GGH flue gas heat exchanger low-leakage fan redundancy control method, system and equipment and storage medium

Abstract

The invention relates to the technical field of flue gas desulfurization control, in particular to a GGH flue gas heat exchanger low-leakage fan redundancy control method, system and equipment and a storage medium. In order to solve the problems of single-point fault risk and lack of an automatic switching mechanism in traditional single low-leakage fan configuration, double-fan redundancy configuration is constructed, and a switching trigger point is determined by monitoring fan operation parameters to establish an association diagram of performance and a system leakage rate; a fault pre-judgment model is constructed based on historical data weighting processing and pressure fluctuation analysis, and a dual-drive switching threshold value is calculated in combination with the coupling relation between the fan operation characteristics and the sealing effect; cooperative actions of a fan and a flue are controlled through an interlocking logic matrix, system stability is evaluated in combination with a time sequence attenuation function, and intelligent redundancy control is achieved. The problem of system sealing failure caused by single-point failure of the low-leakage fan of the flue gas heat exchanger is solved, automatic seamless switching of the main fan and the standby fan is achieved, continuous and stable operation of the desulfurization system is ensured, and the risk of environmental protection exceeding is avoided.

Description

Technical Field

[0001] This invention relates to the field of flue gas desulfurization control technology, and in particular to a method, system, equipment and storage medium for redundant control of low-leakage fans in GGH flue gas heat exchangers. Background Technology

[0002] Flue gas heat exchangers are key equipment in limestone-gypsum wet flue gas desulfurization systems in coal-fired power plants. Their main function is to absorb the heat from the raw flue gas before it enters the absorption tower, thus reheating the clean flue gas before it enters the chimney. This heat recovery and utilization significantly increases the temperature of the clean flue gas before it enters the chimney, effectively reducing low-temperature corrosion problems in the terminal flue and chimney.

[0003] During the operation of the flue gas heat exchanger, in order to reduce the leakage rate of raw flue gas to the clean flue gas side through the rotor, the desulfurization system is usually equipped with a low-leakage fan. This fan draws the clean flue gas out of the airflow leading to the chimney and recirculates it back to the heat exchanger at a higher pressure. This recirculated gas is discharged into the heat exchanger through the openings in the top fan-shaped plate, forming an air curtain to effectively isolate the raw flue gas before it enters the clean flue gas side.

[0004] Currently, flue gas heat exchangers in desulfurization systems of coal-fired power plants are typically equipped with only a single low-leakage fan. This configuration can meet the system's sealing requirements during normal operation. However, when the low-leakage fan fails and stops operating, a large amount of raw flue gas leaks from the heat exchanger to the clean flue gas side through the rotor, causing a significant decrease in desulfurization efficiency and severely impacting the operation of the entire desulfurization system.

[0005] Traditional low-leakage fan configurations for flue gas heat exchangers lack effective backup measures. If the main fan fails, operators need to spend considerable time manually intervening and repairing the equipment, during which the system's sealing performance is significantly reduced. Furthermore, existing control logic is relatively simple, lacking automated fault response and switching mechanisms, and cannot achieve rapid and seamless switching between fans. Summary of the Invention

[0006] In view of the problems existing in the prior art, the present invention is proposed.

[0007] Therefore, the main technical problem solved by this invention is: addressing the issues of high single-point failure risk, lack of effective backup measures, and long failure response time of low-leakage fans in existing flue gas heat exchangers, this invention proposes a redundant control method. By adding backup low-leakage fans and establishing a complete interlocking control logic, the automatic switching between main and backup fans is achieved, ensuring the continuity and reliability of the sealing performance of the flue gas heat exchanger and avoiding the risk of reduced desulfurization efficiency and exceeding environmental standards due to fan failure.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0009] In a first aspect, embodiments of the present invention provide a low-leakage fan redundancy control method for a GGH flue gas heat exchanger, which includes: acquiring flue gas heat exchange system parameters and constructing a dual-fan redundancy configuration; analyzing operating status data through the dual-fan redundancy configuration; determining the switching conditions for the main and backup fans; and establishing a fan status monitoring relationship.

[0010] A fault prediction model is dynamically constructed based on the fan operating status and system pressure parameters for first-stage fan monitoring. Based on the first-stage monitoring results, the coupling relationship between the fan operating characteristics and the system sealing effect is analyzed to obtain the dual-drive switching threshold for second-stage fan control.

[0011] Based on the interlocking logic matrix, a redundant control model is obtained by combining the coordinated characteristics of fan status and flue control. The fault response result is obtained by combining the redundant control model with the fault response strategy.

[0012] As a preferred embodiment of the low-leakage fan redundancy control method for the GGH flue gas heat exchanger described in this invention, the following steps are taken: The operating status data is analyzed through the dual-fan redundancy configuration to determine the switching conditions for the main and backup fans. This includes monitoring operating parameters through the dual-fan redundancy configuration to obtain a correlation diagram between fan performance and system leakage rate; and determining the critical region between abnormal fan operation and system sealing failure, where the critical region is the switching trigger point.

[0013] As a preferred embodiment of the low-leakage fan redundancy control method for the GGH flue gas heat exchanger described in this invention, the step of establishing the fan status monitoring relationship includes: monitoring the operating status based on the dual-fan configuration, and calculating the performance degradation model by setting the fan efficiency coefficient, flue gas pressure difference change rate, and thermo-coupling correction parameters in combination with real-time operating data.

[0014] Based on the monitoring of the operation status of the dual-fan configuration, the load distribution model is obtained by calculating the fan load index, temperature-related efficiency parameters, system resistance coefficient, and real-time pressure.

[0015] As a preferred embodiment of the low-leakage fan redundancy control method for GGH flue gas heat exchangers described in this invention, the following steps are taken: a fault prediction model is dynamically constructed based on the fan operating status and system pressure parameters. This includes: weighting the performance difference values ​​within historical operating cycles based on the current main operating fan status value and performing trend correction; introducing a coupling effect term of pressure fluctuation and load change; and judging the trend of fan efficiency decline based on the performance decay curve function to obtain the fault prediction result.

[0016] As a preferred embodiment of the low-leakage fan redundancy control method for the GGH flue gas heat exchanger described in this invention, the following steps are taken: analyzing the coupling relationship between the fan operating characteristics and the system sealing effect to obtain the dual-drive switching threshold and performing second-stage fan control, including obtaining the fan performance operating matrix through operating data statistics; and obtaining the allowable sealing effect range matrix based on the system design parameters.

[0017] Based on the impact of fan performance changes on system sealing effect through dual-matrix quantification, a performance correction term is obtained; by setting an efficiency attenuation coefficient combined with temperature, the impact of the operating environment on the fan load capacity is quantified, resulting in an environmental correction term; based on the two correction terms and the baseline operating parameters, a dual-drive switching threshold is obtained; based on the dual-drive switching threshold, different warning levels are set and compared with actual operating parameters to perform switching control and warning.

[0018] As a preferred embodiment of the low-leakage fan redundancy control method for GGH flue gas heat exchanger described in this invention, the redundancy control model is obtained based on the interlocking logic matrix and the synergistic characteristics of the fan status and flue control. This includes obtaining the control deviation based on the actual control actions under different operating conditions and the standard control sequence under the corresponding operating conditions, combined with the synergistic index of the fan and flue.

[0019] Simultaneously, a time-series decay function is introduced to obtain the impact of the control response on system stability; a redundant control model is obtained by combining control deviation and stability impairment assessment.

[0020] As a preferred embodiment of the low-leakage fan redundancy control method for GGH flue gas heat exchanger described in this invention, the following steps are taken: obtaining the fault response result through the redundancy control model and the fault response strategy, including obtaining the system's reliability capacity through the redundancy control model; and obtaining the switching response time through the ratio of reliability capacity to fault evolution rate.

[0021] Secondly, embodiments of the present invention provide a low-leakage fan redundancy control system for a GGH flue gas heat exchanger, which includes a monitoring module that acquires flue gas heat exchange system parameters and constructs a dual-fan redundancy configuration, analyzes operating status data through the dual-fan redundancy configuration, determines the switching conditions for the main and backup fans, and establishes a fan status monitoring relationship.

[0022] The control module dynamically constructs a fault prediction model based on the fan operating status and system pressure parameters for first-stage fan monitoring. Based on the first-stage monitoring results, it analyzes the coupling relationship between the fan operating characteristics and the system sealing effect, obtains the dual-drive switching threshold, and performs second-stage fan control.

[0023] The output module, based on the interlocking logic matrix and combined with the synergistic characteristics of fan status and flue control, obtains a redundant control model. Through the redundant control model, combined with the fault response strategy, the fault response result is obtained.

[0024] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the low-leakage fan redundancy control method for GGH flue gas heat exchangers as described in the first aspect of the present invention.

[0025] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the low-leakage fan redundancy control method for GGH flue gas heat exchangers as described in the first aspect of the present invention are implemented.

[0026] The beneficial effects of this invention are as follows: This invention solves the risk problem of traditional single-point failure by constructing a dual-fan redundant configuration. The correlation diagram between fan performance and system leakage rate established in the early steps can accurately identify critical switching points, avoiding the subjective lag of manual judgment. The fault prediction model and dual-drive switching threshold in the intermediate steps achieve multi-level intelligent monitoring, transforming passive emergency response into proactive prevention and control, and shortening fault response time. The interlocking logic matrix and redundant control model in the later steps ensure the coordination and stability of the switching process, eliminating the risk of human error in traditional manual switching. The transformation from a single-fan configuration to intelligent redundant control solves the reliability problem of flue gas heat exchanger sealing systems under high-temperature and high-pressure environments, providing continuous and stable operational assurance for desulfurization systems in coal-fired power plants. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of 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.

[0028] Figure 1 A flowchart for the redundant control method of low-leakage fan in GGH flue gas heat exchanger;

[0029] Figure 2 A computer equipment diagram for a low-leakage fan redundancy control method for GGH flue gas heat exchangers;

[0030] Figure 3 This is a flowchart of another overall scheme for redundant control of low-leakage fans in GGH flue gas heat exchangers. Detailed Implementation

[0031] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0032] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0033] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0034] Example 1

[0035] Reference Figure 1 - Figure 2 This is the first embodiment of the present invention, which provides a low-leakage fan redundancy control method for a GGH flue gas heat exchanger, including:

[0036] S100: Obtain parameters of the flue gas heat exchange system and construct a dual-fan redundant configuration. Analyze the operating status data through the dual-fan redundant configuration, determine the switching conditions of the main and standby fans, and establish the fan status monitoring relationship.

[0037] S200: Based on the fan operating status and system pressure parameters, a fault prediction model is dynamically constructed for the first-stage fan monitoring. Combined with the first-stage monitoring results, the coupling relationship between the fan operating characteristics and the system sealing effect is analyzed to obtain the dual-drive switching threshold for the second-stage fan control.

[0038] S300: Based on the interlocking logic matrix, a redundant control model is obtained by combining the coordinated characteristics of fan status and flue control. The fault response result is obtained by combining the redundant control model with the fault response strategy.

[0039] It should be noted that during operation, the pressure difference, temperature changes, and medium flow rate between the raw and clean flue gas in the GGH (Gas Heater) of a coal-fired power plant flue gas heat exchanger flue gas system fluctuate significantly, causing complex coupled changes in the operating status of the low-leakage fan and the system's sealing effect. The heat exchanger operates under high temperature and high pressure, making accurate monitoring of fan performance and system leakage rate difficult, and fault warnings are often delayed. Furthermore, due to the lack of effective backup measures in the single-unit low-leakage fan configuration, a failure of the main fan can drastically reduce the system's sealing performance, thereby jeopardizing the entire desulfurization system's operation. The high temperature and high pressure operating environment presents significant technical challenges for real-time monitoring and rapid switching control of the fan status. Therefore, redundant configuration and intelligent control of low-leakage fans are crucial.

[0040] Therefore, to address the aforementioned single-point failure risk and lack of intelligent control, the S100-S300 steps construct a simulation of the operating status of a dual-fan redundant configuration system under typical working conditions, obtaining the system sealing effect relationship under the influence of fan performance degradation, thus achieving accurate prediction of fault states; enabling real-time monitoring of fan operating status, dynamically constructing a fault prediction model, and providing early warning of abnormal increases in fan performance; simultaneously, based on interlocking logic control and redundancy switching models, achieving intelligent assurance and automatic response for system operational reliability.

[0041] Example 2

[0042] Reference Figure 2 - Figure 3 This is the second embodiment of the present invention.

[0043] In this embodiment, step S100 involves analyzing the operating status data of the dual-fan redundant configuration, determining the switching conditions for the primary and backup fans, and establishing fan status monitoring relationships, including the following steps A1-A4:

[0044] A1: By monitoring operating parameters through a dual-fan redundant configuration, a correlation diagram between fan performance and system leakage rate is obtained;

[0045] A2: Determine the critical region between abnormal fan operation and system seal failure. This critical region is the switching trigger point.

[0046] Specifically, in A1, pressure sensors, flow meters, and temperature sensors installed at the inlet and outlet of the main and standby fans are used to collect the operating parameters of each fan in real time. Key parameters monitored include: fan inlet pressure P. in Export pressure P out Actual traffic Q actual Fan speed n, current I, power P power and bearing temperature T bearing Rated voltage difference ΔP ratedMeanwhile, differential pressure sensors are installed on both the raw flue gas side and the clean flue gas side of the flue gas heat exchanger to monitor the system leakage rate index ΔP. leak .

[0047] After obtaining these operating parameters, the correlation between fan performance and system leakage rate is established through data processing. First, the fan efficiency is calculated:

[0048]

[0049] Then calculate the system leakage rate:

[0050]

[0051] By improving the efficiency η of the wind turbine fan As the horizontal axis, the system leakage rate R leak A real-time correlation graph is plotted using the vertical axis. As the fan efficiency decreases, the system's sealing effect deteriorates, and the leakage rate increases accordingly.

[0052] In A2, based on statistical analysis of long-term operating data, the criteria for judging abnormal fan operation are determined. An abnormal operation is defined as when the fan efficiency drops below 85% of the rated efficiency, or when the system leakage rate exceeds 150% of the design value. Analysis of historical fault data reveals that when the fan efficiency is below 80% and the system leakage rate exceeds 180%, the system faces a serious risk of seal failure.

[0053] Therefore, the intersection of 80% fan efficiency and 180% system leakage rate is defined as the boundary condition of the critical region. The reason for using this critical region as the switching trigger point is that, under this condition, continued operation will lead to a sharp drop in desulfurization efficiency, while starting the backup fan can restore the system's sealing performance within 3 to 5 minutes, avoiding the risk of exceeding environmental standards.

[0054] A3: Based on monitoring the operation status of dual-fan configuration, a performance degradation model is obtained by setting the fan efficiency coefficient, flue gas pressure difference change rate, and thermal coupling correction parameters in combination with real-time operation data.

[0055] A4: Based on the dual-fan configuration and monitoring of the operating status, the load distribution model is obtained by calculating the fan load index, temperature-related efficiency parameters, system resistance coefficient, and real-time pressure.

[0056] Specifically, in A3, the process of establishing the performance degradation model is as follows: First, the wind turbine efficiency degradation coefficient α is set. decay =0.002 / month, indicating that the fan efficiency decreases by 0.2% per month under normal operating conditions. The flue gas pressure difference change rate is obtained by continuously monitoring the inlet and outlet pressure difference, and the calculation formula is:

[0057]

[0058] Thermo-coupling correction parameter β thermal Considering the impact of temperature changes on fan performance, fan efficiency decreases by approximately 1.5% for every 10°C increase in ambient temperature. A performance degradation model is established by combining these parameters with real-time operating data:

[0059]

[0060] Where t is the running time (in months), η rated Rated efficiency, the standard efficiency value (%) of the fan under design operating conditions, where T is the current temperature. ref The reference temperature is 25℃.

[0061] In A4, the load sharing model is used to calculate the load sharing strategy when two wind turbines are running. First, the wind turbine load index is defined:

[0062]

[0063] Among them, Q actual The actual flow rate is the real flow rate (m³ / s) currently being generated by the wind turbine. 3 / h); Q rated Rated flow rate (m³) 3 / h); P actual P represents the actual pressure (Pa); rated Rated pressure;

[0064] Temperature-dependent efficiency parameters consider the impact of high temperatures on the performance of the fan impeller material, while the system drag coefficient reflects changes in the internal resistance of the flue. The load distribution model is as follows:

[0065]

[0066] Among them, Q total R represents the total system flow demand; η is the general symbol for fan efficiency. sys The system resistance coefficient is represented by the subscripts A and B, which indicate the main fan and the standby fan, respectively.

[0067] In this embodiment, step S200 dynamically constructs a fault prediction model based on the fan operating status and system pressure parameters, analyzes the coupling relationship between fan operating characteristics and system sealing effect, obtains the dual-drive switching threshold, and performs second-stage fan control, including the following steps B1-B7:

[0068] B1: Based on the current status value of the main operating fan, a weighted average of the performance difference values ​​within the historical operating cycle is applied, and a trend correction is performed;

[0069] Specifically, in B1, the real-time status values ​​of the currently operating wind turbines are first acquired through a data acquisition system, including turbine efficiency, bearing temperature, vibration amplitude, and current waveform. The system collects historical data from the same period over the past 168 hours (7 days) at hourly intervals, forming a time-series database. Then, performance difference values ​​are calculated by comparing the current turbine efficiency with historical efficiency over the same period, yielding daily and weekly difference values. These difference values ​​are weighted according to time decay weights, with recent data weighted at 0.4, data from a week ago at 0.1, and intermediate periods weighted linearly decreasing. During trend correction, the system uses the least squares method to linearly fit the weighted difference values, obtaining the slope of the performance change trend line. When the slope is <-0.02, it indicates a significant downward trend in turbine performance, requiring increased monitoring frequency. A seasonal correction factor is also introduced to consider the periodic impact of seasonal changes in ambient temperature on turbine performance.

[0070] Specifically, in B1, the fault prediction model first collects key state parameters of the currently operating main fan, including efficiency, vibration value, bearing temperature, and current fluctuation ΔI. Then, it analyzes performance data from the same time period each day over the past 7 days to calculate the performance difference value.

[0071] Δη i =η today -η day-i

[0072] Where, η today For today's efficiency, the wind turbine efficiency value (%) for the same period on the current date is used to calculate the daily difference value; η day-i For historical efficiency, the wind turbine efficiency value (%) of the same period before day i is used for trend analysis and weighted calculation;

[0073] These differences are weighted, with more recent data receiving higher weights:

[0074]

[0075] Among them, the weighting coefficient w i = (8-i) / 28, ensuring that recent data has a larger weight. Trend correction uses linear regression analysis to analyze the efficiency change trend over the next 24 hours.

[0076] B2: Introduce the coupling effect of pressure fluctuation and load change; based on the performance degradation curve function, determine the trend of fan efficiency decline to obtain fault prediction results.

[0077] In section B2, pressure fluctuation monitoring is achieved using high-precision pressure sensors installed at the inlet and outlet of the fan, with a sampling frequency of 1Hz, continuously recording pressure change data. Load change monitoring is achieved using fan current and speed sensors, calculating the load change rate. When the load change rate exceeds 15% / minute of the rated load, the system is considered to have experienced a drastic load change. The calculation of the coupling effect term considers the interaction between pressure fluctuations and load changes. Historical data analysis revealed that when the pressure fluctuation coefficient is greater than 0.05 and the load change rate is greater than 10% / minute, the probability of fan failure increases by 2.3 times. Therefore, the coupling coefficient is set as the product of the pressure fluctuation coefficient and the load change rate, multiplied by an amplification factor of 2.3. The coupling effect of pressure fluctuations and load changes is quantified by monitoring the system's pressure change rate and load change rate. The pressure fluctuation coefficient is defined as:

[0078]

[0079] Among them, P i The pressure value (Pa) at the i-th sampling point is used for statistical analysis to calculate the pressure fluctuation coefficient; The average pressure (Pa) is the arithmetic mean of all pressure sampling points within a certain time window.

[0080] The load variation coefficient is:

[0081]

[0082] Among them, Q i The flow rate value (m) at the i-th sampling point 3 / h), used for statistical analysis to calculate the load variation coefficient; Average flow rate (m 3 / h), the arithmetic mean of all flow sampling points within a certain time window;

[0083] The coupling effect term is:

[0084] F coupling =C pressure ×C load ×0.3

[0085] This coefficient was obtained by fitting historical data.

[0086] B3: Obtain the operating matrix of the fan performance through statistical methods of operating data; obtain the allowable range matrix of sealing effect based on system design parameters;

[0087] The performance degradation curve function was established based on a large amount of historical operating data from the same type of wind turbine. The system analyzed over three years of operating records from 80 wind turbines, identifying typical efficiency degradation patterns. Normal degradation exhibits an exponential function decline, while the early stages of a fault show linear accelerated degradation. The judgment process consists of three stages: first, calculating the percentage decrease in current efficiency relative to the design efficiency; second, analyzing whether the degradation rate exceeds the normal range (monthly degradation rate > 0.5%); and finally, combining auxiliary parameters such as vibration and temperature for a comprehensive judgment. When the efficiency degradation rate exceeds the threshold for three consecutive days and auxiliary parameters are abnormal, the system outputs a fault warning signal and initiates the second-level control program.

[0088] In B3, a performance degradation curve function is established based on collected historical fault data. Analysis of operating data from 50 similar wind turbines reveals that turbine efficiency degradation follows an exponential decay law:

[0089] η predict (t)=η0×e -λt +F coupling

[0090] Wherein, λ is the attenuation constant, which is obtained by least squares fitting as λ = 0.0018 / day. When the prediction efficiency is below 85%, the system is judged to be in a fault warning state.

[0091] B4: Based on the dual-matrix quantification of the impact of fan performance changes on system sealing performance, a performance correction term is obtained;

[0092] Specifically, in B4, the operational data statistical method first establishes data classification standards. Wind turbine performance is divided into five levels based on efficiency range: Excellent (efficiency ≥ 95%), Good (90% ≤ efficiency < 95%), Average (85% ≤ efficiency < 90%), Poor (80% ≤ efficiency < 85%), and Severe (efficiency < 80%). Each level corresponds to different operating conditions and maintenance requirements.

[0093] The system sealing performance is divided into four levels based on leakage rate: Normal (leakage rate < 120% of design value), Caution (120% ≤ leakage rate < 150%), Warning (150% ≤ leakage rate < 200%), and Danger (leakage rate ≥ 200%). By collecting nearly 90 days of operating data, the probability distribution of leakage rate under each efficiency level is statistically analyzed to form a 5×4 dimension wind turbine performance operation matrix.

[0094] The sealing performance applicable range matrix is ​​determined according to system design specifications and environmental protection requirements. Each element in the matrix represents the maximum allowable operating time under the corresponding operating condition. For example, excellent efficiency - normal leakage condition can run continuously for 720 hours, while poor efficiency - warning leakage condition can only allow a maximum of 2 hours of operation.

[0095] The leakage rate is defined as follows: <120% is acceptable, 120% to 150% is within the range of attention, 150% to 200% is within the range of warning, and >200% is within the range of danger.

[0096] B5: By setting the efficiency decay coefficient combined with temperature, the impact of the operating environment on the wind turbine's load-bearing capacity is quantified, and an environmental correction term is obtained;

[0097] In B5, the performance correction term is calculated by comparing corresponding elements of the two matrices:

[0098]

[0099] Among them, M seal (i,j) are matrix elements representing the allowable sealing effect range, indicating the maximum permissible operating time (in hours) under the i-th efficiency level and j-th leakage rate level; m=5, n=4 are the matrix dimensions. When C performance A value >0.3 indicates that the fan performance significantly affects the system's sealing effect.

[0100] The quantification process for performance correction items employs a weighted deviation analysis method. First, the deviation values ​​of corresponding elements between the operating matrix and the allowable range matrix are calculated. Then, weights are assigned according to importance: 0.4 for hazardous conditions, 0.3 for warning conditions, 0.2 for caution conditions, and 0.1 for normal conditions.

[0101] In the deviation calculation formula, the numerator is the weighted sum of the differences between the actual operating time and the allowable time under each operating condition, and the denominator is the total number of elements in the matrix. When the calculation result is greater than 0.3, it indicates that the current fan performance is seriously affecting the system's sealing effect, and switching to the standby fan needs to be considered. The performance correction term also considers the changing trend of operating conditions, predicting the future performance evolution direction by comparing the matrix changes of the most recent 7 days with previous data.

[0102] B6: The dual-drive switching threshold is obtained based on two correction terms and the baseline operating parameters;

[0103] In B6, the environmental correction term considers the effect of temperature on fan efficiency. Based on experimental data, a temperature correction function is established:

[0104]

[0105] When the ambient temperature deviates significantly from 25℃, the correction factor decreases significantly.

[0106] The environmental correction parameters are set based on the mechanism of temperature's influence on the performance of fan materials and fluid characteristics. Experimental tests showed that when the ambient temperature changes from 25℃ to 45℃, the fan efficiency decreases by an average of 3.2%; when the temperature decreases from 25℃ to 5℃, the efficiency decreases by 2.1%.

[0107] The temperature correction factor is calculated using a piecewise linear function: when the temperature is within the range of 15℃-35℃, the correction factor is 1.0; for every 10℃ increase or decrease in temperature, the correction factor decreases by 0.015. The effect of the rate of temperature change is also considered; a rapid temperature change (5℃ / hour) will incur an additional correction of 0.02.

[0108] The environmental correction term also includes the combined effects of factors such as humidity and atmospheric pressure. Through multiple linear regression analysis, a correlation model between environmental parameters and fan performance is established to ensure that the correction term accurately reflects the impact of the actual operating environment on the system.

[0109] B7: Based on the dual-drive switching threshold, different warning levels are set and compared with actual operating parameters to perform switching control and warning.

[0110] In B7, the dual-drive switching threshold is determined by combining baseline operating parameters:

[0111] Θ switch =Θ base ×C performance ×C environment

[0112] Where, Θ base =0.82 is the baseline switching threshold (corresponding to a wind turbine efficiency of 82%). When the actual threshold calculation result is lower than 0.80, the wind turbine switching procedure is triggered.

[0113] The dual-drive switching threshold was determined using a multi-factor comprehensive evaluation method. The baseline operating parameter Θ_base was set to 0.82, corresponding to the critical point of 82% turbine efficiency. This safety threshold was determined based on a large amount of operating experience and fault statistics.

[0114] In the calculation of the switching threshold, the performance correction term has a weight of 0.6, and the environmental correction term has a weight of 0.4, reflecting the dominant role of wind turbine performance in the switching decision. When the calculated threshold is below 0.80, the system immediately starts the backup wind turbine; when the threshold is between 0.80 and 0.85, the system enters a pre-switching state and increases the monitoring frequency; when the threshold is above 0.85, the system maintains normal operation mode.

[0115] The handover decision-making process also incorporates an anti-jitter mechanism, requiring that the threshold be calculated for three consecutive times (with a 5-minute interval) and remain below the handover point before executing a handover operation, thus avoiding erroneous handovers caused by momentary fluctuations. Simultaneously, a handover history record is established to analyze handover frequency and effectiveness, continuously optimizing the threshold parameters.

[0116] In this embodiment, step S300, based on the interlocking logic matrix and combined with the coordinated characteristics of the fan status and flue control, obtains a redundant control model. Through this redundant control model, combined with a fault response strategy, the fault response result is obtained, including the following steps C1-C5:

[0117] C1: Based on the actual control actions under different operating conditions and the standard control sequence under the corresponding operating conditions, combined with the coordination index of the fan and flue, the control deviation is obtained;

[0118] Specifically, in C1, the interlocking logic matrix first defines the standard control sequence under various operating conditions. Under normal startup conditions, the standard sequence is: check system status (2 seconds) → open outlet damper (8 seconds) → start fan (5 seconds) → adjust inlet damper (10 seconds) → reach stable operating state (15 seconds), with a total time of 40 seconds.

[0119] In emergency switching conditions, the standard sequence is as follows: fault detection confirmation (3 seconds) → start the standby fan outlet damper (5 seconds) → start the standby fan (3 seconds) → standby fan reaches rated speed (12 seconds) → close the faulty fan inlet damper (8 seconds) → stop the faulty fan (2 seconds) → close the faulty fan outlet damper (5 seconds), with a total time of 38 seconds.

[0120] Actual control actions are obtained by monitoring the response time of each device, and the coordination index is defined as:

[0121]

[0122] Among them, T standard For standard control time, T actual N represents the actual control time. success N represents the number of successful actions. total This represents the total number of actions.

[0123] The control deviation is calculated as follows:

[0124] Δ control =|S coordination -1|×W importance

[0125] Among them, W importance As an importance weighting, the fan control weight is 0.6, and the damper control weight is 0.4.

[0126] Actual control actions are obtained through position feedback sensors and status monitoring devices installed on each actuator. The system records the actual execution time of each action and calculates the time deviation by comparing it with the standard time. In the calculation of the coordination index, a successful action is defined as an action completed within ±20% of the standard time; actions exceeding this range are considered abnormal.

[0127] The calculation of control deviation takes into account the differences in importance of different control actions. The importance weight of the fan start-up and shutdown action is 0.4, the weight of the damper control is 0.3, and the weight of the regulating valve control is 0.3. When the control deviation is greater than 0.3, it indicates that there is a significant problem with the control system, and it is necessary to enter the redundant control mode.

[0128] C2: Simultaneously, a time-series decay function is introduced to obtain the impact of the control response on system stability;

[0129] In C2, the timing decay function describes the impact of control response delay on system stability:

[0130] F decay (t)=e -t / τ ×sin(ωt+φ)

[0131] Where τ is the time constant of 15 seconds, and ω is the angular frequency. This refers to the phase angle. When the control response delay exceeds 30 seconds, the system stability decreases significantly.

[0132] The time-series decay function describes the cumulative effect of control response delay on system stability. A mathematical model of response delay and stability impairment is established, with the time constant τ set to 15 seconds to reflect the response characteristics of the flue gas system.

[0133] When the control action is delayed, the system stability decays exponentially. With a 10-second delay, the stability remains at 90%; with a 20-second delay, it drops to 75%; and with a 30-second delay, it drops to 60%. The sine function term describes the system's oscillation characteristics, the angular frequency ω is determined based on the system's natural frequency, and the phase angle... Obtained through system identification.

[0134] The cumulative effect of stability impairment is calculated through integration, taking into account the superimposed effects of multiple delayed actions. When the cumulative impairment exceeds a threshold of 0.4, the system is deemed to have severely compromised stability, requiring protective measures.

[0135] C3: A redundant control model is obtained by combining control deviation and stability impairment assessment.

[0136] In C3, the redundant control model comprehensively considers both control deviation and stability impairment:

[0137] M redundancy =1-(Δ control +F decay )×K safety

[0138] Among them, K safety With a safety factor of 1.2. When M redundancy When the value is less than 0.7, the system enters redundant control mode.

[0139] The redundant control model comprehensively evaluates control deviation and stability impairment. The model uses a linear weighting method, with a control deviation weight of 0.6 and a stability impairment weight of 0.4, reflecting the significant impact of control accuracy on system reliability.

[0140] Safety factor K safety The value is set to 1.2 to provide a 20% safety margin for the system. When the redundant control model outputs value M... redundancy When the value is below 0.7, the system automatically enters the redundant control mode and activates the backup control loop and backup actuator.

[0141] In redundant control mode, the system employs a dual confirmation mechanism, requiring simultaneous confirmation from two independent controllers before execution of critical control commands. This also reduces control response speed and extends the intervals between actions to ensure system stability.

[0142] C4: Obtain the system's reliability capacity through a redundancy control model;

[0143] Specifically, in C4, the system's reliability capacity is determined by analyzing the output of the redundancy control model. Reliability capacity is defined as the system's ability to continue operating safely under its current state.

[0144] R capacity =M redundancy ×(1-P fault )×F reserve

[0145] Among them, P fault Let F be the current failure probability. reserve The system reserve coefficient (dimensionless) is 0.85, which means that the system retains a 15% safety margin and is used for reliability capacity calculation.

[0146] The reliability capacity of the system operation is determined through multi-dimensional evaluation. Firstly, based on the output M of the redundancy control model... redundancy This value reflects the current health status of the control system. When M redundancy When the value is greater than 0.8, the system is in a high-reliability state; when it is 0.7... <M redundancy When M ≤ 0.8, the system is in a state of moderate reliability; redundancy When the value is ≤0.7, the system is in a state of low reliability.

[0147] Failure probability P fault The model, calculated using a Bayesian network, integrates multiple fault indicator parameters, including wind turbine efficiency, vibration level, temperature anomalies, and control response time. Trained on historical fault data, the network model can update fault probability estimates in real time.

[0148] System reserve coefficient Freserve A value of 0.85 indicates that the system retains a 15% safety margin. This parameter is determined based on reliability engineering theory, taking into account uncertainties such as equipment aging, environmental changes, and operational errors. The calculated reliability capacity directly affects the system's operating strategy and maintenance plan.

[0149] C5: The switching response time is obtained by the ratio of reliability capacity to failure evolution rate.

[0150] In C5, the fault evolution rate is calculated by monitoring the rate of change of key parameters:

[0151]

[0152] Where α = 0.5, β = 0.3, and γ = 0.2 are weighting coefficients. The switching response time is:

[0153]

[0154] Among them, K margin With a safety margin of 1.5, sufficient time is allowed to complete the wind turbine switching operation.

[0155] Efficiency Change Rate The efficiency was obtained through continuous monitoring of the fan, with a weighting of α = 0.5 reflecting the dominant influence of efficiency on system reliability. Leakage rate change rate. Weight β = 0.3, temperature change rate Weight γ = 0.2.

[0156] The calculation of the fault evolution rate takes into account the nonlinear characteristics of different parameter variations. When efficiency decreases rapidly... The risk of failure increases dramatically; when the leakage rate rises rapidly. The system faces the risk of seal failure; when the temperature changes abnormally... The equipment may be operating abnormally.

[0157] The calculation results of the switching response time guide the system's emergency response strategy. When the calculated response time is greater than 30 minutes, the system can adopt a gradual switching strategy; when the response time is between 10 and 30 minutes, the system adopts a rapid switching strategy; when the response time is less than 10 minutes, the system immediately executes an emergency switching procedure.

[0158] Safety margin coefficient K margin Setting it to 1.5 ensures sufficient time margin for actual switching operations, avoiding switching failures caused by calculation errors or execution delays.

[0159] In summary, the correlation diagram analysis method established in steps A1-A2 can reflect the dynamic coupling relationship between fan performance and system sealing effect in real time, breaking through the limitations of traditional static threshold judgment and making switching decisions more scientific and accurate. The performance degradation model and load distribution model constructed in steps A3-A4 introduce multi-dimensional environmental factors such as temperature and pressure, solving the problem that single-parameter monitoring cannot comprehensively reflect the health status of equipment and improving the accuracy and timeliness of fault prediction. The multi-level monitoring system in steps B1-B7, through weighted processing of historical data and trend analysis, realizes the transformation from passive monitoring to predictive maintenance, effectively reducing the impact of sudden failures on system operation. The interlocking control logic in steps C1-C5, through standardized control sequences and synergy index evaluation, ensures the smooth and reliable switching process of dual fans, avoiding system disturbances and sealing performance fluctuations during the switching process. The entire embodiment establishes a complete intelligent monitoring and control system, upgrading traditional experience-driven decision-making to data-driven precision control, improving the adaptability and reliability of the flue gas heat exchanger system under complex operating conditions.

[0160] Example 3

[0161] The above is a schematic scheme of a redundant control method for a low-leakage fan in a GGH flue gas heat exchanger. It should be noted that the technical solution of this redundant control system for a low-leakage fan in a GGH flue gas heat exchanger belongs to the same concept as the aforementioned redundant control method. Details not described in detail in this embodiment can be found in the description of the aforementioned redundant control method.

[0162] This embodiment also provides a redundant control system for a low-leakage fan in a GGH flue gas heat exchanger, including:

[0163] The monitoring module acquires parameters of the flue gas heat exchange system and constructs a dual-fan redundancy configuration. Through the dual-fan redundancy configuration, it analyzes the operating status data, determines the switching conditions for the main and standby fans, and establishes fan status monitoring relationships.

[0164] The control module dynamically constructs a fault prediction model based on the fan operating status and system pressure parameters for first-stage fan monitoring. Based on the first-stage monitoring results, it analyzes the coupling relationship between the fan operating characteristics and the system sealing effect, obtains the dual-drive switching threshold, and performs second-stage fan control.

[0165] The output module, based on the interlocking logic matrix and combined with the synergistic characteristics of fan status and flue control, obtains a redundant control model. Through the redundant control model, combined with the fault response strategy, the fault response result is obtained.

[0166] This embodiment also provides an electronic device suitable for redundant control of low-leakage fans in GGH flue gas heat exchangers, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the redundant control method for low-leakage fans in GGH flue gas heat exchangers proposed in the above embodiment.

[0167] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the low-leakage fan redundancy control method for GGH flue gas heat exchangers as proposed in the above embodiments.

[0168] The storage medium proposed in this embodiment and the method for achieving low-leakage fan redundancy control of GGH flue gas heat exchanger proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0169] Based on the above description of the implementation methods, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0170] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for redundant control of low-leakage fans in a GGH flue gas heat exchanger, characterized in that: This includes acquiring parameters of the flue gas heat exchange system and constructing a dual-fan redundancy configuration, analyzing the operating status data through the dual-fan redundancy configuration, determining the switching conditions for the main and standby fans, and establishing fan status monitoring relationships. A fault prediction model is dynamically constructed based on the fan operating status and system pressure parameters for first-stage fan monitoring. Based on the first-stage monitoring results, the coupling relationship between the fan operating characteristics and the system sealing effect is analyzed to obtain the dual-drive switching threshold for second-stage fan control. Based on the interlocking logic matrix, a redundant control model is obtained by combining the coordinated characteristics of fan status and flue control. The fault response result is obtained by combining the redundant control model with the fault response strategy.

2. The low-leakage fan redundancy control method for GGH flue gas heat exchanger as described in claim 1, characterized in that: By analyzing the operating status data through the dual-fan redundancy configuration, the switching conditions for the main and standby fans are determined, including by monitoring the operating parameters through the dual-fan redundancy configuration to obtain a correlation diagram between fan performance and system leakage rate. Identify the critical region between abnormal fan operation and system seal failure; this critical region serves as the switching trigger point.

3. The low-leakage fan redundancy control method for GGH flue gas heat exchanger as described in claim 2, characterized in that: The establishment of the fan status monitoring relationship includes monitoring the operating status based on the dual-fan configuration, and calculating the performance degradation model by setting the fan efficiency coefficient, flue gas pressure difference change rate, and thermal coupling correction parameters in combination with real-time operating data. Based on the monitoring of the operation status of the dual-fan configuration, the load distribution model is obtained by calculating the fan load index, temperature-related efficiency parameters, system resistance coefficient, and real-time pressure.

4. The low-leakage fan redundancy control method for GGH flue gas heat exchanger as described in claim 3, characterized in that: A fault prediction model is dynamically constructed based on the wind turbine operating status and system pressure parameters. This includes weighted processing of the current main operating wind turbine status value, superimposed with the performance difference values ​​within the historical operating cycle, and trend correction; and the introduction of... The coupling effect of pressure fluctuations and load changes; Based on the performance degradation curve function, the trend of wind turbine efficiency decline is determined to obtain fault prediction results.

5. The low-leakage fan redundancy control method for GGH flue gas heat exchanger as described in claim 4, characterized in that: The coupling relationship between the operating characteristics of the fan and the sealing effect of the system is analyzed to obtain the dual-drive switching threshold and perform second-stage fan control, including obtaining the operating matrix of the fan performance through the statistical method of operating data; The allowable range matrix of sealing effect is obtained based on the system design parameters; Based on the dual-matrix quantification of the impact of fan performance changes on system sealing performance, a performance correction term is obtained; By setting an efficiency attenuation coefficient that incorporates temperature, the impact of the operating environment on the wind turbine's load-bearing capacity is quantified, resulting in an environmental correction term. Based on the two correction terms and benchmark operating parameters, a dual-drive switching threshold is obtained. Different warning levels are set based on the dual-drive switching threshold and compared with actual operating parameters to perform switching control and warning.

6. The low-leakage fan redundancy control method for GGH flue gas heat exchanger as described in claim 5, characterized in that: Based on the interlocking logic matrix, a redundant control model is obtained by combining the coordination characteristics of fan status and flue control. This model includes obtaining the control deviation based on the actual control actions under different operating conditions and the standard control sequence under the corresponding operating conditions, combined with the coordination index of fan and flue. Simultaneously, a time-series decay function is introduced to obtain the impact of the control response on system stability; a redundant control model is obtained by combining control deviation and stability impairment assessment.

7. The low-leakage fan redundancy control method for GGH flue gas heat exchanger as described in claim 6, characterized in that: The fault response results are obtained by combining the aforementioned redundancy control model with the fault response strategy, including obtaining the system's reliability capacity through the redundancy control model and obtaining the switching response time through the ratio of reliability capacity to fault evolution rate.

8. A low-leakage fan redundancy control system for a GGH flue gas heat exchanger, based on the low-leakage fan redundancy control method for a GGH flue gas heat exchanger as described in any one of claims 1 to 7, characterized in that: It also includes a monitoring module, which acquires parameters of the flue gas heat exchange system and constructs a dual-fan redundancy configuration. Through the dual-fan redundancy configuration, it analyzes the operating status data, determines the switching conditions of the main and standby fans, and establishes the fan status monitoring relationship. The control module dynamically constructs a fault prediction model based on the fan operating status and system pressure parameters for first-stage fan monitoring. Based on the first-stage monitoring results, it analyzes the coupling relationship between the fan operating characteristics and the system sealing effect, obtains the dual-drive switching threshold, and performs second-stage fan control. The output module, based on the interlocking logic matrix and combined with the synergistic characteristics of fan status and flue control, obtains a redundant control model. Through the redundant control model, combined with the fault response strategy, the fault response result is obtained.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the low-leakage fan redundancy control method for the GGH flue gas heat exchanger according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the low-leakage fan redundancy control method for the GGH flue gas heat exchanger as described in any one of claims 1 to 7.

Images