CN111832830B - Tail water level-based big data optimization operation method for radial flow type hydropower station - Google Patents
Tail water level-based big data optimization operation method for radial flow type hydropower station Download PDFInfo
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Abstract
The invention provides a tail water level-based runoff type hydropower station big data optimization operation method, which forms a big data platform for optimizing the operation of a hydropower station by acquiring the tail water level of the operation of the hydropower station and the output conditions of all units.
Description
Technical Field
The invention relates to water conservancy projects, in particular to a big data optimization operation method of a radial flow type hydropower station based on a tail water level.
Background
The radial flow type hydropower station is a common hydropower station arrangement form, incoming flow cannot be adjusted, although an optimized operation method of the radial flow type hydropower station is researched in the prior art, the operation of the radial flow type hydropower station mostly depends on some theoretical data, the theoretical data mostly generate errors with actual operation of the hydropower station, and an optimized result has errors.
In the running process of the radial flow type hydropower station, the radial flow type hydropower station mostly depends on upstream inflow amount, but because errors of a measuring device and installation conditions are not available, the conventional radial flow type hydropower station mostly adopts an empirical estimation method and is started according to experience, the starting cannot be optimized, and an analysis platform of big data cannot be formed. There is a large optimization space for the hydropower station.
Disclosure of Invention
Based on the above, the invention provides a tail water level-based big data optimization operation method for a radial flow type hydropower station, wherein the radial flow type hydropower station is provided with a tail water level acquisition device and output acquisition devices of all units, and the method is characterized in that: the method comprises the following steps:
s1: after the water level of the front pool stably operates, collecting the output power and the tail water level of each unit in the operation period of the power station, wherein the output power and the tail water level of each unit are in one-to-one correspondence in the time relation;
s2: adding the output of each unit to obtain the total output of each unit in each time period, and further obtaining the one-to-one corresponding relation of the tail water level, the total output of the unit and the output of each unit, wherein the corresponding relation is the corresponding relation in time;
s3: and performing data processing on the corresponding relation to obtain hydropower station operation big data, wherein the data processing comprises the following steps: for a plurality of unit working conditions corresponding to the same tail water level, selecting a group of working conditions with the maximum total output of the unit, and recording the tail water level, the total output of the unit under the working conditions and the output of each unit to form big data; in the subsequent operation process, once the total output of the unit corresponding to the tail water level is greater than the total output of the unit obtained from the big data, the larger output value is adopted to replace the total output of the unit in the original big data, the larger total output of the unit corresponding to the tail water level and the output of each unit corresponding to the larger total output of the unit corresponding to the tail water level are recorded, the data in the original big data are replaced, and new big data are formed;
s4: in the operation process of the hydropower station, the front pool water level is kept to stably operate, the tail water level is collected, the optimal output of a unit corresponding to the tail water level is searched in big data, and the searching mode is as follows: if the collected tail water level is equal to a certain tail water level in the big data, finding out the output of each unit corresponding to the tail water level of the big data, wherein the output is the optimal output of the unit; if the collected tail water level is not equal to a certain tail water level in the big data, finding out two tail water levels adjacent to the collected tail water level for difference, and correspondingly obtaining a differential value of the output of each unit, wherein the differential value of the output is the optimal output of the unit;
s5: and adjusting the output of the unit to the optimal output obtained in the step S4.
Preferably, the tail water level acquisition device has acquisition precision of 1cm and acquisition time interval of 3s.
Preferably, the operation mode of the hydropower station in the step S4 is as follows: and starting up and operating according to the incoming flow according to experience or starting up and operating according to the incoming flow in an optimized operation mode.
Preferably, the front pool water level stable operation means that the front pool water level is kept unchanged within a certain time, the front pool water level is stabilized at a normal high water level, the water consumption of the power station is equal to the water inflow, and the front pool water level change amplitude is not more than 2cm within 1h after the certain time is kept unchanged.
The principle of the invention is as follows:
for the quoted flow of the power station, the tail water level is relatively stable, and for the generating water consumption of the hydropower station, the generating water consumption is discharged into a tail water pool or a tail water channel through a unit, so the height of the tail water level can reflect the water consumption condition of the power station, the generating flow of the power station is reflected by the tail water level, and the error is small through a tail water level acquisition device;
in addition, by utilizing a big data analysis platform, the maximum output working condition under various working conditions corresponding to any tail water level is automatically found out by collecting long series operation data of the hydropower station, the tail water level corresponds to the power generation flow of the hydropower station, and a big data platform is formed by recording. The output combination is the optimal working condition corresponding to the tail water level, and the optimal operation of the hydropower station can be realized by means of the automatic updating function of big data.
The invention has the advantages that:
the invention provides a tail water level-based big data optimized operation method for a radial flow type hydropower station.
The specific implementation mode is as follows: the structure defined in the present invention will be explained in detail with reference to the embodiments.
The invention provides a big data optimization operation method of a radial-flow type hydropower station based on a tail water level, wherein the radial-flow type hydropower station is provided with a tail water level acquisition device and output acquisition devices of all units, and the big data optimization operation method is characterized in that: the method comprises the following steps:
s1: after the water level of the front pool stably operates, collecting the output power and the tail water level of each unit in the operation period of the power station, wherein the output power and the tail water level of each unit are in one-to-one correspondence in the time relation;
s2: adding the output of each unit to obtain the total output of each unit in each time period, and further obtaining the one-to-one corresponding relation of the tail water level, the total output of the unit and the output of each unit, wherein the corresponding relation is the corresponding relation in time;
s3: and performing data processing on the corresponding relation to obtain hydropower station operation big data, wherein the data processing comprises the following steps: for a plurality of unit working conditions corresponding to the same tail water level, selecting a group of working conditions with the maximum total output of the unit, and recording the tail water level, the total output of the unit under the working conditions and the output of each unit to form big data; in the subsequent operation process, once the total output of the unit corresponding to the tail water level is greater than the total output of the unit obtained from the big data, the larger output value is adopted to replace the total output of the unit in the original big data, the larger total output of the unit corresponding to the tail water level and the output of each unit corresponding to the larger total output of the unit corresponding to the tail water level are recorded, the data in the original big data are replaced, and new big data are formed;
s4: in the operation process of the hydropower station, the water level of a front pool is kept to stably operate, the tail water level is collected, the optimal output of a unit corresponding to the tail water level is searched in big data, and the searching mode is as follows: if the collected tail water level is equal to a certain tail water level in the big data, finding out the output of each unit corresponding to the tail water level of the big data, wherein the output is the optimal output of the unit; if the collected tail water level is not equal to a certain tail water level in the big data, finding out two tail water levels adjacent to the collected tail water level for difference, and correspondingly obtaining a differential value of the output of each unit, wherein the differential value of the output is the optimal output of the unit;
s5: and adjusting the output of the unit to the optimal output obtained in the step S4.
Because the tail water level adjusted back and forth is not changed, the flow quoted by the tail water level adjusted back and forth is not changed, and therefore, the hydropower station can ensure the normal high-water-level operation of the forebay in the state.
Normal high level operation of the forebay means that the hydropower station forebay is maintained at a normal high level which is the normal level of operation of the hydropower station and which may generally be chosen to be 5-15cm below the overflow weir.
Preferably, the tail water level acquisition device has acquisition precision of 1cm and acquisition time interval of 3s.
Preferably, the operation mode of the hydropower station in the step S4 is as follows: and starting up and operating according to the incoming flow according to experience or starting up and operating according to the incoming flow in an optimized operation mode.
Preferably, the front pool water level stable operation means that the front pool water level is kept unchanged within a certain time, the front pool water level is stabilized at a normal high water level, the water consumption of the power station is equal to the water inflow, and the front pool water level change amplitude is not more than 2cm within 1h after the certain time is kept unchanged.
The principle of the invention is as follows:
for the quoted flow of the power station, the tail water level is relatively stable, and for the generating water consumption of the hydropower station, the generating water consumption is discharged into a tail water pool or a tail water channel through a unit, so the height of the tail water level can reflect the water consumption condition of the power station, the generating flow of the power station is reflected by the tail water level, and the error is small through a tail water level acquisition device;
in addition, by utilizing a big data analysis platform, the maximum output working condition under various working conditions corresponding to any tail water level is automatically found out by collecting long series operation data of the hydropower station, the tail water level corresponds to the power generation flow of the hydropower station, and a big data platform is formed by recording. The output combination is the optimal working condition corresponding to the tail water level, and the optimal operation of the hydropower station can be realized by means of the automatic updating function of big data.
And for the data with the difference exceeding the big data, carrying out epitaxial difference processing by adopting two adjacent data.
According to the method, the problem of errors of upstream water supply is not considered, the tail water level reflects the flow quoted by the power station, the errors are small, the large data platform is continuously updated for the accumulation of the running time, and the optimal running of the hydropower station is better realized. The optimized operation does not need an optimized operation principle, only depends on the operation historical data of the hydropower station, is simple to operate, and can be popularized and applied in the hydropower station.
The above-described embodiments are only preferred embodiments of the present invention, and the scope of the present invention should not be construed as being limited to the specific forms set forth in the examples, but also includes equivalent technical means which can be conceived by those skilled in the art from the present inventive concept.
Claims (4)
1. A big data optimization operation method of a radial flow type hydropower station based on a tail water level is characterized in that the radial flow type hydropower station is provided with a tail water level acquisition device and output acquisition devices of all units, and the method comprises the following steps: the method comprises the following steps:
s1: after the water level of the front pool stably operates, collecting the output power and the tail water level of each unit in the operation period of the power station, wherein the output power and the tail water level of each unit are in one-to-one correspondence in the time relation;
s2: adding the output of each unit to obtain the total output of each unit in each time period, and further obtaining the one-to-one corresponding relation of the tail water level, the total output of the unit and the output of each unit, wherein the corresponding relation is the corresponding relation in time;
s3: and performing data processing on the corresponding relation to obtain hydropower station operation big data, wherein the data processing comprises the following steps: selecting a set of working conditions with the maximum total output of the set for a plurality of set working conditions corresponding to the same tail water level, and recording the tail water level, the total output of the set under the working conditions and the output of each set to form big data; in the subsequent operation process, once the total output of the unit corresponding to the tail water level is greater than the total output of the unit obtained from the big data, the larger output value is adopted to replace the total output of the unit in the original big data, the larger total output of the unit corresponding to the tail water level and the output of each unit corresponding to the larger total output of the unit corresponding to the tail water level are recorded, the data in the original big data are replaced, and new big data are formed;
s4: in the operation process of the hydropower station, the water level of a front pool is kept to stably operate, the tail water level is collected, the optimal output of a unit corresponding to the tail water level is searched in big data, and the searching mode is as follows: if the collected tail water level is equal to a certain tail water level in the big data, finding out the output of each unit corresponding to the tail water level of the big data, wherein the output is the optimal output of the unit; if the collected tail water level is not equal to a certain tail water level in the big data, finding out two tail water levels adjacent to the collected tail water level for difference, and correspondingly obtaining a differential value of the output of each unit, wherein the differential value of the output is the optimal output of the unit;
s5: and adjusting the output of the unit to the optimal output obtained in the step S4.
2. The tail water level-based big data optimization operation method of the radial flow type hydropower station according to claim 1, wherein the method comprises the following steps: the tail water level acquisition device has the acquisition precision of 1cm and the acquisition time interval of 3s.
3. The tail water level-based big data optimization operation method of the radial flow type hydropower station according to claim 1, wherein the method comprises the following steps: the hydropower station operation mode in the step S4 is as follows: and starting up and operating according to the incoming flow according to experience or starting up and operating according to the incoming flow in an optimized operation mode.
4. The method for optimizing operation of big data of a runoff type hydropower station based on tail water level as claimed in claim 1, wherein the method comprises the following steps: the front pool water level stable operation means that the front pool water level is kept unchanged within a certain time, the front pool water level is stable at a normal high water level, the water consumption of the power station is equal to the water inflow amount, and the front pool water level change amplitude is not more than 2cm within 1h after the front pool water level is kept unchanged within the certain time.
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