Performance optimization control system and method for thermal power generating unit

Abstract

The invention provides a thermal power generating unit performance optimization control system and method, and the system comprises a data collection module which collects various real-time data in the operation process of a thermal power generating unit; the load prediction module is used for establishing a load prediction model and predicting a load change trend by comprehensively considering a historical load condition, a load change rate, a load change acceleration and an external interference factor; the optimization control module is used for dynamically adjusting fuel, an air door and air quantity according to a control strategy in combination with the load prediction result and the unit operation state; the actions of the regulating valve, the air door actuator and the water supply regulating valve are correspondingly controlled; the monitoring feedback module is used for realizing closed-loop control and self-correction; according to the method, a load prediction model based on the combination of time sequence analysis and a physical mechanism and a control strategy comprehensively considering multi-parameter collaborative optimization are utilized, so that quick response to load change, balance consideration of steam quality and combustion efficiency, self-adaptive stable operation under complex working conditions and improvement of overall performance are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal power generation, and in particular to a thermal power unit performance optimization control system and method. Background Art

[0002] With the continuous growth of electricity demand and the gradual evolution of power system structure, the automation control technology of thermal power units has experienced a significant development process; early thermal power unit control mainly focused on the stability of basic operating parameters, such as maintaining steam pressure, temperature and unit load within a certain range through simple feedback control loops; with the rise of electronic technology and computer technology, control systems began to be digitized, realizing more precise control algorithms and more complex logical operations; this enables thermal power units to optimize operation to a certain extent and improve power generation efficiency;

[0003] The existing thermal power unit control system often has a certain response lag when facing load changes; the traditional control strategy usually performs feedback adjustment based on the current operating parameters, and lacks effective prediction of future load change trends; when the grid load changes rapidly, the thermal power unit cannot adjust key parameters such as fuel supply and air volume in time, resulting in a large deviation between the unit output power and the grid demand; this not only affects the stability of the grid, but may also cause the unit to operate under non-optimal conditions, reducing power generation efficiency;

[0004] The operation of thermal power units involves multiple subsystems and numerous parameters, such as the combustion system and the steam-water system. The subsystems are interrelated and influence each other. However, most of the existing control strategies are based on independent optimization of a single subsystem or a few parameters, lacking consideration of the coordinated optimization of the entire unit system. For example, when adjusting the fuel flow to meet the load demand, the combined impact on combustion efficiency, steam temperature and pollutant emissions may not be fully considered. This local optimization rather than overall coordination control method makes it difficult to maximize the overall performance of the unit, and may even cause other parameters to deteriorate while optimizing a certain parameter.

[0005] Maintaining steam quality (such as keeping steam temperature and pressure stable within an appropriate range) and high combustion efficiency are key goals for thermal power units, but in actual operation, both are often difficult to achieve simultaneously. Traditional control methods may interfere with the combustion process and affect combustion efficiency when adjusting steam temperature.

[0006] At the same time, the operating environment of thermal power units is complex and changeable. Fuel quality and external interference factors (such as power grid fluctuations, changes in meteorological conditions, changes in surrounding industrial loads, etc.) may change at any time. However, most existing control systems use fixed control parameters and control strategies, lacking the ability to adapt to these changes. When external conditions change, the original control strategy may no longer be applicable, resulting in a decline in unit performance.

[0007] Therefore, there is an urgent need in the art for a thermal power unit performance optimization control system and method to solve the above-mentioned problems. Summary of the invention

[0008] The present invention provides a thermal power unit performance optimization control system and method, aiming to solve the above-mentioned problems existing in the prior art; by constructing a performance optimization control system including data acquisition, load prediction, optimization control, actuators and monitoring feedback modules, using a load prediction model based on the combination of time series analysis and physical mechanism and a control strategy that comprehensively considers multi-parameter collaborative optimization, the thermal power unit can achieve rapid and accurate response to load changes, balanced consideration of steam quality and combustion efficiency, adaptive and stable operation under complex working conditions, and significant improvement in overall performance.

[0009] In one aspect, the present invention provides a thermal power unit performance optimization control system, comprising:

[0010] Data acquisition module, which is used to collect various real-time data during the operation of thermal power units, including unit characteristic parameters and unit operation parameters;

[0011] A load prediction module, which is connected to the data acquisition module, is used to establish a load prediction model based on various real-time data, predict future load change trends by comprehensively considering the historical load conditions of the unit, load change rate, load change acceleration and external interference factors, and generate load prediction results;

[0012] An optimization control module, which is connected to the load prediction module, is used to dynamically adjust the fuel, air damper and air volume parameters according to the control strategy based on the load prediction results and the current operating status data of the unit, and generate adjustment instructions to optimize the coal-water ratio in real time to ensure that the unit can quickly adapt to load changes;

[0013] An actuator connected to the optimization control module, used to receive adjustment instructions and correspondingly control the actions of the fuel regulating valve, the damper actuator, and the water supply regulating valve;

[0014] The monitoring feedback module is connected to the actuator and the data acquisition module, and is used to monitor the changes in the unit's operating parameters in real time, compare and analyze the actual operating parameters with the target parameters, and form a feedback signal. When it is found that the operating parameters deviate greatly from the target range or the system is abnormal, an alarm is issued in time, and the feedback information is transmitted to the optimization control module to adjust and optimize the control strategy to achieve closed-loop control and self-correction.

[0015] According to a thermal power unit performance optimization control system provided by the present invention, the data acquisition module comprises:

[0016] The unit characteristic parameter acquisition unit is used to acquire fixed physical characteristic parameters of the thermal power unit itself, including the unit's rated load, steam rated target temperature, flue gas rated target pressure, main reheat steam temperature rated target value, constant k1 related to the unit's rated load and rated fuel consumption, constant k3 related to the chemical reaction equivalence ratio of fuel to air, and constant k5 related to coal-water specific heat characteristics;

[0017] The constant k1 is determined by the proportional relationship between the load and fuel consumption under the rated working condition of the unit through experiments; the constant k3 is determined by the ratio of fuel to air required for the theoretical chemical reaction based on the fuel composition through experiments; the constant k5 is determined by the physical properties of the experimental analysis based on the calorific value of the fuel and the specific heat of water;

[0018] The unit operation parameter acquisition unit is used to collect the dynamic parameters of the unit during operation, including the unit real-time load, fuel flow, damper opening, air volume, flue gas pressure, steam temperature, main reheat steam temperature, coal quality parameters, combustion efficiency η b (t), heat transfer efficiency η t (t), fuel density ρ f (t), Excess air coefficient Fuel calorific value h f (t), feed water enthalpy h w (t) and external interference factors;

[0019] Among them, the combustion efficiency η b (t) Calculated through analysis of combustion products, used to reflect the completeness of fuel combustion;

[0020] Heat transfer efficiency η t (t) Determined by heat balance test based on physical parameters such as cleanliness of heated surface and resistance of steam-water flow;

[0021] Fuel density ρ f (t) Derived from the fuel type and real-time temperature and pressure;

[0022] Excess air coefficient Determined by experimental analysis of the composition of the combustion products;

[0023] Fuel calorific value h f (t) Determined by experimental analysis of fuel composition;

[0024] Feed water enthalpy h w (t) Based on feed water temperature and pressure.

[0025] According to a thermal power unit performance optimization control system provided by the present invention, in the unit operation parameter acquisition unit, the external interference factors include:

[0026] Interference from power grid fluctuation frequency, interference from the start and stop of surrounding large-scale industrial power equipment, interference from changes in meteorological conditions, and interference from fluctuations in the intermittent access of new energy power generation to the power grid.

[0027] According to a thermal power unit performance optimization control system provided by the present invention, the load prediction model is:

[0028]

[0029]

[0030] Among them, L(t+Δt) is the load forecast result, Δt is the time interval; L(t-iΔt) is the unit load data at different time points in the historical process, and i represents the sequence number of the past time point; is the load change rate of the unit at different time points, is the load change acceleration of the unit at different time points, P j (t) is the impact of the jth external interference factor on the unit load at time i; n is the number of preset past time points, and is the preset number of external interference factors;

[0031] Among them, a i represents the relative importance of the unit load at different time points in the past to the future load forecast; b i Represents the contribution of the load change rate of the unit at different time points in the past to the future load forecast; c i Represents the impact of the load change acceleration of the unit at different time points in the past on the future load forecast; d j Represents the degree of impact of each external interference factor on future load; all are determined based on the analysis of historical data.

[0032] According to a thermal power unit performance optimization control system provided by the present invention, the optimization control module comprises:

[0033] A fuel flow command calculation unit is connected to the load prediction module, and establishes a first command analysis model based on the load prediction result, combustion efficiency, heat transfer efficiency, steam temperature and constant k1 to determine the fuel flow command;

[0034] An air volume instruction calculation unit is connected to the fuel flow instruction calculation unit, and establishes a second instruction analysis model based on the fuel flow instruction, fuel density, excess air coefficient, flue gas pressure, and constant k3 to determine the air volume instruction;

[0035] A feed water flow instruction calculation unit is connected to the fuel flow instruction calculation unit, and a third instruction analysis model is established based on the fuel flow instruction, fuel calorific value, feed water enthalpy value, main reheat steam temperature and constant k5 to determine the feed water flow instruction.

[0036] According to a thermal power unit performance optimization control system provided by the present invention, the first instruction analysis model is:

[0037]

[0038] Among them, F r (t) is the fuel flow command, L(t+Δt) is the load prediction result, η b (t) is the combustion efficiency, η t (t) is the heat transfer efficiency, T s (t) is the rated target temperature of steam, ΔT s (t) is the current steam temperature and the steam rated target temperature T s (t) deviation; k2 is the first correction coefficient, which is used to adjust the fuel flow rate according to the steam temperature deviation to maintain the steam temperature stable, and is determined through experiments based on the thermal characteristics and control requirements of the unit.

[0039] According to a thermal power unit performance optimization control system provided by the present invention, the second instruction analysis model is:

[0040]

[0041] Among them, A q (t) is the air volume command, F r (t) is the fuel flow command, ρ f (t) is the fuel density, is the excess air coefficient, P g (t) is the rated target pressure of flue gas, ΔP g (t) is the current flue gas pressure and the rated target flue gas pressure P g (t) deviation; k4 is the second correction coefficient, which is used to adjust the air volume according to the flue gas pressure deviation to ensure the stability of the combustion process and is determined through experiments.

[0042] According to a thermal power unit performance optimization control system provided by the present invention, the third instruction analysis model is:

[0043]

[0044] Among them, W f (t) is the water flow command, F r (t) is the fuel flow command, h f (t) is the calorific value of fuel, h w (t) is the feed water enthalpy, T r (t) Rated target value of main reheat steam temperature, ΔT r (t) is the current main reheat steam temperature and the rated target value of the main reheat steam temperature T r(t) deviation; k6 is the third correction coefficient, which is used to adjust the feed water flow rate according to the main reheat steam temperature deviation to ensure the stability of the steam temperature, and is obtained through experiments and model analysis.

[0045] According to a thermal power unit performance optimization control system provided by the present invention, the monitoring feedback module adjusts the values ​​of the first correction coefficient k2, the second correction coefficient k4, and the third correction coefficient k6 accordingly based on the feedback signal to achieve self-correction.

[0046] In another aspect, the present invention provides a thermal power unit performance optimization control method, comprising:

[0047] Step 1: Collect various real-time data during the operation of the thermal power unit, including unit characteristic parameters and unit operation parameters;

[0048] Step 2: Establish a load forecasting model based on various real-time data, and predict future load change trends by comprehensively considering the historical load conditions, load change rate, load change acceleration, and external interference factors of the unit to generate load forecast results;

[0049] Step 3: Combine the load forecast results and the current operating status data of the unit, dynamically adjust the fuel, air damper, and air volume parameters according to the control strategy, and generate adjustment instructions to optimize the coal-water ratio in real time to ensure that the unit can quickly adapt to load changes;

[0050] Step 4, receiving the adjustment instruction through the actuator and correspondingly controlling the actions of the fuel regulating valve, the air door actuator, and the water supply regulating valve;

[0051] Step five: monitor the changes in the unit's operating parameters in real time, compare and analyze the actual operating parameters with the target parameters, and form a feedback signal. When it is found that the operating parameters deviate greatly from the target range or the system is abnormal, an alarm is issued in time, and the feedback information is transmitted to the optimization control module to adjust and optimize the control strategy to achieve closed-loop control and self-correction.

[0052] Compared with the prior art, the beneficial effects of this application are:

[0053] By comprehensively considering the historical load conditions, load change rate, load change acceleration and external interference factors of the unit, the load prediction module of the present application can more accurately predict the future load change trend; the optimization control module can adjust the fuel, air damper, air volume and other parameters in advance according to the load prediction results, so that the unit can quickly adapt to the load change; compared with the traditional control method based on current parameter feedback, the present application avoids the response lag problem and effectively improves the peak load regulation capacity of the thermal power unit in the power grid; during the peak power consumption period, the unit can quickly increase the power generation to ensure the stability of the power grid; during the trough period, the load can be reduced in time to reduce energy waste, thereby improving the economy of the unit operation and the reliability of the power grid operation;

[0054] The optimization control module of the present application realizes the coordinated optimization of multiple key parameters such as fuel, air, and water supply by establishing calculation models of fuel flow instructions, air volume instructions, and water supply flow instructions; when adjusting the fuel flow, factors such as combustion efficiency, heat transfer efficiency, and steam temperature are considered at the same time to ensure that the combustion process is efficient and stable while meeting the load demand; for example, the first instruction analysis model in the fuel flow instruction calculation unit reasonably adjusts the fuel flow according to the load prediction results and the steam temperature deviation, which not only ensures the output power of the unit, but also helps to stabilize the steam temperature; the air volume instruction calculation unit and the water supply flow instruction calculation unit accurately control the air volume and water supply flow according to the fuel flow instruction and other related parameters respectively; by reasonably matching the proportional relationship between fuel, air, and water supply, the relationship between steam quality (such as steam temperature and pressure stability) and combustion efficiency is effectively balanced; the problem of unstable combustion or reduced steam quality caused by single parameter adjustment in traditional control methods is avoided, and the safety and economy of unit operation are improved;

[0055] The monitoring feedback module continuously monitors the deviation between the unit operating parameters and the target parameters, generates feedback signals and adjusts the control strategy, thus achieving self-correction and optimization of the system. During the operation of the unit, if it is found that the steam temperature, flue gas pressure, main reheat steam temperature and other parameters deviate from the target range, the system will automatically adjust the corresponding correction coefficients, optimize the fuel, air and feed water flow instructions, and ensure that the unit always operates stably at the best performance state, thereby improving the reliability and stability of the unit under complex working conditions and extending the service life of the equipment.

[0056] Accurate load prediction and multi-parameter coordinated optimization control enable the unit to operate in a more efficient manner under various load conditions; it avoids energy waste caused by load response lag, improper control, etc. For example, when operating at low load, it can reasonably adjust the combustion and steam-water system parameters to reduce fuel consumption and plant power consumption, improve the energy utilization efficiency of the unit, and reduce power generation costs; stable combustion process and optimized control strategy help reduce the generation of pollutants; by accurately controlling the fuel-air ratio, it ensures full combustion of the fuel and reduces the emission of pollutants such as carbon monoxide and nitrogen oxides; at the same time, the optimized steam quality control also helps to improve the efficiency of the turbine, indirectly reducing the additional energy consumption and pollutant emissions caused by low power generation efficiency, meeting environmental protection requirements, and promoting the sustainable development of thermal power units.

[0057] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0058] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0060] Figure 1 It is a structural schematic diagram of a thermal power unit performance optimization control system provided by an embodiment of the present invention;

[0061] Figure 2 It is a flow chart of a thermal power unit performance optimization control method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0063] Embodiment 1:

[0064] The embodiment of the present invention provides a thermal power unit performance optimization control system, please refer to Figure 1 ,include:

[0065] Data acquisition module, which is used to collect various real-time data during the operation of thermal power units, including unit characteristic parameters and unit operation parameters;

[0066] The load prediction module is connected to the data acquisition module and is used to establish a load prediction model based on various real-time data, predict the future load change trend by comprehensively considering the historical load conditions of the unit, the load change rate, the load change acceleration and external interference factors, and generate load prediction results;

[0067] The optimization control module is connected to the load prediction module and is used to dynamically adjust the fuel, air damper and air volume parameters according to the control strategy based on the load prediction results and the current operating status data of the unit, and generate adjustment instructions to optimize the coal-water ratio in real time to ensure that the unit can quickly adapt to load changes;

[0068] An actuator, which is connected to the optimization control module and is used to receive adjustment instructions and correspondingly control the actions of the fuel regulating valve, the damper actuator, and the water supply regulating valve;

[0069] The monitoring feedback module is connected to the actuator and the data acquisition module. It is used to monitor the changes in the unit's operating parameters in real time, compare and analyze the actual operating parameters with the target parameters, and form a feedback signal. When it is found that the operating parameters deviate greatly from the target range or the system is abnormal, an alarm is issued in time, and the feedback information is transmitted to the optimization control module to adjust and optimize the control strategy to achieve closed-loop control and self-correction.

[0070] The principles and beneficial effects of this embodiment are as follows: through accurate load forecasting and real-time optimization control, the operating efficiency of the unit can be significantly improved and unnecessary energy waste can be reduced; continuous monitoring and immediate feedback mechanisms can help to promptly discover potential safety hazards and prevent accidents; optimized operating methods can reduce operating costs, extend equipment life, and improve the quality and stability of power output; more efficient operating modes can help reduce greenhouse gas emissions and the generation of other pollutants, in line with the requirements of sustainable development.

[0071] In order to further optimize the above embodiment, the data acquisition module includes:

[0072] The unit characteristic parameter acquisition unit is used to acquire fixed physical characteristic parameters of the thermal power unit itself, including the unit's rated load, steam rated target temperature, flue gas rated target pressure, main reheat steam temperature rated target value, constant k1 related to the unit's rated load and rated fuel consumption, constant k3 related to the chemical reaction equivalence ratio of fuel to air, and constant k5 related to coal-water specific heat characteristics;

[0073] The constant k1 is determined by the proportional relationship between the load and fuel consumption under the rated working condition of the unit through experiments; the constant k3 is determined by the ratio of fuel to air required for the theoretical chemical reaction based on the fuel composition through experiments; the constant k5 is determined by the physical properties of the experimental analysis based on the calorific value of the fuel and the specific heat of water;

[0074] The unit operation parameter acquisition unit is used to collect the dynamic parameters of the unit during operation, including the unit real-time load, fuel flow, damper opening, air volume, flue gas pressure, steam temperature, main reheat steam temperature, coal quality parameters, combustion efficiency η b (t), heat transfer efficiency η t (t), fuel density ρ f (t), Excess air coefficient Fuel calorific value h f (t), feed water enthalpy h w (t) and external interference factors;

[0075] Among them, the combustion efficiency η b (t) Calculated through analysis of combustion products, used to reflect the completeness of fuel combustion;

[0076] Heat transfer efficiency ηt (t) Determined by heat balance test based on physical parameters such as cleanliness of heated surface and resistance of steam-water flow;

[0077] Fuel density ρ f (t) Derived from the fuel type and real-time temperature and pressure;

[0078] Excess air coefficient Determined by experimental analysis of the composition of the combustion products;

[0079] Fuel calorific value h f (t) Determined by experimental analysis of fuel composition;

[0080] Feed water enthalpy h w (t) Based on feed water temperature and pressure.

[0081] It should be noted that k1 is a constant related to the rated load and rated fuel consumption of the unit, which reflects the basic proportional relationship between the load and fuel consumption of the unit under rated conditions. For example, if the rated load of the unit is Lrated and the rated fuel consumption is Frated, then k1 = Frated / Lrated.

[0082] Combustion efficiency is a parameter that changes over time and depends on factors such as fuel composition and burner characteristics. Combustion efficiency can be calculated by analyzing combustion products such as carbon dioxide, carbon monoxide, unburned hydrocarbons, etc. For example, the ratio of the amount of carbon dioxide produced during complete combustion to the theoretical amount of carbon dioxide can be used as a reference indicator for combustion efficiency.

[0083] Heat transfer efficiency is related to physical parameters such as cleanliness of the heating surface and resistance of the steam-water flow. Heat transfer efficiency can be determined by heat balance testing. For example, the heat transfer efficiency is calculated based on the ratio of the heat absorbed by steam to the heat released by fuel combustion.

[0084] k3 is a constant related to the equivalence ratio of fuel to air in chemical reactions. For chemical reactions of complete combustion, the ratio of fuel to air required for complete reaction can be determined theoretically based on the composition of the fuel (such as the content of elements such as carbon, hydrogen, and sulfur). This ratio is the basis of k3. For example, for the combustion reaction of hydrocarbon fuels, k3 can be determined based on the stoichiometric relationship;

[0085] Fuel density, which is calculated based on the fuel type and the current temperature and pressure. Changes in fuel density affect the mass flow of the fuel, and thus the amount of air required.

[0086] The excess air coefficient is determined by analyzing the composition of the combustion products. The excess air coefficient is used to ensure that the fuel is fully burned, and it also affects the combustion efficiency and pollutant emissions.

[0087] k5 is a constant related to the coal-water specific heat characteristics, which is determined based on the calorific value of the fuel, the specific heat of water and other physical properties. For example, k5 is obtained through theoretical calculation and experimental verification based on the relationship between the heat released by fuel combustion and the heat required for water evaporation;

[0088] The calorific value of fuel is determined based on the analysis of fuel composition. The calorific value of fuel directly affects the heat released during the combustion process, thereby affecting the demand for feed water flow.

[0089] Feedwater enthalpy is calculated based on feedwater temperature and pressure. Feedwater enthalpy reflects the energy state of feedwater and is closely related to the steam generation process.

[0090] In order to further optimize the above embodiment, in the unit operation parameter acquisition unit, the external interference factors include:

[0091] Interference from power grid fluctuation frequency, interference from the start and stop of surrounding large-scale industrial power equipment, interference from changes in meteorological conditions, and interference from fluctuations in the intermittent access of new energy power generation to the power grid.

[0092] It should be noted that when the grid frequency fluctuates, the speed control system of the thermal power unit will respond. Generally speaking, according to the speed control characteristics of the unit, when the frequency increases, the unit load will increase accordingly; when the frequency decreases, the unit load will decrease. This is because the speed control system of the unit will adjust the steam intake of the turbine according to the frequency change, thereby changing the output power (load) of the unit.

[0093] The unit of grid frequency is Hertz (Hz), and its fluctuation range is usually small. For example, in a normally operating grid, the frequency fluctuation may be within ±0.2Hz. For the impact on unit load, its unit can be megawatt (MW) / Hz, that is, the change in unit load caused by each Hertz frequency change. This impact can be determined by the speed regulation characteristic curve of the unit. For example, the speed regulation characteristic curve of a unit shows that for every 1Hz change in grid frequency, the unit load will change by 10MW. Then when the grid frequency fluctuates △f=0.1Hz, the impact on the unit load is P1(t)=10*0.1=1MW;

[0094] When large industrial electrical equipment in the vicinity starts up, it will instantly draw a large amount of power from the grid, causing the grid voltage to drop and the frequency to fluctuate. Thermal power units will detect these changes and respond by adjusting their own output power to maintain the stability of the grid. Similarly, when large equipment stops running, the load on the grid suddenly decreases, and the units also need to adjust their output accordingly.

[0095] For the start and stop of large industrial electrical equipment, the unit of its impact on the unit load is also megawatt (MW). Quantifying this impact requires considering factors such as the equipment's starting current, power factor, and electrical connection relationship with the unit. For example, when a large motor is started, its rated power is 50MW, the starting current is 6 times the rated current, and the power factor is 0.8. By calculating the instantaneous power drawn from the grid when the motor starts, and considering the electrical connection relationship between the unit and the motor in the grid (such as the transformer ratio, line impedance, etc.), its impact on the unit load can be determined. Assuming that after calculation, when this motor starts, the unit load will increase by 30MW instantly, then this 30MW is its impact on the unit load.

[0096] Temperature changes can affect the performance of the cooling system. For example, in a high temperature environment, the cooling efficiency of the cooling tower decreases, resulting in an increase in the exhaust pressure of the steam turbine and a decrease in the output power of the unit. Wind speed changes have an impact on outdoor transmission lines. Strong winds may increase line losses or cause line failures, indirectly affecting the output power of the unit. In addition, for some units using air cooling systems, wind speed and temperature directly affect the cooling effect of the air cooling radiator, which in turn affects the back pressure and load of the unit.

[0097] The unit of temperature is Celsius (℃), and the unit of wind speed is meter per second (m / s). The unit of the impact on the unit load is megawatt (MW). For example, for a unit using a wet cooling system, experiments and simulations have found that for every 10℃ increase in ambient temperature, the unit back pressure increases by 1kPa, and the unit output power decreases by 5MW. So when the temperature increases by △T = 20 degrees Celsius, the impact on the unit load is P2(t) = -5*(20 / 10) = -10MW (the negative sign indicates a load decrease). For the impact of wind speed, it is assumed through field testing and analysis that when the wind speed exceeds a certain threshold (such as 15m / s), for every 1m / s increase in wind speed, the unit load decreases by 0.5MW due to increased transmission line losses.

[0098] As the proportion of renewable energy generation in the power grid increases, its intermittency and volatility have an impact on the stability of the power grid. For example, the output power of wind power changes dramatically with changes in wind speed, and the output power of photovoltaic power generation changes with changes in sunshine intensity. When the power of renewable energy generation suddenly increases or decreases, the thermal power unit needs to adjust its output power accordingly to balance the supply and demand of the power grid.

[0099] The unit of its impact on the unit load is megawatt (MW). For example, in a hybrid power grid that includes wind power and thermal power, the installed capacity of wind power is 100MW. When the wind speed suddenly drops, the wind power output power drops from 80MW to 30MW, a decrease of 50MW. In order to maintain the power balance of the power grid, the thermal power unit needs to increase its output power by 50MW. This 50MW is the impact of wind power fluctuations on the load of thermal power units.

[0100] In order to further optimize the above embodiment, the load forecasting model is:

[0101]

[0102] Among them, L(t+Δt) is the load forecast result, Δt is the time interval; L(t-iΔt) is the unit load data at different time points in the historical process, and i represents the sequence number of the past time point; is the load change rate of the unit at different time points, is the load change acceleration of the unit at different time points, P j (t) is the impact of the jth external interference factor on the unit load at time i; n is the number of preset past time points, and is the preset number of external interference factors;

[0103] Among them, a i represents the relative importance of the unit load at different time points in the past to the future load forecast; b i Represents the contribution of the load change rate of the unit at different time points in the past to the future load forecast; c i Represents the impact of the load change acceleration of the unit at different time points in the past on the future load forecast; d j Represents the degree of impact of each external interference factor on future load; all are determined based on the analysis of historical data.

[0104] It should be noted that a i , b i , c i , d j It is determined based on the physical characteristics of the unit. This requires detailed testing and analysis of the unit. For example, the unit's step response test can be used to obtain the unit's dynamic response data to load changes, thereby determining b i and c i For a i , can be determined by analyzing the load correlation of the unit at different stable operation stages. j It is necessary to monitor the actual changes in the unit load when external interference factors occur, and determine the corresponding weight of each external interference factor through statistical analysis.

[0105] The selection of the time interval Δt depends on the dynamic response speed of the unit and the frequency of data acquisition. If the unit responds quickly, a smaller value can be selected to more finely capture the change trend of the unit load. For example, for a unit that can respond significantly to load changes within a few minutes, Δt can be selected as 1-2 minutes.

[0106] The size of n determines how many past time points of load, load change rate, and load change acceleration are considered to predict future loads. Its selection requires a trade-off between prediction accuracy and computational complexity. Generally speaking, by analyzing the historical data of the unit, the value of n that minimizes the prediction error is selected; the selection of m depends on the number of external interference factors that can be identified and quantified. If multiple external interference factors that have a significant impact on the unit load can be identified, then m will be correspondingly larger to fully consider the impact of these factors on future loads.

[0107] In order to further optimize the above embodiment, the optimization control module includes:

[0108] A fuel flow command calculation unit is connected to the load prediction module, and establishes a first command analysis model based on the load prediction result, combustion efficiency, heat transfer efficiency, steam temperature and constant k1 to determine the fuel flow command;

[0109] An air volume instruction calculation unit is connected to the fuel flow instruction calculation unit, and establishes a second instruction analysis model based on the fuel flow instruction, fuel density, excess air coefficient, flue gas pressure and constant k3 to determine the air volume instruction;

[0110] The feed water flow instruction calculation unit is connected to the fuel flow instruction calculation unit, and establishes a third instruction analysis model based on the fuel flow instruction, fuel calorific value, feed water enthalpy value, main reheat steam temperature and constant k5 to determine the feed water flow instruction.

[0111] The first instruction analysis model is:

[0112]

[0113] Among them, F r (t) is the fuel flow command, L(t+Δt) is the load prediction result, η b (t) is the combustion efficiency, η t (t) is the heat transfer efficiency, T s (t) is the rated target temperature of steam, ΔT s (t) is the current steam temperature and the steam rated target temperature T s(t) deviation; k2 is the first correction coefficient, which is used to adjust the fuel flow rate according to the steam temperature deviation to maintain the steam temperature stable, and is determined through experiments based on the thermal characteristics and control requirements of the unit.

[0114] The second instruction analysis model is:

[0115]

[0116] Among them, A q (t) is the air volume command, F r (t) is the fuel flow command, ρ f (t) is the fuel density, is the excess air coefficient, P g (t) is the rated target pressure of flue gas, ΔP g (t) is the current flue gas pressure and the rated target flue gas pressure P g (t) deviation; k4 is the second correction coefficient, which is used to adjust the air volume according to the flue gas pressure deviation to ensure the stability of the combustion process and is determined through experiments.

[0117] The third instruction analysis model is:

[0118]

[0119] Among them, W f (t) is the water flow command, F r (t) is the fuel flow command, h f (t) is the calorific value of fuel, h w (t) is the feed water enthalpy, T r (t) Rated target value of main reheat steam temperature, ΔT r (t) is the current main reheat steam temperature and the rated target value of the main reheat steam temperature T r (t) deviation; k6 is the third correction coefficient, which is used to adjust the feed water flow rate according to the main reheat steam temperature deviation to ensure the stability of the steam temperature, and is obtained through experiments and model analysis.

[0120] It should be noted that It is used to consider the adjustment of fuel flow rate due to steam temperature deviation. When the steam temperature is lower than the target temperature, the fuel flow rate needs to be increased to increase the steam temperature; otherwise, the fuel flow rate needs to be reduced. k2 is determined according to the thermal characteristics and control requirements of the unit. It can be obtained through a series of experiments and simulations. For example, during the unit commissioning phase, a fuel flow adjustment experiment under different steam temperature deviations is carried out. When the steam temperature deviation is a certain value (such as 10 degrees Celsius), the fuel flow adjustment ratio required to restore the steam temperature to the target temperature is recorded. This ratio is the k2 part. Through multiple experiments, tests are carried out under different steam temperature deviations, and data fitting methods such as the least squares method are used to determine a suitable k2 value, so that the adjustment of the fuel flow rate can effectively correct the steam temperature deviation without causing excessive system fluctuations.

[0121] The adjustment of the air volume due to the flue gas pressure deviation is considered. When the flue gas pressure deviates from the target pressure, the air volume needs to be adjusted to maintain the stability of the combustion process. The determination of k4 also requires experiments and data analysis. During the operation of the unit, different degrees of flue gas pressure deviation are artificially created, and the air volume adjustment ratio required to restore the flue gas pressure to the target pressure is observed and recorded. By testing under multiple different flue gas pressure deviation conditions, a suitable k4 value is determined using regression analysis and other methods, so that the adjustment of the air volume can effectively correct the flue gas pressure deviation while ensuring the stability and economy of the combustion process.

[0122] The adjustment of the feed water flow rate due to the deviation of the main reheat steam temperature is considered. When the main reheat steam temperature deviates from the target value, the feed water flow rate needs to be adjusted to control the steam temperature. k6 is obtained through experiments and model analysis. During the operation of the unit, different degrees of main reheat steam temperature deviations are created, and the feed water flow adjustment ratio required to restore the main reheat steam temperature to the target value is observed and recorded. By testing under a variety of main reheat steam temperature deviation conditions, a suitable k6 value is determined using statistical analysis and control theory methods, so that the adjustment of the feed water flow rate can effectively correct the main reheat steam temperature deviation and maintain the stable operation of the unit.

[0123] In order to further optimize the above embodiment, the monitoring feedback module adjusts the values ​​of the first correction coefficient k2, the second correction coefficient k4, and the third correction coefficient k6 accordingly based on the feedback signal to achieve self-correction.

[0124] It should be noted that the implementation process is:

[0125] Collect a large amount of historical data of the unit under different operating conditions, including various operating parameters (such as load, steam temperature, flue gas pressure, steam temperature, etc.) and the actual values ​​of the corresponding first correction coefficient, second correction coefficient, and third correction coefficient. Analyze the relationship between the unit operating performance (such as combustion efficiency, steam quality, unit stability, etc.) and the correction coefficient under different parameter deviations (such as steam temperature deviation, flue gas pressure deviation, main reheat steam temperature deviation, etc.).

[0126] For example, when the steam temperature deviation is continuously positive and exceeds a certain range, and the combustion efficiency begins to decrease, the value range of k2 and the performance change trend of the unit are recorded. By analyzing many similar data points, the rules of how k2 should be adjusted in order to maintain or improve unit performance under different operating conditions are summarized.

[0127] Refer to the physical model of the thermal power unit to understand the internal connection between steam generation, combustion process, heat transfer and correction coefficient. For example, according to the principle of heat transfer, analyze the quantitative relationship between steam temperature change and fuel flow adjustment (through the influence of k2) to determine the reasonable value range of k2 under different heat transfer conditions.

[0128] At the same time, combined with the experts' experience in the operation and control of thermal power units, the adjustment rules derived from data are supplemented and optimized. Experts can make some suggestions for adjusting the correction coefficient under special conditions (such as rapid load changes, sudden changes in fuel quality, etc.) based on their understanding of the overall operating characteristics of the unit. These suggestions can be used as part of the rule base to guide the self-correction process of the monitoring feedback module.

[0129] The monitoring feedback module continuously monitors the unit's operating parameters, including but not limited to steam temperature, flue gas pressure, main reheat steam temperature, and related operating status indicators (such as combustion efficiency, steam flow, unit vibration, etc.). These data are collected at a higher frequency (such as every second or every minute) to ensure that the changing trend of the parameters can be captured in a timely manner.

[0130] The comparative analysis unit calculates the deviation between the actual operating parameters and the target parameters, such as the deviation between the current steam temperature and the rated target steam temperature, the deviation between the current flue gas pressure and the rated target flue gas pressure, the deviation between the current main reheat steam temperature and the rated target value of the main reheat steam temperature, etc. At the same time, the impact of these deviations on the unit's operating performance is evaluated to determine whether they exceed the preset normal range. For example, if the steam temperature deviation exceeds ±5 degrees Celsius and lasts for more than a certain period of time (such as 5 minutes), it is considered that k2 may need to be adjusted.

[0131] When the deviation judgment unit determines that the correction coefficient needs to be adjusted, the monitoring feedback module searches for matching adjustment rules in the pre-established correction coefficient adjustment rule base according to the current operating parameter deviation and the unit operating status. For example, if the current steam temperature deviation is positive and large, the combustion efficiency has decreased, and the unit load is at a medium level, then the corresponding adjustment strategy of k2 under this condition is found from the rule base, such as increasing the value of k2 to increase the fuel flow adjustment range, thereby increasing the steam temperature.

[0132] There is a complex coupling relationship between the operating parameters of thermal power units. Therefore, when adjusting the correction coefficient, it is necessary to comprehensively consider the joint impact of multiple parameter deviations. For example, when the flue gas pressure deviation and steam temperature deviation exist at the same time, it is necessary to analyze the comprehensive impact of the interaction between the two on the operation of the unit. If the flue gas pressure deviation leads to incomplete combustion, which in turn affects the steam temperature, then when adjusting k4 (affecting the air volume instruction) and k2 (affecting the fuel flow instruction), it is necessary to coordinate the adjustment range of the two to achieve the best control effect and avoid further deterioration of other parameters due to the adjustment of a single correction coefficient.

[0133] According to the adjustment decision, the monitoring feedback module sends the new correction coefficient value to the optimization control module. The optimization control module uses the updated correction coefficient in subsequent calculations, thereby changing the calculation results of the fuel flow command, air volume command and water supply flow command, and realizing further optimization and adjustment of the unit operating parameters.

[0134] After the correction coefficient is updated, the monitoring feedback module continues to closely monitor the changes in the unit's operating parameters to observe whether the parameter deviation has been improved and whether the unit's operating performance is developing in the expected direction (such as whether the steam temperature is approaching the target value, whether the combustion efficiency is improved, whether the unit's stability is enhanced, etc.). If after a period of observation (such as 10-15 minutes), it is found that the parameter deviation has not been effectively improved, or new problems have emerged (such as excessive steam pressure fluctuations, unstable feed water flow, etc.), then the adjustment strategy will be re-evaluated. It may be necessary to adjust the correction coefficient again, or take other auxiliary control measures (such as adjusting the load change rate, optimizing the burner operation mode, etc.) until the unit operation reaches a satisfactory state. Through the continuous cycle of monitoring, adjustment, and verification, the monitoring feedback module can dynamically optimize the correction coefficient and self-correct the system to ensure that the thermal power unit can maintain efficient and stable operation under various operating conditions.

[0135] Embodiment 2:

[0136] The embodiment of the present invention provides a thermal power unit performance optimization control system, please refer to Figure 2 ,include:

[0137] Step 1: Collect various real-time data during the operation of the thermal power unit, including unit characteristic parameters and unit operation parameters;

[0138] Step 2: Establish a load forecasting model based on various real-time data, and predict future load change trends by comprehensively considering the historical load conditions, load change rate, load change acceleration, and external interference factors of the unit to generate load forecast results;

[0139] Step 3: Combine the load forecast results and the current operating status data of the unit, dynamically adjust the fuel, air damper, and air volume parameters according to the control strategy, and generate adjustment instructions to optimize the coal-water ratio in real time to ensure that the unit can quickly adapt to load changes;

[0140] Step 4, receiving the adjustment instruction through the actuator and correspondingly controlling the actions of the fuel regulating valve, the air door actuator, and the water supply regulating valve;

[0141] Step five: monitor the changes in the unit's operating parameters in real time, compare and analyze the actual operating parameters with the target parameters, and form a feedback signal. When it is found that the operating parameters deviate greatly from the target range or the system is abnormal, an alarm is issued in time, and the feedback information is transmitted to the optimization control module to adjust and optimize the control strategy to achieve closed-loop control and self-correction.

[0142] It should be noted that 1. Data collection: This is the basis of the entire process, which involves collecting a large amount of real-time operating data from thermal power units. These data include but are not limited to the unit's operating status parameters (such as temperature, pressure, flow, etc.), operating parameters (such as power output, fuel consumption, etc.), and other factors that may affect the unit's performance.

[0143] 2. Load forecasting: Based on the data collected in the first step, use appropriate mathematical models or machine learning algorithms to predict future load demand. This step needs to comprehensively consider factors such as the historical load situation of the unit, the load change trend, and the impact of the external environment to ensure the accuracy and reliability of the forecast.

[0144] 3. Optimize control decisions: Use the load forecast results of the previous step and combine them with the current operating status of the unit to develop the optimal control strategy. This includes deciding how to adjust key parameters such as fuel supply, air door opening, and air supply, in order to maintain the most ideal coal-water ratio so that the unit can respond quickly and effectively to load changes.

[0145] 4. Execution control command: After receiving the adjustment instruction generated by the optimized control decision, the actuator will operate the fuel control valve, damper actuator, water supply control valve and other equipment accordingly to realize the actual change of the unit's operating status.

[0146] 5. Monitoring and feedback: This step is the key to ensure the effective operation of the entire control system. By continuously monitoring the actual operating parameters of the unit and comparing and analyzing them with the predetermined target parameters, deviations or abnormalities can be discovered in a timely manner, and feedback signals can be sent to the optimization control module. This helps to continuously adjust and improve the control strategy and achieve self-correction and optimization of the system.

[0147] The core value of this method lies in the fact that it realizes comprehensive monitoring, accurate prediction and efficient management of the operating status of thermal power units through a series of automated and intelligent technical means, thereby improving the efficiency and quality of power production, reducing operating costs, and also contributing to environmental protection.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A thermal power unit performance optimization control system, characterized in that: include: Data acquisition module, which is used to collect various real-time data during the operation of thermal power units, including unit characteristic parameters and unit operation parameters; A load prediction module, which is connected to the data acquisition module, is used to establish a load prediction model based on various real-time data, predict future load change trends by comprehensively considering the historical load conditions of the unit, load change rate, load change acceleration and external interference factors, and generate load prediction results; An optimization control module is connected to the load prediction module and is used to dynamically adjust the fuel, air damper and air volume parameters according to the control strategy in combination with the load prediction results and the current operation status data of the unit, and generate adjustment instructions to optimize the coal-water ratio in real time to ensure that the unit can quickly adapt to load changes; An actuator connected to the optimization control module, used to receive adjustment instructions and correspondingly control the actions of the fuel regulating valve, the damper actuator, and the water supply regulating valve; The monitoring feedback module is connected to the actuator and the data acquisition module, and is used to monitor the changes in the unit's operating parameters in real time, compare and analyze the actual operating parameters with the target parameters, and form a feedback signal. When it is found that the operating parameters deviate greatly from the target range or the system is abnormal, an alarm is issued in time, and the feedback information is transmitted to the optimization control module to adjust and optimize the control strategy to achieve closed-loop control and self-correction.

2. A thermal power unit performance optimization control system according to claim 1, characterized in that: The data acquisition module comprises: The unit characteristic parameter acquisition unit is used to acquire fixed physical characteristic parameters of the thermal power unit itself, including the unit's rated load, steam rated target temperature, flue gas rated target pressure, main reheat steam temperature rated target value, constant k1 related to the unit's rated load and rated fuel consumption, constant k3 related to the chemical reaction equivalence ratio of fuel to air, and constant k5 related to coal-water specific heat characteristics; The constant k1 is determined by the proportional relationship between the load and fuel consumption under the rated working condition of the unit through experiments; the constant k3 is determined by the ratio of fuel to air required for the theoretical chemical reaction based on the fuel composition through experiments; the constant k5 is determined by the physical properties of the experimental analysis based on the calorific value of the fuel and the specific heat of water; The unit operation parameter acquisition unit is used to collect the dynamic parameters of the unit during operation, including the unit real-time load, fuel flow, damper opening, air volume, flue gas pressure, steam temperature, main reheat steam temperature, coal quality parameters, combustion efficiency η b (t), heat transfer efficiency η t (t), fuel density ρ f (t), Excess air coefficient Fuel calorific value h f (t), feed water enthalpy h w (t) and external interference factors; Among them, the combustion efficiency η b (t) Calculated through analysis of combustion products, used to reflect the completeness of fuel combustion; Heat transfer efficiency η t (t) Determined by heat balance test based on physical parameters such as cleanliness of heated surface and resistance of steam-water flow; Fuel density ρ f (t) Derived from the fuel type and real-time temperature and pressure; Excess air coefficient Determined by experimental analysis of the composition of the combustion products; Fuel calorific value h f (t) Determined by experimental analysis of fuel composition; Feed water enthalpy h w (t) Based on feed water temperature and pressure.

3. A thermal power unit performance optimization control system according to claim 2, characterized in that: In the unit operation parameter acquisition unit, the external interference factors include: Interference from power grid fluctuation frequency, interference from the start and stop of surrounding large-scale industrial power equipment, interference from changes in meteorological conditions, and interference from fluctuations in the intermittent access of new energy power generation to the power grid.

4. A thermal power unit performance optimization control system according to claim 3, characterized in that: The load forecasting model is: Among them, L(t+Δt) is the load forecast result, Δt is the time interval; L(t-iΔt) is the unit load data at different time points in the historical process, and i represents the sequence number of the past time point; is the load change rate of the unit at different time points, is the load change acceleration of the unit at different time points, P j (t) is the impact of the jth external interference factor on the unit load at time i; n is the number of preset past time points, and is the preset number of external interference factors; Among them, a i represents the relative importance of the unit load at different time points in the past to the future load forecast; b i Represents the contribution of the load change rate of the unit at different time points in the past to the future load forecast; c i Represents the impact of the load change acceleration of the unit at different time points in the past on the future load forecast; d j Represents the degree of impact of each external interference factor on future load; all are determined based on the analysis of historical data.

5. A thermal power unit performance optimization control system according to claim 4, characterized in that: The optimization control module includes: A fuel flow command calculation unit is connected to the load prediction module, and establishes a first command analysis model based on the load prediction result, combustion efficiency, heat transfer efficiency, steam temperature and constant k1 to determine the fuel flow command; An air volume instruction calculation unit is connected to the fuel flow instruction calculation unit, and establishes a second instruction analysis model based on the fuel flow instruction, fuel density, excess air coefficient, flue gas pressure, and constant k3 to determine the air volume instruction; A feed water flow instruction calculation unit is connected to the fuel flow instruction calculation unit, and a third instruction analysis model is established based on the fuel flow instruction, fuel calorific value, feed water enthalpy value, main reheat steam temperature and constant k5 to determine the feed water flow instruction.

6. A thermal power unit performance optimization control system according to claim 5, characterized in that: The first instruction analysis model is: Among them, F r (t) is the fuel flow command, L(t+Δt) is the load prediction result, η b (t) is the combustion efficiency, η t (t) is the heat transfer efficiency, T s (t) is the rated target temperature of steam, ΔT s (t) is the current steam temperature and the steam rated target temperature T s (t) deviation; k2 is the first correction coefficient, which is used to adjust the fuel flow rate according to the steam temperature deviation to maintain the steam temperature stable, and is determined through experiments based on the thermal characteristics and control requirements of the unit.

7. A thermal power unit performance optimization control system according to claim 6, characterized in that: The second instruction analysis model is: Among them, A q (t) is the air volume command, F r (t) is the fuel flow command, ρ f (t) is the fuel density, is the excess air coefficient, P g (t) is the rated target pressure of flue gas, ΔP g (t) is the current flue gas pressure and the rated target flue gas pressure P g (t) deviation; k4 is the second correction coefficient, which is used to adjust the air volume according to the flue gas pressure deviation to ensure the stability of the combustion process and is determined through experiments.

8. A thermal power unit performance optimization control system according to claim 7, characterized in that: The third instruction analysis model is: Among them, W f (t) is the water flow command, F r (t) is the fuel flow command, h f (t) is the calorific value of fuel, h w (t) is the feed water enthalpy, T r (t) Rated target value of main reheat steam temperature, ΔT r (t) is the current main reheat steam temperature and the rated target value of the main reheat steam temperature T r (t) deviation; k6 is the third correction coefficient, which is used to adjust the feed water flow rate according to the main reheat steam temperature deviation to ensure the stability of the steam temperature, and is obtained through experiments and model analysis.

9. A thermal power unit performance optimization control system according to claim 8, characterized in that: The monitoring feedback module adjusts the values ​​of the first correction coefficient k2, the second correction coefficient k4, and the third correction coefficient k6 accordingly based on the feedback signal to achieve self-correction.

10. A thermal power unit performance optimization control method, based on the system described in claims 1 to 9, characterized in that: include: Step 1: Collect various real-time data during the operation of the thermal power unit, including unit characteristic parameters and unit operation parameters; Step 2: Establish a load forecasting model based on various real-time data, and predict future load change trends by comprehensively considering the historical load conditions, load change rate, load change acceleration, and external interference factors of the unit to generate load forecast results; Step 3: Combine the load forecast results and the current operating status data of the unit, dynamically adjust the fuel, air damper, and air volume parameters according to the control strategy, and generate adjustment instructions to optimize the coal-water ratio in real time to ensure that the unit can quickly adapt to load changes; Step 4, receiving the adjustment instruction through the actuator and correspondingly controlling the actions of the fuel regulating valve, the air door actuator, and the water supply regulating valve; Step five: monitor the changes in the unit's operating parameters in real time, compare and analyze the actual operating parameters with the target parameters, and form a feedback signal. When it is found that the operating parameters deviate greatly from the target range or the system is abnormal, an alarm is issued in time, and the feedback information is transmitted to the optimization control module to adjust and optimize the control strategy to achieve closed-loop control and self-correction.

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