CN115630266A - Thermal power plant wall temperature statistical analysis method based on variation coefficient change - Google Patents
Thermal power plant wall temperature statistical analysis method based on variation coefficient change Download PDFInfo
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- CN115630266A CN115630266A CN202211228586.8A CN202211228586A CN115630266A CN 115630266 A CN115630266 A CN 115630266A CN 202211228586 A CN202211228586 A CN 202211228586A CN 115630266 A CN115630266 A CN 115630266A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
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- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
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- G01K1/024—Means for indicating or recording specially adapted for thermometers for remote indication
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
Abstract
The invention discloses a thermal power plant wall temperature statistical analysis method based on variation coefficient, which comprises the following steps: selecting a pipe wall temperature related index of the power plant according to a service application scene, and extracting equipment related operation historical data from a database; abnormal value detection and missing value filling are carried out on historical data, effectiveness judgment is carried out on the obtained wall temperature data, wall temperature data which are not in an effective range are removed, and abnormal recording is carried out on related wall temperature measuring points; respectively calculating the maximum value, the minimum value, the average value, the median, the alternating rate, the overrun duration and the distribution probability of the overrun limit value of the data according to the calculation rule of the abnormal coefficient; and monitoring the wall temperature of the tube in real time according to the transaction coefficient, and obtaining an abnormal ranking. The wall temperature statistical analysis method based on the comprehensive index variation coefficient is used for monitoring the wall temperature, finding out the wall temperature pipe number with larger abnormal fluctuation of the wall temperature, helping operating personnel to adjust equipment in real time, preventing overtemperature and ensuring the safe and stable operation of a unit.
Description
Technical Field
The invention relates to the technical field of wall temperature calculation of a thermal power plant, in particular to a thermal power plant wall temperature statistical analysis method based on change of a transaction coefficient.
Background
With the development of modern thermal power plant units towards high parameters and large capacity, the allowable over-temperature margin of boiler pipes is smaller and smaller, and the over-temperature phenomenon is easy to generate. When the temperature of the pipe wall is high to a certain degree, the creep speed of the pipe is increased, the diameter of the pipe is gradually thickened, the pipe wall is thinned, the service life of the pipe is influenced, and even the overtemperature pipe explosion is possible. The over-temperature of the heating surface of the boiler is one of the important factors causing the damage of the pipeline of the heating surface. When one boiler has a pipe explosion accident, besides the material cost for unit repair and the oil cost for cold start, the unit will lose a large amount of generated energy during the boiler repair, and the economic loss caused by the loss is quite huge. Therefore, in order to ensure long-term, safe and stable operation of the thermal power generating unit, the wall temperature condition of the heating surface of the boiler needs to be mastered by the operating personnel of the power plant all the time, when the heating surface of the boiler is over-heated, the operating personnel can adjust the operation mode of the unit in time, the wall temperature of the heating surface is controlled in a reasonable range, and the pipe explosion accident caused by long-time over-temperature of the heating surface is avoided. Therefore, the boiler wall temperature on-line monitoring system has very important significance for ensuring the safe and economic operation of the power plant.
Firstly, the existing boiler wall temperature on-line monitoring system detects through a temperature sensor, detected data are generally directly transmitted to a background and screened by staff or independent DCS judgment logic, the efficiency of the calculation method is low, the process is complex, and pipes with unstable wall temperature fluctuation cannot be checked at the first time.
Secondly, the design of each heating surface of the existing boiler of the thermal power plant tends to be multi-tube-loop multi-loop arrangement, the number of all tubes is generally more than 500, the data after the wall temperature detection of the existing boiler is generally only the over-limit evaluation of a single heating surface tube, and the scientific and normative evaluation of the safety state of a single heating surface tube and the overall safety state of the heating surface cannot be carried out.
Disclosure of Invention
The invention aims to provide a thermal power plant wall temperature statistical analysis method based on variation of a transaction coefficient.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a thermal power plant wall temperature statistical analysis method based on variation coefficient variation comprises the following steps:
s1, collecting historical data: selecting a pipe wall temperature related index of the power plant according to a service application scene, and extracting equipment related operation historical data from a database;
s2, data preprocessing: abnormal value detection and missing value filling are carried out on historical data, effectiveness judgment is carried out on the obtained wall temperature data, wall temperature data which are not in an effective range are removed, and abnormal recording is carried out on related wall temperature measuring points;
s3, respectively calculating the maximum value, the minimum value, the average value, the median, the crossover rate and the distribution probability of the overrun frequency of the data according to the abnormal coefficient calculation rule;
s4, calculating a transaction coefficient;
and S5, monitoring the wall temperature of the tube in real time according to the abnormal coefficient, and obtaining an abnormal ranking.
Preferably, the outlier detection and deficiency filling employ local anomaly factors and 3 σ rule.
Preferably, the step of calculating the probability of the maximum distribution is as follows:
(1) The pipe wall temperature data at each moment in the statistical data period are arranged from large to small;
(2) Counting the pipe number of the maximum wall temperature at each moment;
(3) Calculating the proportion time of the maximum pipe number in the statistical period;
(4) Tube a maximum time/statistical cycle time = a1%;
(5) Arranging time ratios in sequence: a first name a1%, a second name b1%, a third name c1%, a fourth name d1%, and a fifth name e1%.
Preferably, the step of calculating the probability of the minimum distribution is as follows:
(1) The pipe wall temperature data at each moment in the statistical data period are arranged from small to large;
(2) Counting the pipe number of the minimum value of the wall temperature at each moment;
(3) Calculating the proportion time of the minimum pipe number in the statistical period;
(4) Tube a minimum time/statistical cycle time = a2%;
(5) Arranging the time ratios in sequence: a first name a2%, a second name b2%, a third name c2%, a fourth name d2%, and a fifth name e2%.
Preferably, the step of calculating the mean distribution probability is as follows:
(1) Calculating the average value of the pipe wall temperature data at each moment in the data statistics period;
(2) Counting the tube number of which the wall temperature is closest to the average value at each moment;
(3) Calculating the proportion time of the pipe number closest to the average value in the statistical period;
(4) Tube a time closest to the mean/statistical cycle time = a3%;
(5) Arranging the time ratios in sequence: a first name a3%, a second name b3%, a third name c3%, a fourth name d3%, and a fifth name e3%.
Preferably, the step of calculating the probability of the distribution of the time-out-of-limit duration is as follows:
(1) Calculating whether the pipe wall temperature data at each moment in the data statistics period exceeds the limit or not;
(2) Counting the number of the pipe with the wall temperature exceeding the limit at each moment;
(3) Calculating the proportion time of the overrun pipe number in the statistical period;
(4) The total overrun time of all the tubes in the accumulated overrun corresponding time/statistical period of the tube A is = a4%;
(5) Arranging the time ratios in sequence: first a4%, second b4%, third c4%, fourth d4%, and fifth e4%.
Preferably, the step of calculating the probability of the overrun amplitude distribution is as follows:
(1) Calculating whether the pipe wall temperature data at each moment in the data statistics period exceeds the limit or not;
(2) Counting the number of the pipe with the wall temperature exceeding the limit at each moment;
(3) Calculating the temperature over-limit value of the over-limit pipe number in the statistical period;
(4) The accumulated overrun amplitude value of the A tube/the total overrun amplitude value of all the tubes in the statistical period is = a5%;
(5) Arranging the amplitude ratios in sequence: first a5%, second b5%, third c5%, fourth d5%, and fifth e5%.
Preferably, the step of calculating the cross-over rate is as follows:
(1) Counting the maximum value and the minimum value of each tube in a data period;
(2) Calculating the average value of the wall temperature data of each pipe in the data statistics period;
(3) Upper cross-change rate = (maximum per tube-mean per tube)/(maximum per tube-minimum per tube);
(4) Lower cross-over rate = (average per tube-minimum per tube)/(maximum per tube-minimum per tube);
(5) The total crossover rate per tube = the upper crossover rate + the lower crossover rate per tube;
preferably, the formula of the abnormal motion coefficient is as follows: the variation coefficient = maximum value distribution probability + minimum value distribution probability + total alternation rate + overrun duration distribution probability + overrun amplitude distribution probability.
Compared with the prior art, the invention has the advantages that:
the wall temperature statistical analysis method based on the comprehensive index variation coefficient monitors the wall temperature and judges whether the wall temperature exceeds the temperature, the variation coefficient is obtained by comprehensively calculating three aspects (four indexes) of probability distribution based on measuring points, the degree of alternation in a data period and data overrun frequency, the greater the variation coefficient is, the more unstable the wall temperature fluctuation is, the greater the possibility of the excess temperature is, and the wall temperature pipe number with larger fluctuation can be found out by the method, so that operating personnel can be helped to adjust equipment in real time, the excess temperature is prevented, and the safe and stable operation of a unit is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a thermal power plant wall temperature statistical analysis method based on variation of transaction coefficients according to the present invention;
FIG. 2 is an interface diagram of the monitoring results of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, and the scope of the present invention will be more clearly and clearly defined.
If 960 pieces of root canal data with high re-wall temperature of a thermal power plant at a certain period of time are selected, taking one piece of data every 5 minutes, and counting 8639 pieces of data, wherein the wall temperature is measured by installing a temperature sensor on the outer surface of a wall to be measured, and ranking the 960 pieces of root canal data with high re-wall temperature for one month, which comprises the following steps:
carrying out abnormal value detection and missing value filling on historical data by using local abnormal factors and a3 sigma rule, removing wall temperature data which is not in an effective range to finally obtain 8550 pieces of data, (the 3 sigma principle is a Lauda rule, the Lauda rule means that a group of detection data is supposed to only contain random errors, the detection data is calculated to obtain standard deviation, an interval is determined according to a certain probability, the error which exceeds the interval is considered not to belong to the random errors but to be coarse errors, and the data containing the errors are removed);
calculate the maximum value distribution profile of 8550 pieces of data:
calculating 960 pieces of wall temperature data of each moment of data in one month, arranging from large to small, calculating the pipe number of the maximum value of the wall temperature at each moment, and calculating the proportion time of the maximum pipe number in a statistical period according to a formula (certain pipe maximum value time/statistical period time);
finding out the tubes at the top 5, and arranging the time ratios in sequence: the first high re-wall temperature is 28-7 (22.08%), the second high re-wall temperature is 78-3 (21.88%), the third high re-wall temperature is 19-8 (16.04%), the fourth high re-wall temperature is 77-8 (11.25%), and the fifth high re-wall temperature is 698-70 (8.54%).
Calculating the minimum value distribution probability:
calculating 960 pieces of wall temperature data of each moment of data in one month, arranging from small to large, calculating the pipe number of the minimum value of the wall temperature at each moment, and calculating the proportion time of the pipe number of the minimum value in a statistical period according to a formula (certain pipe minimum time/statistical period time);
finding out the tubes at the top 5, and arranging the time ratios in sequence: the first high re-wall temperature is 95-10 (97.71%), the second high re-wall temperature is 01-1 (2.29%), the third high re-wall temperature is 02-1 (0%), the fourth high re-wall temperature is 03-1 (0%), and the fifth high re-wall temperature is 04-1 (0%).
Calculating the mean distribution probability:
calculating average values of 960 pieces of wall temperature data of each moment of data in one month, calculating the tube number of which the wall temperature is closest to the average value at each moment, and calculating the proportion time of the tube number closest to the average value in a statistical period according to a formula (the time of the tube closest to the average value/the time of the statistical period);
finding out the tubes at the top 5, and arranging the time ratios in sequence: the first high re-wall temperature 47-1 (2.92%), the second high re-wall temperature 46-1 (2.5%), the third high re-wall temperature 44-1 (2.08%), the fourth high re-wall temperature 47-2 (2.08%), and the fifth high re-wall temperature 54-1 (1.67%).
Calculating median distribution probability:
calculating 960 median values of the wall temperature data at each moment of the data in one month, calculating the number of the pipe with the wall temperature closest to the median value at each moment, and calculating the proportion time of the number of the pipe closest to the median value in the statistical period according to a formula (the time of a certain pipe closest to the median value/the statistical period time);
finding out the tubes with the top 5 ranks, and arranging the time ratios in sequence: the first high re-wall temperature is 42-2 (1.25%), the second high re-wall temperature is 14-2 (1.04%), the third high re-wall temperature is 62-4 (1.04%), the fourth high re-wall temperature is 83-4 (1.04%), and the fifth high re-wall temperature is 62-6 (1.04%).
And (3) calculating the number of overrun times:
and (4) counting the number of times of over-limit of 960 pieces of wall temperature data at each moment of the data within one month, wherein no over-temperature data is found.
Calculating the crossover rate:
1) Counting the maximum value and the minimum value of each tube in a data period;
(2) Calculating the average value of the wall temperature data of each pipe in the data statistics period;
(3) Upper cross-over rate = (maximum per tube-mean per tube)/(maximum per tube-minimum per tube);
(4) Lower cross-change rate = (average per tube-minimum per tube)/(maximum per tube-minimum per tube);
(5) The total crossover rate per tube = the upper crossover rate + the lower crossover rate per tube; (ii) a
Finding out the tubes with the top 5 ranks, and arranging the time ratios in sequence: the first high re-wall temperature 89-1 (5.38%), the second high re-wall temperature 87-2 (5.29%), the third high re-wall temperature 88-1 (5.2%), the fourth high re-wall temperature 87-1 (5.19%), and the fifth high re-wall temperature 87-5 (5.19%).
Calculating a transaction coefficient:
counting data in one month, and calculating the abnormal motion coefficient according to a formula: the variation coefficient = maximum value distribution probability + minimum value distribution probability + alternation rate + overrun frequency;
finding out the tubes at the top 5, and arranging the time ratios in sequence: first high re-wall temperature 89-1 (5.38%), second high re-wall temperature 87-2 (5.29%), third high re-wall temperature 88-1 (5.2%), fourth high re-wall temperature 87-1 (5.19%), fifth high re-wall temperature 87-5 (5.19%)
And (3) performing real-time monitoring and abnormal ranking on 960 root canals with high re-wall temperature according to the abnormal coefficient by using the data to obtain the wall temperature number of 5 before ranking (as shown in a monitoring result interface of fig. 2): the first high re-wall temperature is 89-1, the second high re-wall temperature is 87-2, the third high re-wall temperature is 88-1, the fourth high re-wall temperature is 87-1, and the fifth high re-wall temperature is 87-5.
A ranking system comprises a server, a temperature measuring terminal and a display terminal, wherein the server comprises a processor, the processor stores the formula for processing data, the temperature measuring terminal receives wall temperature data and sends the wall temperature data to the server for processing, the processed data are sent to the display terminal, and the display terminal is used for displaying a result interface as shown in figure 2.
By the method, the wall temperature pipe number with large fluctuation can be found out, so that operating personnel can adjust the equipment in real time, overtemperature is prevented, and the safe and stable operation of the unit is ensured.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, various changes or modifications may be made by the patentees within the scope of the appended claims, and within the scope of the invention, as long as they do not exceed the scope of the invention described in the claims.
Claims (9)
1. A thermal power plant wall temperature statistical analysis method based on variation coefficient is characterized by comprising the following steps:
s1, collecting historical data: selecting a pipe wall temperature related index of the power plant according to a service application scene, and extracting equipment related operation historical data from a database;
s2, data preprocessing: abnormal value detection and missing value filling are carried out on historical data, effectiveness judgment is carried out on the obtained wall temperature data, wall temperature data which are not in an effective range are removed, and abnormal recording is carried out on related wall temperature measuring points;
s3, respectively calculating the maximum value, the minimum value, the average value, the median, the crossover rate and the distribution probability of the overrun frequency of the data according to the abnormal coefficient calculation rule;
s4, calculating a transaction coefficient;
and S5, monitoring the wall temperature of the tube in real time according to the abnormal coefficient, and obtaining an abnormal rank.
2. The thermal power plant wall temperature statistical analysis method based on variation of the coefficient of variation according to claim 1, characterized in that: the outlier detection and deficiency filling employ local anomaly factors and 3 σ rule.
3. The thermal power plant wall temperature statistical analysis method based on variation of transaction coefficient as claimed in claim 1, wherein the step of calculating the probability of maximum distribution is as follows:
(1) The pipe wall temperature data at each moment in the statistical data period are arranged from large to small;
(2) Counting the pipe number of the maximum wall temperature at each moment;
(3) Calculating the proportion time of the maximum pipe number in the statistical period;
(4) Tube a maximum time/statistical cycle time = a1%;
(5) Arranging time ratios in sequence: a first name a1%, a second name b1%, a third name c1%, a fourth name d1%, and a fifth name e1%.
4. The thermal power plant wall temperature statistical analysis method based on variation coefficient of thermal anomaly according to claim 1, characterized in that the step of calculating the probability of the minimum value distribution is as follows:
(1) The pipe wall temperature data at each moment in the statistical data period are arranged from small to large;
(2) Counting the pipe number of the minimum value of the wall temperature at each moment;
(3) Calculating the proportion time of the minimum pipe number in the statistical period;
(4) Tube a minimum time/statistical cycle time = a2%;
(5) Arranging the time ratios in sequence: a first name a2%, a second name b2%, a third name c2%, a fourth name d2%, and a fifth name e2%.
5. The thermal power plant wall temperature statistical analysis method based on variation of abnormal coefficient according to claim 1, characterized in that the step of calculating the mean value distribution probability is as follows:
(1) Calculating the average value of the pipe wall temperature data at each moment in the data statistics period;
(2) Counting the pipe number of which the wall temperature is closest to the average value at each moment;
(3) Calculating the proportion time of the pipe number closest to the average value in the statistical period;
(4) Tube a time closest to the mean/statistical cycle time = a3%;
(5) Arranging the time ratios in sequence: a first name a3%, a second name b3%, a third name c3%, a fourth name d3%, and a fifth name e3%.
6. The thermal power plant wall temperature statistical analysis method based on variation of transaction coefficients as claimed in claim 1, wherein the step of calculating the distribution probability of the overrun time duration is as follows:
(1) Calculating whether the pipe wall temperature data at each moment in the data statistics period exceeds the limit or not;
(2) Counting the number of the pipe with the wall temperature exceeding the limit at each moment;
(3) Calculating the proportion time of the overrun pipe number in the statistical period;
(4) The cumulative overrun corresponding time of the A tube/statistical period is the total overrun time of all tubes = a4%;
(5) Arranging the time ratios in sequence: first a4%, second b4%, third c4%, fourth d4%, and fifth e4%.
7. The thermal power plant wall temperature statistical analysis method based on variation of transaction coefficient as claimed in claim 1, wherein the step of calculating the probability of the distribution of the overrun amplitude values is as follows:
(1) Calculating whether the pipe wall temperature data at each moment in the data statistics period exceeds the limit or not;
(2) Counting the number of the pipe with the wall temperature exceeding the limit at each moment;
(3) Calculating the temperature over-limit value of the over-limit pipe number in the statistical period;
(4) The accumulated overrun amplitude value of the A tube/the total overrun amplitude value of all the tubes in the statistical period is = a5%;
(5) Arranging the amplitude ratios in sequence: first name a5%, second name b5%, third name c5%, fourth name d5%, fifth name e5%.
8. The thermal power plant wall temperature statistical analysis method based on variation coefficient of variation according to claim 1, characterized in that the step of calculating the crossover rate is as follows:
(1) Counting the maximum value and the minimum value of each tube in a data period;
(2) Calculating the average value of the wall temperature data of each pipe in the data statistics period;
(3) Upper cross-change rate = (maximum per tube-mean per tube)/(maximum per tube-minimum per tube);
(4) Lower cross-over rate = (average per tube-minimum per tube)/(maximum per tube-minimum per tube);
(5) Total turnover rate per tube = upper turnover rate per tube + lower turnover rate per tube.
9. The thermal power plant wall temperature statistical analysis method based on variation of the abnormal movement coefficient according to claim 1, characterized in that the formula of the abnormal movement coefficient is as follows: the variation coefficient = maximum value distribution probability + minimum value distribution probability + total alternation rate + overrun duration distribution probability + overrun amplitude distribution probability.
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| CN117725541A (en) * | 2024-02-07 | 2024-03-19 | 上海强华实业股份有限公司 | Intelligent monitoring and fault diagnosis system for running state of annealing furnace |
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| CN117725541A (en) * | 2024-02-07 | 2024-03-19 | 上海强华实业股份有限公司 | Intelligent monitoring and fault diagnosis system for running state of annealing furnace |
| CN117725541B (en) * | 2024-02-07 | 2024-04-16 | 上海强华实业股份有限公司 | Intelligent monitoring and fault diagnosis system for running state of annealing furnace |
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