CN113359435B - Correction method for dynamic working condition data of thermal power generating unit - Google Patents
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Abstract
The invention discloses a method for correcting dynamic working condition data of a thermal power generating unit, relates to the technical field of thermal power generating unit dynamic data correction, and solves the technical problems that the number of steady-state samples is relatively small and the working condition distribution is unbalanced in the operation process of the conventional thermal power generating unit. And finally, calculating the state parameter value and the performance index value of the modified dynamic working condition sample by taking the history nearest steady-state working condition with the minimum Minkowski distance as a reference. The method has higher parameter stability and meets the actual requirements of engineering; the utilization degree of dynamic operation data is improved, and the problem that data mining precision is limited due to unbalanced distribution of working condition samples can be effectively solved.
Description
Technical Field
The disclosure relates to the technical field of thermal power generating unit dynamic data correction, in particular to a correction method for thermal power generating unit dynamic working condition data.
Background
In the data-driven modeling of the thermal process, a steady-state working condition sample becomes a main data source for model training due to good regularity dominance and less noise. However, in actual operation, the unit operation parameters are also in continuous fluctuation due to the influence of peak regulation requirements and four-season transition, and the continuous change of external conditions such as unit load instructions and environmental factors. In addition, the frequency and amplitude of field data deviation will be further aggravated by the influence of internal factors such as human regulation intervention of the power plant and hysteresis characteristics of a thermodynamic system. Frequent and violent fluctuation processes can be seen, so that the number of steady-state working conditions is usually much smaller than that of dynamic working conditions in the unit operation historical database.
In fact, a fully steady state operating condition does not exist during the generation of the power by the unit. Even the steady-state data obtained by the steady-state determination is quasi-steady-state data having a small fluctuation range and less noise. In order to improve the accuracy and efficiency of data mining, a smaller steady state threshold value is adopted to screen historical data, and although higher-quality working condition information can be obtained, the number of reserved samples is greatly reduced, and the problem of unbalanced working condition distribution is further aggravated. Lack of sufficient and comprehensive high-quality samples to participate in model training will affect the mining effect of the unit operation rule, which is obviously fatal to the unit performance evaluation and diagnosis optimization under all conditions.
For data mining, it is always critical to improve the quality of raw data. The difficulty in the field of data mining is that mass data accumulated during unit operation are unevenly distributed in a high-dimensional space. The running state of the unit is in a complex and various state, and the deviation of the working condition is reflected on the deviation of the running parameter, so that the deviation of the energy consumption of the system is caused. The study on the working condition change rule in the whole operation range is bound to face the problems of high dimension, high coupling, high error and the like. The operation performance of the system is determined by boundary parameters and operation parameters, and the difference of the energy consumption of the adjacent samples is also caused by the common influence of the difference of the boundary parameters and the operation related parameters. Therefore, how to modify data in the dynamic process of the unit to obtain high-quality data is an urgent problem to be solved, so that a more complete and clean data basis is provided for mining of an operation optimization rule.
Disclosure of Invention
The invention provides a method for correcting dynamic working condition data of a thermal power generating unit, which aims to solve the problems of relatively small quantity of steady-state samples, unbalanced working condition distribution and the like in the operation process of the existing thermal power generating unit and discloses a method for correcting dynamic process data so as to obtain a large quantity of steady-state samples and solve the problems of unbalanced working condition distribution and the like.
The technical purpose of the present disclosure is achieved by the following technical solutions:
a method for correcting dynamic working condition data of a thermal power generating unit comprises the following steps:
primarily screening first characteristic parameters related to system operation performance through mechanism analysis, and selecting second characteristic parameters related to system operation performance indexes from the first characteristic parameters through a grey correlation degree algorithm;
calculating the statistic of the second characteristic parameter to obtain a steady-state factor describing the stability of the working condition, comparing the steady-state factor with a steady-state threshold value, and considering that the steady-state factor is smaller than the steady-state threshold value as a steady-state working condition sample;
computing stationThe Minkowski distance of the steady-state operating condition sample and the dynamic operating condition sample S of the known boundary condition is less than a distance threshold d if the Minkowski distance of the first steady-state operating condition in the steady-state operating condition sample and the dynamic operating condition sample S is less than ε If the first steady-state working condition is a neighboring working condition, screening out samples { w ] of the neighboring working condition from the samples of the steady-state working condition through Minkowski distance 1 ,w 2 ,...,w K ,w N And f, obtaining (K +1) neighbor working conditions in the neighbor working condition samples, w N Representing the nearest neighbor working condition to the dynamic working condition sample S;
calculating the neighbor condition samples { w 1 ,w 2 ,...,w K ,w N Nuclear density distribution of };
performing least square estimation on the energy consumption evaluation index of the dynamic working condition sample S and the correction coefficient of the related parameter according to the nuclear density distribution to obtain a final correction coefficient;
correcting the dynamic working condition sample S according to the final correction coefficient to obtain a corrected quasi-steady-state working condition sample S';
wherein the dynamic condition sample S is represented as
I S The energy consumption evaluation index of the system under the dynamic working condition is shown,
representing the boundary parameters of the system under dynamic conditions,
and expressing the relevant parameters of the system under the dynamic working condition, wherein u expresses the boundary parameters, r expresses the relevant parameters, and m and n respectively express the number of the boundary parameters and the relevant parameters.
The beneficial effect of this disclosure lies in: historical operating data are obtained from an operating database of a power plant, preliminary preprocessing such as steady state judgment and working condition division is carried out, then adjacent samples similar to boundary parameters of dynamic working condition samples to be corrected in all steady state working condition samples are screened out according to a certain distance threshold, and correction coefficient estimation is carried out based on a least square method and a nuclear density weighting method. And finally, calculating the state parameter value and the performance index value of the modified dynamic working condition sample by taking the history nearest steady-state working condition with the minimum Minkowski distance as a reference.
Compared with the traditional modeling supplement method, the steady-state data supplement method is higher in speed and parameter stability, and meets the actual requirements of engineering; the utilization degree of dynamic operation data is improved, and the problem that data mining precision is limited due to unbalanced distribution of working condition samples can be effectively solved. And the application does not need complex hardware equipment and has low price.
Drawings
FIG. 1 is a flow chart of a method described herein;
FIG. 2 is a schematic view of the heat dissipation of the steam turbine along with the load distribution under different working conditions;
FIG. 3 is a schematic diagram of the mean distribution of the heat rate of the steam turbine in different load intervals.
Detailed Description
The technical scheme of the disclosure will be described in detail with reference to the accompanying drawings. In the description of the present application, it is to be understood that the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated, but merely as distinguishing between different technical features.
Fig. 1 is a flowchart of a method for correcting dynamic condition data of a thermal power generating unit according to the present application, and as shown in fig. 1, the method includes:
s1: the method comprises the steps of preliminarily screening first characteristic parameters related to system operation performance through mechanism analysis, and selecting second characteristic parameters related to system operation performance indexes in the first characteristic parameters through a grey correlation algorithm.
Specifically, the mechanism is used for analyzing relevant parameters of the primary screening system operation, the characteristic variable with high association degree with the unit performance index is selected through a grey association degree algorithm, reduction is carried out through a certain association degree threshold value, and energy consumption characteristic parameters are obtainedNumber, let X ═ X u ,X r The characteristic parameter of energy consumption of a certain system is represented by an uncontrollable characteristic parameter X u (boundary parameters) and controllable characteristic parameters r (relevant parameters). Here, a suitable grey correlation reduction threshold needs to be selected.
S2: and calculating the statistic of the second characteristic parameter to obtain a steady-state factor describing the stability of the working condition, comparing the steady-state factor with a steady-state threshold value, and considering that the steady-state factor is smaller than the steady-state threshold value as a steady-state working condition sample. This process can be accomplished by the R-test method.
S3: calculating the Minkowski distance between the steady-state operating condition sample and the dynamic operating condition sample S of known boundary conditions, if the Minkowski distance between the first steady-state operating condition sample and the dynamic operating condition sample S is less than a distance threshold d ε If the first steady-state working condition is a neighboring working condition, screening out samples { w ] of the neighboring working condition from the samples of the steady-state working condition through Minkowski distance 1 ,w 2 ,...,w K ,w N And f, obtaining (K +1) neighbor working conditions in the neighbor working condition samples, w N Representing the nearest neighbor condition to the dynamic condition sample S.
When the adjacent working conditions are screened, the samples in the adjacent grids can be preferentially selected to calculate the distance, the function similar to the search range reduction is performed, namely, the working conditions are divided, and the working conditions are divided generally by adopting an equal-width method.
Distance threshold d ε Will depend on the actual system operating conditions.
Specifically, the minkowski distance is expressed as:
wherein d (A, B) represents any two points A (a) in m-dimensional space 1 ,a 2 ,...a m ) And B (B) 1 ,b 2 ,...b m ) Minkowski distance, A (a) 1 ,a 2 ,...a m ) Represents any one of the dynamic conditions, B (B) 1 ,b 2 ,...b m ) Indicating that any of the steady state operating condition samples is stableAnd (3) under a constant working condition, wherein p represents a variable parameter, and p is 2.
M not only represents the number of boundary parameters, but also represents the dimension number of the division working condition. Each working point corresponds to m boundary parameters, and when the distance in the high-dimensional space is calculated, the difference value of the boundary parameters in each dimension between the two points needs to be calculated, and then the difference value is summed to obtain the final distance.
The minkowski distance from any working condition in the nearest neighbor working condition sample to the dynamic working condition sample S needs to be calculated respectively from the nearest neighbor working condition to each working condition in the dynamic working condition sample S, and then the maximum minkowski distance from the nearest neighbor working condition to the working condition in the dynamic working condition sample S is the minkowski distance from the nearest neighbor working condition to the dynamic working condition sample S.
Before calculating the Minkowski distance, the boundary parameters for the operating conditions need to be normalized.
S4: calculating the neighbor condition samples { w 1 ,w 2 ,...,w K ,w N The kernel density distribution of the image can be estimated by selecting a Gaussian kernel function.
Specifically, neighbor condition samples { w 1 ,w 2 ,...,w K ,w N A nuclear density distribution of { includes:
wherein f is h (d) Representing any near neighbor working condition and nearest neighbor working condition w N When the distance of (d) is the corresponding probability density, d k Indicating representative neighbor conditions w k And w N K1, 2, K, h denotes the bandwidth, h d ε The/10, g (.) represents the kernel function.
S5: and performing least square estimation on the energy consumption evaluation index of the dynamic working condition sample S and the correction coefficient of the related parameter according to the nuclear density distribution to obtain a final correction coefficient.
The method specifically comprises the following steps:
S6: and correcting the dynamic working condition sample S according to the final correction coefficient to obtain a corrected quasi-steady-state working condition sample S'.
Dynamic regime sample S is shown as
I S Indicating system under dynamic conditionsThe energy consumption evaluation index of (1) is,
representing the boundary parameters of the system under dynamic conditions,
and expressing the relevant parameters of the system under the dynamic working condition, wherein u expresses the boundary parameters, r expresses the relevant parameters, and m and n respectively express the number of the boundary parameters and the relevant parameters.
The correction process comprises the following steps:
As a specific embodiment, the field DCS sampling data is stored in a historical database of a plant-level monitoring information system (SIS), and in combination with a specific system to be analyzed, the field data is subjected to preprocessing such as data cleaning and steady-state screening to obtain a steady-state operation condition database for subsequent analysis. And then selecting energy consumption characteristic variables in the operation process of the steam turbine through a grey correlation algorithm, dividing the steady-state data by taking the relatively uncontrollable characteristic parameters as boundary conditions, and taking the rest parameters as operation related parameters. For the dynamic working condition sample to be corrected, the boundary parameters can be operated in a steady stateAnd matching in the working condition library, screening steady-state samples within the boundary similar working conditions, and respectively calculating Minkowski distances between the steady-state samples and the working conditions to be corrected. Setting a distance threshold d of Minkowski distance ε And screening a certain number of neighboring working condition samples, calculating the nuclear density probability of the Minkowski distance of each sample as a weight, fitting the correction coefficient of each relevant parameter by a least square method, and finally giving a parameter result of the corrected working condition.
The method is combined with a steam turbine system of a 600MW subcritical air cooling unit of a certain power plant in inner Mongolia to analyze the practicability of the method. Taking steam turbine heat consumption as an example, the original data come from an SIS system PI database of the unit, the time is 8 months 1 day to 8 months 15 days in 2020, the sampling interval is 1min, and 21600 groups of data are totally collected. The steady state threshold was taken to be 1.5, resulting in 8765 steady state samples, with the remaining 12835 dynamic samples.
The grey correlation analysis was performed on the samples under steady state conditions, and the results are shown in table 1.
TABLE 1
And (3) taking the gray correlation degree reduction threshold value as 0.75, and for the operation working condition of the steam turbine, screening the main steam pressure, the main steam temperature, the reheat steam temperature, the vacuum degree, the load, the main steam flow, the regulation stage pressure and the regulation stage temperature as energy consumption characteristic variables of the steam turbine. The main steam pressure, the main steam temperature, the reheated steam temperature, the vacuum degree and the load are boundary parameters of the operation condition of the steam turbine, and the main steam flow, the adjusting stage pressure and the adjusting stage temperature are related parameters of the operation condition of the steam turbine. According to the historical fluctuation range of the parameters, the division interval of the working condition intervals can be artificially determined, and the specific division parameters are shown in table 2.
| Main steam pressure/MPa | Temperature of main steam/. degree.C | Reheat steam temperature/. degree.C | Vacuum degree/kPa | |
| Extent of variation of parameter | 13-17 | 520-570 | 500-570 | 7-18 |
| Interval of interval | 0.5 | 5 | 5 | 1 |
TABLE 2
For each piece of dynamic process data to be corrected, the 40 operating conditions that are the nearest minkowski distance are taken as reference samples. Based on a least square estimation method, the correction coefficients of operation related parameters such as load, main steam flow, high-pressure cylinder exhaust pressure and the like are estimated according to the difference values of the parameters such as main steam pressure, main steam temperature, reheated steam temperature, vacuum degree and the like. The parameter distribution conditions of part of the original dynamic working conditions, the corrected working conditions and the nearest neighbor working conditions are shown in table 3, fig. 2 is a scatter diagram of the steam turbine heat consumption along with the load distribution under different working conditions, table 4 is the steam turbine heat consumption standard deviation and deviation rate corresponding to different working conditions under all load intervals, and fig. 3 is the average value distribution of the steam turbine heat consumption rate of different load intervals. As can be seen from FIG. 2, the projected area of the heat rate of the operating condition after the interpolation correction of the neighboring operating condition is significantly reduced. As is apparent from table 4 and fig. 3, the corrected heat loss deviation ratio is greatly reduced, and the fluctuation is suppressed. From the average heat consumption value of each load interval, the trend that the heat consumption of the corrected steam turbine is gradually reduced along with the load is more obvious, the numerical value is closer to the result in the test report, and the effect of the dynamic sample after correction is proved to be ideal, so that the engineering requirement can be met.
TABLE 3
TABLE 4
In order to further verify the reproducibility of the corrected working condition parameters, the coupling between the energy consumption characteristic parameters is verified by using a steady-state heat consumption prediction model. Input parameters of the neural network model are energy consumption characteristic parameters, 500 steady-state working condition samples are screened in total, the first 400 steady-state working condition samples are used for training a steady-state heat consumption prediction model based on a BP neural network algorithm, the last 100 steady-state working condition samples and 100 dynamically corrected working condition samples are used for evaluating errors of the model in the test set, evaluation indexes of the errors are Mean Relative Error (MRE) and Root Mean Square Error (RMSE), and the errors of the training set and the test set are shown in Table 5. As can be seen from Table 5, the error of the corrected operation parameter for predicting the heat consumption of the steam turbine is slightly larger than the prediction error of the steady-state working condition sample, but the MRE is also within 1.5%, so that the coupling relation between all relevant parameters is still guaranteed, and the method still has guiding significance in actual operation optimization.
TABLE 5
The method makes full use of the historical steady-state data of the unit to correct the dynamic working conditions with complex changes, has higher speed, smaller calculated amount and higher parameter stability compared with the traditional modeling and supplementing method, and is suitable for being used in the field of data mining and operation optimization of thermal power units to supplement sparse data areas and samples.
The foregoing is an exemplary embodiment of the present application, and the scope of the present application is defined by the claims and their equivalents.
Claims (6)
1. A correction method for dynamic working condition data of a thermal power generating unit is characterized by comprising the following steps:
primarily screening first characteristic parameters related to system operation performance through mechanism analysis, and selecting second characteristic parameters related to system operation performance indexes from the first characteristic parameters through a grey correlation algorithm;
calculating the statistic of the second characteristic parameter to obtain a steady-state factor describing the stability of the working condition, comparing the steady-state factor with a steady-state threshold value, and considering the steady-state factor smaller than the steady-state threshold value as a steady-state working condition sample;
calculating the Minkowski distance between the steady-state operating condition sample and the dynamic operating condition sample S of known boundary conditions, if the Minkowski distance between the first steady-state operating condition sample and the dynamic operating condition sample S is less than a distance threshold d ε If the first steady-state working condition is a neighboring working condition, screening out samples { w ] of the neighboring working condition from the samples of the steady-state working condition through Minkowski distance 1 ,w 2 ,...,w K ,w N And f, obtaining (K +1) neighbor working conditions in the neighbor working condition samples, w N Representing the nearest neighbor working condition to the dynamic working condition sample S;
calculating the neighbor condition samples { w 1 ,w 2 ,...,w K ,w N Nuclear density distribution of };
performing least square estimation on the energy consumption evaluation index of the dynamic working condition sample S and the correction coefficient of the related parameter according to the nuclear density distribution to obtain a final correction coefficient;
correcting the dynamic working condition sample S according to the final correction coefficient to obtain a corrected quasi-steady-state working condition sample S';
wherein the dynamic condition sample S is represented as
I S The energy consumption evaluation index of the system under the dynamic working condition is shown,
representing the boundary parameters of the system under dynamic conditions,
and expressing the relevant parameters of the system under the dynamic working condition, wherein u expresses the boundary parameters, r expresses the relevant parameters, and m and n respectively express the number of the boundary parameters and the relevant parameters.
2. A method as claimed in claim 1 wherein the Minkowski distance is expressed as:
wherein d (A, B) represents any two points A (a) in m-dimensional space 1 ,a 2 ,...a m ) And B (B) 1 ,b 2 ,...b m ) Minkowski distance, A (a) 1 ,a 2 ,...a m ) Represents any one of the dynamic conditions, B (B) 1 ,b 2 ,...b m ) And representing any stable working condition in the steady-state working condition samples, wherein p represents a variable parameter.
3. The method of claim 2, wherein the computing the neighbor condition samples { w } 1 ,w 2 ,...,w K ,w N A nuclear density distribution of { includes:
4. The method according to claim 3, wherein the least square estimation is performed on the energy consumption evaluation index of the dynamic working condition sample S and the correction coefficient of the related parameter according to the nuclear density distribution to obtain a final correction coefficient,
the method comprises the following steps:
wherein,
representing boundary parameters
Energy consumption evaluation index I S The correction coefficient of (a) is determined,
representing boundary parameters
For j relevant parameter
J is within [1, n ]]；
Indicating neighbor condition w k Energy consumption evaluation index of (1) and w N The difference of the energy consumption evaluation indexes;
indicating neighbor condition w k Boundary parameter and w N The set of difference values of the boundary parameters of (a);
indicating neighbor condition w k J-th correlation parameter of (1) and w N The difference of the jth boundary parameter of (a); f. of h (d k ) Represents any neighbor operating condition and w N A distance of d k Probability density of temporal correspondence; θ 1, θ 2 represent parameters that minimize the argmin (.) result.
5. The method according to claim 1, wherein the modifying the dynamic condition sample S according to the final modification coefficient to obtain a modified quasi-steady state condition sample S' comprises:
wherein,
boundary parameter and w representing dynamic condition sample S N A set of difference values of the boundary parameters of (a);
denotes w N The energy consumption evaluation index of (1);
denotes w N The j-th correlation parameter of (1); finally obtaining a quasi-steady state working condition sample
6. The method of claim 2, wherein p is 2.
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