CN113806351B - Abnormal value processing method and device for power generation data of thermal power generating unit - Google Patents
Abnormal value processing method and device for power generation data of thermal power generating unit Download PDFInfo
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Abstract
A method and a device for processing abnormal values of power generation data of a thermal power generating unit are characterized by comprising the following steps: step 1, selecting a main measuring point and a plurality of auxiliary measuring points from all measuring points of the thermal power generating unit, and collecting operation data of the main measuring point and the auxiliary measuring points; step 2, performing relevance analysis on the operation data of the master measuring point and the operation data of the slave measuring points, and establishing a linear relation with highest relevance; and 3, generating an operation data threshold value of the slave measuring point under the main measuring point based on the linear relation of the highest correlation, and eliminating invalid operation data according to the threshold value. The method has simple steps, high efficiency and accurate result, and can not cause the pressure of data processing in the system.
Description
Technical Field
The invention relates to the field of power generation, in particular to a method and a device for processing abnormal values of power generation data of a thermal power generating unit.
Background
With the rapid development and progress of the power industry, the demand of each industry on power is gradually increased, so that the technologies of actual operation state, performance detection, fault diagnosis and early warning and the like of each link in the thermal power unit are more and more emphasized.
At present, in order to enable each device and each link in a thermal power unit to be accurately monitored and detected in real time, a large number of data monitoring and collecting points are usually arranged in a thermal power unit system and used for monitoring abnormal data. However, since the thermal power generating unit generally operates in a severe environment, all data collected by the data monitoring and collecting point may not necessarily represent the operating condition of the thermal power generating unit device well. Therefore, in order to accurately predict the operation condition of the equipment in the thermal power unit system, an operation of removing abnormal values from the collected operation data is required.
In the prior art, an abnormal value elimination method in thermal power unit operation data generally aims at comparing data, collected by a single measuring point at different time periods, for representing a single parameter, so as to obtain an abnormal value. The abnormal value eliminating method is complex, the eliminating efficiency is low, and the prediction requirement of the intelligent thermal power unit system cannot be met.
Therefore, a method and a device for processing abnormal values of power generation data of a thermal power generating unit are needed.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a novel method and a novel device for processing abnormal values of power generation data of a thermal power generating unit.
The invention adopts the following technical scheme.
The invention relates to a method for processing abnormal values of power generation data of a thermal power generating unit, wherein the method comprises the following steps: step 1, selecting a main measuring point and a plurality of auxiliary measuring points from all measuring points of a thermal power generating unit, and collecting operation data of the main measuring point and the auxiliary measuring points; step 2, performing relevance analysis on the operation data of the master measuring point and the operation data of the slave measuring points, and establishing a linear relation with highest relevance; and 3, generating an operation data threshold of the slave measuring point under the master measuring point based on the linear relation of the highest correlation, and removing invalid operation data according to the threshold.
Preferably, the main and the secondary measuring points are arranged in the boiler equipment, the steam turbine equipment, the generator equipment and the related auxiliary equipment which are connected in sequence.
Preferably, acquiring basic indexes for measuring the operation capacity and the power generation capacity of the thermal power generating unit as main measuring points; the index is obtained from a power plant production database.
Preferably, the main measuring point is the active power representing the load of the thermal power generating unit.
Preferably, the measuring points are selected from all measuring points arranged on the thermal power generating unit in a manner of selecting measuring points having correlation factors with the main measuring point.
Preferably, the correlation factors include total coal feed, total water feed, and water feed temperature.
Preferably, step 2 further comprises: step 2.1, performing correlation operation on the operation data of the master measuring point and the operation data of each slave measuring point to obtain a correlation coefficient between the current slave measuring point and the master measuring point; step 2.2, traversing all the slave measuring points, comparing the correlation coefficients of all the slave measuring points and extracting the slave measuring point with the maximum correlation coefficient; and 2.3, generating a linear relation based on the operation data of the main measuring point and the operation data of the secondary measuring point with the maximum correlation coefficient.
Preferably, the linear relationship is
(ii) a Wherein the content of the first and second substances,
is the operation data of the main measuring point,
representing the operation data of the slave measuring point with the maximum correlation coefficient in a vector form;
、
is a constant.
Preferably, the obtaining mode of the operation data threshold of the main measuring point is as follows: the method comprises the steps of determining the operation data of all measuring points of the thermal power generating unit according to the operation data, and determining the operation data of all measuring points of the thermal power generating unit according to the operation data.
Preferably, when the operation data of the slave measuring point exceeds the operation data threshold range of the slave measuring point under the master measuring point, the operation data of the slave measuring point is judged to be invalid operation data.
Preferably, a regression function model of the support vector machine is established, and fitting regression analysis is performed on the data so as to fill the rejected invalid operation data.
The invention relates to a device for processing abnormal values of power generation data of a thermal power generating unit, wherein the device comprises a data acquisition module, an association analysis module and an abnormal elimination module; the data acquisition module is used for selecting a main measuring point and a plurality of auxiliary measuring points from all measuring points of the thermal power generating unit and acquiring operation data of the main measuring point and the auxiliary measuring points; the correlation analysis module is used for performing correlation analysis on the operation data of the master measuring point and the operation data of the slave measuring points and establishing a linear relation with the highest correlation; and the abnormal elimination module is used for generating a running data threshold value of the slave measuring point under the main measuring point based on the linear relation with the highest correlation and eliminating invalid running data according to the threshold value.
Compared with the prior art, the method and the device for processing the abnormal values of the power generation data of the thermal power generating unit have the advantages that the linear relation between the measuring points can be obtained by reasonably selecting the main measuring point and the auxiliary measuring point and carrying out acquisition and correlation analysis on the operation data of the main measuring point and the auxiliary measuring point, and invalid operation data can be eliminated. The method has simple steps, high efficiency and accurate result, and can not cause the pressure of data processing in the system.
The beneficial effects of the invention also include:
1. the method can combine the characteristics of actual operation parameters of the thermal power generating unit, correlation analysis is carried out on the parameters corresponding to the measuring points of the unit, so that correlation between variables is obtained, and irrelevant redundant data and parameters are removed by establishing a correlation relationship, so that the screening efficiency and the screening quality of the data are improved.
2. In the process of parameter selection, the method removes a large number of irrelevant or redundant or low-accuracy parameters, so that in order to ensure that the data actually utilized in the method is sufficient, a vector machine model method is adopted for data filling, the power failure of the whole data is prevented, and the continuity of the data is enhanced.
3. In the prior art, a calculation method of a linear relation is very simple and efficient, however, the calculation method of the linear relation cannot well characterize the correlation characteristics between data. Based on the technical problem, the invention adopts a creative method to select the main measuring point and the auxiliary measuring point, so that the linear relation equation can well represent the correlation among all data, and the elimination of invalid operation data is effectively realized.
4. The effective data obtained by the method of the invention without the invalid operation data can be used as the basis of various requirements, and the accurate analysis, prediction and control of the power generation condition of the thermal power generating unit can be realized.
Drawings
FIG. 1 is a schematic flow chart illustrating steps of a method for processing abnormal values of power generation data of a thermal power generating unit according to the present invention;
FIG. 2 is a schematic diagram of a linear relationship of highest correlation in the abnormal value processing method for power generation data of a thermal power generating unit according to the present invention;
fig. 3 is a schematic block structure diagram of an abnormal value processing device for power generation data of a thermal power generating unit according to the present invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
Fig. 1 is a schematic flow chart illustrating steps of a method for processing abnormal values of power generation data of a thermal power generating unit according to the present invention. As shown in fig. 1, a first aspect of the present invention relates to a method for processing abnormal values of power generation data of a thermal power generating unit, wherein the method includes the following steps: step 1, selecting a main measuring point and a plurality of auxiliary measuring points from all measuring points of the thermal power generating unit, and collecting operation data of the main measuring point and the auxiliary measuring points.
It is understood that a plurality of measuring points can be arranged in the thermal power generating unit firstly in the invention. Specifically, the measurement point refers to a point at which operating data of the thermal power generating unit can be acquired. The measuring point can be operation data of each device in the thermal power generating unit, such as unit load, load rate and other data; or the environmental parameters such as temperature and humidity data of the operating environment of the thermal power unit and the like acquired by acquisition equipment such as sensors and the like arranged on main equipment of the thermal power unit.
Preferably, the main and the secondary measuring points are arranged in the boiler equipment, the steam turbine equipment, the generator equipment and the related auxiliary equipment which are connected in sequence.
In an embodiment of the present invention, the thermal power generating unit may include a boiler device, a steam turbine device, a generator device, and operation data acquired from each of related auxiliary devices of the thermal power generating unit that do not participate in power generation. A plurality of measuring points can be arranged on each device according to actual conditions, and the plurality of measuring points can be used for collecting the same data in the same device for multiple times and collecting different data in the same device.
It can be understood that in the process of measuring point setting, a preliminary association relationship can be established according to the association attributes between the measuring points. For example, the temperature data of a certain measuring point on the generator equipment should have a correlation with the temperature data of other measuring points on the generator. The reason is that the environmental temperature is generally consistent, and when the temperature data of a certain measuring point and the temperature data of other measuring points have a larger difference, it indicates that there may be an abnormality in the position of the device where the current measuring point is located, or there may be an abnormality in the data acquisition process of the current measuring point itself. For another example, since main devices such as a boiler, a steam turbine, and a generator should be connected in sequence to achieve the overall power generation goal, the operation data of the generator device should have a certain correlation to some extent, and specifically, the unit load of the generator, the unit load of the steam turbine, and the coal consumption per unit time of the boiler should have a certain correlation.
Preferably, acquiring an optimal basic index for measuring the operation capacity and the power generation capacity of the thermal power generating unit as a main measuring point;
the basic indexes are obtained from a power plant production database.
It is understood that, in the present invention, the main point may be selected according to the principle that a certain data has the strongest correlation, or the data is most representative as the basic. In the method, the operation capacity and the power generation capacity of the thermal power generating unit are considered, the operation condition of the thermal power generating unit can be represented most, and therefore the index capable of representing the condition is selected on the premise of the condition. Preferably, the main measuring point is the active power representing the load of the thermal power generating unit.
Since the indexes are not unique in the production data of the power plant, the most basic index can be selected from the indexes to be used as a main measuring point. Specifically, for example, the data capable of characterizing the operation capacity and the power generation capacity of the thermal power generating unit may include a plurality of contents, such as unit active power, unit load rate, and the like. The active power is the premise of the load and the load rate of the computer unit, and therefore the data is used as a basic index and is selected as a main measuring point. Preferably, the measuring points are selected from all measuring points arranged on the thermal power generating unit in a manner of selecting measuring points having correlation factors with the main measuring point.
It will be appreciated that after the master station is selected, not all of the other stations in the system are slave stations. The slave measuring points are extracted from all the measuring points based on the natural association relationship of the slave measuring points and the master measuring point. Specifically, after different master points are selected according to the requirement of data analysis, the slave points can be automatically generated according to the association relationship between the slave points and the master points.
In the process of data acquisition and storage, data among various measuring points can be extracted in a plurality of different associated modes. For example, all operational data is assigned a plurality of different correlation factors. The correlation factor may characterize whether the data has one or more correlation properties. For example, the temperature can be used as a correlation factor and distributed to all the operation data acquired by using the temperature sensor as a measuring point; in addition, the power consumption may be assigned to the unit load data of the generator, the unit load data of the turbine, and the coal consumption data per unit time of the boiler as a correlation factor.
Preferably, the correlation factors include total coal feed, total water feed, and water feed temperature.
In the invention, after the active power is selected as the main measuring point, other auxiliary measuring points related to the active power can be data measuring points related to coal feeding, water feeding and the like. Therefore, the correlation factors can be set to the total coal feeding amount, the total water feeding amount and the water feeding temperature, so that the relevant measurement indexes of the coal feeding water can be obtained.
By the mode, one datum may have a plurality of different correlation factors, and when one datum is used as the operation datum of the main measuring point, the corresponding operation datum of the auxiliary measuring point can be searched according to the plurality of correlation factors. And the slave measuring point can be corresponded according to the searched data.
And 2, performing relevance analysis on the operation data of the main measuring point and the operation data of the secondary measuring point, and establishing a highest-relevance linear relation.
After the operation data of the master measuring point and the slave measuring point are obtained, the association relationship between the master measuring point and the slave measuring point can be established for each slave measuring point, and the association relationship is analyzed.
Preferably, step 2 further comprises: step 2.1, performing correlation operation on the operation data of the master measuring point and the operation data of each slave measuring point to obtain a correlation coefficient between the current slave measuring point and the master measuring point; step 2.2, traversing all the slave measuring points, comparing the correlation coefficients of all the slave measuring points and extracting the slave measuring point with the maximum correlation coefficient; and 2.3, generating a linear relation based on the operation data of the main measuring point and the operation data of the secondary measuring point with the maximum correlation coefficient.
It can be understood that the correlation operation of the data between the master measuring point and the slave measuring point can be realized in a plurality of different ways. In an embodiment of the present invention, the correlation coefficient may be used for calculation.
It will be appreciated that the correlation coefficient is expressed as
Wherein, in the step (A),
respectively the operation data of the main measuring point and the slave measuring point,
wherein E is the number of atoms desired,
is the variance. According to the formula, the correlation coefficient between the two can be calculated. In this formula, unless the correlation coefficient between two identical items of data is equal to 1 and the correlation coefficient between two completely unrelated items of data is equal to 0, most of the other items have a correlation relationshipShould be in the interval range of 0 to 1.
In addition, after data of each slave measuring point is traversed and compared with the master measuring point, the slave measuring point with the highest relevance with the master measuring point can be obtained. From a comparison of operational data between this slave site and the master site, a linear relationship can be generated.
Preferably, the linear relationship is
(ii) a Wherein the content of the first and second substances,
is the operation data of the main measuring point,
representing the operation data of the slave measuring point with the maximum correlation coefficient in a vector form;
、
is a constant.
It is understood that the linear relationship in the present invention can be obtained from multiple data between two measuring points. For example, data collected at different time points by two measuring points can be combined into a data vector form, and a linear equation can be obtained by performing linear fitting according to the two data vectors.
In one embodiment of the invention, the operational data of one of the slave stations most relevant to the unit load data is unit load rate data. Therefore, a linear equation can be established based on the correlation between the two data.
Fig. 2 is a schematic diagram of a highest correlation linear relationship in the abnormal value processing method for power generation data of a thermal power generating unit. As shown in FIG. 2, the formula can be obtained from a linear fitting method
. In the formula, in the above-mentioned formula,
representing the load rate data of the unit,
representing unit load data. While
、
It is a constant obtained by a linear fitting method. In particular, since the linear fitting method approximates the correlation between two operation data, it can also be used to characterize the correlation factor between the master measuring point and the slave measuring point having the largest correlation coefficient.
And 3, generating an operation data threshold of the slave measuring point under the master measuring point based on the linear relation of the highest correlation, and removing invalid operation data according to the threshold.
Because the main measuring point and the most relevant auxiliary measuring point have a linear relation, it can be predicted from the linear relation that when the data of the main measuring point is in a certain range and floats up and down, the auxiliary measuring point should be kept in a certain range, for example, in fig. 2, when the unit load data is in a range of 40MW to 80MW, the unit load rate data should fluctuate in a range of 60% to 100%. From this data, a threshold range of operational data from the survey point can be determined.
Preferably, the obtaining mode of the operation data threshold of the main measuring point is as follows: the method comprises the steps of determining the operation data of all measuring points of the thermal power generating unit according to the operation data, and determining the operation data of all measuring points of the thermal power generating unit according to the operation data.
It will be appreciated that the data threshold for the slave stations in FIG. 2 is determined based on the threshold for the master station. The threshold range of the main measuring point can be determined in advance according to prior data, for example, historical data of the main measuring point is collected and analyzed, and the range of the main measuring point in normal conditions is found. In addition, the operation data of all measuring points of the thermal power generating unit can be analyzed, so that when the data of the main measuring point belong to the data under the normal operation condition, the operation data of all the measuring points of the thermal power generating unit can be obtained.
Preferably, when the operation data of the slave measuring point exceeds the operation data threshold range of the slave measuring point under the master measuring point, the operation data of the slave measuring point is judged to be invalid operation data.
It will be appreciated that after a threshold range of operational data from a station has been determined based on the above method, other data from stations exceeding that range can be culled. The rest data are valid data in a normal range and can be used for representing the normal operation condition of the thermal power generating unit.
Preferably, a regression function model of the support vector machine is established, and fitting regression analysis is performed on the data so as to fill the rejected invalid operation data.
In the invention, a regression function model of the support vector machine can be established, and new simulation data is generated through the model, so that the removed data is reasonably filled. Because the new data generated by the support vector machine can meet the basic characteristics of effective operation data, the analysis and the processing of subsequent data cannot be stressed, and meanwhile, sufficient source data is provided for the subsequent data analysis process.
A second aspect of the present invention relates to a thermal power generating unit power generation data abnormal value processing apparatus 100 as described in the first aspect of the present invention.
Fig. 3 is a schematic block structure diagram of an abnormal value processing device for power generation data of a thermal power generating unit according to the present invention. As shown in fig. 3, the apparatus 100 includes a data acquisition module 101, an association analysis module 102, and an exception culling module 103; the data acquisition module 101 is used for selecting a main measuring point and a plurality of auxiliary measuring points from all measuring points of the thermal power generating unit and acquiring operation data of the main and auxiliary measuring points; the correlation analysis module 102 is used for performing correlation analysis on the operation data of the master measuring point and the operation data of the slave measuring points and establishing a linear relation with the highest correlation; and the exception eliminating module 103 is used for generating a running data threshold value of the slave measuring point under the master measuring point based on the highest associated linear relation, and eliminating invalid running data according to the threshold value.
Compared with the prior art, the method and the device for processing the abnormal values of the power generation data of the thermal power generating unit have the advantages that the linear relation between the measuring points can be obtained by reasonably selecting the main measuring point and the auxiliary measuring point and carrying out acquisition and correlation analysis on the operation data of the main measuring point and the auxiliary measuring point, and invalid operation data can be eliminated. The method has simple steps, high efficiency and accurate result, and can not cause the pressure of data processing in the system.
While the present invention has been particularly shown and described with reference to the preferred embodiments thereof, it will be understood by those skilled in the art that the foregoing description is only for the purpose of illustrating the preferred embodiments of the present invention, and the detailed description is only for the purpose of facilitating the reader's understanding of the spirit of the present invention, rather than limiting the scope of the present invention, and any modification or change made to the present invention based on the spirit of the present invention should be considered to fall within the scope of the present invention.
Claims (10)
1. A thermal power generating unit power generation data abnormal value processing method is characterized by comprising the following steps:
step 1, selecting a main measuring point and a plurality of auxiliary measuring points from all measuring points of the thermal power generating unit, and collecting operation data of the main measuring point and the auxiliary measuring points;
step 2, performing relevance analysis on the operation data of the master measuring point and the operation data of the slave measuring points, and establishing a linear relation with highest relevance; the method specifically comprises the following steps:
step 2.1, performing correlation operation on the operation data of the master measuring point and the operation data of each slave measuring point to obtain a correlation coefficient between the current slave measuring point and the master measuring point;
step 2.2, traversing all the slave measuring points, comparing the correlation coefficients of all the slave measuring points and extracting the slave measuring point with the maximum correlation coefficient;
step 2.3, generating a linear relation based on the operation data of the main measuring point and the operation data of the secondary measuring point with the maximum correlation coefficient;
the linear relation is
；
Wherein the content of the first and second substances,
is the operation data of the main measuring point,
representing the operation data of the slave measuring point with the maximum correlation coefficient in a vector form;
and 3, generating an operation data threshold value of the slave measuring point under the main measuring point based on the linear relation of the highest correlation, and eliminating invalid operation data according to the threshold value.
2. The abnormal value processing method for the power generation data of the thermal power generating unit as claimed in claim 1, wherein:
the main measuring point and the auxiliary measuring point are arranged in boiler equipment, steam turbine equipment, generator equipment and related accessory equipment which are connected in sequence.
3. The abnormal value processing method for the power generation data of the thermal power generating unit as claimed in claim 2, wherein:
acquiring basic indexes for measuring the operation capacity and the power generation capacity of the thermal power generating unit as the main measuring points;
the index is obtained from a power plant production database.
4. The abnormal value processing method for the power generation data of the thermal power generating unit according to claim 3, characterized in that:
and the main measuring point represents the active power of the load of the thermal power generating unit.
5. The method for processing the abnormal value of the power generation data of the thermal power generating unit according to claim 4, wherein the method comprises the following steps:
and the slave measuring points are selected in a manner that measuring points with correlation factors with the main measuring point are selected from all measuring points arranged on the thermal power generating unit.
6. The abnormal value processing method for the power generation data of the thermal power generating unit as claimed in claim 5, wherein:
the correlation factors comprise total coal feeding amount, total water feeding amount and water feeding temperature.
7. The abnormal value processing method for the power generation data of the thermal power generating unit as claimed in claim 1, wherein:
the acquisition mode of the operation data threshold of the main measuring point is as follows: the method comprises the steps of determining the operation data of all measuring points of the thermal power generating unit according to the operation data, wherein the operation data are determined in advance based on a priori data, or are determined based on the operation data of all measuring points of the thermal power generating unit.
8. The abnormal value processing method for the power generation data of the thermal power generating unit according to claim 7, characterized in that:
and when the operation data of the slave measuring point exceeds the operation data threshold range of the slave measuring point under the master measuring point, judging that the operation data of the slave measuring point is invalid operation data.
9. The abnormal value processing method for the power generation data of the thermal power generating unit according to claim 8, characterized in that:
and establishing a regression function model of the support vector machine, and performing fitting regression analysis on the data so as to fill the rejected invalid operation data.
10. A thermal power generating unit power generation data abnormal value processing apparatus according to a thermal power generating unit power generation data abnormal value processing method of any one of claims 1 to 9, characterized in that:
the device comprises a data acquisition module, an association analysis module and an exception eliminating module; wherein the content of the first and second substances,
the data acquisition module is used for selecting a main measuring point and a plurality of auxiliary measuring points from all measuring points of the thermal power generating unit and acquiring operation data of the main measuring point and the auxiliary measuring points;
the correlation analysis module is used for performing correlation analysis on the operation data of the master measuring point and the operation data of the slave measuring points and establishing a linear relation with the highest correlation;
and the abnormal removing module is used for generating a running data threshold value of the slave measuring point under the master measuring point based on the highest associated linear relation and removing invalid running data according to the threshold value.
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| CN106596110A (en) * | 2016-11-30 | 2017-04-26 | 国网福建省电力有限公司 | Online data-based hydroelectric generating set hydraulic unbalance fault automatic analysis and diagnosis method |
| CN107061186A (en) * | 2017-06-09 | 2017-08-18 | 北京金风慧能技术有限公司 | Vibration of wind generating set abnormity early warning method and apparatus |
| CN109298697A (en) * | 2018-11-13 | 2019-02-01 | 远光软件股份有限公司 | Thermal power plant's various parts working state evaluation method based on DBM Dynamic Baseline Model |
| CN110058971A (en) * | 2019-04-24 | 2019-07-26 | 华中科技大学无锡研究院 | The intelligent restorative procedure of wind power plant SCADA data missing |
| CN111191881A (en) * | 2019-12-13 | 2020-05-22 | 大唐东北电力试验研究院有限公司 | Thermal power generating unit industrial equipment state monitoring method based on big data |
| CN111275288A (en) * | 2019-12-31 | 2020-06-12 | 华电国际电力股份有限公司十里泉发电厂 | XGboost-based multi-dimensional data anomaly detection method and device |
| CN112687407A (en) * | 2020-12-28 | 2021-04-20 | 山东鲁能软件技术有限公司 | Nuclear power station main pump state monitoring and diagnosing method and system |
| CN112633583A (en) * | 2020-12-29 | 2021-04-09 | 南方电网调峰调频发电有限公司 | Generator set vibration prediction method and device, computer equipment and storage medium |
| CN112664410A (en) * | 2020-12-30 | 2021-04-16 | 苏州绿科智能机器人研究院有限公司 | Big data-based modeling method for unit online monitoring system |
| CN112784373A (en) * | 2021-01-19 | 2021-05-11 | 河北大学 | Fault early warning method for wind turbine generator gearbox |
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