trend type and fluctuation amplitude label into the index group to form a wind pressure trend index group.

[0033] S202: Based on the wind pressure trend index group, compare the outlet flow field shear velocity sequence of the corresponding wind turbine, analyze the changes in shear velocity distribution during each period, identify the temperature change process of the terminal heat exchange zone, calculate the correspondence between temperature trend and wind pressure trend in time stage, and obtain the linkage interference feature group. For each included wind turbine, its outlet flow field shear velocity sequence for the corresponding cycle is retrieved. The difference in shear velocity between the upper and lower layers during each cycle is compared. By recording the changes in the difference between each cycle, the evolution trajectory of the shear velocity over time is determined, analyzing whether it is continuously increasing, continuously decreasing, or exhibiting oscillation. Simultaneously, temperature data of the terminal heat exchange zone corresponding to the shear velocity change cycle is extracted. Temperature data needs to be obtained from the monitoring points at the end of the heat exchanger. The temperature change process within multiple cycles is organized in cyclical order. Then, the direction of shear velocity change within each cycle is matched one-to-one with the direction of temperature change. A time node alignment mechanism is set in the matching process to ensure that the data is compared within the same time window, determining whether the increase in shear velocity is accompanied by an increase in temperature, or whether the opposite trend occurs. If there are two or more cycles where the shear velocity increases while the temperature rises, the analysis is performed. If the temperature rises by more than 1.5℃, it is considered to have a linkage relationship. At the same time, the direction of the wind pressure trend is compared with the direction of the temperature change. For example, if the wind pressure increase, shear enhancement, and temperature increase are all within the same period, it is judged as a significant linkage interference type. Conversely, if the trend directions are different or the temperature change is less than 1℃, it is considered a weak linkage type. The three types of parameter trends of each fan are combined and judged to classify their linkage interference level. The fan number, shear trend, wind pressure trend, temperature trend, number of trend consistency periods, and linkage level are organized and classified into linkage interference characteristic groups. For example, if the shear velocity of fan B increases by 0.6m / s, the wind pressure increases by 140Pa, and the terminal temperature rises from 41.2℃ to 43.1℃ in the 3rd to 5th period, the number of trend consistency periods is 3, it is classified as a strong linkage type and included in the linkage interference characteristic group.

[0034] S203: Based on the linkage interference feature group, determine the proportion of the wind pressure change trend of each fan in the joint feature combination, adjust the priority of temperature change and shear rate in the interference classification, and analyze the order and level of interference influence of each target fan to obtain the ranking result of the dominant influence. The number of wind pressure trend records corresponding to each fan in the feature group is statistically analyzed. For example, the number of times the "increasing" or "decreasing" wind pressure trend occurs for each fan number in the feature combination is counted. This number is then divided by the total number of trend combinations to calculate the proportion of the wind pressure trend and determine its weight in the overall linkage trend. A judgment benchmark value of 0.6 is set. If the proportion of the wind pressure trend is greater than this value, wind pressure is marked as the dominant interference factor; otherwise, it is a secondary factor. Based on this judgment process, the importance of temperature change and shear rate in the linkage factors of each fan is adjusted in turn. If wind pressure is dominant, then temperature and shear rate are judged in order of frequency and intensity. For example, if the average increase in shear rate exceeds 0... If the wind speed is less than 0.5 m / s and the temperature change is less than 1.0℃, then shear rate is listed as the second factor and temperature as the third factor. Further, the ranking of the dominant factors of each fan is used as the priority key, supplemented by a weighted average of the total shear rate change and the total temperature change, to generate an interference intensity value. The interference intensity is then ranked according to this value to obtain the influence level of each fan in the current cycle, and these fans are ranked and numbered to obtain the dominant influence ranking result. For example, if fan C has a wind pressure trend proportion of 0.72, it is judged as the main cause, with a cumulative shear rate change of 2.2 m / s and a cumulative temperature change of 1.6℃, it is classified as a Class 1 influencing fan, and fan number C is ranked first in the dominant influence ranking result.

[0035] like Figure 4 As shown, the specific steps for obtaining the key propagation node criteria are as follows: S301: Based on the ranking of dominant influences, select the top-ranked wind turbine nodes as targets, monitor the outlet wind direction sequence of the target wind turbine in each operating cycle, calculate the angle change between wind direction vectors in adjacent cycles, determine the continuous deflection trend and transfer trajectory of wind direction, and obtain the wind direction deflection feature group. The wind turbine node data in the prior sorting results is filtered. Priority thresholds are set during the filtering process; for example, only the top 20% of wind turbines are selected as target nodes. The wind turbine numbers and their corresponding cycle ranges are extracted. Then, outlet wind direction data is collected for each target wind turbine within its covered operating cycle. The wind direction data is represented by standard polar coordinate angle values, with one wind direction vector corresponding to each cycle. The wind direction vectors of adjacent cycles are then paired, and the angle between the vectors is obtained through trigonometric function transformation. This angle represents the magnitude of wind direction change between two cycles. For example, if the wind direction of wind turbine A in cycles T1 to T4 is 45°, 60°, 75°, and 90° respectively, the angle change is 15°, 15°, and 15°. Further analysis of the angle change magnitude determines whether the wind direction exhibits a continuous deflection trend. When making judgments, a threshold for the consistency of angle changes is set. For example, if the direction of the angle change is the same for three consecutive cycles and each change is between 10° and 25°, it is marked as "continuous deflection". If the direction of the angle change is inconsistent or the angle is less than 5°, it is considered as "fluctuation" or "stable". In addition, the positive and negative directions of each angle are recorded to track the wind direction transfer path, whether it is deflected in a clockwise or counterclockwise direction. The wind turbine number, cycle sequence, angle change value for each cycle, deflection direction, deflection amplitude and continuity mark are summarized to form feature data and categorized into wind direction deflection feature groups. For example, if the wind direction of wind turbine B is 90°, 110°, 130° and 150° between cycles T2 and T5, the continuous angle change is 20°, the direction is consistent, the change amplitude is stable, and the continuous deflection cycle is 3, this wind turbine is recorded in the wind direction deflection feature group.

[0036] S302: Based on the wind direction deflection feature group, compare the angle deviation between the main axis direction of each wind turbine and the wind path, analyze the relationship between the wind trajectory and the main axis, determine the continuous deviation state of the wind direction during the operating cycle, and combine the wind turbine interference level data for group comparison to obtain the wake interference impact sequence. The outlet wind direction path of each fan is compared with its main axis direction. The main axis direction is determined by the fan installation location and equipment structure, and is usually a fixed value. For example, if the fan main axis is 0°, it indicates the air outlet direction is directly in front. The angle values ​​of the wind direction path data in different cycles can be directly calculated by subtracting from the main axis direction. The result is the wind direction deviation angle. If the deviation angle is greater than the set benchmark threshold of 15°, it is recorded as a "deviation event". If the deviation occurs continuously for multiple cycles and the direction is highly consistent, it is defined as a "continuous deviation state". During the judgment process, the main axis angle of each fan needs to be obtained from the equipment layout, and the wind direction data needs to be calibrated by measuring points to prevent orientation errors from interfering with the judgment accuracy. Then, each fan... The deviation angle, deviation direction, and duration of the data are recorded and matched with the preceding wind turbine interference level data. The disturbance level of the wind turbine is obtained based on the number association. For example, if wind turbine C has an interference level of II and its wind direction deviates from the main axis direction by 20° for four consecutive cycles, and the direction is always clockwise, it is classified as "medium-level interference + continuous wind direction deviation". Then, a two-level classification matrix is ​​established for all wind turbines according to the interference level and the intensity of wind direction deviation. Nodes with strong wind direction deviation in the same disturbance level are grouped and marked to form a wake disturbance impact sequence. For example, if wind turbine D has a level I interference and its wind direction deviates from the main axis by an angle of 35° for more than 3 consecutive cycles, it is marked as a high-level wake disturbance wind turbine node in the sequence.

[0037] S303: Based on the wake interference impact sequence, determine the consistency between the wind direction continuity of each wind turbine and the disturbance level response, identify wind turbine nodes with continuous propagation trend and high response level, and obtain the key propagation node criteria. The consistency between the wind direction continuity status of each wind turbine and its response to the preceding disturbance level is determined sequentially. Specifically, the duration of wind direction deflection cycles, the consistency of deflection direction, and whether the deflection angle is within the previously defined high-intensity range (greater than 25°) for each wind turbine are extracted. Then, a correlation analysis is performed with its corresponding disturbance level. If a wind turbine has a disturbance level of I and its wind direction deflection angle is consistently greater than 25°, the number of cycles exceeds 3, and the deflection direction maintains a single trend, such as continuous clockwise deflection, it is determined to have strong wind direction continuity capability. Conversely, if the wind direction deflection direction is unstable or the cycle duration is less than 2, it does not have propagation characteristics. Combined with the disturbance level response, that is, the wind turbine in the interference mapping... If a wind turbine is repeatedly marked as a primary cause of a cascading event or a node with a high proportion of wind pressure, it is considered a critical propagation node. Based on this judgment, propagation representativeness criteria are set: continuous wind direction shift period ≥ 3, deviation angle ≥ 25°, disturbance level I or II, and wind pressure dominance exceeding 60%. If a wind turbine meets all the criteria, it is classified as a critical propagation node, and its number, influence path number, response level, and propagation sequence number are recorded. Criteria for critical propagation nodes are output. For example, if the wind direction of wind turbine E continuously shifts by 30° from T1 to T4, the shift direction remains unchanged, the disturbance level is I, and the wind pressure dominance is 72%, wind turbine E is identified as a critical propagation node and added to the criterion set.

[0038] like Figure 5 As shown, the specific steps for obtaining the linkage risk classification results are as follows: S401: Based on the key propagation node criteria, locate the main steam outlet area and the cold end negative pressure section, monitor the wind pressure change process in each area, compare the periodic trends of wind pressure data between each monitoring point, determine the synchronicity and amplitude of wind pressure fluctuations in each area, and obtain the regional wind pressure synchronization characteristics. The spatial location area associated with the propagation node is clearly identified by comparing the fan number with the unit layout diagram to pinpoint the steam flow channel where the key node is located. Following the downstream propagation path of the disturbance, the location is traced back to the main steam outlet area and the cold-end negative pressure section. The main steam outlet area is the hot-end exhaust channel outlet in the structure, while the cold-end negative pressure section is typically located in the induced draft area or vacuum holding area between the condenser and the fan. After confirming these two areas, multiple wind pressure monitoring points are set up, for example, P1 and P2 in the main steam outlet area and P3 and P4 in the cold-end negative pressure section. Wind pressure data for each point is collected in 10-second increments within a 5-minute cycle, forming a time series. The data from each monitoring point is then periodically segmented into time periods T1, T2, and T3. The increase or decrease in wind pressure within each cycle is analyzed, i.e., the wind pressure difference between adjacent cycles is calculated for each monitoring point. Finally, the changing trends of multiple monitoring points within the same cycle are compared to determine whether they exhibit the same trend within the same cycle. For example, if both P1 and P2 show a decrease in wind pressure within cycle T1, with differences of 120 Pa and 105 Pa respectively, they are considered to have a synchronous trend. The magnitude of their wind pressure differences is then compared, and a fluctuation threshold of 100 Pa is set. If the wind pressure change at adjacent monitoring points exceeds this value, it is recorded as a high-amplitude fluctuation event. The data is then structured and recorded according to monitoring point number, cycle segment number, direction of wind pressure change, and magnitude of wind pressure change. If multiple points show a synchronous change direction with consistent wind pressure fluctuations in two consecutive cycles, and their fluctuation magnitudes are all higher than the threshold, the area is marked as a synchronous fluctuation state, forming a regional wind pressure synchronous characteristic. For example, if the main steam outlet P1 and P2 both show a decrease in wind pressure in T2 and T3, with magnitudes of 110 Pa and 130 Pa respectively, while the negative pressure segments P3 and P4 show an increase of 110 Pa and 95 Pa respectively, but in opposite directions, this indicates that the main steam outlet area is a synchronous fluctuation area, and the cold end negative pressure segment is an asynchronous response area. The results are then included in the regional wind pressure synchronous characteristic dataset.

[0039] S402: Based on the regional wind pressure synchronization characteristics, the temperature rise change process of the main steam terminal is detected synchronously, the temporal correspondence between wind pressure change and temperature rise change in each time period is analyzed, the time periods when wind pressure and temperature rise changes are synchronous or have similar trends are identified, and temperature rise and wind pressure correlation data are obtained. Further, temperature monitoring data from the main steam terminal is extracted within the corresponding period. The corresponding monitoring point is located in the middle or end of the steam outlet channel at the end of the heat exchange. The acquisition frequency is consistent with the wind pressure acquisition, set to once every 10 seconds. The temperature data and wind pressure data are aligned and synchronously organized into a time series matrix. Then, the temperature rise value within each time period is processed, and the temperature rise is defined as the increment of the monitoring point temperature relative to the previous period. For example, if the temperature at the end of period T1 is 42.0℃ and the temperature at the end of period T2 is 43.5℃, then the temperature rise is 1.5℃. Next, the temperature rise is compared with the wind pressure change within the same period. If the temperature rise is positive and the wind pressure shows a decreasing trend, it is recorded as "reverse correlation". If the temperature rise and wind pressure are in the same direction (e.g., the temperature rise is positive and the wind pressure is also increasing positively), it is recorded as "positive correlation". In addition, for each The time window within a cycle is compared by sliding to find the time difference between the start time of temperature rise and the start time of wind pressure change. If the difference is less than 30 seconds, it is considered "trend proximity"; otherwise, it is recorded as "trend lag". This pairing is used to determine the temperature and pressure correspondence within all data collection cycles. The wind pressure change direction, change magnitude, temperature rise value, temperature rise trend direction, start time, and trend time difference are integrated. The frequency and proportion of consistent or proximity trends in each region are statistically analyzed to form temperature rise and wind pressure correlation data. For example, in the cold end negative pressure zone, the wind pressure decreases in T4 and T5, the temperature rise values ​​are 1.6℃ and 1.9℃ respectively, and the time difference is 20 seconds and 25 seconds respectively. Then, the two cycles are classified as trend proximity segments, recorded as medium-intensity correlation data, and included in the temperature rise and wind pressure correlation dataset.

[0040] S403: Based on the correlation data of temperature rise and wind pressure, determine the degree of coupling between wind pressure and temperature rise changes in each region, classify regional risks according to the linkage standard, compare the region numbers and characteristics under each risk level, and obtain the linkage risk classification results. The number of matching occurrences between wind pressure and temperature rise changes in each region across different periodic segments was accumulated, and the number of positive correlations, negative correlations, and trend proximity segments were statistically analyzed. Then, the total correlation frequency was calculated, and the difference between temperature rise and wind pressure amplitudes was statistically analyzed. A baseline value of 1.0℃ was set for temperature rise amplitude, and 100Pa for wind pressure change. The temperature rise or wind pressure changes recorded at each monitoring point were compared with the baseline values. If the difference exceeded 30%, it was considered a strong change. Time periods exhibiting trend consistency or proximity and where the changes were all strong were designated as high-coupling segments. Regions with more than three cumulative high-coupling segments were defined as high-coupling areas. Regions with less than one high-coupling segment were marked as weakly coupled, and the remaining segments were classified as medium-coupling. All monitoring points were assessed according to this standard. The main steam outlet area and the cold end negative pressure section are classified sequentially to generate risk level labels. For example, if four out of five cycles in area A are strongly consistent segments, it is defined as high coupling. If only one cycle in area B is an adjacent segment, it is defined as low coupling. Then, according to the linkage standard, high coupling areas are classified as high risk, medium coupling areas as medium risk, and low coupling areas as low risk. At the same time, under each level, indicators such as area number, temperature rise variation range, maximum wind pressure fluctuation, and trend start interval are further summarized to form a complete risk characteristic item. The linkage risk classification results are compiled. For example, area C is the main steam outlet sub-area with a temperature rise variation range of 2.1℃ and a maximum wind pressure fluctuation of 150Pa. It has three consecutive cycles of positive trend adjacent segments and is classified as a high-risk area. Its number and classification label are recorded and included in the linkage risk classification results.

[0041] like Figure 6 As shown, the specific steps for obtaining the zoning control response indicators are as follows: S501: Based on the results of the linkage risk classification, determine the combination of wind pressure and temperature rise parameters of the fans in high-risk areas, optimize the operating status of the target fans in high-risk areas, adjust the speed according to the risk level of each fan, set the response time delay and wind direction deflection, and obtain the set of high-risk control parameters. Extract the area codes classified as high-risk and locate the corresponding wind turbine nodes. Retrieve the wind pressure and temperature change data for each node, and organize the dataset into periodic segments. Compare and judge each data set to identify periods where wind pressure increases and temperature rises exceed set benchmark values. The benchmark value for wind pressure is 100 Pa, and the benchmark value for temperature rise is 1.0℃. If both wind pressure and temperature rise exceed these benchmark values ​​by more than 30%, it is considered a period of strong disturbance. For example, if wind turbine A experiences a wind pressure increase of 135 Pa and a temperature rise of 1.5℃ in period T2, this period is marked as a high-disturbance period. Then, analyze the wind turbine operating status using this type of periodic data, extracting the current speed, operating load, and outlet direction, recording them as current operating status parameters. Compare this with historical data; if the speed has been continuously increasing over the past three periods but the wind pressure has not yet stabilized, further analysis is needed. If the pressure decreases, it is identified as a target fan node requiring adjustment. The target speed of high-risk fans is then adjusted accordingly. The setting logic is to reduce the speed by 5% for every 100Pa change in air pressure, while setting a response delay of 30 seconds to prevent system oscillation. The wind direction deflection angle is also set. Based on records of airflow deviations from the main axis exceeding 10°, the wind direction deflection range of 5° to 15° is adjusted to correct the disturbance diffusion direction. The risk level, air pressure and temperature rise values, recommended speed adjustment, response delay in seconds, and wind direction deflection angle for each fan are combined and summarized to generate a high-risk control parameter set. For example, if fan B has a periodic air pressure change of +145Pa and a temperature rise of +1.8℃, it is classified as a high-risk node, and a recommended speed reduction of 8%, a response delay of 30 seconds, and a wind direction deflection of 10° are recorded as control parameters for that fan in the set.

[0042] S502: Based on the high-risk control parameter set, monitor the current operating status of wind turbines in medium and low-risk areas, identify the differences between the conventional rate and direction parameters and the actual operation, adjust the wind turbine speed and wind direction in medium and low-risk areas, execute intra-zone coordinated control actions, and obtain the intra-zone coordinated control configuration group. First, the area numbers already classified as medium-risk and low-risk were identified. The current operating status data of the area's fans were retrieved, and the fan speed and outlet direction values ​​for the current period were extracted and compared with the corresponding conventional design parameters for that area. The conventional speed was based on the standard speed indicated on the equipment nameplate. If the current speed deviated from the conventional value by more than ±10%, it was marked as an abnormal state. The conventional airflow direction was based on the main shaft direction; if the angle between the current outlet direction and the main shaft exceeded 15°, it was also considered an abnormal direction state. Subsequently, according to the type of abnormal state, nodes with abnormal speed and abnormal direction were recorded separately. The coordination between medium- and low-risk areas was then derived by referring to the control parameters already set in the high-risk area. The control strategy is set according to the principle that in medium-risk areas, the speed adjustment range should not exceed ±5% and the wind direction adjustment range should be within ±10°. In low-risk areas, the speed adjustment should not exceed ±2% and the wind direction should only be limited to not deviating from the main axis by ±5°. Then, all the wind turbine numbers that need to be adjusted are paired and integrated with their actual operating values, target adjustment values, wind direction correction angles, and the response numbers corresponding to the current cycle to generate collaborative control configuration items. For example, if wind turbine C is a medium-risk node, and its current speed is 12% higher than normal and the wind direction is deviated from the main axis by 20°, the speed needs to be reduced to 95% of the normal value and the wind direction needs to be corrected to a main axis angle of less than 10°. This is recorded as the control parameters of node C and included in the zone collaborative control configuration group.

[0043] S503: Based on the zoned coordinated control configuration group, collect disturbance information of each zone, analyze the operating conditions of wind turbines in the region, allocate control actions according to the risk level, and obtain zoned control response indicators; The system begins by collecting disturbance information from each configured node, including wind pressure changes, temperature rise changes, wind direction changes, and shear velocity fluctuations. Five samples are collected in each cycle, and the average is calculated to obtain the cycle's average disturbance value. Based on the collected results, the current operating conditions of each wind turbine node are analyzed to determine if its disturbance level is consistent with the original risk classification. If the average disturbance parameters of a low-risk area wind turbine exceed the set warning threshold (e.g., wind pressure change exceeding 120 Pa, temperature rise exceeding 1.4℃), the node is temporarily upgraded to a medium-risk level, the control strategy is reloaded, and a new control command is generated. If the current parameters do not exceed the threshold, the original control scheme is maintained. Subsequently, each... Each wind turbine extracts corresponding adjustment instructions from the control configuration group based on its risk level. The instructions must include the target speed value, speed adjustment range, wind direction offset angle, and response delay time. A response parameter structure is then generated based on this. All wind turbine instructions are then integrated into a control index summary by zone, and the output is the zone control response index. For example, if wind turbine D has a current cycle wind pressure change of 135Pa, a temperature rise of 1.6℃, and a wind direction offset of 20°, it is determined to be upgraded from low risk to medium risk. The speed is reset to 95% of the original value, the response delay is set to 20 seconds, and the wind direction offset is adjusted until the main shaft offset angle is less than 10°. This wind turbine control data is integrated into one of the medium-risk zone response instructions and recorded in the zone control response index set.

[0044] like Figure 7 As shown, a deep peak-shaving control system for a heating unit includes: The disturbance trend identification module is based on the cold end fan cluster. It collects the axial velocity gradient of each fan, continuously tracks the changes in shear velocity and wind pressure at the fan outlet, identifies the shear disturbance trend, screens key nodes that change in the same direction, and obtains a set of disturbance trend features. The interference ranking monitoring module selects the target fan in the condenser exhaust section based on the disturbance trend feature set, continuously monitors the wind pressure change trend, flow field shear velocity and terminal heat exchange zone temperature, performs fan interference degree ranking, and obtains the dominant influence ranking result. The propagation node screening module is based on the ranking of dominant influences, collects wind direction change trends, measures the angle of the main axis direction, and combines wind direction change and periodic data to determine the continuity of wake interference, screens key propagation nodes, and obtains the criteria for key propagation nodes. The risk classification and judgment module is based on the criteria of key propagation nodes. It locates the main steam outlet area and the cold end negative pressure section, monitors the wind pressure changes in each area, compares the correlation between the two in a synchronous manner, classifies the risk level, and obtains the linkage risk classification results. Based on the results of the linkage risk classification, the zone response control module adjusts the fan speed in high-risk areas, sets time delay and wind direction deflection, and uses conventional rate and direction parameters for coordinated control in medium and low-risk areas. It judges the zone operation status, executes response control actions, and obtains the zone control response index.

[0045] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A deep peak-shaving control method for heating units, characterized in that, The method includes: S1: Based on the cold-end fan cluster, collect the axial velocity gradient of each fan, continuously track the changes in fan outlet shear velocity and wind pressure, identify shear disturbance trends, screen key nodes with the same direction of change, and obtain a set of disturbance trend features. S2: Based on the set of disturbance trend characteristics, select the target fan in the condenser exhaust section, continuously monitor the wind pressure change trend, flow field shear velocity and end heat exchange zone temperature, perform fan disturbance degree ranking, and obtain the dominant influence ranking result; S3: Based on the ranking results of the dominant influences, collect the wind direction change trend, measure the main axis direction angle, combine wind direction change and periodic data to determine the continuity of wake interference, screen key propagation nodes, and obtain the criteria for key propagation nodes. S4: Based on the key propagation node criteria, locate the main steam outlet area and the cold end negative pressure section, monitor the wind pressure changes in each area, compare the correlation between the two simultaneously, classify the risk level, and obtain the linkage risk classification results. S5: Based on the aforementioned linkage risk classification results, adjust the fan speed for high-risk areas, set time delay and wind direction deflection, and use conventional rate and direction parameters for coordinated control in medium and low-risk areas. Determine the zone operation status, execute response control actions, and obtain zone control response indicators.

2. The deep peak-shaving control method for heating units according to claim 1, characterized in that, The disturbance trend feature set includes disturbance origin number, trend duration period, and shear consistency category; the dominant influence ranking result includes priority ranking level, key affected wind turbines, and response thermal section; the key propagation node criterion includes propagation node number, influence path identifier, and propagation sequence level; the linkage risk classification result includes high-risk area label, medium-risk area label, and low-risk area label; and the zonal control response index includes adjustment speed parameter, response timing parameter, and control command number.

3. The deep peak-shaving control method for heating units according to claim 1, characterized in that, The specific steps for obtaining the set of disturbance trend features are as follows: S101: Based on the cold-end fan cluster, analyze the axial velocity data frames of the fans in a fixed period, compare the velocity data of each monitoring position along the fan axis, calculate the velocity change rate between adjacent monitoring points, determine the spatial difference of velocity distribution, and obtain the velocity distribution feature set. S102: Based on the velocity distribution feature set, continuously compare the outlet shear velocity and wind pressure data of the corresponding fan in multiple cycles, calculate the difference in shear velocity between layers in the cycle, identify the wind pressure change trend, combine the shear velocity fluctuation and wind pressure trend, determine the fan operation characteristics and associate them with the number to obtain the operation disturbance mapping group. S103: Based on the operational disturbance mapping group, determine the periodic shear direction sequence of each wind turbine, identify wind turbine nodes whose shear direction remains consistent within a continuous period, and organize the shear direction change trajectory, trend duration period, and number of the nodes to obtain a disturbance trend feature set.

4. The deep peak-shaving control method for heating units according to claim 1, characterized in that, The specific steps for obtaining the dominant influence ranking results are as follows: S201: Based on the set of disturbance trend features, identify the fans marked as trend deviations in the exhaust section of the condenser tail gas, monitor the change process of the outlet air pressure of the target fan, determine the consistency of the direction and magnitude of the air pressure change in adjacent cycles, and obtain the air pressure trend index group. S202: Based on the wind pressure trend index group, compare the outlet flow field shear velocity sequence of the corresponding fan, analyze the changes in shear velocity distribution during each period, identify the temperature change process of the terminal heat exchange zone, calculate the correspondence between temperature trend and wind pressure trend in time stage, and obtain the linkage interference feature group. S203: Based on the aforementioned linkage interference feature group, determine the proportion of the wind pressure change trend of each fan in the joint feature combination, adjust the priority of temperature change and shear rate in the interference classification, and analyze the order and level of interference influence of each target fan to obtain the ranking result of dominant influence.

5. The deep peak-shaving control method for heating units according to claim 1, characterized in that, The specific steps for obtaining the key propagation node criterion are as follows: S301: Based on the ranking results of the dominant influence, select the wind turbine nodes with the highest ranking as targets, monitor the outlet wind direction sequence of the target wind turbine in each operating cycle, calculate the angle change between wind direction vectors in adjacent cycles, determine the continuous deflection trend and transfer trajectory of wind direction, and obtain the wind direction deflection feature group. S302: Based on the wind direction deflection feature group, compare the angle deviation between the main axis direction of each wind turbine and the wind path, analyze the relationship between the wind trajectory and the main axis, determine the continuous deviation state of the wind direction during the operating cycle, and combine the wind turbine interference level data for group comparison to obtain the wake interference impact sequence. S303: Based on the wake interference effect sequence, determine the consistency between the wind direction continuity of each wind turbine and the disturbance level response, identify wind turbine nodes with continuous propagation trend and high response level, and obtain the key propagation node criterion.

6. The deep peak-shaving control method for heating units according to claim 1, characterized in that, The specific steps for obtaining the linked risk classification results are as follows: S401: Based on the key propagation node criteria, locate the main steam outlet area and the cold end negative pressure section, monitor the wind pressure change process in each area, compare the periodic trends of wind pressure data between each monitoring point, determine the synchronicity and amplitude of wind pressure fluctuations in each area, and obtain the regional wind pressure synchronization characteristics. S402: Based on the regional wind pressure synchronization characteristics, synchronously detect the temperature rise change process at the main steam terminal, analyze the time correspondence between wind pressure change and temperature rise change in each time period, identify the time periods when wind pressure and temperature rise changes are synchronous or have similar trends, and obtain temperature rise and wind pressure correlation data. S403: Based on the temperature rise and wind pressure correlation data, determine the degree of coupling between wind pressure and temperature rise changes in each region, classify regional risks according to the linkage standard, compare the region numbers and characteristic performance under each risk level, and obtain the linkage risk classification results.

7. The deep peak-shaving control method for heating units according to claim 1, characterized in that, The specific steps for obtaining the zonal control response indicators are as follows: S501: Based on the aforementioned linkage risk classification results, determine the combination of wind pressure and temperature rise parameters of the fans in high-risk areas, optimize the operating status of the target fans in high-risk areas, adjust the speed according to the risk level of each fan, set the response time delay and wind direction deflection, and obtain a set of high-risk control parameters. S502: Based on the high-risk control parameter set, monitor the current operating status of wind turbines in medium- and low-risk areas, identify the differences between the conventional speed and direction parameters and the actual operation, adjust the wind turbine speed and wind direction in medium- and low-risk areas, execute intra-zone collaborative control actions, and obtain the intra-zone collaborative control configuration group. S503: Based on the aforementioned zoned coordinated control configuration group, collect disturbance information from each zone, analyze the operating conditions of wind turbines within the region, allocate control actions according to the risk level, and obtain zoned control response indicators.

8. The deep peak-shaving control method for heating units according to claim 1, characterized in that, The axial velocity gradient refers to the rate of change of airflow velocity with spatial position along the fan axis, the shear velocity refers to the velocity difference of airflow at different levels in the tangential direction, and the wind pressure change refers to the change of static pressure at the fan outlet.

9. The deep peak-shaving control method for heating units according to claim 1, characterized in that, The condenser exhaust section refers to the cold-end fan section of the heating unit responsible for discharging the waste heat gas from the condenser outlet to the atmosphere, and the air pressure change trend refers to the direction and rate of change of the fan outlet air pressure over time.

10. A deep peak-shaving control system for a heating unit, the system being used to implement the deep peak-shaving control method for a heating unit as described in any one of claims 1-9, characterized in that, The system includes: The disturbance trend identification module is based on the cold end fan cluster. It collects the axial velocity gradient of each fan, continuously tracks the changes in shear velocity and wind pressure at the fan outlet, identifies the shear disturbance trend, screens key nodes that change in the same direction, and obtains a set of disturbance trend features. Based on the set of disturbance trend features, the interference ranking monitoring module selects the target fan in the condenser exhaust section, continuously monitors the wind pressure change trend, flow field shear velocity and terminal heat exchange zone temperature, performs fan interference degree ranking, and obtains the dominant influence ranking result. Based on the ranking results of the dominant influences, the propagation node screening module collects the wind direction change trend, measures the main axis direction angle, and combines wind direction change and periodic data to determine the continuity of wake interference, screens key propagation nodes, and obtains the key propagation node criteria. Based on the key propagation node criteria, the risk classification and determination module locates the main steam outlet area and the cold end negative pressure section, monitors the wind pressure changes in each area, compares the correlation between the two simultaneously, classifies the risk level, and obtains the linkage risk classification result. Based on the linkage risk classification results, the zone response control module adjusts the fan speed for high-risk areas, sets time delay and wind direction deflection, and uses conventional rate and direction parameters for coordinated control in medium and low-risk areas. It judges the zone operation status, executes response control actions, and obtains the zone control response index.

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