CN117236159B - Method for adjusting runout working condition of hydropower station unit based on radial basis function neural network model - Google Patents
Method for adjusting runout working condition of hydropower station unit based on radial basis function neural network model Download PDFInfo
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Abstract
The invention provides a method for adjusting the runout working conditions of a hydroelectric generating set based on a radial basis neural network model. And the influence rule of the water head and the unit output on the unit runout working condition is quantitatively researched, and the method is used for guiding the adjustment work of the running operator runout working condition and improving the working condition adjustment work efficiency.
Description
Technical Field
The invention relates to the technical field of hydropower station unit fault diagnosis, in particular to a method for adjusting the runout working condition of a hydropower station unit based on a radial basis function neural network model.
Background
The research on the stability of the operation of the hydroelectric generating set has been a worldwide problem, and the complexity of the research is that the stability of the hydroelectric generating set involves a plurality of factors such as waterpower, machinery, electricity and the like, and the factors act together to cause the main excitation load of the vibration of the water turbine. At present, the related application of the operation working condition of the hydroelectric generating set, the water head and the active power of the set is mostly based on the operation characteristic curve of the hydroelectric generating set, and the data of the related application are derived from years of design operation and maintenance experience, model simulation, fixed proportion shrinkage model simulation test, set operation working condition test under different water heads and the like. The method can not carry out targeted processing on the vibration swing rule difference in the actual operation working conditions caused by different unit installation details, long-term abrasion and the like. And the data density is limited, so that the quantitative adjustment of the operation working condition of the unit is difficult to effectively guide.
The vibration value of the hydroelectric generating set has a great influence on the long-term stable operation of the set. When a large-capacity mixed flow hydroelectric generating set of a certain type of hydropower station runs under a low-head working condition in a flood season, the set can generate a condition that the vibration swing degree is large, and even the primary and secondary alarms of the swing are achieved. The hydropower station has high power generation requirement in the flood season, the unit is in an operation state for a long time, cavitation abrasion of the blades and the water guide mechanism of the water turbine can be aggravated if the unit is operated under the working condition that the vibration swing degree exceeds the standard for a long time, and the equipment is possibly loosened, displaced and even part of elements are torn and damaged.
At present, a hydroelectric generating set is generally provided with a set state monitoring, analyzing and fault diagnosis system, so that different monitoring positions of the set, such as a vibration swing degree peak value of a water guide bearing, a generator frame and the like, a large-shaft swing curve and the like, can be reflected in real time, and an alarm is sent out when the vibration swing degree peak value and the large-shaft swing curve are out of limit. At present, a certain hydropower station unit is configured with a TN8000 hydropower station unit state monitoring analysis fault diagnosis system. When the hydro-generator set generates a runout alarm, an operation operator always monitors, analyzes and analyzes a runout monitoring value displayed by the fault diagnosis system according to the TN8000 hydro-generator set state, and improves the operation working condition of the set by adjusting the active power, reactive power, large-axis forced air supplement and other methods of the set according to operation experience, so that the runout alarm is reset.
The problems of the prior art are:
(1) Under different water heads, the influence rule of the output of the hydro-generator set runout random unit is often complex, the current adjustment of the running condition of the set is mostly dependent on the experience of an operator on duty, a long time is required to be taken for multiple attempts, blindness is achieved, and the set runout can be even adjusted to a worse running condition interval in the adjustment process;
(2) The influence rules of vibration swing degree random unit output in actual operation conditions caused by installation details of different units, long-term abrasion and the like often have differences, and the adjustment of the unit operation conditions under the guidance of the current model and data does not have universality;
(3) When active power of a plurality of units participates in Automatic Generation Control (AGC) joint debugging, after the active power of a single unit is regulated, the active power of other units also changes, and after the vibration working condition of the single unit is improved, the vibration working condition of other units possibly worsens.
(4) Because the hydropower station in flood season has high power generation requirement, the adjustable allowance of active power is often small, and the manual attempt of adjustment often has the problems of poor adjustment effect, blindness in adjustment direction, long time consumption, insufficient precision and the like.
Disclosure of Invention
The invention provides a method for adjusting the runout working conditions of a hydroelectric generating set based on a radial basis neural network model. And the influence rule of the water head and the unit output on the unit runout working condition is quantitatively researched, and the method is used for guiding the adjustment work of the running operator runout working condition and improving the working condition adjustment work efficiency.
In order to achieve the technical characteristics, the aim of the invention is realized in the following way: a method for adjusting the runout working condition of a hydropower station unit based on a radial basis function neural network model comprises the following steps:
Step one, collecting historical data of hydropower station unit runout alarm generated in a flood season in the last year, wherein the historical data specifically comprises a unit water head, active power and a vibration peak value;
Obtaining the influence rule of different water heads and active power of the unit on the vibration peak value by using a radial basis neural network model, and fitting to obtain a change trend chart of the water heads and the active power of the unit on the vibration peak value of the position on the premise of meeting the index of the model accuracy R 2, and continuously fitting and iterating until the requirement is met if the index of the model accuracy R 2 is not met;
step three, optionally selecting a certain water head, carrying out profile treatment on the radial basis function neural network model, and obtaining a change trend chart of the active power of the vibration peak-to-peak value random unit under the water head of the unit, thereby obtaining the influence rule of the active power of the unit on the vibration peak-to-peak value under the water head; under the water head, an operator can refer to a change trend graph to adjust active power, so that the vibration peak value of the unit is reduced, and the vibration alarm of the unit is eliminated;
Developing a unit runout working condition adjustment auxiliary system, respectively constructing an approximate model based on a radial basis function neural network for characteristic values of runout alarm items of a plurality of units and a plurality of monitoring positions, and integrating a plurality of models in the auxiliary system; the system can manually input the current water head through selecting the machine set number and the runout alarm type, generate a runout random set active power change trend chart corresponding to the machine set part, and an operator on duty refers to the runout random set active power change trend chart to adjust the active power to a direction with low runout amplitude, so that the running working condition of the machine set is improved.
In the first step, invalid and low-efficiency data in the shutdown time period and the startup and shutdown process of the unit need to be removed from the historical data, and the data quantity of each data point is 2000-3000.
In the second step, the size range of the model accuracy R 2 is [0,1], the higher the numerical value is, the better the model fitting degree is, 1 is the complete fitting, 0 is the worst fitting degree, and the calculation formula of the correlation index R 2 is shown as the formula (1):
R2=SSE/SST （1）
Wherein: SSE and SST respectively represent goodness of fit and total variation; the goodness of fit is the sum of squares of the differences between the fit data with the deviation and the actual value, and the total variation is the sum of squares of the differences between the actual value and the average value.
The water head is the difference between the upstream water level and the downstream water level.
The invention has the beneficial effects that:
1. according to the invention, the influence rule of the water head and the unit output on the unit runout working condition is quantitatively researched, so that the runout amplitude of the unit during low water head operation is conveniently reduced, and the unit operation working condition is improved.
2. The invention provides a guiding basis for the operation working condition adjustment work of the operation operator and improves the working condition adjustment work efficiency.
3. The invention reduces the manual trend analysis workload of the operator on duty and saves human resources.
Drawings
The invention is further described below with reference to the drawings and examples.
Fig. 1 is a rule model of influence of water head and active power on peak and peak values of vertical vibration in X direction of a machine frame under a certain machine set.
Fig. 2 is a plan view of a model of the influence rule of the water head and the active power on the peak value of the vertical vibration of the machine frame X direction under a certain machine set.
FIG. 3 is a graph showing the active power variation trend of a vertical vibration peak-to-peak random unit in the X direction of a machine frame under a certain water head.
FIG. 4 is an illustration of the system for aiding in adjusting the runout condition of the machine set of the present invention.
FIG. 5 is a diagram illustrating the operation of the auxiliary system for adjusting the vibration condition of the machine set according to the present invention.
Detailed Description
Embodiments of the present invention will be further described with reference to the accompanying drawings.
Example 1:
Referring to fig. 1-5, a method for adjusting the runout working condition of a hydropower station unit based on a radial basis function neural network model comprises the following steps:
Step one, collecting historical data of hydropower station unit runout alarm generated in a flood season in the last year, wherein the historical data specifically comprises a unit water head, active power and a vibration peak value; invalid and low-efficiency data in the shutdown time period and the startup and shutdown process of the unit are required to be removed from the historical data, and the data quantity of each data point is 2000-3000.
Obtaining the influence rule of different water heads and active power of the unit on the vibration peak value by using a radial basis neural network model, and fitting to obtain a change trend chart of the water heads and the active power of the unit on the vibration peak value of the position on the premise of meeting the index of the model accuracy R 2, and continuously fitting and iterating until the requirement is met if the index of the model accuracy R 2 is not met;
In the second step, the size range of the model accuracy R 2 is [0,1], the higher the numerical value is, the better the model fitting degree is, 1 is the complete fitting, 0 is the worst fitting degree, and the calculation formula of the correlation index R 2 is shown as the formula (1):
R2=SSE/SST （1）
Wherein: SSE and SST respectively represent goodness of fit and total variation; the goodness of fit is the sum of squares of the differences between the fit data with the deviation and the actual value, and the total variation is the sum of squares of the differences between the actual value and the average value.
Step three, optionally selecting a certain water head, carrying out profile treatment on the radial basis function neural network model, and obtaining a change trend chart of the active power of the vibration peak-to-peak value random unit under the water head of the unit, thereby obtaining the influence rule of the active power of the unit on the vibration peak-to-peak value under the water head; under the water head, an operator can refer to a change trend graph to adjust active power, so that the vibration peak value of the unit is reduced, and the vibration alarm of the unit is eliminated;
Developing a unit runout working condition adjustment auxiliary system, respectively constructing an approximate model based on a radial basis function neural network for characteristic values of runout alarm items of a plurality of units and a plurality of monitoring positions, and integrating a plurality of models in the auxiliary system; the system can manually input the current water head through selecting the machine set number and the runout alarm type, generate a runout random set active power change trend chart corresponding to the machine set part, and an operator on duty refers to the runout random set active power change trend chart to adjust the active power to a direction with low runout amplitude, so that the running working condition of the machine set is improved.
Further, the water head is the difference between the upstream water level and the downstream water level.
Example 2:
According to the method, common runout alarm types of a plurality of hydroelectric generating sets are arranged according to a record of the running duty of a certain hydropower station in a flood season and a set runout data record table, historical data of running water heads, active power and vibration runout peaks and peaks of different monitoring positions of the plurality of sets are extracted, an approximate model is respectively built for characteristic values of the runout alarm items of the plurality of sets and the monitoring positions by using a radial basis neural network model, and the plurality of models are integrated into one set runout working condition adjustment auxiliary software for use. The influence rule of the water head and the unit output on the unit runout working condition is quantitatively researched, the adjustment work of the running operator runout working condition can be guided, and the working condition adjustment work efficiency is improved.
The implementation process of the application method based on the radial basis function neural network model in the aspect of adjustment of the runout working condition of the hydropower station unit is as follows: taking vertical vibration of a machine frame X direction under a certain machine set as an example.
Step one: collecting historical data of a certain unit for generating a lower frame X-direction vertical vibration runout alarm in a last year flood period, wherein the historical data comprises a water head, active power and a lower frame X-direction vertical vibration peak value of the unit, and removing invalid and low-efficiency data in a unit shutdown time period and a unit shutdown starting process, wherein the data amount of each data point is 2000-3000;
Step two: and acquiring the influence rule of different water heads and active power of the unit on the vertical vibration peak value of the lower frame in the X direction by using a radial basis function neural network model, and fitting to obtain a change trend chart of the unit water heads and active power on the vibration swing peak value of the position on the premise of meeting the index of the model accuracy R 2, and if the index of the model accuracy R 2 is not met, continuing fitting iteration until the requirement is met. As shown in fig. 1 and 2. The blue area is small in runout amplitude and good in unit operation condition; the red area is large in runout amplitude and poor in unit operation condition;
The radial basis function neural network model accuracy R 2 index is 0.76, and the data change trend can be effectively reflected. The size range of R 2 is [0,1], the higher the numerical value is, the better the model fitting degree is, 1 is the complete fitting, 0 is the complete fitting, the fitting degree is the worst, and the calculation formula of the correlation index R 2 is shown as the formula (1):
R2=SSE/SST （1）
wherein SSE and SST respectively represent goodness of fit and total variation; the goodness of fit is the sum of squares of the differences between the fit data with the deviation and the actual value, and the total variation is the sum of squares of the differences between the actual value and the average value.
Step three: optionally, a certain water head is selected, the radial basis function neural network model is subjected to section processing, a change trend chart of active power of the lower rack X-direction vertical vibration peak value random unit under the water head is obtained, and as shown in figure 3, the influence rule of the active power of the unit under the water head on the lower rack X-direction vertical vibration peak value can be found. Under the water head, an operator can refer to the trend chart to adjust active power, so that the vertical vibration peak value of the machine frame X direction under the machine set is reduced, and the machine set runout alarm disappears.
Step four: in order to facilitate the application of the runout condition adjustment by operators operating under different water heads, a unit runout condition adjustment auxiliary system is developed, as shown in fig. 4. And respectively constructing an approximate model based on a radial basis neural network for the characteristic values of the runout alarm items of the multiple units and the multiple monitoring parts, and integrating the multiple models in an auxiliary system. The system can manually input the current water head by selecting the number of the machine set and the type of the runout alarm, and generate a runout random set active power change trend chart corresponding to the machine set part. The operator can refer to the trend chart, and adjust the active power to the direction with lower runout amplitude (namely, blue area), so as to improve the operation condition of the unit.
Referring to fig. 5, in a specific operation and use process of the auxiliary system for adjusting the vibration working condition of the unit, the auxiliary system comprises the following specific steps:
step one, selecting a machine group number;
Secondly, selecting a runout alarm type;
thirdly, manually typing in the current water head (upstream-downstream), and only analyzing the water head working condition in the range of 77-99;
And fourthly, clicking to generate a relation trend graph and the current head runout to change along with the output.
Claims (4)
1. A method for adjusting the runout working condition of a hydropower station unit based on a radial basis function neural network model is characterized by comprising the following steps:
Step one, collecting historical data of hydropower station unit runout alarm generated in a flood season in the last year, wherein the historical data specifically comprises a unit water head, active power and a vibration peak value;
Obtaining the influence rule of different water heads and active power of the unit on the peak value of the vibration peak by using a radial basis function neural network model, and fitting to obtain a change trend chart of the water heads and the active power of the unit on the peak value of the vertical vibration peak of the lower frame X of the unit on the premise of meeting the index of the model accuracy R 2, and continuously fitting and iterating until meeting the requirement if the index of the model accuracy R 2 is not met;
step three, optionally selecting a certain water head, carrying out profile treatment on the radial basis function neural network model, and obtaining a change trend chart of the active power of the vibration peak-to-peak value random unit under the water head of the unit, thereby obtaining the influence rule of the active power of the unit on the vibration peak-to-peak value under the water head; under the water head, an operator can refer to a change trend graph to adjust active power, so that the vibration peak value of the unit is reduced, and the vibration alarm of the unit is eliminated;
Developing a unit runout working condition adjustment auxiliary system, respectively constructing an approximate model based on a radial basis function neural network for characteristic values of runout alarm items of a plurality of units and a plurality of monitoring positions, and integrating a plurality of models in the auxiliary system; the system can generate a vibration random set active power change trend chart corresponding to a set position by selecting a set number and a vibration alarm type and manually inputting a water head within a range of 77-99, and an operator on duty refers to the vibration random set active power change trend chart to adjust the active power to a direction with low vibration amplitude and improve the running working condition of the set.
2. The method for adjusting the runout working condition of the hydropower station unit based on the radial basis function neural network model according to claim 1, wherein in the first step, invalid and low-efficiency data in the shutdown time period and the startup and shutdown process of the unit are removed from historical data, and the data quantity of each data point is 2000-3000.
3. The method for adjusting the runout condition of the hydroelectric generating set based on the radial basis function neural network model according to claim 1, wherein in the second step, the size range of the model accuracy R 2 is [0,1], the higher the numerical value is, the better the model fitting degree is, 1 is, 0 is, the two are not related, the fitting degree is worst, and the calculation formula of the correlation index R 2 is shown as formula (1):
R2=SSE/SST （1）
Wherein: SSE and SST respectively represent goodness of fit and total variation; the goodness of fit is the sum of squares of the differences between the fit data with the deviation and the actual value, and the total variation is the sum of squares of the differences between the actual value and the average value.
4. The method for adjusting the runout condition of a hydroelectric generating set based on a radial basis function neural network model according to claim 1, wherein the water head is a difference value between an upstream water level and a downstream water level.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103036232A (en) * | 2012-12-13 | 2013-04-10 | 大唐（云南）水电联合开发有限责任公司 | Vibration-area-crossing method of automatic gain control (AGC) system for different water heads in hydroelectric plant |
| CN105811473A (en) * | 2016-05-19 | 2016-07-27 | 华能澜沧江水电股份有限公司 | Active power output control parameter pre-processing method for hydropower station during automatic power generation |
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| CN111654068B (en) * | 2020-07-02 | 2021-07-27 | 华能澜沧江水电股份有限公司 | Active power control method for hydroelectric generating set in double-suggestion operation area |
| CN112855408B (en) * | 2021-01-15 | 2022-03-11 | 湖南五凌电力科技有限公司 | Early warning method and early warning device for hydroelectric generating set |
| CN113236581B (en) * | 2021-06-29 | 2022-02-22 | 江苏大学 | Intelligent parallel pump system and optimal adjustment method |
| CN113792940A (en) * | 2021-09-30 | 2021-12-14 | 湖南五凌电力科技有限公司 | Hydro-turbo generator set runout data prediction method and device based on deep learning |
| CN115795999B (en) * | 2022-10-26 | 2023-08-01 | 国网新源控股有限公司 | Early warning method for abnormal performance of long-term service pumped storage unit |
| CN116201680A (en) * | 2023-03-20 | 2023-06-02 | 华能澜沧江水电股份有限公司 | AGC distribution method for controlling and limiting operation time of hydroelectric generating set |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103036232A (en) * | 2012-12-13 | 2013-04-10 | 大唐（云南）水电联合开发有限责任公司 | Vibration-area-crossing method of automatic gain control (AGC) system for different water heads in hydroelectric plant |
| CN105811473A (en) * | 2016-05-19 | 2016-07-27 | 华能澜沧江水电股份有限公司 | Active power output control parameter pre-processing method for hydropower station during automatic power generation |
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