CN113359435A - Correction method for dynamic working condition data of thermal power generating unit - Google Patents
Correction method for dynamic working condition data of thermal power generating unit Download PDFInfo
- Publication number
- CN113359435A CN113359435A CN202110517846.2A CN202110517846A CN113359435A CN 113359435 A CN113359435 A CN 113359435A CN 202110517846 A CN202110517846 A CN 202110517846A CN 113359435 A CN113359435 A CN 113359435A
- Authority
- CN
- China
- Prior art keywords
- working condition
- steady
- dynamic
- state
- condition
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000012937 correction Methods 0.000 title claims abstract description 25
- 238000005265 energy consumption Methods 0.000 claims description 23
- 238000011156 evaluation Methods 0.000 claims description 17
- 238000012216 screening Methods 0.000 claims description 10
- 230000007246 mechanism Effects 0.000 claims description 4
- 230000002123 temporal effect Effects 0.000 claims description 2
- 238000012986 modification Methods 0.000 claims 1
- 230000004048 modification Effects 0.000 claims 1
- 230000008569 process Effects 0.000 abstract description 10
- 238000007418 data mining Methods 0.000 abstract description 6
- 238000005457 optimization Methods 0.000 description 4
- 230000009467 reduction Effects 0.000 description 4
- 238000012549 training Methods 0.000 description 4
- 238000010977 unit operation Methods 0.000 description 4
- 230000033228 biological regulation Effects 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 239000013589 supplement Substances 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000017525 heat dissipation Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Testing And Monitoring For Control Systems (AREA)
- Monitoring And Testing Of Nuclear Reactors (AREA)
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 thermal process data-driven modeling, 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 intervention of artificial control of the power plant and the influence of internal factors such as the hysteresis characteristic of the 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 operation optimization rules.
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 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 dεIf 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 distance1,w2,...,wK,wNAnd f, obtaining (K +1) neighbor working conditions in the neighbor working condition samples, wNRepresenting the nearest neighbor working condition to the dynamic working condition sample S;
calculating the neighbor condition samples { w1,w2,...,wK,wNNuclear 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
ISThe 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, mechanism analysis is used for preliminarily screening relevant parameters of system operation, a characteristic variable with a large association degree with a unit performance index is selected through a grey association degree algorithm, reduction is carried out through a certain association degree threshold value to obtain an energy consumption characteristic parameter, and X is recorded as X ═ X { (X)u,XrThe characteristic parameter of energy consumption of a certain system is represented by an uncontrollable characteristic parameter Xu(boundary parameters) and controllable characteristic parametersr(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 dεIf 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 distance1,w2,...,wK,wNAnd f, obtaining (K +1) neighbor working conditions in the neighbor working condition samples, wNRepresenting 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 dεWill 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 space1,a2,...am) And B (B)1,b2,...bm) Minkowski distance, A (a)1,a2,...am) Represents any one of the dynamic conditions, B (B)1,b2,...bm) And representing any one stable working condition in the steady-state working condition samples, 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 { w1,w2,...,wK,wNThe kernel density distribution of the image can be estimated by selecting a Gaussian kernel function.
Specifically, neighbor condition samples { w1,w2,...,wK,wNA nuclear density distribution of { includes:
wherein f ish(d) Representing any near neighbor working condition and nearest neighbor working condition wNWhen the distance of (d) is the corresponding probability density, dkIndicating representative neighbor conditions wkAnd wNK1, 2, K, h denotes the bandwidth, h dεThe/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
ISThe 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 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 samples to be corrected, the boundary parameters of the dynamic working condition samples to be corrected can be matched in a steady-state operation working condition library, steady-state samples within similar working conditions of the boundary are screened out, and the Minkowski distance between the steady-state samples and the working conditions to be corrected is respectively calculated. Setting a distance threshold d of Minkowski distanceεAnd 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 grey correlation degree reduction threshold value as 0.75, and for the operation condition of the steam turbine, screening main steam pressure, main steam temperature, reheated steam temperature, vacuum degree, load, main steam flow, regulation stage pressure and 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 | |
| Range 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 (5)
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;
minkowski distance of the steady-state operating condition samples from the dynamic operating condition samples S of known boundary conditions is calculated,if the Minkowski distance between the first one of the steady-state condition samples and the dynamic condition sample S is less than a distance threshold dεIf 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 distance1,w2,...,wK,wNAnd f, obtaining (K +1) neighbor working conditions in the neighbor working condition samples, wNRepresenting the nearest neighbor working condition to the dynamic working condition sample S;
calculating the neighbor condition samples { w1,w2,...,wK,wNNuclear 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
ISThe 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. The method as claimed in claim 1, wherein said minkowski distance is expressed as:
wherein d (A, B) represents any two points A (a) in m-dimensional space1,a2,...am) And B (B)1,b2,...bm) Minkowski distance, A (a)1,a2,...am) Represents any one of the dynamic conditions, B (B)1,b2,...bm) And representing any one stable working condition in the steady-state working condition samples, wherein p represents a variable parameter, and p is 2.
3. The method of claim 2, wherein the computing the neighbor condition samples { w }1,w2,...,wK,wNA nuclear density distribution of { includes:
4. The method according to claim 3, wherein the performing least square estimation on the energy consumption evaluation index of the dynamic condition sample S and the correction coefficient of the related parameter according to the nuclear density distribution to obtain a final correction coefficient comprises:
wherein,
representing boundariesParameter(s)
Energy consumption evaluation index ISThe correction coefficient of (a) is determined,
representing boundary parameters
For j relevant parameter
J is within [1, n ]]；
Indicating neighbor condition wkEnergy consumption evaluation index of (1) and wNThe difference of the energy consumption evaluation indexes;
indicating neighbor condition wkBoundary parameter and wNA set of difference values of the boundary parameters of (a);
indicating neighbor condition wkJ-th correlation parameter of (1) and wNThe difference of the jth boundary parameter of (a); f. ofh(dk) Represents any neighbor operating condition and wNA distance of dkProbability 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 SNA set of difference values of the boundary parameters of (a);
denotes wNThe energy consumption evaluation index of (1);
denotes wNThe j-th correlation parameter of (1); finally obtaining a quasi-steady state working condition sample
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110517846.2A CN113359435B (en) | 2021-05-12 | 2021-05-12 | Correction method for dynamic working condition data of thermal power generating unit |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110517846.2A CN113359435B (en) | 2021-05-12 | 2021-05-12 | Correction method for dynamic working condition data of thermal power generating unit |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113359435A true CN113359435A (en) | 2021-09-07 |
| CN113359435B CN113359435B (en) | 2022-08-12 |
Family
ID=77526371
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110517846.2A Active CN113359435B (en) | 2021-05-12 | 2021-05-12 | Correction method for dynamic working condition data of thermal power generating unit |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113359435B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114398779A (en) * | 2022-01-07 | 2022-04-26 | 华北电力科学研究院有限责任公司 | Method and device for determining coal consumption characteristic curve of thermal power generating unit |
| CN115875091A (en) * | 2021-09-26 | 2023-03-31 | 国能智深控制技术有限公司 | Method and device for monitoring flow characteristics of turbine valve and readable storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102636991A (en) * | 2012-04-18 | 2012-08-15 | 国电科学技术研究院 | Method for optimizing running parameters of thermal power unit and based on fuzzy set association rule |
| US20130013255A1 (en) * | 2011-07-06 | 2013-01-10 | Honeywell International Inc. | Automatic identification of operating parameters for power plants |
| CN104503406A (en) * | 2014-12-16 | 2015-04-08 | 华润电力湖北有限公司 | Optimized operation evaluation system and method for thermal power plant |
| CN110310033A (en) * | 2019-06-28 | 2019-10-08 | 清华大学 | Transient Stability Evaluation method and system based on trend vector Minkowski Distance |
| CN112749840A (en) * | 2020-12-29 | 2021-05-04 | 华北电力大学 | Method for acquiring reference value of energy efficiency characteristic index of thermal power generating unit |
-
2021
- 2021-05-12 CN CN202110517846.2A patent/CN113359435B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130013255A1 (en) * | 2011-07-06 | 2013-01-10 | Honeywell International Inc. | Automatic identification of operating parameters for power plants |
| CN102636991A (en) * | 2012-04-18 | 2012-08-15 | 国电科学技术研究院 | Method for optimizing running parameters of thermal power unit and based on fuzzy set association rule |
| CN104503406A (en) * | 2014-12-16 | 2015-04-08 | 华润电力湖北有限公司 | Optimized operation evaluation system and method for thermal power plant |
| CN110310033A (en) * | 2019-06-28 | 2019-10-08 | 清华大学 | Transient Stability Evaluation method and system based on trend vector Minkowski Distance |
| CN112749840A (en) * | 2020-12-29 | 2021-05-04 | 华北电力大学 | Method for acquiring reference value of energy efficiency characteristic index of thermal power generating unit |
Non-Patent Citations (1)
| Title |
|---|
| 杨利: "基于运行数据的火电机组性能指标评价方法研究", 《硕士学位论文全文库》, 31 March 2020 (2020-03-31) * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115875091A (en) * | 2021-09-26 | 2023-03-31 | 国能智深控制技术有限公司 | Method and device for monitoring flow characteristics of turbine valve and readable storage medium |
| CN115875091B (en) * | 2021-09-26 | 2024-01-09 | 国能智深控制技术有限公司 | Method and device for monitoring flow characteristics of steam turbine valve and readable storage medium |
| CN114398779A (en) * | 2022-01-07 | 2022-04-26 | 华北电力科学研究院有限责任公司 | Method and device for determining coal consumption characteristic curve of thermal power generating unit |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113359435B (en) | 2022-08-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113359435B (en) | Correction method for dynamic working condition data of thermal power generating unit | |
| CN110163429B (en) | Short-term load prediction method based on similarity day optimization screening | |
| CN105868853B (en) | Method for predicting short-term wind power combination probability | |
| CN111260116A (en) | Time-interval refined short-term load prediction method based on BOA-SVR and fuzzy clustering | |
| CN108520269A (en) | A kind of wind speed forecasting method and forecasting wind speed system | |
| CN112987666A (en) | Power plant unit operation optimization regulation and control method and system | |
| CN111709454B (en) | Multi-wind-field output clustering evaluation method based on optimal copula model | |
| CN110263998B (en) | Double-layer correction method for multisource numerical weather forecast set | |
| CN105893669A (en) | Global simulation performance predication method based on data digging | |
| CN110765703B (en) | Wind power plant aggregation characteristic modeling method | |
| CN115511657A (en) | Wind power output and photovoltaic output evaluation method based on combined prediction model | |
| CN112819238A (en) | Short-term wind power prediction method based on chaotic chicken flock optimization algorithm | |
| CN114066069A (en) | Combined weight byproduct gas generation amount prediction method | |
| CN112669168A (en) | Short-term wind power prediction method | |
| CN109193791B (en) | Wind power convergence tendency state-based quantification method based on improved shape value | |
| CN115640874A (en) | Transformer state prediction method based on improved grey model theory | |
| CN111709585A (en) | Air conditioner load prediction method and device and storage medium | |
| CN114330915A (en) | Short-term wind power combination model prediction method | |
| CN114693076A (en) | Dynamic evaluation method for running state of comprehensive energy system | |
| CN110705099A (en) | Method for verifying output correlation of wind power plant | |
| CN110555784A (en) | wind power prediction method based on gated cyclic neural network wind speed correction | |
| CN114970698B (en) | Metering equipment operation performance prediction method based on improved LWPS | |
| CN116307250A (en) | Short-term load prediction method and system based on typical daily feature selection | |
| CN116451826A (en) | Photovoltaic power generation prediction method based on improved long-term and short-term memory neural network | |
| CN116703022A (en) | Power equipment optimal carbon emission evaluation method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |