CN117688307A - Intelligent monitoring method and system for state evaluation of coal-fired unit - Google Patents
Intelligent monitoring method and system for state evaluation of coal-fired unit Download PDFInfo
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Abstract
The application discloses an intelligent monitoring method and system for state evaluation of a coal-fired unit, which relate to the technical field of coal-fired units and are used for preprocessing data by acquiring historical data and real-time data related to the running state of the coal-fired unit; performing state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting a state evaluation method of experience and rule scoring to obtain a first evaluation result; carrying out accurate and comprehensive state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting an ANFIS evaluation algorithm optimized through a PSO algorithm to obtain a second evaluation result; synthesizing the first evaluation result and the second evaluation result to obtain a comprehensive evaluation result; and sending the comprehensive evaluation result to a display interface for display. According to the method, the running state of the coal-fired unit is comprehensively estimated in real time by adopting a machine learning algorithm on the basis of the traditional state estimation, so that the running state of the coal-fired unit can be accurately mastered, and the omnibearing intelligent monitoring of the coal-fired unit can be realized.
Description
Technical Field
The application relates to the technical field of coal-fired units, in particular to an intelligent monitoring method and system for state evaluation of a coal-fired unit.
Background
The coal-fired unit is used as the main force of power supply and plays an important role in electric energy supply and dispatching operation of a power grid. The coal-fired unit is a strong-coupling, multivariable and nonlinear complex system, and different components are mutually coupled, so that accurate modeling is difficult to realize.
The traditional coal-fired unit state evaluation method generally adopts an empirical and rule scoring mode for evaluation, and mainly has the following defects:
false alarm problem: since the conventional method mainly depends on the experience and rules of the expert, the problem of false alarm is easy to occur. This is because the expert's experience is limited and may not be able to cover all fault conditions or there is a deviation in subjective judgment during the evaluation. False alarms can lead to unnecessary maintenance and downtime, increasing maintenance costs and production losses.
The workload is huge: the traditional method needs to check and evaluate each coal-fired unit one by one, which requires a great deal of manpower and time, and especially for large-scale power plants, the units are numerous, and the workload is very huge. Moreover, due to the existence of human factors, the accuracy of the evaluation result cannot be guaranteed.
It is difficult to meet the real-time requirements: the traditional method is generally carried out periodically, and the real-time monitoring and judging requirements on the state of the unit cannot be met. If a fault occurs in the evaluation period, the fault can not be found and processed in time, so that the troubleshooting and repairing time of the fault is delayed, and the normal operation of the unit is affected.
Meanwhile, as the operation time of the coal-fired unit is continuously increased, the operation state of the coal-fired unit is gradually developed from a normal state to a fault state, the degradation process of the coal-fired unit can be better monitored through state evaluation, and the health state of the coal-fired unit is better evaluated.
Disclosure of Invention
Therefore, the application provides an intelligent monitoring method and system for state evaluation of a coal-fired unit, which are used for solving the problems of false alarm, large workload and difficulty in meeting real-time requirements of the state evaluation method of the coal-fired unit in the prior art.
In order to achieve the above object, the present application provides the following technical solutions:
in a first aspect, an intelligent monitoring method for evaluating a state of a coal-fired unit includes:
step 1: acquiring historical data and real-time data related to the running state of the coal-fired unit;
step 2: performing data preprocessing on the historical data and the real-time data;
step 3: performing state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting a state evaluation method of experience and rule scoring to obtain a first evaluation result;
step 4: carrying out accurate and comprehensive state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting an ANFIS evaluation algorithm optimized through a PSO algorithm to obtain a second evaluation result;
step 5: synthesizing the first evaluation result and the second evaluation result to obtain a comprehensive evaluation result;
step 6: and sending the comprehensive evaluation result to a display interface for display.
Preferably, in the step 1, the historical data and the real-time data related to the operation state of the coal-fired unit are obtained through a coal-fired power plant SIS system.
Preferably, in the step 1, the historical data and the real-time data related to the operation state of the coal-fired unit include the historical data and the real-time data of the boiler combustion system and the historical data and the real-time data of the steam-water system.
Preferably, in the step 2, the data preprocessing includes data cleaning and feature parameter selection.
Preferably, the data cleansing includes noise processing, missing value replenishment, and outlier removal.
In a second aspect, an intelligent monitoring system for coal-fired unit status assessment, comprising:
the monitoring system data acquisition module is used for acquiring historical data and real-time data related to the running state of the coal-fired unit;
the monitoring system data processing module is used for carrying out data preprocessing on the historical data and the real-time data;
the monitoring system core algorithm evaluation module is used for performing state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting a state evaluation method of experience and rule scoring to obtain a first evaluation result; carrying out accurate and comprehensive state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting an ANFIS evaluation algorithm optimized through a PSO algorithm to obtain a second evaluation result; synthesizing the first evaluation result and the second evaluation result to obtain a comprehensive evaluation result;
and the monitoring system evaluation display module is used for sending the comprehensive evaluation result to a display interface for display.
Preferably, the data acquisition module of the monitoring disc system comprises an HD acquisition unit and an RTD acquisition unit;
the HD acquisition unit is used for acquiring historical data related to the running state of the coal-fired unit;
and the RTD acquisition unit is used for acquiring real-time data related to the running state of the coal-fired unit.
Preferably, the data processing module of the monitoring disc system comprises a data cleaning unit and a characteristic parameter selecting unit;
the data cleaning unit is used for carrying out noise processing, missing value supplementation and abnormal value removal on the historical data and the real-time data;
the characteristic parameter selection unit is used for selecting key characteristics in the historical data and the real-time data.
Preferably, the monitoring system core algorithm evaluation module comprises an experience state evaluation unit and a machine learning state evaluation unit;
the experience state evaluation unit is used for performing state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting a state evaluation method of experience and rule scoring to obtain a first evaluation result;
the machine learning state evaluation unit is used for accurately and comprehensively evaluating the state of the preprocessed historical data and the preprocessed real-time data by adopting an ANFIS evaluation algorithm optimized through a PSO algorithm to obtain a second evaluation result.
Preferably, the machine learning state evaluation unit includes a PSO optimizing unit and an ANFIS evaluating unit;
the PSO optimization unit is used for optimizing an ANFIS evaluation algorithm by adopting a PSO algorithm;
the ANFIS evaluation unit is used for carrying out accurate and comprehensive state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting an ANFIS evaluation algorithm optimized through a PSO algorithm to obtain a second evaluation result.
Compared with the prior art, the application has the following beneficial effects:
the application provides an intelligent monitoring method and system for state evaluation of a coal-fired unit, wherein historical data and real-time data related to the running state of the coal-fired unit are obtained; carrying out data preprocessing on the historical data and the real-time data; performing state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting a state evaluation method of experience and rule scoring to obtain a first evaluation result; carrying out accurate and comprehensive state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting an ANFIS evaluation algorithm optimized through a PSO algorithm to obtain a second evaluation result; synthesizing the first evaluation result and the second evaluation result to obtain a comprehensive evaluation result; and sending the comprehensive evaluation result to a display interface for display. According to the method, the running state of the coal-fired unit is comprehensively estimated in real time by adopting a machine learning algorithm on the basis of the traditional state estimation, so that the running state of the coal-fired unit can be accurately mastered, and the omnibearing intelligent monitoring of the coal-fired unit can be realized.
Drawings
For a more visual description of the prior art and the present application, exemplary drawings are presented below. It should be understood that the specific shape and configuration shown in the drawings should not be considered in general as limiting upon the practice of the present application; for example, based on the technical concepts and exemplary drawings disclosed herein, those skilled in the art have the ability to easily make conventional adjustments or further optimizations for the add/subtract/assign division, specific shapes, positional relationships, connection modes, dimensional scaling relationships, etc. of certain units (components).
FIG. 1 is a flowchart of an intelligent monitoring method for evaluating the status of a coal-fired unit according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an intelligent monitoring system for evaluating the status of a coal-fired unit according to a second embodiment of the present application;
fig. 3 is a partially-displayed view of a monitoring main interface of an intelligent monitoring system for evaluating the status of a coal-fired unit according to a second embodiment of the present application;
fig. 4 is a state evaluation trend chart of an intelligent monitoring system for state evaluation of a coal-fired unit according to a second embodiment of the present application.
Reference numerals illustrate:
1. the monitoring disc system data acquisition module; 11. an HD acquisition unit; 12. an RTD acquisition unit; 2. the monitoring system data processing module; 21. a data cleaning unit; 22. a characteristic parameter selection unit; 3. the monitoring system core algorithm evaluation module; 31. an empirical state evaluation unit; 32. a machine learning state evaluation unit; 321. a PSO optimizing unit; 322. an ANFIS evaluation unit; 4. and the monitoring disc system evaluates and displays the module.
Detailed Description
The present application is further described in detail below with reference to the attached drawings.
In the description of the present application: unless otherwise indicated, the meaning of "a plurality" is two or more. The terms "first," "second," "third," and the like in this application are intended to distinguish between the referenced objects without a special meaning in terms of technical connotation (e.g., should not be construed as emphasis on degree or order of importance, etc.). The expressions "comprising", "including", "having", etc. also mean "not limited to" (certain units, components, materials, steps, etc.).
The terms such as "upper", "lower", "left", "right", "middle", and the like, as used in this application, are generally used for the purpose of facilitating an intuitive understanding with reference to the drawings and are not intended to be an absolute limitation of the positional relationship in actual products.
Example 1
Referring to fig. 1, the present embodiment provides an intelligent monitoring method for evaluating the status of a coal-fired unit, where the intelligent monitoring refers to digitizing and modeling operation knowledge and experience of the coal-fired unit by using an intelligent means, and predicting and evaluating operation status parameters of the coal-fired unit, so as to achieve the purpose of using a computer to replace operators to review pictures, analyze parameters and evaluate the status, and includes:
s1: acquiring historical data and real-time data related to the running state of the coal-fired unit;
specifically, historical data and real-time data related to the running state of the coal-fired unit are obtained from the coal-fired power plant SIS system, and the historical data and the real-time data related to the running state of the coal-fired unit comprise the historical data and the real-time data of the boiler combustion system and the historical data and the real-time data of the steam-water system.
S2: carrying out data preprocessing on the historical data and the real-time data;
specifically, the step carries out data cleaning and characteristic parameter selection on the obtained historical data and real-time data related to the running state of the coal-fired unit, and provides data support for a subsequent monitoring core evaluation algorithm. The data cleaning is used for carrying out noise processing, missing value supplementing and abnormal data clearing on the acquired historical data and real-time data, so that the integrity and the effectiveness of the acquired data are ensured; the characteristic parameters are selected to screen key characteristics of the coal-fired unit from the acquired original data characteristics, so that the key characteristics of the coal-fired unit are reduced as much as possible on the basis of ensuring that the original data characteristics are not influenced, and the modeling speed and the modeling accuracy of a monitoring core evaluation algorithm are improved.
S3: performing state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting a state evaluation method of experience and rule scoring to obtain a first evaluation result;
specifically, the method adopts the traditional state evaluation method of experience and rules to evaluate the running state of the coal-fired unit, and the running state of the coal-fired unit is scored by an intuitive method of manually inspecting results and monitoring data, so that the method has the characteristics of intuitiveness and simplicity, and plays an auxiliary evaluation role in a machine learning state evaluation method (a monitoring core evaluation algorithm).
S4: carrying out accurate and comprehensive state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting an ANFIS evaluation algorithm optimized through a PSO algorithm to obtain a second evaluation result;
specifically, the operation state of the coal-fired unit is comprehensively evaluated by adopting a machine learning algorithm, the health degree of the coal-fired unit is predicted, and real-time guarantee is provided for the health operation of the coal-fired unit.
The machine learning state evaluation method of the step optimizes the ANFIS evaluation algorithm by adopting the PSO algorithm, and then carries out accurate and comprehensive evaluation on the running state of the coal-fired unit by adopting the optimized ANFIS evaluation algorithm, so as to achieve real-time and accurate overall evaluation on the coal-fired unit.
Wherein the PSO algorithm updates the particle velocity by equation (1):
updating the position of the particles by formula (2):
in the above formulas (1) and (2),for the kth iteration particle a in n-dimensional velocity vector,/for the k-th iteration particle a>For the position vector of the kth iteration particle a in the n dimension, d 1 And d 2 R is the acceleration coefficient 1 And R is 2 Is [0,1]Random number of->For the individual optimal extreme point position of the kth iteration particle a in n dimensions,/for the individual optimal extreme point position of the kth iteration particle a in n dimensions>And for the position of the global optimal extreme point of the kth iterative particle a in the n dimension, w is the inertial weight.
The ANFIS evaluation algorithm solves the blur layer by (3):
Q 1,i ＝μ Ai (x 1 )i＝1,2
Q 1,i ＝μ Bi (x 2 ) i＝1,2 (3)
in the formula (3), x 1 And x 2 For input of node i, x 1 Divided into fuzzy sets A1 and A2, x 2 Divided into fuzzy sets B1 and B2, Q i,j For fuzzy set membership value, μ is a Gaussian membership function.
Solving for the excitation intensity layer by equation (4):
Q 2,i ＝ω i ＝μ Ai (x 1 )×μ Bi (x 2 ) i＝1,2 (4)
solving for the excitation intensity normalization layer by equation (5):
solving the functional group layer by equation (6):
Q 4i ＝β i (p i x 1 +q i x 2 +r i )i＝1,2 (6)
in formula (6), p i 、q i And r i Is 3 parameters in the function group layer.
Finally, an output layer is obtained by the formula (7):
Q 5 ＝β 1 (p 1 x 1 +q 1 x 2 +r 1 ) 1 +β 2 (p 2 x 1 +q 2 x 2 +r 2 ) (7)
in the step, the PSO algorithm is adopted to optimize the ANFIS evaluation algorithm, so that the ANFIS evaluation algorithm is optimal, the operation state of the coal-fired unit can be accurately mastered, and the omnibearing intelligent monitoring of the coal-fired unit can be realized; the ANFIS evaluation algorithm comprehensively considers the reasoning capacity of the fuzzy reasoning system and the self-adaptive capacity of the neural network, so that the fuzzy problem can be solved, the self-learning capacity is high, the current real running state of the coal-fired unit can be effectively known, and the safe and reliable running of the coal-fired unit is ensured.
S5: synthesizing the first evaluation result and the second evaluation result to obtain a comprehensive evaluation result;
s6: and sending the comprehensive evaluation result to a display interface for display.
The evaluation result of the monitoring core evaluation algorithm is subjected to man-machine display, so that operation and maintenance personnel and management personnel can grasp the operation state of the coal-fired unit more accurately and efficiently.
According to the intelligent monitoring method for the state evaluation of the coal-fired unit, the machine learning algorithm is adopted to comprehensively evaluate the running state of the coal-fired unit in real time on the basis of the traditional state evaluation method, so that the running state of the coal-fired unit can be accurately mastered, the omnibearing intelligent monitoring of the coal-fired unit can be realized, the reasoning capacity of a fuzzy reasoning system and the self-adaptive capacity of a neural network are comprehensively considered by the adopted ANFIS evaluation algorithm, the ambiguity problem can be processed, and the self-learning capacity is very high.
Example two
Referring to fig. 2, the present embodiment provides an intelligent monitoring system for evaluating a status of a coal-fired unit, including:
the monitoring system data acquisition module 1 is used for acquiring historical data and real-time data related to the running state of the coal-fired unit;
specifically, the monitoring system data acquisition module 1 comprises an HD acquisition unit 11 and an RTD acquisition unit 12, wherein the HD acquisition unit 11 is used for acquiring historical data related to the running state of the coal-fired unit; the RTD acquisition unit 12 is used to acquire real-time data related to the operational status of the coal-fired unit.
The monitoring system data processing module 2 is used for preprocessing historical data and real-time data;
specifically, the data processing module 2 of the prison disc system includes a data cleaning unit 21 and a characteristic parameter selecting unit 22, where the data cleaning unit 21 is used for performing noise processing, missing value supplementation and abnormal value clearing on historical data and real-time data; the feature parameter selection unit 22 is used for selecting key features in the historical data and the real-time data.
The monitoring system core algorithm evaluation module 3 is used for performing state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting a state evaluation method of experience and rule scoring to obtain a first evaluation result; carrying out accurate and comprehensive state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting an ANFIS evaluation algorithm optimized through a PSO algorithm to obtain a second evaluation result; synthesizing the first evaluation result and the second evaluation result to obtain a comprehensive evaluation result;
specifically, the monitoring system core algorithm evaluation module 3 includes an experience state evaluation unit 31 and a machine learning state evaluation unit 32; wherein, the experience state evaluation unit 31 is configured to perform state evaluation on the preprocessed historical data and the preprocessed real-time data by using a state evaluation method of experience and rule scoring, so as to obtain a first evaluation result; the machine learning state evaluation unit 32 is configured to perform accurate and comprehensive state evaluation on the preprocessed historical data and the preprocessed real-time data by using an ANFIS evaluation algorithm optimized by a PSO algorithm, so as to obtain a second evaluation result.
More specifically, the machine learning state evaluation unit 32 includes a PSO optimization unit 321 and an ANFIS evaluation unit 322, where the PSO optimization unit 321 is configured to optimize the ANFIS evaluation algorithm using a PSO algorithm; the ANFIS evaluation unit 322 is configured to perform accurate and comprehensive state evaluation on the preprocessed historical data and the preprocessed real-time data by using an ANFIS evaluation algorithm optimized by a PSO algorithm, so as to obtain a second evaluation result.
And the monitoring system evaluation display module 4 is used for sending the comprehensive evaluation result to a display interface for display.
Referring to fig. 3 and fig. 4, fig. 3 is a partially-developed view of a monitoring main interface of the intelligent monitoring system according to the present embodiment; fig. 4 is a state evaluation trend chart of the intelligent monitoring disc system provided in this embodiment (taking the temperature of the a bearing of the oxidizing machine a of the 1# unit as an example).
The specific implementation content of each module in an intelligent monitoring system for coal-fired unit state evaluation can be referred to as the limitation of an intelligent monitoring method for coal-fired unit state evaluation, and the detailed description is omitted here.
Any combination of the technical features of the above embodiments may be performed (as long as there is no contradiction between the combination of the technical features), and for brevity of description, all of the possible combinations of the technical features of the above embodiments are not described; these examples, which are not explicitly written, should also be considered as being within the scope of the present description.
Claims (10)
1. An intelligent monitoring method for evaluating the state of a coal-fired unit is characterized by comprising the following steps:
step 1: acquiring historical data and real-time data related to the running state of the coal-fired unit;
step 2: performing data preprocessing on the historical data and the real-time data;
step 3: performing state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting a state evaluation method of experience and rule scoring to obtain a first evaluation result;
step 4: carrying out accurate and comprehensive state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting an ANFIS evaluation algorithm optimized through a PSO algorithm to obtain a second evaluation result;
step 5: synthesizing the first evaluation result and the second evaluation result to obtain a comprehensive evaluation result;
step 6: and sending the comprehensive evaluation result to a display interface for display.
2. The intelligent monitoring method for coal-fired unit status assessment according to claim 1, wherein in step 1, the historical data and the real-time data related to the coal-fired unit operation status are obtained through a coal-fired power plant SIS system.
3. The intelligent monitoring method for coal-fired unit status assessment according to claim 1, wherein in step 1, the historical data and the real-time data related to the coal-fired unit operation status include the historical data and the real-time data of the boiler combustion system and the historical data and the real-time data of the steam-water system.
4. The intelligent monitoring method for coal-fired unit status assessment according to claim 1, wherein in step 2, the data preprocessing includes data cleaning and feature parameter selection.
5. The intelligent monitoring method for coal-fired unit status assessment according to claim 4, wherein the data cleaning includes noise handling, missing value replenishment and outlier removal.
6. An intelligent monitoring system for coal-fired unit status assessment, comprising:
the monitoring system data acquisition module is used for acquiring historical data and real-time data related to the running state of the coal-fired unit;
the monitoring system data processing module is used for carrying out data preprocessing on the historical data and the real-time data;
the monitoring system core algorithm evaluation module is used for performing state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting a state evaluation method of experience and rule scoring to obtain a first evaluation result; carrying out accurate and comprehensive state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting an ANFIS evaluation algorithm optimized through a PSO algorithm to obtain a second evaluation result; synthesizing the first evaluation result and the second evaluation result to obtain a comprehensive evaluation result;
and the monitoring system evaluation display module is used for sending the comprehensive evaluation result to a display interface for display.
7. The intelligent monitoring system for coal-fired unit state assessment according to claim 6, wherein the monitoring system data acquisition module comprises an HD acquisition unit and an RTD acquisition unit;
the HD acquisition unit is used for acquiring historical data related to the running state of the coal-fired unit;
and the RTD acquisition unit is used for acquiring real-time data related to the running state of the coal-fired unit.
8. The intelligent monitoring system for coal-fired unit state evaluation according to claim 6, wherein the monitoring system data processing module comprises a data cleaning unit and a characteristic parameter selection unit;
the data cleaning unit is used for carrying out noise processing, missing value supplementation and abnormal value removal on the historical data and the real-time data;
the characteristic parameter selection unit is used for selecting key characteristics in the historical data and the real-time data.
9. The intelligent monitoring system for coal-fired unit status assessment according to claim 6, wherein the monitoring system core algorithm assessment module comprises an empirical status assessment unit and a machine learning status assessment unit;
the experience state evaluation unit is used for performing state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting a state evaluation method of experience and rule scoring to obtain a first evaluation result;
the machine learning state evaluation unit is used for accurately and comprehensively evaluating the state of the preprocessed historical data and the preprocessed real-time data by adopting an ANFIS evaluation algorithm optimized through a PSO algorithm to obtain a second evaluation result.
10. The intelligent monitoring system for coal-fired unit status assessment according to claim 9, wherein the machine learning status assessment unit comprises a PSO optimization unit and an ANFIS assessment unit;
the PSO optimization unit is used for optimizing an ANFIS evaluation algorithm by adopting a PSO algorithm;
the ANFIS evaluation unit is used for carrying out accurate and comprehensive state evaluation on the preprocessed historical data and the preprocessed real-time data by adopting an ANFIS evaluation algorithm optimized through a PSO algorithm to obtain a second evaluation result.
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