CN111651938A - Variable coal quality unit output prediction method based on thermodynamic calculation and big data - Google Patents
Variable coal quality unit output prediction method based on thermodynamic calculation and big data Download PDFInfo
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Abstract
The invention discloses a coal-fired boiler unit output research technical field, in particular to a coal-quality unit output prediction method based on thermodynamic calculation and big data, which comprises the following steps: s1: preprocessing the historical operating data, removing abnormal data, and S2: and analyzing the data by using an average influence value method, and acquiring main factors influencing the output of the unit, namely the main steam flow and related secondary factors. The output prediction method of the coal quality varying unit based on the thermal computation and the big data has the advantages of being more accurate, more intelligent and more reliable by taking historical operating data as reference, and by utilizing a sliding window method, when new data samples enter a model, the oldest samples are directly removed, model parameters are updated, so that the accuracy of the model can be guaranteed, and compared with model reconstruction, the computation amount and the time for model updating are greatly reduced.
Description
Technical Field
The invention relates to the field of output research of coal-fired boiler units, in particular to a coal-quality-variable unit output prediction method based on thermodynamic calculation and big data.
Background
Under the background of the current big data era, big data analysis technology is widely applied to various social fields. The information and digitalization levels of coal-fired power stations in China are greatly improved, and Distributed Control Systems (DCS), plant information monitoring systems (SIS), Management Information Systems (MIS) and the like are generally applied to coal-fired power stations. A large amount of actual operation data of power plants generated by the systems are mostly idle, and the data lack deep utilization, so that resource waste is caused. Therefore, the invention provides a variable coal quality unit output prediction method based on thermal calculation and big data, and the method can fully utilize massive actual operation data of a power plant and establish a relevant model to predict the unit output. By using a sliding window method, when a new data sample enters a model, the oldest sample is directly removed, and model parameters are updated. Therefore, the accuracy of the model can be guaranteed, compared with model reconstruction, the calculated amount and the time for updating the model are greatly reduced, convenience is provided for power plant output prediction, and guidance is provided for power grid dispatching.
Disclosure of Invention
The method fully utilizes massive actual operation data in a power plant Distributed Control System (DCS), a plant-level information monitoring system (SIS) and a Management Information System (MIS), and firstly preprocesses the data and eliminates abnormal data; analyzing the data by using an average influence value method, and acquiring main factors influencing the output of the unit, namely main steam flow and related secondary factors; then, according to coal quality analysis, performing overall thermodynamic calculation based on a checking method on the existing boiler, and predicting main steam flow of different coal types for combustion of the existing boiler; and finally, establishing a theoretical output prediction model based on a feedforward type training algorithm, taking historical operation data of relevant factors influencing the output of the unit as input variables of the model, and taking the output of the unit as output variables of the model. And selecting the optimal number of hidden layer neurons through a contrast test, determining a network structure, and optimizing a weight threshold of the network by using a genetic algorithm. And predicting the output of the unit by using the optimized model. By using a sliding window method, when a new data sample enters a model, the oldest sample is directly removed, and model parameters are updated. Therefore, the accuracy of the model can be guaranteed, compared with model reconstruction, the calculated amount and the time for updating the model are greatly reduced, convenience is provided for power plant output prediction, and guidance is provided for power grid dispatching. Compared with the traditional method, the method provided by the invention has the advantages of more accuracy, more intelligence and more reliability by taking historical operating data as a reference.
To solve the above technical problem, according to an aspect of the present invention, the present invention provides the following technical solutions:
the invention provides a coal quality changing unit output prediction method based on thermodynamic calculation and big data, which comprises the following steps:
s1: preprocessing the historical operating data and eliminating abnormal data;
s2: analyzing data by using an average influence value method, and acquiring main factors influencing the output of the unit, namely main steam flow and related secondary factors;
s3: according to coal quality analysis, performing overall thermodynamic calculation based on a checking method on the existing boiler, and predicting main steam flow of different coal types for combustion of the existing boiler;
s4: establishing a theoretical output prediction model based on a feedforward type training algorithm, taking historical operation data of relevant factors influencing the output of a unit as an input variable of the model, and taking the output of the unit as an output variable of the model;
s5: selecting the optimal number of hidden layer neurons through a contrast test, determining a network structure, and optimizing a weight threshold of the network by using a genetic algorithm;
s6: performing performance evaluation on the actual optimization model by adopting a test set;
s7: and inputting data obtained by thermal calculation into a calculation model to obtain the unit output under the condition of coal quality change.
Preferably, the sources of the historical operation data in step S1 include a power plant distributed control system, a plant-level information monitoring system, a management information system, and a monitoring information system.
Preferably, the preprocessing of the historical operating data in step S1 is to perform abnormal data elimination on the historical operating data according to a rale criterion, and perform normalization processing.
Preferably, in the step S2, the data is analyzed and processed by using an average influence value method, and the obtained main steam flow is a decisive factor affecting the unit output and other related factors affecting the unit output less.
Preferably, in the step S4, a genetic algorithm is used to optimize a weight threshold of the network, so as to improve accuracy of the prediction model.
Preferably, the output prediction method includes the following steps:
a module for preprocessing the historical operating data, eliminating abnormal data and carrying out normalization processing;
the module is used for analyzing and processing the data and acquiring specific relevant factors influencing the output of the unit;
module for performing overall thermodynamic calculation based on checking method on existing boiler according to coal quality analysis and predicting main steam flow of different coal types for combustion of existing boiler
A module for establishing a theoretical unit output prediction model based on a feedforward type training algorithm, taking historical operation data of relevant factors influencing the unit output as input variables of the model, and taking the unit output as output variables of the model;
selecting the optimal number of hidden layer neurons through a contrast test, and determining a module of a network structure;
a module for optimizing the weight threshold of the network by using a genetic algorithm;
the module is used for evaluating the performance of the actual optimization model by adopting the test set;
and the prediction module is used for calculating the unit output under the condition of coal quality change.
Compared with the prior art: (1) based on big data analysis, a large amount of idle historical operation data values of the power plant are fully mined, resources are effectively utilized, and resource waste is reduced.
(2) By utilizing the overall thermodynamic calculation of the boiler, the parameter values under the condition of changing the coal quality are calculated, and the problem of lack of unknown coal data quantity is avoided.
(3) The method comprises the steps of establishing a unit output prediction system model by adopting a big data modeling theory, directly removing the oldest sample and updating model parameters when new data samples enter the model by utilizing a sliding window method. Therefore, the accuracy of the model can be ensured, and compared with model reconstruction, the calculation amount and the time for updating the model are greatly reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and detailed embodiments, wherein:
FIG. 1 is a flow chart of a big data based crew contribution prediction method of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described herein, and it will be apparent to those of ordinary skill in the art that the present invention may be practiced without departing from the spirit and scope of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
With reference to fig. 1, the present invention provides a coal quality unit output prediction method based on thermodynamic calculation and big data, wherein the method includes:
s1: and preprocessing the historical operating data and eliminating abnormal data.
The historical operating data extracted from various sources are not uniform in format, and the data content is complicated and disordered, so that the data cannot be directly applied, and the data needs to be preprocessed. The step of preprocessing the historical operating data refers to the step of performing data cleaning, data integration, data transformation and data specification on the historical operating data.
S2: and analyzing the data by using an average influence value method, and acquiring main factors influencing the output of the unit, namely the main steam flow and related secondary factors.
According to the content, the preprocessed data volume is still large, particularly the coal-fired power plant equipment is numerous, even if only parameter data under a stable working condition are selected, the calculation speed is higher and better when the subsequent optimization model needs to respond to actual requirements in the running process, and correspondingly, the calculation quantity is smaller and better, so that the data are analyzed and calculated by utilizing an average influence value method capable of directly reflecting the influence degree of each variable on the dependent variable. After data provided by a power station system are processed and analyzed, the obtained main steam flow is a decisive factor influencing the output of a unit.
S3: according to the coal quality analysis, the overall thermodynamic calculation based on the checking method is carried out on the existing boiler, and the main steam flow of different coal types for burning of the existing boiler is predicted.
The Matlab language is used for programming, and the whole calculation process is divided into eight modules in total, namely a structure calculation module, a fuel calculation module, a combustion-generated flue gas characteristic module, a flue gas enthalpy thermometer module, a water vapor enthalpy value temperature calculation module, a boiler heat balance module, a heating surface heat exchange calculation module and an alarm program module added in consideration of restriction factors. According to the flow of the smoke and the steam water of the boiler, program calculation is carried out, related modules are called, finally, the smoke exhaust temperature is solved, the thermal efficiency of the boiler is obtained, and the main steam flow of the boiler is predicted.
S4: establishing a theoretical output prediction model based on a feedforward type training algorithm, taking historical operation data of relevant factors influencing the output of a unit as an input variable of the model, and taking the output of the unit as an output variable of the model;
the modeling data after the dimension reduction of the average influence value is utilized, a feedforward type training algorithm is adopted to establish an initial optimization model, and the initial optimization model established by the method can fully utilize historical operating data.
S5: selecting the optimal number of hidden layer neurons through a contrast test, determining a network structure, and optimizing a weight threshold of the network by using a genetic algorithm;
and analyzing the relative error and the root mean square error of the output value and the expected value under different hidden layer neuron numbers through experimental comparison, and finally selecting an optimal hidden layer neuron number optimization model. And after the weight and the threshold of the network are optimized by using a genetic algorithm, the accuracy of model prediction is improved.
S6: and (4) evaluating the performance of the actual optimization model by adopting a test set.
And if the evaluation score obtained by evaluating the performance of the actual optimization model by adopting the test set is lower than the set performance evaluation score threshold value, judging that the actual optimization model is not audited, returning to the step S5 to search the global optimal solution of the parameters in the initial optimization model again, otherwise, judging that the actual optimization model is audited to be passed, and putting the actual optimization model into actual operation.
S7: and inputting data obtained by thermal calculation into a calculation model to obtain the unit output under the condition of coal quality change.
The main steam flow and other related parameter values under the condition of coal quality change are obtained by thermal calculation and input into a prediction model to obtain the unit output under the condition of coal quality change.
Based on the method, the invention also provides a coal quality unit output prediction method based on thermodynamic calculation and big data, and the prediction method comprises the following modules:
1) a module for preprocessing the historical operating data, eliminating abnormal data and carrying out normalization processing;
2) the module is used for analyzing and processing the data and acquiring relevant factors influencing the specific output of the unit;
3) a module for performing overall thermodynamic calculation based on a checking method on the existing boiler according to coal quality analysis and predicting main steam flow of different coal types for combustion of the existing boiler;
4) a module for establishing a theoretical unit output prediction model based on a feedforward type training algorithm, taking historical operation data of relevant factors influencing the unit output as input variables of the model, and taking the unit output as output variables of the model;
5) selecting the optimal number of hidden layer neurons through a contrast test, and determining a module of a network structure;
6) a module for optimizing the weight threshold of the network by using a genetic algorithm;
7) the module is used for evaluating the performance of the actual optimization model by adopting the test set;
8) and the prediction module is used for calculating the unit output under the condition of coal quality change.
The implementation case carries out model establishment on the Jingtai power plant unit, and the method for establishing the unit output prediction model of the implementation case comprises the following specific steps:
1) establishing a theoretical model according to theoretical analysis and actual operation conditions, and selecting actual operation data from DCS, MIS and SIS systems of the coal-fired power plant unit;
2) preprocessing the historical operating data and eliminating abnormal data;
3) analyzing data by using an average influence value method, and acquiring main factors influencing the output of the unit, namely main steam flow and related secondary factors;
4) according to coal quality analysis, performing overall thermodynamic calculation based on a checking method on the existing boiler, and predicting main steam flow of different coal types for combustion of the existing boiler;
5) establishing a theoretical output prediction model based on a feedforward type training algorithm, taking historical operation data of relevant factors influencing the output of a unit as an input variable of the model, and taking the output of the unit as an output variable of the model;
6) selecting the optimal number of hidden layer neurons through a contrast test, determining a network structure, and optimizing a weight threshold of the network by using a genetic algorithm;
7) performing performance evaluation on the actual optimization model by adopting a test set;
8) and inputting data obtained by thermal calculation into a calculation model to obtain the unit output under the condition of coal quality change.
While the invention has been described above with reference to an embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the various features of the disclosed embodiments of the invention may be used in any combination, provided that no structural conflict exists, and the combinations are not exhaustively described in this specification merely for the sake of brevity and resource conservation. Therefore, it is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (6)
1. The coal quality unit output prediction method based on thermal calculation and big data is characterized by comprising the following steps:
s1: preprocessing the historical operating data and eliminating abnormal data;
s2: analyzing data by using an average influence value method, and acquiring main factors influencing the output of the unit, namely main steam flow and related secondary factors;
s3: according to coal quality analysis, performing overall thermodynamic calculation based on a checking method on the existing boiler, and predicting main steam flow of different coal types for combustion of the existing boiler;
s4: establishing a theoretical output prediction model based on a feedforward type training algorithm, taking historical operation data of relevant factors influencing the output of a unit as an input variable of the model, and taking the output of the unit as an output variable of the model;
s5: selecting the optimal number of hidden layer neurons through a contrast test, determining a network structure, and optimizing a weight threshold of the network by using a genetic algorithm;
s6: performing performance evaluation on the actual optimization model by adopting a test set;
s7: and inputting data obtained by thermal calculation into a calculation model to obtain the unit output under the condition of coal quality change.
2. The method of claim 1, wherein the sources of the historical operating data in the step S1 include a power plant distributed control system, a plant-level information monitoring system, a management information system, and a monitoring information system.
3. The method for predicting the output of the coal-varying unit based on the thermal computation and the big data as claimed in claim 1, wherein the step S1 of preprocessing the historical operation data is to perform abnormal data elimination and normalization processing on the historical operation data according to the rales criterion.
4. The method as claimed in claim 1, wherein the step S2 is performed by analyzing and processing the data by using an average influence value method, and the obtained main steam flow is a determining factor affecting the output of the coal-to-liquids unit and other related factors having smaller influence.
5. The variable coal quality unit output prediction method based on thermal computation and big data as claimed in claim 1, wherein in step S4, a weight threshold of a network is optimized by using a genetic algorithm, so as to improve the accuracy of the prediction model.
6. The variable coal quality unit output prediction method based on thermal computation and big data as claimed in claim 1, wherein the output prediction method comprises the following parts:
a module for preprocessing the historical operating data, eliminating abnormal data and carrying out normalization processing;
the module is used for analyzing and processing the data and acquiring specific relevant factors influencing the output of the unit;
module for performing overall thermodynamic calculation based on checking method on existing boiler according to coal quality analysis and predicting main steam flow of different coal types for combustion of existing boiler
A module for establishing a theoretical unit output prediction model based on a feedforward type training algorithm, taking historical operation data of relevant factors influencing the unit output as input variables of the model, and taking the unit output as output variables of the model;
selecting the optimal number of hidden layer neurons through a contrast test, and determining a module of a network structure;
a module for optimizing the weight threshold of the network by using a genetic algorithm;
the module is used for evaluating the performance of the actual optimization model by adopting the test set;
and the prediction module is used for calculating the unit output under the condition of coal quality change.
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