阅读说明：本技术 一种确定火电机组运行参数基准值的方法及装置 (Method and device for determining operating parameter reference value of thermal power generating unit ) 是由 齐敏芳 罗华耿 于 2018-07-26 设计创作，主要内容包括：本发明的实施例公开一种确定火电机组运行参数基准值的方法及装置,涉及燃煤发电机组能耗分析技术领域,能够通过对火电机组的历史运行数据进行聚类分析,从而获得最优运行参数基准值,提高火电机组煤耗预测的准确率。该方法包括：获取火电机组在预定时间长度内的历史运行数据；将所述历史运行数据划分,生成至少两个机组工况区间；根据第一预定算法对每个机组工况区间内的所述历史运行数据进行聚类,生成至少一组聚类数据簇及至少一个聚类中心；在所述至少一组聚类数据簇中根据预设条件选取最优聚类数据簇；将所述最优聚类数据簇对应的聚类中心作为每个机组工况区间的运行参数基准值。本发明实施例应用于电力系统。(The embodiment of the invention discloses a method and a device for determining a thermal power generating unit operation parameter reference value, relates to the technical field of coal-fired power generating unit energy consumption analysis, and aims to obtain an optimal operation parameter reference value and improve the accuracy of thermal power generating unit coal consumption prediction by performing cluster analysis on historical operation data of a thermal power generating unit. The method comprises the following steps: acquiring historical operating data of the thermal power generating unit within a preset time length; dividing the historical operating data to generate at least two unit working condition intervals; clustering the historical operating data in the working condition interval of each unit according to a first preset algorithm to generate at least one group of clustering data clusters and at least one clustering center; selecting an optimal clustering data cluster from the at least one group of clustering data clusters according to a preset condition; and taking the clustering center corresponding to the optimal clustering data cluster as the reference value of the operating parameters of each unit working condition interval. The embodiment of the invention is applied to the power system.)

1. A method for determining a reference value of an operating parameter of a thermal power generating unit is characterized by comprising the following steps:

acquiring historical operating data of the thermal power generating unit within a preset time length; wherein the historical operating data comprises: the system comprises a main steam temperature, a main steam pressure, a reheated steam temperature, a water supply temperature, a smoke exhaust temperature, a smoke oxygen content, condenser vacuum and power supply coal consumption;

dividing the historical operating data to generate at least two unit working condition intervals;

clustering the historical operating data in the working condition interval of each unit according to a first preset algorithm to generate at least one group of clustering data clusters and at least one clustering center; wherein the at least one group of clustered data clusters and the at least one clustering center are in a one-to-one correspondence relationship;

selecting an optimal clustering data cluster from the at least one group of clustering data clusters according to a preset condition;

and taking the clustering center corresponding to the optimal clustering data cluster as the reference value of the operating parameters of each unit working condition interval.

2. The method for determining the reference value of the operating parameter of the thermal power generating unit according to claim 1, wherein the step of dividing the historical operating data to generate at least two unit operating condition intervals specifically comprises the steps of:

performing unit working condition division on the historical operation data according to a preset boundary condition to generate at least two unit working condition intervals; the preset boundary conditions comprise unit load, ambient temperature and coal quality characteristics.

3. The method for determining the reference value of the operating parameter of the thermal power generating unit according to claim 1, wherein the clustering center corresponding to the optimal clustering data cluster is used as the reference value of the operating parameter of each unit operating condition interval, and then the method further comprises:

collecting the operating parameter reference values of the operating condition intervals of each unit to construct a reference value operating condition library;

and establishing a power supply coal consumption reference value model by adopting a second preset algorithm according to the reference value working condition library.

4. The method for determining the reference value of the operating parameter of the thermal power generating unit according to claim 1, wherein updating the reference value of the operating parameter according to a predetermined period specifically includes:

acquiring the latest historical operating data within a preset time length according to a preset period;

generating a latest operating parameter reference value according to the latest historical operating data;

and replacing the original operation parameter reference value in the same unit working condition interval with the latest operation parameter reference value.

5. The method for determining the reference value of the operating parameter of the thermal power generating unit according to claim 1, wherein the method comprises the following steps: the first predetermined algorithm is a spectral clustering algorithm.

6. The method for determining the reference value of the operating parameter of the thermal power generating unit according to claim 1, wherein the obtaining of the historical operating data within the preset time length further comprises: and removing or replacing abnormal historical operating data in the historical operating data.

7. An apparatus for determining a baseline value of an operating parameter of a thermal power generating unit, comprising:

the acquisition module is used for acquiring historical operating data of the thermal power generating unit within a preset time length; wherein the historical operating data comprises: the system comprises a main steam temperature, a main steam pressure, a reheated steam temperature, a water supply temperature, a smoke exhaust temperature, a smoke oxygen content, condenser vacuum and power supply coal consumption;

the first processing module is used for dividing the historical operating data acquired by the acquisition module to generate at least two unit working condition intervals;

the first processing module is further used for clustering the historical operating data in the working condition interval of each unit according to a first preset algorithm to generate at least one group of clustering data clusters and at least one clustering center; wherein the at least one group of clustered data clusters and the at least one clustering center are in a one-to-one correspondence relationship;

the first processing module is further configured to select an optimal clustered data cluster from the at least one group of clustered data clusters according to a preset condition;

and the first processing module is also used for taking the clustering center corresponding to the optimal clustering data cluster as the reference value of the operating parameters of the working condition interval of each unit.

8. The apparatus for determining the reference value of the operating parameter of the thermal power generating unit according to claim 7, comprising:

the first processing module is specifically used for dividing the unit working conditions of the historical operating data according to a preset boundary condition to generate at least two unit working condition intervals; the preset boundary conditions comprise unit load, ambient temperature and coal quality characteristics.

9. The apparatus for determining the reference value of the operating parameter of the thermal power generating unit according to claim 7, comprising:

the first processing module is used for collecting the operating parameter reference values of the working condition intervals of each unit to construct a reference value working condition library;

and the second processing module is used for establishing a power supply coal consumption reference value model by adopting a second preset algorithm according to the reference value working condition library established by the first processing module.

10. The apparatus for determining the reference value of the operating parameter of the thermal power generating unit according to claim 7, comprising:

the acquisition module is used for acquiring the latest historical operating data within a preset time length according to a preset period;

the first processing module is used for generating a latest operating parameter reference value according to the latest historical operating data acquired by the acquisition module;

and the first processing module is also used for replacing the latest running parameter reference value with the original running parameter reference value in the same unit working condition interval.

11. The apparatus for determining the reference value of the operating parameter of the thermal power generating unit according to claim 7, comprising: the first predetermined algorithm is a spectral clustering algorithm.

12. The apparatus for determining the reference value of the operating parameter of the thermal power generating unit according to claim 7, comprising:

the first processing module is used for removing or replacing abnormal historical operating data in the historical operating data acquired by the acquiring module.

Technical Field

The embodiment of the invention relates to the technical field of energy consumption analysis of coal-fired power generating units, in particular to a method and a device for determining a reference value of an operating parameter of a thermal power generating unit.

Background

The thermal power generating unit consumption difference analysis is the basis of unit energy conservation and consumption reduction and operation optimization, and is the premise for carrying out analysis and calculation on power plant economy evaluation, energy conservation management and performance evaluation. The accurate determination of the operation parameter reference value directly influences the accuracy of the consumption difference analysis result and is also a reliable guarantee for the unit operation optimization guidance.

The traditional operation parameter reference value can be a design value, an optimized test value, a variable working condition calculation value and the like. However, in recent years, coal-fired units frequently participate in peak shaving and load reduction operations so that new energy can be consumed by a power grid. And taking the design value as the reference value of the operation parameter is not suitable for the unit which operates for long-term variable load. The operation parameter reference value obtained by the test method has a good effect on the initial operation stage of the system, but changes along with the long-term operation of the unit and the change of the operation state of the unit; the optimization test is expensive, the obtained reference working condition is limited, and the optimization test cannot be performed frequently, so that the reference value of the operation parameter is inconsistent with the actual operation state of the unit. The variable working condition calculation value depends on the availability and the accuracy of a variable working condition thermodynamic calculation model, and the obtained optimized operation parameter reference value is a theoretical value and is difficult to achieve in the actual operation of the unit.

Disclosure of Invention

The embodiment of the invention provides a method and a device for determining a thermal power unit operation parameter reference value, which can obtain an optimal operation parameter reference value and improve the accuracy of thermal power unit coal consumption prediction by performing cluster analysis on historical operation data of the thermal power unit.

In a first aspect, a method for determining a reference value of an operating parameter of a thermal power generating unit is provided, and the method is characterized by comprising the following steps: acquiring historical operating data of the thermal power generating unit within a preset time length; wherein the historical operating data comprises: the system comprises a main steam temperature, a main steam pressure, a reheated steam temperature, a water supply temperature, a smoke exhaust temperature, a smoke oxygen content, condenser vacuum and power supply coal consumption; dividing historical operating data to generate at least two unit working condition intervals; clustering historical operating data in the working condition interval of each unit according to a first preset algorithm to generate at least one group of clustering data clusters and at least one clustering center; wherein at least one group of cluster data clusters and at least one cluster center are in one-to-one correspondence; selecting an optimal clustering data cluster from at least one group of clustering data clusters according to preset conditions; and taking the clustering center corresponding to the optimal clustering data cluster as the reference value of the operating parameters of each unit working condition interval.

In the method, historical operating data of the thermal power generating unit in a preset time length is obtained firstly; dividing historical operating data to generate at least two unit working condition intervals; clustering historical operating data in the working condition interval of each unit according to a first preset algorithm to generate at least one group of clustering data clusters and at least one clustering center; wherein at least one group of cluster data clusters and at least one cluster center are in one-to-one correspondence; selecting an optimal clustering data cluster from at least one group of clustering data clusters according to preset conditions; and taking the clustering center corresponding to the optimal clustering data cluster as the reference value of the operating parameters of each unit working condition interval. According to the method, the optimal operation parameter reference value is obtained by clustering and analyzing the historical operation data of the thermal power generating unit, and the accuracy of coal consumption prediction of the thermal power generating unit is improved.

Optionally, the historical operating data is divided to generate at least two unit operating condition intervals, which specifically includes: the method comprises the steps that unit working conditions of historical operation data are divided according to preset boundary conditions, and at least two unit working condition intervals are generated; the preset boundary conditions comprise unit load, ambient temperature and coal quality characteristics.

Optionally, the method further includes, using a clustering center corresponding to the optimal clustering data cluster as an operating parameter reference value of each unit operating condition interval: collecting the operating parameter reference values of each unit working condition interval to construct a reference value working condition library; and establishing a power supply coal consumption reference value model by adopting a second preset algorithm according to the reference value working condition library.

Optionally, the updating the reference value of the operating parameter according to the predetermined period specifically includes: acquiring the latest historical operating data within a preset time length according to a preset period; generating a latest operation parameter reference value according to the latest historical operation data; and replacing the original operation parameter reference value in the same unit working condition interval with the latest operation parameter reference value.

Optionally, the first predetermined algorithm is a spectral clustering algorithm.

Optionally, obtaining historical operating data within a predetermined time period, and then further includes: and removing or replacing abnormal historical operating data in the historical operating data.

In a second aspect, an apparatus for determining a reference value of an operating parameter of a thermal power generating unit is provided, the apparatus comprising:

the acquisition module is used for acquiring historical operating data of the thermal power generating unit within a preset time length; wherein the historical operating data comprises: main steam temperature, main steam pressure, reheated steam temperature, feed water temperature, exhaust gas temperature, flue gas oxygen content, condenser vacuum and power supply coal consumption.

And the first processing module is used for dividing the historical operating data acquired by the acquisition module to generate at least two unit working condition intervals.

The first processing module is further used for clustering historical operating data in the working condition interval of each unit according to a first preset algorithm to generate at least one group of clustering data clusters and at least one clustering center; wherein at least one group of cluster data clusters and at least one cluster center are in one-to-one correspondence.

The first processing module is further used for selecting the optimal clustering data cluster from the at least one group of clustering data clusters according to a preset condition.

And the first processing module is also used for taking the clustering center corresponding to the optimal clustering data cluster as the reference value of the operating parameters of the working condition interval of each unit.

Optionally, the first processing module is specifically configured to divide the unit operating conditions of the historical operating data according to a preset boundary condition, so as to generate at least two unit operating condition intervals; the preset boundary conditions comprise unit load, ambient temperature and coal quality characteristics.

Optionally, the first processing module is configured to set the reference values of the operating parameters of each unit operating condition interval to construct a reference value operating condition library.

And the second processing module is used for establishing a power supply coal consumption reference value model by adopting a second preset algorithm according to the reference value working condition library established by the first processing module.

Optionally, the obtaining module is configured to obtain latest historical operating data within a predetermined time length according to a predetermined period;

the first processing module is used for generating a latest operating parameter reference value according to the latest historical operating data acquired by the acquisition module;

and the first processing module is also used for replacing the original operation parameter reference value in the same unit working condition interval with the latest operation parameter reference value.

Optionally, the first predetermined algorithm is a spectral clustering algorithm.

Optionally, the first processing module is configured to remove or replace abnormal historical operating data in the historical operating data acquired by the acquiring module.

It can be understood that the device for determining the reference value of the operating parameter of the thermal power generating unit is used for executing the method corresponding to the first aspect, so that the beneficial effects achieved by the device can refer to the beneficial effects of the method of the first aspect and the corresponding scheme in the following specific implementation, and are not described herein again.

Drawings

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Fig. 1 is a schematic flowchart of a method for determining a reference value of an operating parameter of a thermal power generating unit according to an embodiment of the present invention;

fig. 2 is a schematic flow chart illustrating a process for determining a reference value of an operating parameter of a thermal power generating unit by using a spectral clustering algorithm according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of an apparatus for determining a reference value of an operating parameter of a thermal power generating unit according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The analysis and calculation of the thermal power plant operation parameter reference value is the most key step and the most important link of the thermal power plant operation performance assessment system, provides target values of operation parameters and performance indexes which reflect the current optimal operation state of the unit, and provides a basis and a basis for operation optimization operation guidance. Without the support of the correct and applicable operation parameter reference value, the operation performance assessment also loses significance.

The load of the thermal power plant fluctuates along with the peak and the valley of power utilization, the thermal power generating unit is required to reach variable load power within a certain time, the control and adjustment of various parameters of the large thermal power generating unit during operation have large delay and too fast load change speed, and the actual operation working condition deviates from the stable working condition due to the large load change amplitude and the too short load stabilization time, so that the power supply coal consumption in the variable load operation process of the thermal power generating unit is far greater than the corresponding stable operation coal consumption. In order to enable each operation parameter to be always kept at an operation parameter reference value during operation, a large supercritical thermal power generating unit can exert the advantage of high parameters, the real-time monitoring of the unit is particularly important, and the consumption difference analysis is the most basic theoretical support for energy conservation and is necessary to research the unit. The method is characterized in that according to the difference value between the actual value of the operation parameter and the reference value, the influence degree of the operation index on the heat consumption rate, the unit efficiency (or device efficiency), the coal consumption rate and the service power of the thermal power unit is obtained through analysis and calculation, so that an operator can actively, intuitively and primarily strive to reduce the controllable loss of the unit according to the quantity concepts. At present, the reference value of the operating parameter in the prior art is mostly the optimum value of the operating parameter determined according to a design value (such as a rated value of the parameter), a variable working condition calculation value, an optimization experiment value and the like, so the reference value of the operating parameter is also called as a standard value or a target value of the operating parameter; however, the reference values of the operating parameters determined by the design values (such as the rated values of the parameters), the calculation values of the variable working conditions, the optimized experimental values and the like cannot be updated in time or are difficult to reach in the actual operation of the unit. Therefore, as shown in fig. 1, an embodiment of the present invention provides a method for determining a reference value of an operating parameter of a thermal power generating unit, including:

101. acquiring historical operating data of the thermal power generating unit within a preset time length; wherein the historical operating data comprises: main steam temperature, main steam pressure, reheated steam temperature, feed water temperature, exhaust gas temperature, flue gas oxygen content, condenser vacuum and power supply coal consumption.

In addition, historical operating data within a predetermined time period is acquired, and then the method further comprises the following steps: and removing or replacing abnormal historical operating data in the historical operating data. Preferably, the historical operating data is collected from a digital power plant platform.

102. And dividing historical operating data to generate at least two unit working condition intervals.

In detail, the historical operating data is divided to generate at least two unit working condition intervals, and the method specifically comprises the following steps: the method comprises the steps that unit working conditions of historical operation data are divided according to preset boundary conditions, and at least two unit working condition intervals are generated; the preset boundary conditions comprise unit load, ambient temperature and coal quality characteristics. For example, the unit working conditions are divided according to boundary conditions such as unit load, ambient temperature and coal quality characteristics, the load interval is divided for the full-load working conditions of the unit, and all the load working conditions in which the unit can operate are covered.

It should be noted that the preset boundary conditions include, but are not limited to, unit load, ambient temperature, and coal quality characteristics.

Illustratively, taking a 600MW coal-fired power generating unit as an example, the variation range of the unit load is generally 300MW to 600MW, and the unit load neighborhood interval Δ P is set to be 5 MW; the smaller the unit load neighborhood interval is divided, the more accurate the obtained reference value of the operation parameter is. The variation range of the ambient temperature is generally 5 to 25 ℃, and the ambient temperature neighborhood Δ T may be set to 1 ℃. If the coal-fired generator set is fixed for the burning coal type of the pithead power station, the coal quality characteristic is assumed to be unchanged; if the coal quality of the coal-fired power generating set changes frequently, interval division is carried out on the coal quality characteristics, and the coal quality heat quantity adjacent interval delta Q is set to be 0.1MJ/kg mainly according to the coal quality heat quantity.

103. Clustering historical operating data in the working condition interval of each unit according to a first preset algorithm to generate at least one group of clustering data clusters and at least one clustering center; wherein at least one group of cluster data clusters and at least one cluster center are in one-to-one correspondence.

It should be noted that the first predetermined algorithm is a spectral clustering algorithm.

104. Selecting an optimal clustering data cluster from at least one group of clustering data clusters according to preset conditions; and taking the clustering center corresponding to the optimal clustering data cluster as the reference value of the operating parameters of each unit working condition interval.

In addition, updating the reference value of the operating parameter according to a predetermined period specifically includes: acquiring the latest historical operating data within a preset time length according to a preset period; generating a latest operation parameter reference value according to the latest historical operation data; and replacing the original operation parameter reference value in the same unit working condition interval with the latest operation parameter reference value.

In addition, the clustering center corresponding to the optimal clustering data cluster is used as the reference value of the operating parameter of each unit working condition interval, and then the method further comprises the following steps: collecting the operating parameter reference values of each unit working condition interval to construct a reference value working condition library; and establishing a power supply coal consumption reference value model by adopting a second preset algorithm according to the reference value working condition library.

In detail, when the real-time operation data of the thermal power generating unit is acquired, the power supply coal consumption reference value model generates a new operation parameter reference value under a preset boundary condition according to the real-time operation data of the thermal power generating unit, and the new operation parameter reference value is added into the reference value working condition library so as to improve the reference value working condition library.

Illustratively, the second predetermined algorithm may be, but is not limited to, a support vector machine or a neural network.

For a better understanding, step 103 and the following steps of the above method are described in detail with reference to fig. 2. It should be noted that, according to steps 101 and 102 of the method, historical operating data is divided into p unit working condition intervals; wherein p is a positive integer. In addition, the first predetermined algorithm employs a spectral clustering algorithm. In addition, steps 201-207 correspond to step 103 in the above method; steps 208-210 correspond to step 104 of the above method. The specific steps are as follows:

201. and acquiring a sample matrix X of the k (k is 1,2, …, p) th unit working condition interval.

M unit operating condition sample points exist in the k (k is 1,2, …, p) unit operating condition interval, each unit operating condition sample point contains n cluster analysis indexes (also called historical operating data), and then the sample matrix X is:

wherein x is_ijAnd (i is 1,2, …, m, j is 1,2, …, n) represents the value of the jth cluster analysis index of the ith unit working condition sample point.

202. And constructing a similar matrix W by adopting a Gaussian kernel function according to the sample matrix X.

Typically using a Gaussian kernel function s (x)_i,x_j)＝||x_i-x_j||²And constructing a similarity matrix. w is a_ijIs x_iAnd x_jThe connection weight value between the two is as follows:

wherein σ is a scale parameter.

Introducing information entropy to optimize a scale parameter sigma, and defining a probability function:

the information entropy is:

entropy of information

As a function of the scale parameter sigma. The smaller the value of the information entropy is, the more dispersed the data in the feature are distributed on the corresponding value domain, namely the purity of the feature is higher; on the contrary, if the value of the information entropy is larger, it can be shown that the data in the feature is distributed more uniformly on the corresponding value domain, that is, the purity of the feature is lower.

Optimizing the scale parameter in a certain range, and solving the corresponding scale parameter sigma when the information entropy is minimized.

And selecting the optimal scale parameter sigma to calculate to obtain a similarity matrix W.

Wherein the content of the first and second substances,

203. constructing a degree matrix D based on the similarity matrix W;

the degree matrix D is a diagonal matrix, and the value on the diagonal is the sum of corresponding rows or columns in the W matrix. Namely, it is

204. And constructing a Laplace matrix L (Laplacian) according to the similarity matrix W and the degree matrix D. The formula is as follows:

205. and performing spectral feature analysis on the obtained Laplace matrix L to construct a feature vector space.

Ordering the eigenvalues of the Laplace matrix L from large to small, λ₁≥λ₂≥…≥λ_nIs greater than or equal to 0, and the difference between adjacent characteristic values is defined as spectrum gap delta lambda_i-λ_i+1(i ═ 1,2, …, n-1); the closer the intra-class distribution is, the more separated the inter-class distribution is, the corresponding delta lambda_iThe larger the index is, the lower the index corresponding to the maximum value of the spectrum gap is selected as the number k of clusters in the spectrum gap, and the first k characteristic values lambda are determined according to the index₁≥λ₂≥…≥λ_kThe corresponding feature vector is Z₁,Z₂,…,Z_k(ii) a Constructing a feature vector matrix Z:

206. and normalizing the characteristic vector matrix Z to obtain a normalized characteristic vector space matrix Y.

According to the formula

Wherein Z_ij(i 1,2, …, m; j 1,2, …, k), resulting in a normalized eigenvector space matrix Y:

207. and performing k-means clustering on the characteristic vector space matrix Y to obtain at least one group of clustering data clusters and at least one clustering center of the working condition interval of the kth unit. Wherein at least one group of cluster data clusters and at least one cluster center are in one-to-one correspondence.

208. And judging whether at least one group of clustering data clusters and at least one clustering center of all the unit working condition intervals are obtained. If yes, go to step 209; otherwise, jump to step 201.

209. And selecting the optimal clustering data cluster from at least one group of clustering data clusters in the working condition interval of each unit according to preset conditions.

For example, the preset condition may be that a group of clustered data clusters with the lowest power supply coal consumption in each unit working condition interval is used as an optimal clustered data cluster.

210. And taking the clustering center corresponding to the optimal clustering data cluster of each unit working condition interval as the reference value of the operating parameters of each unit working condition interval.

Wherein, according to the updating operation parameter benchmark value of predetermined cycle, specifically include: acquiring the latest cluster analysis index within a preset time length according to a preset period; generating a latest operating parameter reference value according to the latest clustering analysis index; and replacing the original operation parameter reference value in the same unit working condition interval with the latest operation parameter reference value.

For example, in a stable operation state of the thermal power generating unit, the predetermined period may be selected from 5 days, 10 days, 20 days or one month for updating the model; and if the thermal power unit needs to obtain new thermal power unit operation data after overhaul or equipment fault maintenance, updating the reference value working condition base according to the new operation parameter reference value after generating the new operation parameter reference value.

211. And (4) aggregating the operation parameter reference values of the working condition intervals of each unit to construct a reference value working condition library.

212. And establishing a power supply coal consumption reference value model by adopting a second preset algorithm according to the reference value working condition library.

It should be noted that, there is a possibility that all the preset boundary conditions cannot be covered according to the reference value working condition library obtained under the preset boundary conditions according to the historical operating data within the preset time length. Therefore, a power supply coal consumption reference value model under the working condition corresponding to the operating parameter reference value is constructed on the basis of the reference value working condition library obtained by the spectral clustering algorithm, when the preset boundary condition corresponding to the obtained new operating data does not belong to the reference value working condition library, the new operating data can be input into the power supply coal consumption reference value model under the preset boundary condition to obtain the operating parameter reference value of the preset boundary condition corresponding to the new operating data, and the operating parameter reference value is input into the reference value working condition library. Illustratively, 100 sets of operation parameter reference values obtained according to a spectral clustering algorithm are used for constructing a power supply coal consumption reference value model, when a preset boundary condition corresponding to the acquired new operation data does not belong to a preset boundary condition contained in the 100 sets of operation parameter reference values, a power supply coal consumption reference value corresponding to the preset boundary condition can be generated through the power supply coal consumption reference value model, and the power supply coal consumption reference value is input into a reference value working condition library. And if the coal consumption in actual operation is higher than the reference value, taking measures to adjust the parameters.

In addition, in the actual operation of the thermal power generating unit, the operation data under the real-time boundary condition can be optimized and guided by referring to the reference value working condition library so as to be closer to or reach the reference value of the operation parameter, and therefore lower power supply coal consumption is obtained.

In the method, historical operating data of the thermal power generating unit in a preset time length is obtained firstly; dividing historical operating data to generate at least two unit working condition intervals; clustering historical operating data in the working condition interval of each unit according to a first preset algorithm to generate at least one group of clustering data clusters and at least one clustering center; wherein at least one group of cluster data clusters and at least one cluster center are in one-to-one correspondence; selecting an optimal clustering data cluster from at least one group of clustering data clusters according to preset conditions; and taking the clustering center corresponding to the optimal clustering data cluster as the reference value of the operating parameters of each unit working condition interval. The method comprises the steps of establishing a unit operation parameter reference value working condition library based on actually measured historical operation data of the thermal power unit; clustering historical operating data under a preset boundary condition by adopting a spectral clustering algorithm, and clustering on a sample space with any shape and converging on a global optimal solution by adopting the method; when a similarity matrix is constructed, information entropy weighting optimization is adopted to obtain a scale parameter, so that the sensitivity of a Gaussian function to the scale parameter is reduced, and the clustering effect is improved; and when the spectral characteristic value is analyzed, automatically determining the clustering number based on the difference value of the adjacent characteristic values. And establishing a power supply coal consumption reference value model based on a reference value working condition library obtained by spectral clustering algorithm analysis, and providing dynamic reference power supply coal consumption for thermal power unit consumption difference analysis.

Referring to fig. 3, an embodiment of the present invention provides an apparatus 30 for determining a reference value of an operating parameter of a thermal power generating unit, where the apparatus includes:

the acquiring module 301 is configured to acquire historical operating data of the thermal power generating unit within a preset time length; wherein the historical operating data comprises: main steam temperature, main steam pressure, reheated steam temperature, feed water temperature, exhaust gas temperature, flue gas oxygen content, condenser vacuum and power supply coal consumption.

The first processing module 302 is configured to divide the historical operating data acquired by the acquiring module 301 to generate at least two unit operating condition intervals.

The first processing module 302 is further configured to cluster the historical operating data in the operating condition interval of each unit according to a first predetermined algorithm, and generate at least one group of cluster data clusters and at least one cluster center; wherein at least one group of cluster data clusters and at least one cluster center are in one-to-one correspondence.

The first processing module 302 is further configured to select an optimal clustered data cluster from the at least one group of clustered data clusters according to a preset condition.

The first processing module 302 is further configured to use a clustering center corresponding to the optimal clustering data cluster as an operation parameter reference value of each unit operating condition interval.

In an exemplary scheme, the first processing module 302 is specifically configured to perform unit operating condition division on historical operating data according to a preset boundary condition, so as to generate at least two unit operating condition intervals; the preset boundary conditions comprise unit load, ambient temperature and coal quality characteristics.

In an exemplary scheme, the first processing module 302 is configured to set the reference values of the operating parameters of each unit operating range to construct a reference value operating range library.

And the second processing module 303 is configured to establish a power supply coal consumption reference value model by using a second predetermined algorithm according to the reference value working condition library established by the first processing module 302.

In an exemplary embodiment, the obtaining module 301 is configured to obtain the latest historical operating data within a predetermined time period according to a predetermined period.

A first processing module 302, configured to generate a latest operating parameter reference value according to the latest historical operating data acquired by the acquiring module 301.

The first processing module 302 is further configured to replace the original operating parameter reference value within the same unit operating condition interval with the latest operating parameter reference value.

In one exemplary approach, the first predetermined algorithm is a spectral clustering algorithm.

In an exemplary scheme, the first processing module 302 is configured to remove or replace abnormal historical operating data in the historical operating data acquired by the acquiring module 301.

The device for determining the reference value of the operating parameter of the thermal power generating unit in the embodiment of the present invention may be applied to implement the embodiment of the method described above, so that the technical effects obtained by the device may also refer to the embodiment of the method described above, and the embodiment of the present invention is not described herein again.

The steps of a method or algorithm described in connection with the disclosure herein may be embodied in hardware or in software instructions executed by a processor. For example: the obtaining module, the first processing module and the second processing module may be implemented by separate processors or may be implemented by being integrated in the same processor. Embodiments of the present invention also provide a storage medium, which may include a memory for storing computer software instructions for an apparatus for determining a baseline value of an operating parameter of a thermal power generating unit, the computer software instructions including program code configured to perform a method for determining a baseline value of an operating parameter of a thermal power generating unit. Specifically, the software instructions may be composed of corresponding software modules, and the software modules may be stored in a Random Access Memory (RAM), a flash Memory, a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a register, a hard disk, a removable hard disk, a compact disc Read Only Memory (CD-ROM), or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor.

The embodiment of the invention also provides a computer program which can be directly loaded into the memory and contains software codes, and the computer program can be loaded and executed by a computer to realize the method for determining the reference value of the operating parameter of the thermal power generating unit.

Those skilled in the art will recognize that, in one or more of the examples described above, the functions described in this invention may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.

The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.