Unit full-load real-time intelligent optimization control system based on complex coal type mode

Abstract

The invention discloses a unit full-load real-time intelligent optimization control system based on a complex coal type mode, which comprises a data acquisition and processing module, a coal quality analysis module, a load prediction and optimization module, a combustion control module, a fault diagnosis and early warning module, a man-machine interaction module and a central control unit, the modules and the units are connected through a high-speed communication network, so that real-time transmission of data and issuing of control instructions are realized; the unit full-load real-time intelligent optimization control system can monitor the running state of a unit in real time, formulate an optimal control strategy according to coal quality changes and load requirements, achieve efficient combustion and reduce pollutant emission, timely discover potential faults and send out early warning information through a fault diagnosis and early warning module, and improve the working efficiency of the unit. And safe and stable operation of the unit is ensured.

Description

Technical Field

[0001] The present invention relates to a real-time intelligent optimization control system for full load of a unit, and more specifically, to a real-time intelligent optimization control system for full load of a unit based on a complex coal type mode, and belongs to the field of power plants. Background Art

[0002] As an important part of the current global energy supply, improving the efficiency and environmental performance of coal-fired power generation is crucial to achieving sustainable development goals. However, coal-fired units often face complex operating conditions such as changing coal types and load fluctuations in actual operation, which poses a severe challenge to the stable operation and efficient power generation of the units. In particular, traditional coal-fired power generation units are difficult to achieve efficient and stable operation when faced with complex and changeable coal quality conditions. The uncertainty of coal quality and the volatility of load demand lead to low combustion efficiency, increased pollutant emissions and frequent unit failures. Therefore, it is particularly important to develop an intelligent control system that can adapt to complex coal types and optimize unit load and combustion process in real time. Summary of the invention

[0003] The purpose of the present invention is to provide a real-time intelligent optimization control system for full load of the unit under a complex coal type mode. The system realizes comprehensive monitoring, precise control and efficient optimization of the unit operation status by integrating functional modules such as data acquisition and processing, coal quality analysis, load prediction and optimization, combustion control, fault diagnosis and early warning, human-computer interaction and central control.

[0004] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0005] The present invention provides a real-time intelligent optimization control system for full load of a unit under a complex coal type mode, comprising a data acquisition and processing module, a coal quality analysis module, a load prediction and optimization module, a combustion control module, a fault diagnosis and early warning module, a human-computer interaction module and a central control unit. Each module is connected to another unit through a high-speed communication network to realize real-time data transmission and the issuance of control instructions.

[0006] The unit's full-load real-time intelligent optimization control system can monitor the unit's operating status in real time, and formulate the optimal control strategy according to changes in coal quality and load requirements to achieve efficient combustion and reduce pollutant emissions. It can also detect potential faults in a timely manner and issue warning information through the fault diagnosis and early warning module to ensure the safe and stable operation of the unit.

[0007] Preferably, the data acquisition and processing module: collects the coal quality parameters, operating parameters and environmental parameters of the unit in real time through the sensor network, performs cleaning, denoising and format conversion, and improves the data quality;

[0008] Coal quality analysis module: receives and processes coal quality parameters, performs component analysis, prediction and classification, and provides key basis for combustion control and load optimization;

[0009] Load forecasting and optimization module: Based on historical load data, the module uses forecasting and optimization algorithms to achieve accurate forecasting of load demand and output optimization, ensuring the balance of power grid supply and demand and maximizing energy efficiency;

[0010] Combustion control module: Based on the coal quality analysis results and load optimization instructions, it uses combustion control algorithms and intelligent actuators to accurately control the boiler combustion process;

[0011] Fault diagnosis and early warning module: Use machine learning algorithms to monitor the status of key components of the unit in real time, perform fault diagnosis and issue early warning information;

[0012] Human-computer interaction module: displays the unit's operating status, prediction results and control strategies in real time, and receives instructions from operators;

[0013] Central control unit: coordinates the work of each module, realizes centralized data processing and intelligent decision-making, and issues control instructions.

[0014] Preferably, the data acquisition and processing module transmits the data collected in real time to the central control unit for integration and processing, and the data includes unit operating parameters, coal quality parameters and environmental parameters; the data acquisition and processing module collects the unit coal quality parameters, unit operating parameters and environmental parameters in real time through the sensor network, and cleans, denoises and converts the format of the collected raw data to improve the data quality; the processed data is stored in the data warehouse for subsequent analysis and optimization; the unit operating parameters include temperature, pressure, flow, vibration, and speed; the environmental parameters include air temperature, humidity, and wind speed; the coal quality parameters include ash content, moisture content, and volatile matter.

[0015] Preferably, the coal quality analysis module receives the coal quality parameters transmitted by the data acquisition and processing module, performs component analysis and prediction, and transmits the analysis results to the central control unit;

[0016] Specifically include:

[0017] Coal quality parameter measurement: Use chemical analysis instruments to analyze the composition of coal samples and obtain key parameters of coal: ash, moisture, volatile matter, and fixed carbon;

[0018] Coal quality prediction: Based on historical coal quality parameters and unit operating parameters, machine learning algorithms are used to predict future coal quality trends;

[0019] Coal quality classification: Coal types are divided into different categories based on coal quality parameters to provide a reference for combustion control and load optimization.

[0020] Preferably, the load prediction and optimization module uses prediction algorithms and optimization algorithms to perform load prediction and output optimization based on the historical load data transmitted by the data acquisition and processing module, and transmits the prediction results and optimization strategies to the central control unit to achieve accurate prediction and reasonable scheduling of future load demand of the unit;

[0021] Specifically include:

[0022] Load forecasting: Use time series analysis and machine learning algorithms to forecast unit load demand;

[0023] Load optimization: Based on the load forecast results, combined with coal quality information and unit operating status, optimize the unit output distribution to maximize the energy efficiency of the entire plant;

[0024] Load regulation: Automatically adjust the unit load according to the real-time load demand and unit output to ensure the balance of power grid supply and demand.

[0025] Preferably, the combustion control module receives the combustion strategy and control instructions transmitted by the central control unit, adjusts the boiler combustion process through the intelligent actuator, and uses sensors to monitor the combustion effect, and transmits the monitoring data to the central control unit for feedback adjustment;

[0026] Specifically include:

[0027] Combustion strategy formulation: Develop the optimal combustion strategy based on coal quality parameters and load requirements, including coal feed rate, air volume, and burner configuration.

[0028] Combustion process control: precise control of the boiler combustion process is achieved through intelligent actuators, including coal feeders, fans, and burner regulating valves;

[0029] Combustion effect monitoring: Use sensors to monitor boiler outlet flue gas temperature, oxygen content, and pollutant emission parameters to evaluate combustion effects.

[0030] Preferably, the fault diagnosis and early warning module receives the unit operation status data transmitted by the data acquisition and processing module, and uses a machine learning algorithm to perform real-time monitoring and fault diagnosis; when an abnormality or potential fault is found, it automatically sends an early warning message to the human-computer interaction module and transmits it to the central control unit for processing;

[0031] Fault diagnosis and early warning module: Based on historical fault data, real-time operating parameters and machine learning algorithms, it realizes real-time monitoring and fault diagnosis of the unit's operating status, and issues early warning information in time to avoid the expansion of faults; including real-time monitoring: using sensors to monitor the unit's operating status, including vibration, temperature, and pressure; fault diagnosis: based on machine learning algorithms, in-depth analysis of monitoring data to identify potential fault modes; early warning and alarm: when abnormal data is monitored, early warning information is automatically issued; when the fault reaches a certain level, the alarm mechanism is triggered to notify the operator to take emergency measures.

[0032] Preferably, the human-computer interaction module displays the unit operation status, load forecast results, combustion control strategy, fault diagnosis and early warning information in real time, and receives the operator's command input. The operator can adjust the unit parameters and control strategy through the interface, and the command will be transmitted to the central control unit for issuance and execution;

[0033] Specifically include:

[0034] Information display: real-time display of unit operating status, load forecast results, combustion control strategy, fault diagnosis and early warning information;

[0035] Operation control: remote operation is provided through the communication module, and the operator adjusts the unit parameters and control strategies through the interface;

[0036] Data query and export: Realize the query, export and printing functions of historical data, which is convenient for operators to conduct analysis and report preparation.

[0037] Preferably, the central control unit is responsible for coordinating the work between the modules and units, receiving the data and instructions transmitted by the modules and units, integrating and processing the data, formulating the optimal control strategy, and issuing the control instructions to the corresponding modules and units for execution; and the central control unit monitors the operating status of the system to ensure the normal operation of the modules and units, and automatically adjusts or issues alarm information when an abnormality is found;

[0038] Specifically include:

[0039] Data integration and processing: Receive data transmitted by each module and unit, integrate, clean and pre-process it, and provide a basis for subsequent analysis and control;

[0040] Intelligent decision-making: Based on data analysis results, formulate optimal control strategies, including combustion strategies, load optimization strategies, and fault diagnosis strategies;

[0041] Instruction issuance: convert the decision results into specific control instructions and send them to each module and unit for execution;

[0042] System monitoring and management: Real-time monitoring of system operation status to ensure the normal operation of each module and unit; when abnormalities are found, automatic adjustments or alarm information are issued.

[0043] Beneficial effects: Significantly improve the operating efficiency and energy efficiency of coal-fired power generation units. Reduce pollutant emissions and improve environmental performance. Enhance the fault warning and response capabilities of the units and reduce unplanned downtime. Optimize personnel operation experience and improve operation and maintenance management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0045] The present invention will be further described below in conjunction with the accompanying drawings, but the present invention is not limited to the following embodiments.

[0046] This application aims to design a set of real-time intelligent optimization control system for full load of the unit under complex coal type mode, and realize real-time monitoring, intelligent analysis and optimization control of the operation status of the coal-fired unit through modular and unitized design concepts, so as to improve the energy efficiency of the unit, reduce coal consumption, reduce pollutant emissions, and enhance the adaptability and flexibility of the unit. It is of great significance to improve the energy efficiency of coal-fired power generation units, reduce pollutant emissions, and enhance operational stability.

[0047] like Figure 1 The figure shows a specific embodiment of a real-time intelligent optimization control system for a full load of a unit under a complex coal type mode. The embodiment is a real-time intelligent optimization control system for a full load of a unit under a complex coal type mode, including a data acquisition and processing module, a coal quality analysis module, a load prediction and optimization module, a combustion control module, a fault diagnosis and early warning module, a human-computer interaction module and a central control unit. The modules are connected to each other through a high-speed communication network to realize real-time transmission of data and issuance of control instructions; the real-time intelligent optimization control system for the full load of the unit can monitor the operating status of the unit in real time, and formulate the optimal control strategy according to the changes in coal quality and load requirements, so as to realize efficient combustion and reduce pollutant emissions, and timely detect potential faults and issue early warning information through the fault diagnosis and early warning module to ensure the safe and stable operation of the unit.

[0048] Data acquisition and processing module: collects coal quality parameters, unit operation parameters and environmental parameters in real time through the sensor network. Performs pre-processing operations such as cleaning, denoising and format conversion on the collected raw data to improve data quality. The processed data is transmitted to the central control unit for integration and processing.

[0049] Coal quality analysis module: Receives coal quality data transmitted by the data acquisition and processing module. Uses chemical analysis instruments to analyze the composition of coal samples. Based on historical coal quality data and unit operation data, uses machine learning algorithms to predict future coal quality trends. Coal types are divided into different categories according to coal quality parameters, providing key basis for subsequent combustion control and load optimization.

[0050] Load forecasting and optimization module: Based on the historical load data transmitted by the data acquisition and processing module, the prediction algorithm and optimization algorithm are used to perform load forecasting and output optimization. The future load demand of the unit is predicted using time series analysis, machine learning and other algorithms. According to the load forecast results, combined with coal quality information and unit operating status, the unit output distribution is optimized to maximize the energy efficiency of the entire plant. According to the real-time load demand and unit output, the unit load is automatically adjusted to ensure the balance of power grid supply and demand.

[0051] Combustion control module: Receives combustion strategies and control instructions transmitted by the central control unit. Uses advanced combustion control algorithms and intelligent actuators to achieve precise control of the boiler combustion process. Formulates optimal combustion strategies based on coal quality analysis results and load optimization instructions. Uses sensors to monitor combustion effects and transmits monitoring data to the central control unit for feedback adjustment.

[0052] Fault diagnosis and early warning module: Receives the operating status data of key components of the unit transmitted by the data acquisition and processing module. Uses machine learning algorithms for real-time monitoring and fault diagnosis. Based on historical fault data, real-time operating parameters and machine learning algorithms, it realizes real-time monitoring and fault diagnosis of the unit's operating status. It issues early warning information in a timely manner to perform fault diagnosis, early warning and alarm.

[0053] Human-computer interaction module: real-time display of unit operation status, load forecast results, combustion control strategy, fault diagnosis and early warning information, etc. Receive operator's command input to achieve human-computer interaction.

[0054] Central control unit: responsible for coordinating the work between modules and units. Realize centralized data processing and intelligent decision-making. Based on data analysis results, formulate optimal control strategies, including combustion strategies, load optimization strategies, fault diagnosis strategies, etc. Convert decision results into specific control instructions and send them to each module and unit for execution. Monitor the system operation status in real time to ensure the normal operation of each module and unit; when abnormalities are found, automatically adjust or issue alarm information.

[0055] In a preferred embodiment, the data acquisition and processing module transmits the data collected in real time to the central control unit for integration and processing, and the data includes unit operating parameters, coal quality parameters and environmental parameters; the data acquisition and processing module collects the unit coal quality parameters, unit operating parameters and environmental parameters in real time through a sensor network, and cleans, denoises and converts the format of the collected raw data to improve the data quality; the processed data is stored in a data warehouse for subsequent analysis and optimization; the unit operating parameters include temperature, pressure, flow, vibration, and speed; the environmental parameters include air temperature, humidity, and wind speed; the coal quality parameters include ash content, moisture content, and volatile matter.

[0056] Functional refinement of the data acquisition and processing module: The data acquisition and processing module in the present invention plays a vital role. It is responsible for real-time and accurate collection of various key data generated by the coal-fired power generation unit during operation, and pre-processing it, laying a solid foundation for subsequent analysis and optimization work.

[0057] Data transmission and integration This module can capture diverse data from the sensor network in real time, including but not limited to unit operating parameters, coal quality parameters and environmental parameters. Once collected, these data are quickly transmitted to the central control unit for integration and unified processing. This process ensures the timeliness and integrity of the data and provides comprehensive information support for the system.

[0058] In order to achieve comprehensive and accurate data collection, the present invention adopts advanced sensor network technology. These sensors are carefully arranged at various key parts of the unit, which can monitor and record the changes of the unit's coal quality (such as ash, moisture, volatile matter, etc.), operation (such as temperature, pressure, flow, vibration, speed, etc.) and environmental (such as air temperature, humidity, wind speed, etc.) parameters in real time. This arrangement not only improves the density and accuracy of data collection, but also ensures the comprehensiveness and representativeness of the data.

[0059] Data preprocessing technology Before the raw data is transmitted to the central control unit, it needs to go through a rigorous preprocessing process. This includes data cleaning (removing invalid or erroneous data), denoising (reducing random fluctuations and noise in the data), and format conversion (converting the data into a unified, easy-to-process format). These preprocessing steps greatly improve the quality of the data and provide a reliable foundation for subsequent analysis and optimization work.

[0060] Data storage and management Preprocessed data will be stored in an efficient and secure data warehouse. This data warehouse not only has powerful data storage capabilities, but also supports efficient data retrieval and analysis functions. This ensures that subsequent analysis and optimization work can quickly and accurately obtain the required data resources, thereby further improving the overall performance and efficiency of the system.

[0061] Specific parameter description

[0062] Unit operating parameters: including but not limited to temperature (an important indicator reflecting the thermal efficiency of the unit), pressure (a key parameter for measuring the unit's load-bearing capacity), flow (a key indicator for evaluating the unit's cycle efficiency), vibration (an important means of monitoring the unit's mechanical stability), and speed (a key parameter reflecting the unit's operating speed and efficiency).

[0063] Environmental parameters: including air temperature (an important factor affecting the heat dissipation and energy efficiency of the unit), humidity (affecting the insulation performance and corrosion rate of the unit), and wind speed (having a direct impact on the cooling effect and stability of the unit).

[0064] Coal quality parameters: including ash content (a key factor affecting combustion efficiency and pollutant emissions), moisture (affecting combustion calorific value and combustion stability), and volatile matter (an important indicator reflecting the combustion performance and ignition characteristics of coal).

[0065] In a preferred embodiment, the coal quality analysis module receives the coal quality parameters transmitted by the data acquisition and processing module, performs component analysis and prediction, and transmits the analysis results to the central control unit; the results of coal quality classification can provide intuitive information support for operators of coal-fired equipment, helping them to better understand the coal quality, formulate reasonable combustion strategies, and improve combustion efficiency and equipment operation stability.

[0066] Coal quality parameter measurement: The coal quality analysis module first receives the coal quality parameters transmitted from the data acquisition and processing module. In order to ensure the accuracy and reliability of the data, the system uses chemical analysis instruments to analyze the composition of the coal sample. Instruments include but are not limited to X-ray fluorescence spectrometers, Karl Fischer moisture analyzers, thermogravimetric analyzers, etc., which accurately measure key parameters such as ash, moisture, volatile matter, and fixed carbon in coal. Ash is the general term for inorganic matter in coal, and its content directly affects combustion efficiency and ash treatment; moisture affects the calorific value and combustion stability of coal; volatile matter is the gas and liquid product released by coal during the heating process, which is closely related to the ignition difficulty and combustion speed of coal; fixed carbon is the combustible part remaining in coal after pyrolysis and gasification, and is the main source of calorific value of coal.

[0067] Coal quality prediction: Based on the coal quality parameters, the coal quality analysis module further uses machine learning algorithms to predict the trend of coal quality changes. The system establishes a prediction model by analyzing the correlation between historical coal quality parameters and unit operating parameters (such as load, temperature, pressure, etc.); this model can predict the possible changes in coal quality parameters in the future and provide a decision-making basis for the early adjustment and optimization of coal-fired equipment. In order to achieve this function, the system uses a variety of machine learning algorithms for model training and optimization, including but not limited to support vector machines, random forests, neural networks, etc. These algorithms can handle complex nonlinear relationships and improve the accuracy and robustness of predictions.

[0068] Coal quality classification: In addition to measuring and predicting coal quality parameters, the coal quality analysis module also divides coal into different categories based on coal quality parameters. This function is of great significance for combustion control and load optimization. The system classifies coal according to key parameters such as ash content and volatile matter through algorithms such as cluster analysis and decision trees. Different types of coal have different combustion characteristics and calorific values, so different strategies need to be adopted in combustion control and load optimization.

[0069] In a preferred embodiment, the load prediction and optimization module uses prediction algorithms and optimization algorithms to perform load prediction and output optimization based on the historical load data transmitted by the data acquisition and processing module, and transmits the prediction results and optimization strategies to the central control unit to achieve accurate prediction and reasonable scheduling of future load demand of the unit;

[0070] Functional refinement of the load forecasting and optimization module: The load forecasting and optimization module relies on a large amount of historical load data provided by the data acquisition and processing module. These data record the operating status, load changes and other information of the power units in the past time period. This module deeply mines the laws and trends behind these data and uses advanced prediction algorithms and optimization algorithms to achieve accurate prediction and efficient management of future unit load demand. It is mainly through the comprehensive use of time series analysis, machine learning algorithms, coal quality information, unit status monitoring and other technical means to achieve accurate prediction and reasonable scheduling of future load demand of power units, providing strong support for improving the efficiency of power grid operation and promoting energy conservation and emission reduction.

[0071] Among them, load forecast

[0072] 1) Time series analysis: Load forecasting first uses time series analysis technology, which focuses on identifying and utilizing the periodicity, trend, and seasonality of load data over time. For example, by analyzing the daily load curves of the past few years, it can be found that load peaks are usually higher in summer and winter due to increased cooling and heating demand; at the same time, there are significant differences in load patterns between weekdays and weekends. Based on these rules, the module can build a forecasting model to estimate the load in the next few days, weeks, or even months.

[0073] 2) Machine learning algorithms: Introduce machine learning algorithms, such as support vector machines (SVM), random forests, and long short-term memory networks (LSTM). Automatically learn the complex nonlinear relationships in historical load data, consider more influencing factors (such as weather changes, holiday effects, economic activity levels, etc.), and generate more refined forecast results. For example, the LSTM model can capture long-term dependencies in load data and effectively predict drastic load fluctuations under extreme weather conditions.

[0074] Load Optimization:

[0075] 1) Coal quality information and unit operating status: Based on load forecasting, the load optimization module will comprehensively consider the changes in current coal quality (such as calorific value, sulfur content, etc.) and the operating status of each unit (such as temperature, pressure, degree of wear, etc.), and calculate the energy efficiency of different units under different loads through complex mathematical models. For example, when the calorific value of coal decreases, it may be necessary to adjust the output of high-efficiency but high-coal-consuming units and use a more economical unit combination to meet the load demand, so as to achieve the goal of maximizing the energy efficiency of the entire plant.

[0076] 2) Output distribution optimization: Based on the above analysis, the optimization algorithm will generate an optimal output distribution plan to ensure that while meeting the load demand, energy consumption and emissions are reduced as much as possible to improve the overall economic benefits. This may involve multiple aspects such as unit start and stop decisions and load distribution adjustments. Each optimization may bring significant energy-saving effects.

[0077] Load Regulation:

[0078] 1) Real-time response: The load regulation function emphasizes immediate response to the balance of power grid supply and demand. By continuously monitoring the real-time load demand and the actual output of each unit, it can quickly identify the mismatch between supply and demand and automatically trigger the regulation mechanism. For example, when the grid demand suddenly increases, the system can automatically increase the output of a specific unit or start a standby unit to meet the additional load demand; on the contrary, when the demand decreases, the output can be appropriately reduced to avoid energy waste.

[0079] 2) Intelligent dispatching: In order to achieve more refined load regulation, combined with advanced dispatching strategies such as demand response (DR) programs, users are encouraged to reduce electricity consumption during peak hours or increase electricity consumption during off-peak hours, thereby helping to balance the supply and demand of the power grid and reduce the cost of power grid operation. At the same time, through coordinated dispatching with other energy systems (such as wind power and solar energy), the flexibility and reliability of energy utilization can be improved.

[0080] In a preferred embodiment, the combustion control module receives the combustion strategy and control instructions transmitted by the central control unit, adjusts the boiler combustion process through the intelligent actuator, and uses sensors to monitor the combustion effect, and transmits the monitoring data to the central control unit for feedback adjustment; specifically, by comprehensively using coal quality parameters, load requirements, intelligent actuators and sensor monitoring and other technical means, accurate control and efficient management of the boiler combustion process are achieved. It improves fuel utilization, reduces operating costs, and effectively reduces pollutant emissions, promoting environmental protection and sustainable development.

[0081] The functions of the combustion control module are as follows: it receives the combustion strategy and control instructions from the central control unit, and finely adjusts the combustion process of the boiler through a series of intelligent actuators. At the same time, it uses advanced sensor technology to monitor the combustion effect in real time to ensure that the entire combustion process is both efficient and environmentally friendly. The following is a detailed expansion and specific description of the functions of this module:

[0082] Among them, combustion strategy formulation:

[0083] 1) Coal quality parameters and load requirements: When formulating the combustion strategy, the combustion control module will first comprehensively consider the various parameters of the current coal quality, such as the calorific value, ash content, moisture, sulfur content, etc. of the coal, as well as the current load requirements of the boiler. For example, when the coal quality is poor (such as low calorific value and high ash content), it may be necessary to increase the coal feed to maintain the same heat output, but at the same time, the air volume must also be adjusted accordingly to ensure that the coal powder can be fully burned to avoid excessive unburned carbon.

[0084] 2) Optimal combustion strategy: Based on the above analysis, advanced algorithms (such as optimization algorithms, machine learning models, etc.) are used to calculate the optimal combustion strategy, which includes accurate coal feed, air volume (distribution of primary and secondary air), and burner configuration (such as burner start and stop sequence, tilt angle, etc.). For example, during peak load periods, it may be necessary to fully open all burners and increase the supply of primary and secondary air to quickly increase the heat output of the boiler; during low load periods, energy consumption can be reduced by reducing coal feed and air volume, or shutting down some burners.

[0085] Combustion process control:

[0086] 1) Intelligent actuators: To achieve the precise execution of the combustion strategy, the combustion control module relies on a series of intelligent actuators, including coal feeders, fans (primary air fan and secondary air fan), burner regulating valves, etc. These actuators can quickly and accurately adjust their respective operating states according to control instructions. For example, the coal feeder can precisely adjust the supply rate of pulverized coal according to the set coal feeding amount; the fan changes the air volume by adjusting the rotational speed, thereby controlling the oxygen concentration and air flow distribution in the furnace; the burner regulating valve can adjust the pulverized coal air flow velocity and mixing ratio at the burner outlet to achieve the best combustion effect.

[0087] 2) Precise control: By real-time monitoring of the boiler's operating parameters (such as furnace temperature, oxygen content in flue gas, etc.) and combined with the feedback adjustment of the combustion strategy, the combustion control module can achieve precise control of the boiler combustion process. This control not only helps to improve combustion efficiency but also effectively reduces pollutant emissions.

[0088] Combustion effect monitoring:

[0089] 1) Sensor monitoring: To evaluate the combustion effect, the combustion control module uses sensors installed at the boiler outlet and in the flue to real-time monitor the flue gas temperature, oxygen content, and pollutant emission parameters (such as sulfur dioxide, nitrogen oxides, carbon monoxide, etc.). These sensors can measure the relevant parameters with high precision, providing reliable data support for the evaluation of the combustion effect.

[0090] 2) Evaluating the combustion effect: Based on the data monitored by the sensors, the combustion control module can calculate key indicators such as combustion efficiency and pollutant emission concentration in real-time and evaluate the combustion effect. For example, if the oxygen content in the flue gas is too high, it indicates incomplete combustion, and it may be necessary to increase the coal feeding amount or reduce the air volume; if the pollutant emission concentration exceeds the standard, it may be necessary to adjust the burner configuration or adopt more advanced pollution control technologies.

[0091] In a preferred embodiment, the fault diagnosis and early warning module receives the unit operation status data transmitted by the data acquisition and processing module, and uses the machine learning algorithm for real-time monitoring and fault diagnosis; when an abnormality or potential fault is found, an early warning message is automatically sent to the human-computer interaction module, and transmitted to the central control unit for processing; the fault diagnosis and early warning module: based on historical fault data, real-time operating parameters and machine learning algorithms, it realizes real-time monitoring and fault diagnosis of the unit operation status, and issues early warning information in time to avoid the expansion of faults; including real-time monitoring: using sensors to monitor the unit operation status, including vibration, temperature, and pressure; fault diagnosis: based on machine learning algorithms, deeply analyze the monitoring data and identify potential fault modes; early warning and alarm: when abnormal data is monitored, an early warning message is automatically issued; when the fault reaches a certain level, the alarm mechanism is triggered to notify the operator to take emergency measures. Specifically, by real-time monitoring of the unit operation status, deep analysis using machine learning algorithms, and timely issuing early warning and alarm information, a strong guarantee is provided for the stable operation of the unit. It can reduce the equipment failure rate, extend the service life of the equipment, and improve the overall production efficiency.

[0092] The functions of the fault diagnosis and early warning module are as follows: It is responsible for monitoring the operating status of the unit or equipment, timely discovering and warning potential faults, thereby effectively avoiding production interruptions and safety accidents. It receives high-precision unit operating status data from the data acquisition and processing module, which includes but is not limited to real-time readings of various sensors, such as vibration sensors, temperature sensors, and pressure sensors. Using advanced machine learning algorithms, the module can monitor and deeply analyze these data in real time to achieve comprehensive control of the unit's operating status.

[0093] 1. Real-time monitoring-sensor monitoring:

[0094] 1) Vibration monitoring: Vibration sensors installed on key components of the unit monitor the vibration of the unit in real time during operation. Abnormal vibration often indicates problems such as wear, looseness or imbalance inside the equipment. For example, when the bearing is severely worn, its vibration frequency and amplitude will change significantly.

[0095] 2) Temperature monitoring: Temperature sensors are used to monitor temperature changes in various components of the unit. High temperatures may indicate equipment overload, cooling system failure, or poor lubrication. For example, excessively high generator winding temperatures may cause aging of the insulation material, leading to a short circuit.

[0096] 3) Pressure monitoring: Pressure sensors are used to monitor pressure changes in fluid systems (such as cooling water systems, fuel systems, etc.). Abnormal pressure may indicate problems such as system blockage, leakage or pump failure.

[0097] 2. Fault diagnosis - machine learning algorithm:

[0098] 1) Data preprocessing: First, clean, denoise, and standardize the collected raw data to improve the accuracy of subsequent analysis.

[0099] 2) Feature extraction: Extract key features from the preprocessed data that can reflect the operating state of the unit, such as vibration spectrum, temperature change trend, etc.

[0100] 3) Model training: Use historical fault data and real-time operating parameters to train machine learning models (such as support vector machines, neural networks, etc.) so that they can identify and classify different fault modes.

[0101] 4) In-depth analysis: Compare the real-time monitored data with the trained model to identify potential fault modes. For example, by analyzing specific frequency components in the vibration spectrum, it is possible to determine whether there are bearing wear or imbalance problems.

[0102] 3. Warning and alarm

[0103] 1) Warning information: When abnormal data is detected, the module will automatically send a warning message, display it to the operator through the human-machine interaction module, and at the same time transmit it to the central control unit for processing. The warning information includes the fault type, possible causes, recommended countermeasures, etc. For example, when the vibration sensor detects abnormal vibration of a certain component of the unit, the module may issue a "bearing wear warning" and recommend that the operator check and replace the bearing.

[0104] 2) Alarm mechanism: When the fault reaches a certain level, that is, the monitored data exceeds the preset threshold, the module will trigger the alarm mechanism and send an emergency alarm signal. This usually means that the equipment has or is about to have a serious fault and requires immediate shutdown and emergency measures. For example, when the temperature sensor detects a sharp rise in the temperature of the generator winding, the module may issue a "generator overheating alarm" and require the operator to immediately shut down the machine and check the cooling system.

[0105] In a preferred embodiment, the human-machine interaction module displays the operating state of the unit, the load prediction result, the combustion control strategy, and the information of fault diagnosis and warning in real time, and receives the instruction input of the operator. The operator can adjust the unit parameters and control strategy through the interface, and the instruction will be transmitted to the central control unit for distribution and execution; specifically: the human-machine interaction module displays the operating state of the unit, the load prediction result, the combustion control strategy, the fault diagnosis and warning, etc. through a high-definition, interactive interface in real time, and at the same time receives and processes the instruction input of the operator.

[0106] Function details of the human-machine interaction module method: It serves as a communication bridge between the operator and the unit, enabling the operator to monitor the unit status in real time, receive warning information, and perform remote operations and parameter adjustments as needed.

[0107] Information display - Real-time display function:

[0108] 1) Unit operating status: The operating status of each component of the unit is displayed in real time on the interface, such as the combustion condition of the boiler, the output power of the generator, the temperature of the cooling system, etc. These statuses are usually presented in the form of charts, dashboards, or dynamic icons for the operator to quickly understand the overall condition of the unit.

[0109] 2) Load prediction results: Based on the output of the load prediction module, the predicted load demand of the unit for a period of time in the future, including information such as prediction curves, peaks, and valleys, will be displayed on the interface. This helps the operator make scheduling and preparations in advance.

[0110] 3) Combustion control strategy: Displays the currently executed combustion control strategy, including the set values of parameters such as coal feeding amount, air volume, and burner configuration. The operator can view these parameters through the interface to understand the details of combustion control.

[0111] 4) Fault diagnosis and warning: When the fault diagnosis module detects an anomaly or potential fault, the relevant information will immediately pop up on the interface, accompanied by audible and visual alarms. These information include the fault type, location, severity, and recommended countermeasures.

[0112] Hardware devices:

[0113] 1) Large-screen display system: In the industrial control room, a large-screen display system (such as an LED display screen, DLP splicing screen, etc.) is usually configured so that the operator can intuitively see the operating status of the entire unit.

[0114] Touch-screen workstation: The operator can perform various operations through the interface on the touch-screen workstation, such as viewing data, adjusting parameters, etc. These workstations are usually equipped with high-resolution display screens and sensitive touch screens to ensure the accuracy and convenience of operations.

[0115] Operation control - Remote operation function:

[0116] 1) Parameter adjustment: The operator can directly adjust various parameters of the unit, such as the rotation speed of the coal feeder, the air volume of the fan, etc., through tools such as sliders and input boxes on the interface. These adjustments will take effect in real time and be fed back to the unit.

[0117] 2) Control strategy switching: The interface provides a control strategy switching option, and the operator can select the control strategy that best suits the current working conditions as needed. For example, during peak load periods, a high-efficiency but high-coal consumption strategy can be selected, and during low load periods, an energy-saving but slightly lower output strategy can be selected.

[0118] Communication module:

[0119] 1) Network communication: The human-machine interaction module communicates with the central control unit through industrial Ethernet or a dedicated communication network to ensure real-time transmission and execution of instructions.

[0120] 2) Security authentication: To ensure the security of operations, communication modules usually implement encrypted transmission and identity authentication functions to prevent unauthorized operations.

[0121] Data query and export-historical data function:

[0122] 1) Query function: The interface provides historical data query function, and operators can filter and query according to time range, data type and other conditions. It helps operators analyze the past operating status and performance of the unit.

[0123] 2) Export and print: The historical data can be exported to Excel, CSV and other formats, which is convenient for operators to conduct further analysis and processing. At the same time, the interface also provides a printer connection to print, so that data reports can be printed directly.

[0124] In a preferred embodiment, the central control unit is responsible for coordinating the work between the modules and units, receiving the data and instructions transmitted by the modules and units, integrating and processing the data, formulating the optimal control strategy, and issuing the control instructions to the corresponding modules and units for execution; and the central control unit monitors the operating status of the system to ensure the normal operation of the modules and units, and automatically adjusts or issues alarm information when an abnormality is found; the central control unit receives data and instructions from the modules and units, integrates and processes the data, and thus fully understands the operating status of the system. On this basis, the optimal control strategy is formulated using advanced algorithms, and these strategies are converted into specific control instructions, which are issued to the modules and units for execution. At the same time, the central control unit also bears the heavy responsibility of system monitoring and management, ensuring the normal operation of the modules and units, and taking timely countermeasures when an abnormality is found.

[0125] The functions of the central control unit are detailed: responsible for the overall coordination of the work between the modules and units to ensure the efficient and stable operation of the entire system. The following is a detailed expansion and specific description of the functions of the central control unit, including its data processing, intelligent decision-making, command issuance, and system monitoring. Specifically, through the functions of data integration and processing, intelligent decision-making, command issuance, and system monitoring and management, the overall control and optimization of the entire industrial automation system is achieved. The operating efficiency and economic benefits of the system are improved, and the labor intensity and work complexity of the operators are reduced.

[0126] Data integration and processing - data reception and integration:

[0127] The central control unit receives data from the data acquisition and processing module, fault diagnosis and early warning module, human-computer interaction module, etc. in real time through a high-speed communication network. The data includes unit operation status, load forecast results, combustion control parameters, fault diagnosis information, etc. The received data is integrated and processed to form a unified data format and storage structure, providing a basis for subsequent analysis and control.

[0128] Data cleaning and preprocessing:

[0129] 1) During the data cleaning phase, the central control unit will check the integrity, accuracy and consistency of the data, remove outliers and duplicate data, and ensure data quality.

[0130] 2) During the preprocessing stage, the central control unit will perform operations such as normalization and smoothing on the data to improve the accuracy and efficiency of data analysis.

[0131] Intelligent decision-making - decision-making based on data analysis:

[0132] 1) The central control unit uses advanced technologies such as big data analysis and machine learning to conduct in-depth mining and analysis of the integrated and processed data to reveal the laws and trends behind the data. Based on the data analysis results, the central control unit formulates the optimal control strategy. For example, in terms of combustion control, it will dynamically adjust the configuration and coal supply of the burner according to factors such as coal quality and load demand to achieve efficient and environmentally friendly combustion; in terms of load optimization, it will reasonably arrange the start and stop and output of the unit according to the load forecast results and unit performance to maximize economic benefits.

[0133] 2) Strategy formulation and optimization: The central control unit also has a strategy optimization function. According to the actual effect of system operation, the control strategy is continuously adjusted and optimized to ensure that the system is always in the best operating state.

[0134] Instruction issuance - generation and issuance of control instructions:

[0135] 1) The central control unit converts the optimal control strategy into specific control instructions (the instructions are generated through data feedback from other modules), such as adjusting the speed of the coal feeder, changing the air volume of the fan, switching the burner configuration, etc. The instructions are sent to each module and unit for execution in real time through the communication network. At the same time, the central control unit will also monitor the execution of the instructions to ensure that each operation is carried out as expected.

[0136] System monitoring and management - real-time monitoring of system status:

[0137] 1) The central control unit obtains the operating status information of each module and unit in real time through the monitoring network, including sensor readings, equipment working status, communication status, etc. Comprehensively analyze this information to determine whether the system is in normal operation.

[0138] 2) Exception handling and alarm:

[0139] When a system anomaly is detected, the central control unit will immediately take countermeasures. For example, for a controllable fault or anomaly that deviates from the set threshold, the relevant parameters will be automatically adjusted or the backup equipment will be switched; for a serious fault or accident (deviating from normal data), an alarm message will be immediately issued to notify the operator to handle it. The alarm message usually includes the fault type, location, severity, and recommended countermeasures, etc., to help the operator quickly locate the problem and take effective solutions.

[0140] Example description: Taking a coal-fired power plant as an example, after receiving the boiler combustion data from the data acquisition and processing module, the central control unit will use big data analysis technology to conduct in-depth mining and analysis of these data. By analyzing the changing trends and correlations of indicators such as coal quality, combustion efficiency, and flue gas emissions, the central control unit can formulate the optimal combustion control strategy. For example, in the case of poor coal quality, gradually increase the coal feed and adjust the configuration of the burner to ensure stable combustion and efficient operation of the boiler. At the same time, the central control unit will also monitor the operating status of the system in real time. Once abnormal conditions such as burner blockage and fan failure are detected, it will immediately issue an alarm message and take corresponding countermeasures to ensure the safe and stable operation of the entire system.

[0141] Finally, it should be noted that the present invention is not limited to the above embodiments, and there are many variations. All variations that can be directly derived or associated with the content disclosed by ordinary technicians in this field should be considered as the protection scope of the present invention.

Claims

1. A real-time intelligent optimization control system for full load of a unit under complex coal type mode, characterized by: It includes data acquisition and processing module, coal quality analysis module, load prediction and optimization module, combustion control module, fault diagnosis and early warning module, human-computer interaction module and central control unit. Each module is connected to another through a high-speed communication network to achieve real-time data transmission and the issuance of control instructions. The unit's full-load real-time intelligent optimization control system can monitor the unit's operating status in real time, and formulate the optimal control strategy according to changes in coal quality and load requirements to achieve efficient combustion and reduce pollutant emissions. It can also detect potential faults in a timely manner and issue warning information through the fault diagnosis and early warning module to ensure the safe and stable operation of the unit.

2. According to claim 1, a real-time intelligent optimization control system for full load of a unit under a complex coal type mode is characterized by: Data acquisition and processing module: collects coal quality parameters, operating parameters and environmental parameters of the unit in real time through the sensor network, performs cleaning, denoising and format conversion to improve data quality; Coal quality analysis module: receives and processes coal quality parameters, performs component analysis, prediction and classification, and provides key basis for combustion control and load optimization; Load forecasting and optimization module: Based on historical load data, the module uses forecasting and optimization algorithms to achieve accurate forecasting of load demand and output optimization, ensuring the balance of power grid supply and demand and maximizing energy efficiency; Combustion control module: Based on the coal quality analysis results and load optimization instructions, it uses combustion control algorithms and intelligent actuators to accurately control the boiler combustion process; Fault diagnosis and early warning module: Use machine learning algorithms to monitor the status of key components of the unit in real time, perform fault diagnosis and issue early warning information; Human-computer interaction module: displays the unit's operating status, prediction results and control strategies in real time, and receives instructions from operators; Central control unit: coordinates the work of each module, realizes centralized data processing and intelligent decision-making, and issues control instructions.

3. A real-time intelligent optimization control system for full load of a unit based on a complex coal type mode according to claim 1 or 2, characterized in that: The data acquisition and processing module transmits the data collected in real time to the central control unit for integration and processing, and the data includes unit operating parameters, coal quality parameters and environmental parameters; the data acquisition and processing module collects the unit coal quality parameters, unit operating parameters and environmental parameters in real time through the sensor network, and cleans, denoises and converts the format of the collected raw data to improve the data quality; the processed data is stored in the data warehouse for subsequent analysis and optimization; the unit operating parameters include temperature, pressure, flow, vibration, and speed; the environmental parameters include air temperature, humidity, and wind speed; the coal quality parameters include ash content, moisture content, and volatile matter.

4. According to claim 1, a real-time intelligent optimization control system for full load of a unit under a complex coal type mode is characterized by: The coal quality analysis module receives the coal quality parameters transmitted by the data acquisition and processing module, performs component analysis and prediction, and transmits the analysis results to the central control unit; Specifically include: Coal quality parameter measurement: Use chemical analysis instruments to analyze the composition of coal samples and obtain key parameters of coal: ash, moisture, volatile matter, and fixed carbon; Coal quality prediction: Based on historical coal quality parameters and unit operating parameters, machine learning algorithms are used to predict future coal quality trends; Coal quality classification: Coal types are divided into different categories based on coal quality parameters to provide a reference for combustion control and load optimization.

5. According to claim 4, a real-time intelligent optimization control system for full load of a unit under a complex coal type mode is characterized in that: The load prediction and optimization module uses prediction algorithms and optimization algorithms to perform load prediction and output optimization based on the historical load data transmitted by the data acquisition and processing module, and transmits the prediction results and optimization strategies to the central control unit to achieve accurate prediction and reasonable scheduling of future load demand of the unit; Specifically include: Load forecasting: Use time series analysis and machine learning algorithms to forecast unit load demand; Load optimization: Based on the load forecast results, combined with coal quality information and unit operating status, optimize the unit output distribution to maximize the energy efficiency of the entire plant; Load regulation: Automatically adjust the unit load according to the real-time load demand and unit output to ensure the balance of power grid supply and demand.

6. The real-time intelligent optimization control system for full load of a unit under a complex coal type mode according to claim 5 is characterized by: The combustion control module receives the combustion strategy and control instructions transmitted by the central control unit, adjusts the boiler combustion process through the intelligent actuator, and uses sensors to monitor the combustion effect, and transmits the monitoring data to the central control unit for feedback adjustment; Specifically include: Combustion strategy formulation: formulate the optimal combustion strategy based on coal quality parameters and load requirements, including coal feed, air volume, and burner configuration; Combustion process control: precise control of the boiler combustion process is achieved through intelligent actuators, including coal feeders, fans, and burner regulating valves; Combustion effect monitoring: Use sensors to monitor boiler outlet flue gas temperature, oxygen content, and pollutant emission parameters to evaluate combustion effects.

7. The real-time intelligent optimization control system for full load of a unit under a complex coal type mode according to claim 6 is characterized by: The fault diagnosis and early warning module receives the unit operation status data transmitted by the data acquisition and processing module, and uses the machine learning algorithm to perform real-time monitoring and fault diagnosis; when an abnormality or potential fault is found, it automatically sends an early warning message to the human-computer interaction module and transmits it to the central control unit for processing; Fault diagnosis and early warning module: Based on historical fault data, real-time operating parameters and machine learning algorithms, it can realize real-time monitoring and fault diagnosis of the unit's operating status, and issue early warning information in time to avoid the expansion of faults; Including real-time monitoring: using sensors to monitor the operating status of the unit, including vibration, temperature, and pressure; fault diagnosis: based on machine learning algorithms, in-depth analysis of monitoring data to identify potential fault modes; early warning and alarm: when abnormal data is monitored, early warning information is automatically issued; when the fault exceeds the preset setting, the alarm mechanism is triggered to notify the operator to take emergency measures.

8. The real-time intelligent optimization control system for full load of a unit under a complex coal type mode according to claim 7 is characterized in that: The human-computer interaction module displays the unit operation status, load forecast results, combustion control strategy, fault diagnosis and early warning information in real time, and receives the operator's command input. The operator can adjust the unit parameters and control strategy through the interface, and the command will be transmitted to the central control unit for execution; Specifically include: Information display: The screen displays the unit operating status, load forecast results, combustion control strategy, fault diagnosis and early warning information in real time; Operation control: remote operation is provided through the communication module, and the operator adjusts the unit parameters and control strategies through the interface; Data query and export: Realize the query, export and printing functions of historical data, which is convenient for operators to conduct analysis and report preparation.

9. A real-time intelligent optimization control system for full load of a unit under a complex coal type mode according to claim 1 or 8, characterized in that: The central control unit is responsible for coordinating the work between the modules and units, receiving the data and instructions transmitted by the modules and units, integrating and processing the data, formulating the optimal control strategy, and sending the control instructions to the corresponding modules and units for execution; and the central control unit monitors the operating status of the system to ensure the normal operation of the modules and units, and automatically adjusts or issues alarm information when an abnormality is found; Specifically include: Data integration and processing: Receive data transmitted by each module and unit, integrate, clean and pre-process it, and provide a basis for subsequent analysis and control; Intelligent decision-making: Based on data analysis results, formulate optimal control strategies, including combustion strategies, load optimization strategies, and fault diagnosis strategies; Instruction issuance: convert the decision results into specific control instructions and send them to each module and unit for execution; System monitoring and management: Real-time monitoring of system operation status to ensure the normal operation of each module and unit; when abnormalities are found, automatic adjustments or alarm information are issued.

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