CN117993667B - Multi-element information fusion-based generator set combustion optimization system and method - Google Patents
Multi-element information fusion-based generator set combustion optimization system and method Download PDFInfo
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Abstract
The invention relates to the technical field of generator set combustion optimization, in particular to a generator set combustion optimization system and method based on multivariate information fusion, which can realize real-time monitoring and optimization adjustment of a combustion process and improve the running stability and economy of a generator set; the method comprises the following steps: continuously collecting combustion process multiple information of the generator set according to preset time intervals, wherein the combustion process multiple information comprises fuel components, fuel supply rate, oxygen concentration, oxygen supply rate, hearth flame fluctuation rate, waste gas components, waste gas temperature and boiler hydrodynamic temperature; arranging the acquired combustion process multielement information in time sequence to construct a combustion efficiency characteristic matrix; performing combustion efficiency evaluation on the combustion efficiency characteristic matrix by using a pre-trained combustion efficiency performance evaluation model to obtain a combustion efficiency parameter of the generator set; and obtaining the generating efficiency of the generating set in a time window corresponding to the combustion efficiency characteristic matrix.
Description
Technical Field
The invention relates to the technical field of generator set combustion optimization, in particular to a generator set combustion optimization system and method based on multivariate information fusion.
Background
Under the background of constructing a novel power system, a thermal generator set is operated under a peak regulation working condition for a long time, and when a boiler is operated under a peak regulation state, the operation of the boiler is unstable and even faults occur; especially at low load operation, how to increase the combustion efficiency of the generator set and maintain its stable and efficient operation has become a vital task.
The existing combustion optimization method is often based on single or limited monitoring parameters, cannot comprehensively consider all multi-element information affecting combustion performance, and limits the fine management of the combustion process and the exploitation of optimization potential. Under low load operation, the combustion efficiency may fluctuate due to dynamic changes of combustion conditions and influences of various internal and external factors, so that the overall performance and economy of the unit are affected. Therefore, how to effectively integrate multiple information to realize accurate evaluation and optimization adjustment of the combustion process has become a technical problem to be solved.
Disclosure of Invention
In order to solve the technical problems, the invention provides the generator set combustion optimization method based on the multi-element information fusion, which can realize real-time monitoring, optimization and adjustment of the combustion process and improve the operation stability and economy of the generator set.
In a first aspect, the present invention provides a method for optimizing the combustion of a generator set based on multivariate information fusion, the method comprising:
Continuously collecting combustion process multiple information of the generator set according to preset time intervals, wherein the combustion process multiple information comprises fuel components, fuel supply rate, oxygen concentration, oxygen supply rate, hearth flame fluctuation rate, hearth temperature field, hearth negative pressure, waste gas components, waste gas temperature and boiler hydrodynamic temperature;
arranging the acquired combustion process multielement information in time sequence to construct a combustion efficiency characteristic matrix;
performing combustion efficiency evaluation on the combustion efficiency characteristic matrix by using a pre-trained combustion efficiency performance evaluation model to obtain a combustion efficiency parameter of the generator set;
Acquiring the generating efficiency of the generating set in a time window corresponding to the combustion efficiency characteristic matrix;
presetting a weight coefficient corresponding to the combustion efficiency parameter and the generating efficiency of the generator set, and carrying out weighted calculation according to the preset weight coefficient to obtain the combustion generating performance index of the generator set;
comparing the unit combustion power generation performance index with a preset threshold value: if the unit combustion power generation performance index exceeds a preset threshold, the unit combustion power generation performance index indicates that the current unit combustion state is good, and optimization adjustment is not needed; and if the unit combustion power generation performance index does not exceed the preset threshold value, indicating that the current unit combustion state is to be optimally adjusted.
In another aspect, the present application also provides a generator set combustion optimization system based on multivariate information fusion, the system comprising:
the system comprises a data acquisition module, a power generation module and a power generation module, wherein the data acquisition module is used for continuously acquiring combustion process multiple information of the power generation unit according to a preset time interval, and the combustion process multiple information comprises fuel components, fuel supply rate, oxygen concentration, oxygen supply rate, hearth flame fluctuation rate, hearth temperature field, hearth negative pressure, waste gas components, waste gas temperature and boiler hydrodynamic temperature;
The data processing module is used for arranging the acquired combustion process multiple information in time sequence to construct a combustion efficiency characteristic matrix;
The combustion efficiency evaluation model is used for evaluating the combustion efficiency of the combustion efficiency characteristic matrix by utilizing a pre-trained combustion efficiency performance evaluation model to obtain the combustion efficiency parameters of the generator set;
the generating efficiency acquisition module is used for acquiring the generating efficiency of the generating set in a time window corresponding to the combustion efficiency characteristic matrix;
the combustion performance index calculation module is used for presetting a weight coefficient corresponding to the combustion efficiency parameter and the unit power generation efficiency of the generator set, and carrying out weighted calculation according to the preset weight coefficient to obtain the unit combustion power generation performance index;
The state judging module is used for comparing the unit combustion power generation performance index with a preset threshold value: if the unit combustion power generation performance index exceeds a preset threshold, the unit combustion power generation performance index indicates that the current unit combustion state is good, and optimization adjustment is not needed; and if the unit combustion power generation performance index does not exceed the preset threshold value, indicating that the current unit combustion state is to be optimally adjusted.
In a third aspect, the present application provides an electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, the computer program when executed by the processor implementing the steps of any of the methods described above.
In a fourth aspect, the application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods described above.
Compared with the prior art, the invention has the beneficial effects that:
the invention comprehensively considers the multi-element information influencing the combustion performance, including the fuel component, the fuel supply rate, the oxygen concentration, the oxygen supply rate, the hearth flame fluctuation rate, the hearth temperature field, the hearth negative pressure, the waste gas component, the waste gas temperature, the boiler hydrodynamic temperature and the like, and can more comprehensively evaluate the state and the performance of the combustion process. And the acquired multiple information is integrated by constructing the combustion efficiency characteristic matrix, so that subsequent combustion efficiency evaluation and optimization adjustment are facilitated. The combustion efficiency is evaluated by utilizing a pre-trained combustion efficiency performance evaluation model, so that the combustion efficiency state of the generator set can be rapidly and accurately judged. The weight coefficient of the generating efficiency of the unit is introduced, so that the combustion optimization adjustment is closer to the actual operation requirement, and the economy and the operation efficiency of the unit are improved. By comparing the unit combustion power generation performance index with a preset threshold, whether combustion optimization adjustment is needed or not can be judged quickly, and timeliness and accuracy of the optimization adjustment are improved. The method has good universality and expansibility, and the variety and weight coefficient of the acquired multi-element information of the combustion process can be adjusted according to actual requirements so as to adapt to generator sets of different types and scales. The method can realize real-time monitoring and optimization adjustment of the combustion process, improves the running stability and economy of the generator set, and provides technical support and guarantee for constructing a novel power system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for optimizing the combustion of a generator set based on multivariate information fusion according to an embodiment of the present invention;
FIG. 2 is a hardware architecture diagram of an electronic device according to an embodiment of the present invention;
FIG. 3 is a block diagram of a generator set combustion optimization device based on multivariate information fusion according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a combustion efficiency characterization matrix according to an embodiment of the present invention
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a method for optimizing the combustion of a generator set based on multivariate information fusion, the method comprising:
Step S1, continuously collecting the multiple information of the combustion process of the generator set according to a preset time interval;
s2, arranging the acquired combustion process multiple information in time sequence to construct a combustion efficiency characteristic matrix;
S3, performing combustion efficiency evaluation on the combustion efficiency characteristic matrix by using a pre-trained combustion efficiency performance evaluation model to obtain a combustion efficiency parameter of the generator set;
S4, acquiring the generating efficiency of the generating set in a time window corresponding to the combustion efficiency characteristic matrix;
S5, presetting a weight coefficient corresponding to the combustion efficiency parameter and the generating efficiency of the generator set, and carrying out weighted calculation according to the preset weight coefficient to obtain the combustion generating performance index of the generator set;
S6, comparing the unit combustion power generation performance index with a preset threshold value: if the unit combustion power generation performance index exceeds a preset threshold, the unit combustion power generation performance index indicates that the current unit combustion state is good, and optimization adjustment is not needed; and if the unit combustion power generation performance index does not exceed the preset threshold value, indicating that the current unit combustion state is to be optimally adjusted.
In the present embodiment, by comprehensively considering the multiple information affecting the combustion performance, including the fuel composition, the fuel supply rate, the oxygen concentration, the oxygen supply rate, the furnace flame fluctuation rate, the furnace temperature field, the furnace negative pressure, the exhaust gas composition, the exhaust gas temperature, the boiler hydrodynamic temperature, and the like, the state and performance of the combustion process can be more comprehensively evaluated. And the acquired multiple information is integrated by constructing the combustion efficiency characteristic matrix, so that subsequent combustion efficiency evaluation and optimization adjustment are facilitated. The combustion efficiency is evaluated by utilizing a pre-trained combustion efficiency performance evaluation model, so that the combustion efficiency state of the generator set can be rapidly and accurately judged. The weight coefficient of the generating efficiency of the unit is introduced, so that the combustion optimization adjustment is closer to the actual operation requirement, and the economy and the operation efficiency of the unit are improved. By comparing the unit combustion power generation performance index with a preset threshold, whether combustion optimization adjustment is needed or not can be judged quickly, and timeliness and accuracy of the optimization adjustment are improved. The method has good universality and expansibility, and the variety and weight coefficient of the acquired multi-element information of the combustion process can be adjusted according to actual requirements so as to adapt to generator sets of different types and scales. The method can realize real-time monitoring and optimization adjustment of the combustion process, improves the running stability and economy of the generator set, and provides technical support and guarantee for constructing a novel power system.
The manner in which the individual steps shown in fig. 1 are performed is described below.
For step S1:
step S1 involves continuously collecting the combustion process multi-information of the generator set to ensure that subsequent combustion efficiency assessment and optimization adjustment can be based on sufficient data support; the implementation of this step requires consideration of the following aspects:
s11, selecting a proper sensor and monitoring equipment: in order to obtain the multiple information in the combustion process, proper sensors and monitoring equipment are required to be selected; such devices may include, but are not limited to, fuel composition analyzers, oxygen concentration sensors, temperature sensors, pressure sensors, and the like; these devices should be able to accurately collect data for various parameters during the combustion process;
S12, determining acquisition frequency and time interval: to obtain enough data to support subsequent analysis and evaluation, the frequency and time interval at which the data is acquired needs to be determined; this frequency should be high enough to capture dynamic changes in the combustion process and the time interval should be reasonable to avoid data being too dense to cause unnecessary computational burden;
s13, ensuring the accuracy and reliability of the data: when acquiring multi-element information in the combustion process, the accuracy and the reliability of data need to be ensured; this includes calibrating the sensor, maintaining the health of the monitoring device, and ensuring that the data collection process is not disturbed or damaged;
S14, covering range of the multiple information: the multiple information in the combustion process should cover various aspects affecting the combustion efficiency, such as fuel composition, supply rate, oxygen concentration, furnace temperature, exhaust gas composition, etc.; the performance of the combustion process can be comprehensively evaluated, and an effective basis is provided for subsequent optimization;
s15, designing and deploying a data acquisition system: in order to achieve continuous acquisition, a sophisticated data acquisition system needs to be designed and deployed that should be able to automatically acquire, store and manage the multiple information in the combustion process, and ensure the timeliness and integrity of the data.
More specifically, the multivariate information encompasses key parameters of the combustion process including, but not limited to:
Fuel composition: analysis of fuel chemistry, such as ash, moisture, volatiles, and fixed carbon content of coal, directly affects combustion efficiency and pollutant emissions;
Fuel supply rate: monitoring and recording the fuel flow entering the boiler, and ensuring that the fuel flow is matched with the current load requirement so as to maintain the optimal combustion condition;
Oxygen concentration and oxygen supply rate: monitoring the oxygen concentration in the hearth and the oxygen supply rate of the oxygen supply equipment so as to maintain a proper excess air coefficient and realize high-efficiency and low-pollution combustion;
Hearth flame fluctuation rate: the stability of flame in the hearth is captured and calculated through means such as a thermocouple, infrared imaging and the like, and whether the combustion is uniform or not is evaluated;
And (3) a hearth temperature field: monitoring temperature distribution of different areas of the hearth in real time, and reflecting intensity and heat distribution conditions of combustion reaction;
Hearth negative pressure: maintaining a proper furnace negative pressure helps to control the combustion process and prevents smoke from flowing backward;
Exhaust gas composition and exhaust gas temperature: analyzing the content (such as CO, NOx, SOx and the like) of each component in the discharged waste gas and the temperature of the waste gas, and taking the content and the temperature of the waste gas as important indexes of combustion effect and environmental protection performance;
boiler hydrodynamic temperature: and the temperature of key points in the steam-water system of the boiler is monitored, so that the steam quality and the circulation safety are ensured.
In the step, by collecting the multielement information, all key parameters affecting the combustion efficiency and stability are covered, the combustion process can be deeply analyzed from multiple dimensions, and the omnibearing data support is provided for subsequent evaluation and optimization; the method has the advantages that the data are continuously collected according to the preset time interval, the change of the combustion state can be monitored in real time, abnormal conditions can be found and processed in time, and the method is favorable for realizing the fine management of the combustion process; the high-precision sensor and the monitoring equipment are adopted, and the calibration and the maintenance are carried out regularly, so that the accuracy and the reliability of data are ensured, and scientific and reasonable decisions are made; the reasonable time interval and the acquisition frequency can be set according to actual demands, so that the dynamic combustion change can be captured, and the excessive data processing burden can be avoided; by monitoring environmental protection indexes such as waste gas components, temperature and the like, not only is combustion efficiency concerned, but also environmental protection is considered, and the concept of sustainable development is reflected; in summary, step S1 establishes a solid foundation for subsequent combustion performance evaluation and optimization adjustment by comprehensively, precisely acquiring multiple information in the combustion process in real time, effectively improves the combustion efficiency and stability of the thermal generator set under the low-load working condition, and simultaneously enhances the intelligent management and environmental protection performance of the whole system.
For step S2:
Step S2 involves arranging the acquired combustion process multiple information in time sequence to construct a combustion efficiency characteristic matrix; the key to this step is how to integrate the data from the different monitored parameters in order for subsequent combustion efficiency assessment and optimization adjustment; the method specifically comprises the following steps:
S21, data integration: the method comprises the steps of (1) sorting the multiple information of the combustion process of the generator set continuously collected in the step (1); such information includes, but is not limited to, key parameters such as fuel composition, fuel supply rate, oxygen concentration, oxygen supply rate, furnace flame fluctuation rate, furnace temperature field distribution (including temperature data for multiple zones), furnace negative pressure values, exhaust gas composition and exhaust gas temperature, and boiler hydrodynamic system temperature;
S22, time sequence arrangement: the numerical values of each monitoring parameter at different time points are arranged one by one according to the time sequence of the sampling time; for example, assuming data is collected every five minutes, a time-varying data sequence is formed for the "fueling rate" parameter;
S23, constructing a matrix structure: the data of all parameters at the same time point are assembled to form one row of a matrix; thus, each row represents all relevant information of the combustion process at a point in time over time; finally, the obtained matrix is a multidimensional combustion efficiency characteristic matrix, wherein the number of rows corresponds to different time stamps, and the number of columns corresponds to different combustion performance index parameters; the combustion efficiency characteristic matrix structure is shown in fig. 4; the combustion efficiency characteristic matrix has the function of providing a method for comprehensively and systematically analyzing the combustion efficiency by organizing and visualizing the change trend of each parameter along with time in the combustion process; the method is beneficial to deep mining of potential optimization space in the combustion process, and provides detailed and structured input data for application of a pre-trained combustion efficiency performance evaluation model in the subsequent step S3, so that accurate evaluation and dynamic optimization adjustment of combustion efficiency are realized.
By constructing the combustion efficiency characteristic matrix, not only the change trend of each combustion parameter along with time can be intuitively displayed, but also the inherent relevance and the coupling effect among different parameters can be revealed; the matrixing data processing mode provides rich and structured input data for a combustion efficiency performance evaluation model in a subsequent step, so that the model can accurately evaluate a combustion process and an effective optimization adjustment strategy is provided accordingly.
For step S3:
In the generator set combustion optimization method based on multi-element information fusion, the core of the step S3 is to construct a model capable of accurately evaluating combustion efficiency by utilizing a combustion efficiency characteristic matrix; the model aims at accurately quantifying and predicting the combustion efficiency of the generator set in the current state by analyzing the relevance among multiple information and the actual influence on the combustion efficiency; the specific process for constructing the combustion efficiency performance evaluation model comprises the following steps:
S31, data preparation and preprocessing: using the combustion efficiency characteristic matrix obtained in the step S2 as basic data of model training; the acquired data are cleaned, the problems of missing values, abnormal values and the like are processed, and certain nonlinear related or unevenly distributed data are normalized or standardized according to the needs;
S32, feature selection and engineering: analyzing the influence weights of all parameters on the combustion efficiency, and screening out key features which have obvious influence on the combustion efficiency; performing feature selection and dimension reduction by adopting methods such as correlation analysis, principal component analysis, recursive feature elimination and the like;
S33, establishing a model: selecting or designing a proper machine learning model for training according to the characteristics and the requirements of the problems; possible model types include, but are not limited to, linear regression, support vector machines, decision trees, random forests, neural networks, deep learning models, and the like; inputting the pre-processed and feature-selected combustion efficiency feature matrix into a selected model for training, and continuously adjusting model parameters to optimize model performance in the training process by taking combustion efficiency as a target variable;
s34, model verification and optimization: the method has the advantages that the models are verified and optimized in a cross verification mode, a grid search mode and the like, so that the models are guaranteed to have good generalization capability, and the combustion efficiency can be accurately predicted on new data; in practical application, the performance of the model is continuously monitored, and the model is iteratively updated and optimized according to new operation data.
In the step, parameters of various aspects including fuel, oxygen, hearth, waste gas, boiler hydrodynamic force and the like are considered by considering information of various dimensions in the combustion process; the comprehensive information coverage enables the model to reflect the actual condition of the combustion process more accurately, and the evaluation accuracy is improved; step S3 ensures the quality and consistency of input data through data preparation and preprocessing; the operations such as missing value, abnormal value, data normalization or standardization are processed, noise and deviation are reduced, and the prediction precision of the model is improved; the feature selection and engineering in the step S3 allows the model to adapt to different data sets and operation scenes; the model can reduce the calculation complexity and the risk of overfitting while maintaining the prediction performance by screening key features and dimension reduction processing; the step S3 is helpful for understanding the actual influence of different parameters on the combustion efficiency by analyzing the influence weight of each parameter on the combustion efficiency, and provides a basis for the subsequent combustion optimization; this interpretability helps engineers and operators to better understand the model and make reasonable decisions; the step S3 is used for constructing the combustion efficiency performance evaluation model, and has the advantages of comprehensiveness, accuracy, flexibility, interpretability, robustness and efficiency, so that the model can accurately evaluate the combustion efficiency of the generator set, and powerful support is provided for subsequent combustion optimization.
For step S4:
in the method for optimizing the combustion of the generator set based on the multivariate information fusion, the step S4 is a key link, and the main aim is to accurately calculate and extract the actual power generation efficiency of the generator set in the same time window as the combustion efficiency characteristic matrix; this step is critical to understanding the impact of the combustion process on overall power generation performance and provides the necessary data support for subsequent optimization; the detailed implementation steps are as follows:
S41, data acquisition and integration: acquiring generating capacity data in a time period corresponding to the combustion efficiency characteristic matrix from a real-time monitoring system of the power plant, and ensuring that the time periods of the generating capacity data and the combustion efficiency characteristic matrix are matched and consistent; meanwhile, key operation parameters such as a unit load instruction, actual load output power and the like in the time window are collected so as to comprehensively reflect the working state of the unit in the period;
s42, calculating the generated energy: the generated energy can be obtained by accumulating the readings of an electric energy meter at the outlet of the power grid or by recording an internal energy conversion system of the power plant, so that the accuracy and the instantaneity of the data are ensured;
s43, fuel calorific value determination: according to the fuel composition information in the combustion efficiency characteristic matrix and by combining the chemical composition and physical characteristics of the fuel, the heat value of each fuel unit mass or volume can be calculated; the difference in heating value corresponding to different fuel types and qualities can significantly affect the final power generation efficiency;
s44, fuel consumption estimation: the total fuel quantity consumed by the generator set in the time window is calculated by utilizing the fuel supply rate data and combining the working time and the actual working condition of the fuel conveying equipment;
s44, calculating and correcting the power generation efficiency: using the formula, generating efficiency = generating capacity/(fuel consumption x fuel heating value), to calculate the actual generating efficiency of the generator set; meanwhile, considering that the low-load operation is possibly influenced by factors such as ambient temperature, humidity, atmospheric pressure, internal loss of a unit and the like, proper correction and calibration are required, and the result is ensured to be closer to the real condition.
In the step, the comprehensiveness and the accuracy of information required by calculating the power generation efficiency are ensured by integrating various key operation parameters and power generation amount data in a power plant real-time monitoring system; simultaneously, the fuel heat value is determined by combining the fuel component analysis, and the fuel supply rate is utilized to estimate the consumption, so that the data quality is further improved; the power generation efficiency is evaluated by accurately calculating the power generation amount, the fuel consumption amount and the fuel heat value and adopting a scientific and reasonable formula, so that the evaluation of the actual running state of the unit is more refined, and the internal connection between the combustion process and the overall power generation performance is facilitated to be identified; environmental factors (such as temperature, humidity, atmospheric pressure and the like) which possibly influence the power generation efficiency under the low-load working condition are considered, and correction and calibration are carried out, so that the calculated power generation efficiency is closer to the actual condition, and the adaptability of the model to the complex running condition is enhanced; the power generation efficiency data obtained through the step can provide important basis for subsequent combustion optimization, and technicians can pertinently adjust the combustion strategy according to the change condition of the power generation efficiency parameters, so that the combustion efficiency and the operation stability of the unit under peak shaving and low-load working conditions are improved.
For step S5:
In the generator set combustion optimization method based on multi-element information fusion, the step S5 is a key link, and the main aim is to comprehensively consider the generator set combustion efficiency parameter and the actual power generation efficiency, and evaluate the overall combustion performance by setting the corresponding weight coefficient; the method comprises the following specific steps:
And (5) setting a weight coefficient: presetting a combustion efficiency parameter of a generator set and a weight coefficient corresponding to the generating efficiency of the generator set according to actual conditions and actual demands; the weight coefficients reflect the importance degree of the combustion efficiency and the power generation efficiency in evaluating the overall performance of the unit; generally, the weight coefficients are set based on comprehensive consideration of expert experience, historical data analysis, performance requirements and other aspects; the setting of the weight coefficient is required to follow a certain principle; firstly, they should reflect the actual needs and objectives of the operation of the unit; for example, if the main goal of the unit is to increase the power generation efficiency, the weight coefficient of the power generation efficiency of the unit should be set relatively high; secondly, the setting of the weight coefficient should have rationality and operability, avoiding being too complex or difficult to implement;
The combustion efficiency parameter is integrated with the power generation efficiency: in step S3, obtaining a generator set combustion efficiency parameter through a combustion efficiency performance evaluation model, and in step S4, obtaining generator set power generation efficiency data in a corresponding time window; the method comprises the steps that the combustion efficiency parameters of the generator set and the generating efficiency of the generator set are weighted and summed according to a preset weight coefficient to obtain a combustion generating performance index of the generator set; the unit combustion power generation performance index is a comprehensive index, comprehensively considers two factors of combustion efficiency and power generation efficiency, and can reflect the combustion power generation performance of the unit more comprehensively; by comparing the index with a preset threshold value, whether the combustion state of the current unit is good or not can be judged, and whether optimization adjustment is needed or not can be judged.
In summary, step S5 is an important step in the method for optimizing the combustion of the generator set, and it combines the combustion efficiency and the power generation efficiency by setting the weight coefficient and performing the weighted calculation, so as to provide a powerful tool for evaluating and optimizing the combustion power generation performance of the generator set; by reasonably setting the weight coefficient and applying the weighted calculation method, the combustion power generation performance of the unit can be estimated more comprehensively and accurately, and a scientific basis is provided for subsequent optimization and adjustment.
For step S6:
In the generator set combustion optimization method based on multi-element information fusion, step S6 is a decision stage in the whole optimization flow, and the core objective is to judge whether the current combustion state reaches an ideal level according to the previously calculated generator set combustion power generation performance index, and accordingly make a corresponding optimization adjustment strategy; the specific implementation mode is as follows:
S61, setting a threshold value: the preset threshold is determined based on a large amount of experimental data, theoretical research and actual operation experience, and reflects the ideal stability and high efficiency level which can be achieved by the combustion system under the low-load peak-shaving working condition; the preset threshold represents the lowest standard of the combustion power generation performance index which the unit should reach during normal operation;
S62, calculating a combustion power generation performance index of the unit: in step S5, the unit combustion power generation performance index has been obtained through the weighting calculation; the combustion efficiency and the power generation efficiency of the unit are comprehensively considered by the unit combustion power generation performance index, and the unit combustion power generation performance index is a comprehensive evaluation index;
s63, comparing and analyzing: comparing the calculated unit combustion power generation performance index with a preset threshold value; the purpose of this comparison is to determine whether the current combustion state of the unit meets the preset performance requirements:
If the unit combustion power generation performance index exceeds a preset threshold, the unit is good in the current combustion state, and all performance parameters reach the expected requirements, in this case, no further optimization adjustment is needed, the unit can be kept to continue to operate under the current working condition, and meanwhile, the operation state of the unit still needs to be continuously monitored, so that the stability of the performance is ensured;
If the unit combustion power generation performance index does not reach the preset threshold value, the current combustion state of the unit is indicated to have a problem, or the performance of the unit is still further improved, in which case, the unit needs to be subjected to combustion optimization adjustment, and specific optimization measures may include adjusting fuel supply rate, optimizing oxygen supply, adjusting a hearth temperature field, improving hearth negative pressure and the like, which aims at improving combustion conditions, improving combustion efficiency, and thus improving the power generation efficiency and overall performance of the unit;
s64, continuous optimization and monitoring: step S6 is not just a one-time comparison and decision process, but a continuous process, and as the running state of the unit changes, the combustion and power generation performance index of the unit needs to be continuously re-evaluated and compared with a preset threshold value, so as to ensure that the unit is always in an optimal running state.
In the step, the actual performance of the unit under different operation conditions can be reflected in time by calculating and comparing the combustion power generation performance index with a preset threshold in real time or periodically, and the dynamic evaluation of the system state is realized, so that rapid response to change is facilitated, and flexible adjustment is realized; the preset threshold value is set based on a large amount of experimental data, theoretical research and practical experience, has higher credibility and authority, and provides scientific and reasonable reference for judging whether the unit needs to be optimized; the method not only makes a decision of whether to optimize or not according to the current state, but also emphasizes the importance of long-term monitoring, is beneficial to finding potential problems in advance, avoids sudden faults, simultaneously continuously digs and releases the potential of the unit, and improves the overall operation efficiency; the multiple dimensions such as combustion efficiency, power generation efficiency and the like are comprehensively considered, so that the optimization measures are more comprehensive, each influence factor can be effectively balanced, and accurate control and efficient operation are realized; the step forms an important feedback link in the whole optimization flow, forms a closed-loop control system from monitoring, analysis to decision making, execution to re-monitoring, and is beneficial to self-correction and continuous optimization of the system.
As shown in fig. 2 and 3, the embodiment of the invention provides a generator set combustion optimization system based on multi-element information fusion. The system embodiment may be implemented by software, or may be implemented by hardware or a combination of hardware and software. In terms of hardware, as shown in fig. 2, a hardware architecture diagram of an electronic device where a generator set combustion optimization system based on multi-element information fusion is provided in an embodiment of the present invention, in addition to a processor, a memory, a network interface, and a nonvolatile memory shown in fig. 2, the electronic device where the system is located in the embodiment may generally include other hardware, such as a forwarding chip responsible for processing a message, and so on. Taking a software implementation as an example, as shown in fig. 3, the system in a logic sense is formed by reading a corresponding computer program in a nonvolatile memory into a memory through a CPU of an electronic device where the system is located.
As shown in fig. 3, the generating set combustion optimizing system based on multivariate information fusion provided in this embodiment includes:
the system comprises a data acquisition module, a power generation module and a power generation module, wherein the data acquisition module is used for continuously acquiring combustion process multiple information of the power generation unit according to a preset time interval, and the combustion process multiple information comprises fuel components, fuel supply rate, oxygen concentration, oxygen supply rate, hearth flame fluctuation rate, hearth temperature field, hearth negative pressure, waste gas components, waste gas temperature and boiler hydrodynamic temperature;
The data processing module is used for arranging the acquired combustion process multiple information in time sequence to construct a combustion efficiency characteristic matrix;
The combustion efficiency evaluation model is used for evaluating the combustion efficiency of the combustion efficiency characteristic matrix by utilizing a pre-trained combustion efficiency performance evaluation model to obtain the combustion efficiency parameters of the generator set;
the generating efficiency acquisition module is used for acquiring the generating efficiency of the generating set in a time window corresponding to the combustion efficiency characteristic matrix;
the combustion performance index calculation module is used for presetting a weight coefficient corresponding to the combustion efficiency parameter and the unit power generation efficiency of the generator set, and carrying out weighted calculation according to the preset weight coefficient to obtain the unit combustion power generation performance index;
the state judging module compares the unit combustion power generation performance index with a preset threshold value: if the unit combustion power generation performance index exceeds a preset threshold, the unit combustion power generation performance index indicates that the current unit combustion state is good, and optimization adjustment is not needed; and if the unit combustion power generation performance index does not exceed the preset threshold value, indicating that the current unit combustion state is to be optimally adjusted.
In the embodiment, the system can acquire the multi-element information in the combustion process in real time and continuously through the data acquisition module, so that a plurality of key dimensions such as fuel characteristics, oxygen supply conditions, hearth states, exhaust emission, boiler hydrodynamic force and the like are covered, and the actual combustion conditions can be reflected more comprehensively; the data processing module integrates the multiple information into a combustion efficiency characteristic matrix, and can accurately evaluate the combustion efficiency by combining a pre-trained combustion efficiency evaluation model to obtain combustion efficiency parameters; the multi-dimensional and multi-level data processing mode is beneficial to deep mining of potential optimization space in the combustion process; the system can calculate the combustion performance index according to the generating efficiency and the combustion efficiency parameters of the unit, and realize real-time monitoring and automatic judgment of the combustion state of the unit by comparing the state judgment module with a preset threshold value; when the combustion performance index does not reach the expectation, the system can rapidly identify and prompt that the optimization adjustment is needed, and the dynamic property and the flexibility of the combustion management are effectively improved; the combustion performance index calculation module adopts a weighting coefficient weighting mode, and can carry out quantization weighting according to the influence degree of different factors on the combustion efficiency, so that the evaluation result is more in line with the actual operation requirement, and a more scientific and reasonable combustion optimization strategy is facilitated to be realized; by the system, the combustion efficiency can be improved on the premise of ensuring the combustion stability, so that the working efficiency and the economic benefit of the whole generator set are improved, meanwhile, the fault risk caused by unstable combustion is reduced, and the reliability and the safety of the system are enhanced; in conclusion, the system has important advantages in solving the problems of long-term peak regulation working condition operation, unstable boiler operation, fluctuation of combustion efficiency and the like of the thermal generator set, can realize comprehensive monitoring, fine management and real-time optimization adjustment of the combustion process of the thermal generator set, and improves the stability, reliability and economy of the system.
It will be appreciated that the structure illustrated in the embodiments of the present invention does not constitute a specific limitation of a multiple information fusion-based combustion optimization system for a generator set. In other embodiments of the invention, a genset combustion optimization system based on multivariate information fusion may include more or fewer components than shown, or may combine certain components, or may split certain components, or may have a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
The content of information interaction and execution process between the modules in the system is based on the same concept as the method embodiment of the present invention, and specific content can be referred to the description in the method embodiment of the present invention, which is not repeated here.
The embodiment of the invention also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method for optimizing the combustion of the generator set based on the multi-element information fusion in any embodiment of the invention is realized.
The embodiment of the invention also provides a computer readable storage medium, and the computer readable storage medium is stored with a computer program, when the computer program is executed by a processor, the processor is caused to execute the generator set combustion optimization method based on the multivariate information fusion in any embodiment of the invention.
Specifically, a system or apparatus provided with a storage medium on which a software program code realizing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or apparatus may be caused to read out and execute the program code stored in the storage medium.
In this case, the program code itself read from the storage medium may realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code form part of the present invention.
Examples of storage media for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (e.g., CD-ROMs, CD-R, CD-RWs, DVD-ROMs, DVD-RAMs, DVD-RWs, DVD+RWs), magnetic tapes, nonvolatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer by a communication network.
Further, it should be apparent that the functions of any of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform part or all of the actual operations based on the instructions of the program code.
Further, it is understood that the program code read out by the storage medium is written into a memory provided in an expansion board inserted into a computer or into a memory provided in an expansion module connected to the computer, and then a CPU or the like mounted on the expansion board or the expansion module is caused to perform part and all of actual operations based on instructions of the program code, thereby realizing the functions of any of the above embodiments.
It is noted that relational terms such as first and second, and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: various media in which program code may be stored, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. The utility model provides a generating set combustion optimization method based on multivariate information fusion which is characterized in that the method comprises the following steps:
continuously collecting the multiple information of the combustion process of the generator set according to a preset time interval;
arranging the acquired combustion process multielement information in time sequence to construct a combustion efficiency characteristic matrix;
performing combustion efficiency evaluation on the combustion efficiency characteristic matrix by using a pre-trained combustion efficiency performance evaluation model to obtain a combustion efficiency parameter of the generator set;
Acquiring the generating efficiency of the generating set in a time window corresponding to the combustion efficiency characteristic matrix;
presetting a weight coefficient corresponding to the combustion efficiency parameter and the generating efficiency of the generator set, and carrying out weighted calculation according to the preset weight coefficient to obtain the combustion generating performance index of the generator set;
Comparing the unit combustion power generation performance index with a preset threshold value: if the unit combustion power generation performance index exceeds a preset threshold, the unit combustion power generation performance index indicates that the current unit combustion state is good, and optimization adjustment is not needed; if the unit combustion power generation performance index does not exceed the preset threshold value, indicating that the current unit combustion state is to be optimally adjusted;
The combustion process multiple information comprises fuel components, fuel supply rate, oxygen concentration, oxygen supply rate, hearth flame fluctuation rate, hearth temperature field, hearth negative pressure, waste gas components, waste gas temperature and boiler hydrodynamic temperature;
In the combustion efficiency characteristic matrix, the combustion process multiple information acquired at the same data acquisition time point is positioned in the same row; the combustion process multiple information of the same type is positioned in the same column, and the combustion process multiple information of different data acquisition time points in the same column are arranged in time sequence;
the construction method of the combustion efficiency performance evaluation model comprises the following steps:
using the combustion efficiency characteristic matrix as basic data for training a combustion efficiency performance evaluation model;
Analyzing the influence weights of the parameters on the combustion efficiency, and screening out the characteristics that the influence on the combustion efficiency exceeds a set weight threshold;
Selecting a machine learning model, inputting the preprocessed and feature-selected combustion efficiency feature matrix into the selected model for training, and taking the combustion efficiency as a target variable;
and verifying and optimizing the model, and iteratively updating and optimizing the model according to the new operation data.
2. The method for optimizing the combustion of a generator set based on multi-element information fusion according to claim 1, wherein the method for obtaining the generating efficiency of the generator set comprises the following steps:
acquiring generating capacity data in a time period corresponding to the combustion efficiency characteristic matrix;
Calculating the heat value of the unit mass of the fuel according to the fuel composition information in the combustion efficiency characteristic matrix and by combining the chemical composition and the physical characteristics of the fuel;
calculating the total fuel consumption of the generator set in the time window by using the fuel supply rate data and combining the working time and the actual working condition of the fuel conveying equipment;
The actual power generation efficiency of the generator set is calculated, and the calculation formula is as follows: power generation efficiency=power generation amount/(fuel consumption amount×fuel heat value).
3. The method for optimizing the combustion of the generator set based on the multivariate information fusion according to claim 2, wherein the weight coefficients corresponding to the combustion efficiency parameters and the generating efficiency of the generator set are set based on comprehensive consideration of expert experience, historical data analysis and performance requirements of the generator set.
4. The method for optimizing the combustion of a generator set based on multivariate information fusion according to claim 1, wherein the machine learning model adopts one of linear regression, a support vector machine, a decision tree, a random forest, a neural network or a deep learning model.
5. A multiple information fusion-based generator set combustion optimization system, wherein the system is applied to the multiple information fusion-based generator set combustion optimization method as claimed in claim 1, and the system comprises:
the system comprises a data acquisition module, a power generation module and a power generation module, wherein the data acquisition module is used for continuously acquiring combustion process multiple information of the power generation unit according to a preset time interval, and the combustion process multiple information comprises fuel components, fuel supply rate, oxygen concentration, oxygen supply rate, hearth flame fluctuation rate, hearth temperature field, hearth negative pressure, waste gas components, waste gas temperature and boiler hydrodynamic temperature;
The data processing module is used for arranging the acquired combustion process multiple information in time sequence to construct a combustion efficiency characteristic matrix;
The combustion efficiency evaluation model is used for evaluating the combustion efficiency of the combustion efficiency characteristic matrix by utilizing a pre-trained combustion efficiency performance evaluation model to obtain the combustion efficiency parameters of the generator set;
the generating efficiency acquisition module is used for acquiring the generating efficiency of the generating set in a time window corresponding to the combustion efficiency characteristic matrix;
the combustion performance index calculation module is used for presetting a weight coefficient corresponding to the combustion efficiency parameter and the unit power generation efficiency of the generator set, and carrying out weighted calculation according to the preset weight coefficient to obtain the unit combustion power generation performance index;
the state judging module compares the unit combustion power generation performance index with a preset threshold value: if the unit combustion power generation performance index exceeds a preset threshold, the unit combustion power generation performance index indicates that the current unit combustion state is good, and optimization adjustment is not needed; and if the unit combustion power generation performance index does not exceed the preset threshold value, indicating that the current unit combustion state is to be optimally adjusted.
6. A generator set combustion optimization electronic device based on multivariate information fusion, comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, characterized in that the computer program when executed by the processor realizes the steps in the method according to any of claims 1-4.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1-4.
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