CN108628968B - Turbine measuring point historical database establishing method - Google Patents
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- 238000010606 normalization Methods 0.000 claims abstract description 5
- 238000003672 processing method Methods 0.000 claims description 15
- 238000012216 screening Methods 0.000 claims description 6
- 238000013144 data compression Methods 0.000 claims description 4
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Abstract
The invention discloses a method for establishing a historical database of turbine measuring points, and relates to a method for establishing a historical database. The invention aims to solve the problems that the existing historical database of the steam turbine sensor occupies a large storage space and has low access efficiency. The method comprises the steps of processing data collected by a temperature sensor, a pressure sensor, a flow sensor and a vibration sensor of the steam turbine by using a normalization method; and then compressing the acquired data by using a revolving door compression method, and establishing a historical database of the full life of the steam turbine. The hard disk storage space can be greatly saved, and the data access efficiency is improved. The method is applied to the field of building of the historical database of the large-scale steam turbine.
Description
Technical Field
The invention relates to a historical database establishing method.
Background
The existing turbine sensor data storage database is based on an independent system, the data is not stored in a compression mode, the sampled data volume is large, the stored historical data is limited by the capacity of a hard disk, the historical data cannot be stored for a longer time, data loss and waste are caused, the data volume is too large, the time consumption for calling is long, the resource consumption is large, and the data access efficiency is low.
Disclosure of Invention
The invention provides a method for establishing a historical database of turbine measuring points, which aims to solve the problems of large storage space occupation and low access efficiency of the existing historical database of a turbine sensor.
A method for establishing a historical database of turbine measuring points comprises the following steps:
step A: processing the data collected by the temperature, pressure, flow and vibration sensors of the steam turbine by using a normalization method;
and B: and compressing the acquired data by using a revolving door compression method, and establishing a historical database of the whole life cycle of the steam turbine.
Further, in the step a, the following method is adopted for sensor data processing:
step A1: the sensor data is normalized, and the processing method comprises the following steps:
for the temperature sensor, the data processing method comprises the following steps:
in the formula: t is normalized temperature data, TcDigital temperature signals, T, collected for data acquisitionmaxThe full-load operation temperature of the unit;
for the pressure sensor, the data processing method comprises the following steps:
in the formula: p is normalized pressure data, PcDigital pressure signals, P, collected for data acquisitionmaxThe full load operation pressure of the unit;
for the flow sensor, the data processing method comprises the following steps:
in the formula: l is normalized flow data, LcFor data acquisition by data acquisition unitsIntegrated flow digital signal, LmaxThe flow rate of the unit running at full load;
for the vibration sensor, the data processing method comprises the following steps:
in the formula: w is the normalized vibration data, WcVibration digital signals, W, collected for data acquisitionmaxDesigning a trip value for the unit;
step A2: screening the normalized sensor data, and controlling the storage frequency; the data screening method comprises the following steps:
for temperature, pressure and flow sensors, the variable step length storage method comprises the following steps:
|Ni+n-Ni|＜0.001×n×t
in the formula: n is a radical ofiRepresenting the normalized temperature, pressure or flow sensor data corresponding to the time i; t is a storage time interval, n is a data counter,
if the conditions are met, the storage step length of the temperature, pressure and flow historical database is 60 seconds; if the temperature, the pressure and the flow rate are not satisfied, the storage step length of the historical database is 1 second;
for the vibration sensor, the variable step storage method comprises the following steps:
Wi＜0.4
in the formula: w is the normalized vibration data, WiW corresponding to the moment i;
if the conditions are met, the storage step length of the vibration history database is 60 seconds; if not, the step length of the vibration history database is 0.001 second.
Further, the method for compressing the data of the revolving door in the step B is as follows:
step B1: storing the first data point and the second data point into a database as characteristic data, wherein the two data points are called filing points, and the first data point is used as an initial filing point; starting from a first data point, taking a connecting line between the first data point and a second data point as a central line, and drawing a triangle with the width being 2 times of the compression precision through the two data points; the 2 times of compression precision is exceptional deviation; taking the side corresponding to the second data point as one side of a parallelogram, taking the extension line of the middle line as a middle line of the parallelogram, and taking the third data point as the parallelogram; continuously drawing a new parallelogram by the same method along with the continuous update of the current data point and continuously expanding, wherein when the generated parallelogram can not accommodate the newly added data point, the corresponding parallelogram accommodates the next last data point as a filing point to be stored in a database;
step B2: repeating step B1 with the parallelogram of step B1 accommodating the next last data point as the initial filing point;
and performing data compression on all the archive points.
Further, the specific process of step B1 is as follows:
storing a first data point and a second data point into a database as characteristic data, wherein the two data points are called filing points, and the first data point is used as an initial filing point; starting from the third data point, judging whether the data point needs to be archived, namely when i is larger than or equal to 3, judging whether the data point is the archived point, wherein the expression of the data point is as follows:
b1≤yi≤b2
in the formula: y isiIs the value of the data point, i.e. the processed sensor data, y1And y2Sensor data corresponding to the first and second data points; Δ x is the storage step; z is the exceptional deviation;
if the formula is satisfied, the data can be ignored, and the next group of data can be continuously distinguished; if the formula is not satisfied, the last data point is archived.
Further, the exception deviation Z is different for the database corresponding to each sensor, and the specific exception deviation Z is as follows:
for the temperature history database, the exception deviation is 0.00001-0.00005;
for the pressure history database, the exception bias is 0.00008-0.00016;
for the flow history database, the exception deviation is 0.00002-0.00005;
for the vibration history database, the exception bias is 0.000005-0.00001.
The invention has the following beneficial effects:
1. the data of the steam turbine sensor is subjected to normalization processing, and the data distribution range is reduced;
2. the total data storage amount is reduced;
3. based on revolving door compression technique, according to steam turbine sensor data characteristics, set up novel scheme compression data.
By adopting the method for establishing the turbine measuring point historical database provided by the technical scheme, the storage space of a hard disk can be greatly reduced, and compared with the existing database storage method, the data can be reduced by more than 2/3; one hard disk can store all data collected by the sensors in the whole life cycle of the steam turbine. And meanwhile, the data access efficiency is greatly improved.
Drawings
FIG. 1 is a flow chart of a method for building a historical database of turbine measurement points;
FIG. 2 is a schematic view of a revolving door compression scheme;
FIG. 3 is a flow chart of rotating gate data compression.
Detailed Description
The first embodiment is as follows: the present embodiment is described in connection with figure 1,
a method for establishing a historical database of turbine measuring points comprises the following steps:
step A: processing the data collected by the temperature, pressure, flow and vibration sensors of the steam turbine by using a normalization method;
and B, step B: and (3) compressing the acquired data by using a revolving door compression method, and establishing a steam turbine full-life (30-year) historical database by occupying as little storage space as possible.
The second embodiment is as follows:
in step a of this embodiment, the following method is adopted for processing sensor data:
step A1: the sensor data is normalized, and the processing method comprises the following steps:
for the temperature sensor, the data processing method comprises the following steps:
in the formula: t is normalized temperature data, TcDigital temperature signals, T, collected for data acquisitionmaxThe full load operation temperature of the unit;
for the pressure sensor, the data processing method comprises the following steps:
in the formula: p is normalized pressure data, PcDigital pressure signals, P, collected for data acquisitionmaxThe full load operating pressure of the unit;
for the flow sensor, the data processing method comprises the following steps:
in the formula: l is normalized flow data, LcDigital signal of flow, L, collected for data collectormaxThe flow rate of the unit running at full load;
for the vibration sensor, the data processing method comprises the following steps:
in the formula: w is the normalized vibration data, WcVibration digital signals, W, collected for data acquisitionmaxDesigning a trip value for the unit;
step A2: screening the normalized sensor data, and controlling the storage frequency; the data screening method comprises the following steps:
for temperature, pressure and flow sensors, the variable step length storage method comprises the following steps:
|Ni+n-Ni|＜0.001×n×t
in the formula: n is a radical ofiRepresenting the normalized temperature, pressure or flow sensor data corresponding to the time i; t is a storage time interval, n is a data counter,
if the conditions are met, the storage step length of the temperature, pressure and flow historical database is 60 seconds; if the temperature, the pressure and the flow rate are not satisfied, the storage step length of the historical database is 1 second;
for the vibration sensor, the variable step storage method comprises the following steps:
Wi＜0.4
in the formula: w is the normalized vibration data, WiW corresponding to the moment i;
if the conditions are met, the storage step length of the vibration history database is 60 seconds; if not, the step length of the vibration history database is 0.001 second.
Other steps and parameters are the same as in the first embodiment.
The third concrete implementation mode:
the method for compressing the revolving door data in step B in this embodiment is as follows:
step B1: the data points have different meanings from the filing points, the data points are all data collected by the sensor, the filing points are characteristic data stored in a database after being compressed by the revolving door, and the data points comprise the filing points;
storing the first data point and the second data point into a database as characteristic data, wherein the two data points are called filing points, and the first data point is used as an initial filing point; starting from a first data point, taking a connecting line between the first data point and a second data point as a central line, and drawing a triangle with the width being 2 times of the compression precision through the two data points; the 2-fold compression accuracy is the exception deviation, which corresponds to the exception deviation in fig. 2; taking a side (a vertical side of a triangle in fig. 2, a line segment parallel to the y axis of a straight coordinate system) corresponding to the second data point as one side of the parallelogram, taking an extension line of the middle line (a connecting line between the first point and the second point is the middle line) as a middle line of the parallelogram, and taking the third data point as the parallelogram; continuously drawing a new parallelogram by the same method along with the continuous update of the current data point and continuously expanding, wherein when the generated parallelogram can not accommodate the newly added data point, the corresponding parallelogram accommodates the next last data point as a filing point to be stored in a database;
step B2: repeating step B1 with the parallelogram of step B1 accommodating the next last data point as the initial filing point;
with the continuous progress of the above processes, only points exceeding the tolerance band are filed and stored finally, so that the efficient compression of the data is realized;
and performing data compression on all the archive points.
Other steps and parameters are the same as in the first or second embodiment.
The fourth concrete implementation mode is as follows: the present embodiment is described in connection with figure 3,
the specific process of step B1 in this embodiment is as follows:
storing the first data point and the second data point into a database as characteristic data, wherein the two data points are called filing points, and the first data point is used as an initial filing point; starting from the third data point, judging whether the data point needs to be archived, namely when i is larger than or equal to 3, judging whether the data point is the archived point, wherein the expression of the data point is as follows:
b1≤yi≤b2
in the formula: y isiIs the value of the data point, i.e. the processed sensor data; y is1And y2Sensor data corresponding to the first and second data points, that is, values of vertical coordinates in a coordinate system, that is, vertical coordinates of the top point and the bottom center point of the lower left triangle in fig. 2; Δ x is a storage step length, and Δ x can be adjusted according to a database; z is the exceptional deviation;
in the method for compressing the data of the revolving door, the abscissa of a coordinate axis is the serial number of a data point, and the ordinate is the value of sensor data;
if the formula is satisfied, the data can be ignored, and the next group of data can be continuously distinguished; if the formula is not satisfied, the last data point is archived.
B in FIG. 31≤yi≤b2The case of no determination corresponds to the contents of the start step B2.
Other steps and parameters are the same as those in the third embodiment.
The fifth concrete implementation mode:
the exceptional deviation Z described in the present embodiment is different for each database corresponding to each sensor, and specific exceptional deviations are as follows:
for a temperature history database, the exception bias is 0.00001-0.00005;
for the pressure history database, the exception bias is 0.00008-0.00016;
for the flow history database, the exception deviation is 0.00002-0.00005;
for the vibration history database, the exception bias is 0.000005-0.00001.
Other steps and parameters are the same as in embodiment four.
Claims (1)
1. A method for establishing a historical database of turbine measuring points is characterized by comprising the following steps:
step A: processing the data collected by the temperature, pressure, flow and vibration sensors of the steam turbine by using a normalization method; the following method is adopted for sensor data processing:
step A1: the sensor data is normalized, and the processing method comprises the following steps:
for the temperature sensor, the data processing method comprises the following steps:
in the formula: t is normalized temperature data, TcDigital temperature signals, T, collected for data acquisitionmaxThe full-load operation temperature of the unit;
for the pressure sensor, the data processing method comprises the following steps:
in the formula: p is normalized pressure data, PcDigital pressure signals, P, collected for data acquisitionmaxThe full load operation pressure of the unit;
for the flow sensor, the data processing method comprises the following steps:
in the formula: l is normalized flow data, LcDigital signal of flow, L, collected for data collectormaxFor flows of units operating at full loadAn amount;
for the vibration sensor, the data processing method comprises the following steps:
in the formula: w is the normalized vibration data, WcVibration digital signals, W, collected for data acquisitionmaxDesigning a trip value for the unit;
step A2: screening the normalized sensor data, and controlling the storage frequency; the data screening method comprises the following steps:
for temperature, pressure and flow sensors, the variable step length storage method comprises the following steps:
|Ni+n-Ni|＜0.001×n×t
in the formula: n is a radical of hydrogeniRepresenting the normalized temperature, pressure or flow sensor data corresponding to the moment i; t is a storage time interval, n is a data counter,
if the conditions are met, the storage step length of the temperature, pressure and flow historical database is 60 seconds; if not, the storage step length of the historical database of the temperature, the pressure and the flow is 1 second;
for the vibration sensor, the variable step storage method comprises the following steps:
Wi＜0.4
in the formula: w is the normalized vibration data, WiW corresponding to the moment i;
if the conditions are met, the storage step length of the vibration history database is 60 seconds; if not, the storage step length of the vibration historical database is 0.001 second;
and B: compressing the collected data by using a revolving door compression method to establish a historical database of the whole life of the steam turbine; the method for compressing the data of the revolving door comprises the following steps:
step B1:
storing the first data point and the second data point into a database as characteristic data, wherein the two data points are called filing points, and the first data point is used as an initial filing point; starting from a first data point, taking a connecting line between the first data point and a second data point as a central line, and drawing a triangle with the width being 2 times of compression precision through the two data points; 2 times of compression precision is exceptional deviation; taking the side corresponding to the second data point as one side of a parallelogram, taking the extension line of the middle line as a middle line of the parallelogram, and taking the third data point as the parallelogram; continuously drawing a new parallelogram by the same method along with the continuous update of the current data point and continuously expanding, wherein when the generated parallelogram can not accommodate the newly added data point, the corresponding parallelogram accommodates the next last data point as a filing point to be stored in a database;
the specific process of the step B1 is as follows:
storing the first data point and the second data point into a database as characteristic data, wherein the two data points are called filing points, and the first data point is used as an initial filing point; starting from the third data point, judging whether the data point needs to be archived, namely when i is larger than or equal to 3, judging whether the data point is an archived point, wherein the expression of the data point is as follows:
b1≤yi≤b2
in the formula: y isiIs the value of the data point, i.e. the processed sensor data, y1And y2Sensor data corresponding to the first and second data points; Δ x is the storage step; z is the exceptional deviation;
the exception deviation Z is different for the database corresponding to each sensor, and the specific exception deviation is as follows:
for the temperature history database, the exception deviation is 0.00001-0.00005;
for the pressure history database, the exception bias is 0.00008-0.00016;
for the traffic history database, the exception deviation is 0.00002-0.00005;
for the vibration history database, the exception deviation is 0.000005-0.00001;
if the formula is satisfied, the data can be ignored, and the next group of data can be continuously distinguished; if the formula is not met, archiving the previous data point;
step B2: repeating step B1 with the parallelogram of step B1 accommodating the next last data point as the initial filing point;
and performing data compression on all the archive points.
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