CN116738841A - Algorithm and device for dynamic simulation of water energy of hydropower station - Google Patents
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Abstract
The invention discloses an algorithm and a device for dynamic simulation of water energy of a hydropower station, comprising the following steps: s1, acquiring natural working condition parameters of the operation of a hydroelectric generating set on line; s2, analyzing a comprehensive characteristic curve graph of the turbine model runner by utilizing data acquired by a neural network; s3, collecting relation curve data and output limit line data reflected in a comprehensive characteristic curve graph of the turbine model runner; s4, establishing an objective function of the unit operation condition efficiency C, and obtaining an optimizing solution; s5, describing a comprehensive characteristic curve graph of the turbine model runner, and analyzing by utilizing a neural network to collect data; s6, simulating the whole hydrologic process to obtain the average power generation amount for many years; and S7, obtaining an optimal solution by adopting a weighting algorithm according to the optimal solution and the average power generation quantity for many years. The invention combines the hydrological condition with the generating characteristic of the unit, simulates the real-time efficiency, improves the accuracy and avoids the situation that the generating characteristic of the unit is not consistent with the hydrological condition.
Description
Technical Field
The invention relates to the technical field of water conservancy and hydropower, in particular to an algorithm for hydropower station hydropower dynamic simulation.
Background
Hydropower stations are comprehensive engineering facilities for converting water energy into electric energy, and generally comprise reservoirs formed by water retaining and draining buildings, hydropower station diversion systems, power generation plants, electromechanical equipment and the like. The high-water level water of the reservoir flows into a factory building through a water diversion system to push a water-turbine generator set to generate electric energy, and then is input into a power grid through a step-up transformer, a switching station and a power transmission line.
In the prior art, china patent with the publication number of CN103306886B discloses a hydropower station water energy dynamic simulation algorithm, and the patent adopts the following method steps: 1) Importing the comprehensive characteristic curve graph of the turbine model runner into a database; collecting the reflected graph according to the comprehensive characteristic curve graph of the turbine model runner provided by the manufacturerThe relation curve data and the output limit line data are used for importing the acquired data into a database; 2) Establishing a neural network model, and training the neural network model by data to reflect and characterize +.>A relationship curve, the model meeting sufficient accuracy requirements; 3) Importing a long-year daily flow sequence and water head data of the power station; 4) Introducing water turbine parameters (rotation speed ni, diameter Di, installation Gao Cheng); on the basis of a given rotating wheel and hydrologic process, the power generation process of the power station is mathematically simulated through an intelligent algorithm model of optimizing to obtain annual energy production index of the power station, and reliable basis is provided for hydraulic generator modeling and hydropower station technical and economic analysis.
However, in the prior art, with the improvement of the requirement on the accuracy of water energy calculation, the application of the dynamic simulation method in the field of water energy calculation is gradually updated, and the effective combination of the running working condition and the hydrologic condition of a unit cannot be comprehensively considered, so that the water energy resource waste is caused in the actual running of the hydropower station, the difficulty in calculating the electric energy by adopting the dynamic simulation method is that the adjustment capability and the scheduling mode of the hydropower station are diversified, the dynamic simulation method is complex, and part of parameters of a calculation model need to be limited.
It is therefore desirable to propose an algorithm for hydropower station hydropower dynamic simulation in order to solve the above-mentioned problems.
Disclosure of Invention
The invention provides an algorithm for hydropower station hydropower dynamic simulation, which is used for solving the problems in the background technology, combining an artificial neural network and a genetic algorithm with a hydropower calculation model, calculating hydropower station hydropower energy, selecting a unit model and actually running, establishing a mathematical model for hydropower station hydropower energy influence factors, inputting hydropower station hydrological process parameters, simulating the basic condition of the power station during normal running power generation, and calculating an accurate generated energy result by a weighting algorithm in cooperation with a historical record so as to guide the model selection work of the hydropower station.
The technical scheme of the invention is realized as follows:
an algorithm for hydropower station hydropower dynamic simulation, comprising the following steps:
s1, on-line collecting natural working condition parameters of the operation of a hydroelectric generating set, namely head loss H, set reference flow Q and runner diameter D;
s2, analyzing the comprehensive characteristic curve graph of the turbine model runner by utilizing the neural network to acquire data, and forming a database;
s3, acquiring relation curve data and output limit line data reflected in a comprehensive characteristic curve graph of the turbine model runner, and importing the acquired data into the database;
s4, combining the step S1 on the basis of the rotation speed n of the water turbineCollecting parameters, establishing an objective function of unit operation working condition efficiency C taking unit flow A and unit rotating speed B as parameters, and optimizing according to the objective function, requirements of safety and stability limiting conditions and a database established by a neural network to obtain an optimizing solution E 1 ＝(A,B)；
S5, deriving [ Q ] of each time point of unit history long sequence i (t),H i (t)]And determining the diameter and rotation speed (D j ,n j ) Describing a comprehensive characteristic curve graph of the turbine model runner, and analyzing by utilizing a neural network to collect data;
s6, dynamically simulating the hydrologic process by combining invariant in the running process of a group of units with the hydrologic process of the power station and reservoir dispatching control and taking the maximum generated energy as an objective function, wherein the simulation process is that the dynamic simulation is performed by taking the maximum generated energy as the objective function, analysis is performed by utilizing a neural network acquisition data set, the hydrologic process such as the annual incoming water flow of the power station, the upstream water level, the downstream water level and the like is input into parameters of a water turbine model, and (D) is reselected j ,n j ) m To obtain the average power generation quantity E for many years 2 ＝(A 2 ,B 2 )；
S7, optimizing the requirements of the unit operation working condition efficiency C and the safety and stability limiting conditions and the established neural network database to obtain an optimizing solution E 1 = (a, B) and years average power generation E obtained by analysis using neural network with maximum power generation as objective function 2 ＝(A 2 ,B 2 ) Finally, weighting algorithm is adopted to E 1 And E is 2 Solving to obtain an optimal solution E * 。
In a further optimized technical solution, in step S4, the calculation formula of the unit flow a is:
the calculation formula of the unit rotating speed B is as follows:
further optimizing the technical solution, in step S4, the hydropower station hydropower energy calculation simulation adopts the following model:
in a water energy calculation simulation model of a hydropower station,
wherein: h 1(t) A water head corresponding to the upstream of the time t is H 2(t) The head corresponding to the downstream of the moment t is the head difference between the upstream and the downstream, V 0 The volume of the initial water of the hydropower station is V (t) is the volume of the water corresponding to the hydropower station at the moment t, beta is the opening degree of the blade, beta max The maximum blade opening eta is the efficiency of the water turbine i For generator efficiency, sigma t Q i Drainage flow rate, Q of all water turbines max Is the maximum overflow of the water turbine, alpha is the opening degree of the guide vane, alpha max Is the maximum opening of the guide vane; d is the water turbine efficiency set.
In a further optimized technical scheme, in step S5, the calculation model of the output in the hydropower station is as follows:
wherein: di is the diameter of the ith water turbine, H is the water head, alpha is the opening of the guide vane, beta is the opening of the blade, Q i Is the unit flow rate of the ith water turbine, n i The unit rotating speed of the ith water turbine is defined as eta as generator efficiency.
In a further optimized technical scheme, in step S6, a calculation model of the generated energy is:
further optimizing the technical scheme, in step S7, the optimal solution E * The calculation formula of (2) is as follows:
wherein E is * ＝[(kA 1 +rA 2 ),(kB 1 +rB 2 )]。
By adopting the technical scheme, the invention has the beneficial effects that:
according to the invention, an artificial neural network and a genetic algorithm are combined with a water energy calculation model, a water energy calculation, a unit model selection and actual operation are carried out on the water energy of the hydropower station, a mathematical model is built on water energy influence factors of the hydropower station, the water character process parameters of the power station are input, the basic condition of the power station during normal operation and power generation are simulated, and an accurate generated energy result is calculated through a weighting algorithm in cooperation with a historical record, so that the water power station model selection work is guided, the water character meteorological conditions and the unit power generation characteristics can be effectively combined, the real-time efficiency is simulated, the accuracy is improved, and the condition that the unit characteristics are inconsistent with the water character conditions is avoided.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flow chart of an algorithm for hydropower station water energy dynamic simulation of the present invention.
Detailed Description
The technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments, but it should be understood by those skilled in the art that the embodiments described below are only for illustrating the present invention and should not be construed as limiting the scope of the present invention. All other embodiments, which can be made by one of ordinary skill in the art without undue burden on the person of ordinary skill in the art based on the embodiments of the present invention, are within the scope of the present invention.
The dynamic simulation method is widely applied, and has more application in the aspects of control process, mechanical simulation, water taking and the like, but the application in the aspect of water energy calculation is relatively less at present, and the application of the dynamic simulation method in the field of water energy calculation is gradually popularized along with the improvement of the requirement on the water energy calculation precision by restricting the operation working condition of the water turbine and effectively avoiding the unfavorable operation working condition by adopting the dynamic simulation method.
The invention provides an algorithm for hydropower station hydropower dynamic simulation, which is shown in combination with fig. 1, and comprises the following steps:
s1, on-line collecting natural working condition parameters of the operation of the hydroelectric generating set, namely head loss H, set reference flow Q and runner diameter D.
S2, analyzing the comprehensive characteristic curve graph of the turbine model runner by utilizing data acquired by a neural network (the neural network characterizes a unit operation curve) to form a database.
And S3, acquiring relation curve data and output limit line data reflected in the graph according to the comprehensive characteristic curve graph of the turbine model runner, and importing the acquired data into a database.
S4, establishing a unit flow based on the rotation speed n of the water turbine and combining the acquisition parameters in the step S1And unit rotation speed->Optimizing an objective function of the unit operation working condition efficiency C as a parameter according to the objective function, the requirement of a safety and stability limiting condition and a database established by a neural network to obtain an optimizing solution E 1 ＝(A,B)。
When the water energy calculation is compared with the unit model selection scheme, the model and the algorithm meet the consistency requirement, namely each scheme is compared with other schemes under the condition of acquiring the optimal value, namely 'preferred in preferred mode'. Therefore, the mathematical model of the water energy calculation and the unit selection should be given three contents:
(1) given the runoff and unit parameters, how to distribute the flow (output) of each unit so as to make full use of the water energy.
(2) How to select the unit parameters and flow distribution for given runoff so as to maximize the real-time power generation.
(3) For the long-sequence hydrologic process taking account of the annual change, which model and corresponding invariant parameters are selected, so that the annual average power generation amount is maximized. From the basic principle of the dynamic simulation method, the three parts of contents are indistinguishable and should be unified in one mathematical model. Experience has shown that an effective tool to deal with the above problems is a dynamic programming algorithm. The hydropower station hydropower energy calculation simulation adopts the following model:
in a water energy calculation simulation model of a hydropower station,
wherein: h 1(t) A water head corresponding to the upstream of the time t is H 2(t) The head corresponding to the downstream of the moment t is the head difference between the upstream and the downstream, V 0 The volume of the initial water of the hydropower station is V (t) is the volume of the water corresponding to the hydropower station at the moment t, beta is the opening degree of the blade, beta max The maximum blade opening eta is the efficiency of the water turbine i For generator efficiency, sigma t Q i Drainage flow rate, Q of all water turbines max Is the maximum overflow of the water turbine, alpha is the opening degree of the guide vane, alpha max Is the maximum opening of the guide vane; d is the water turbine efficiency set.
S5, deriving each time point of the unit history long sequence[Q i (t),H i (t)]And determining the diameter and rotation speed (D j ,n j ) And (3) describing a comprehensive characteristic curve graph of the turbine model runner, and analyzing by utilizing data collected by a neural network.
The power output calculation model in the hydropower station is as follows:
wherein: di is the diameter of the ith water turbine, H is the water head, alpha is the opening of the guide vane, beta is the opening of the blade, Q i Is the unit flow rate of the ith water turbine, n i The unit rotating speed of the ith water turbine is defined as eta as generator efficiency.
S6, adopting a dynamic simulation method to combine invariant (specific revolution, diameter, rotating speed, installation elevation, model rotating wheel comprehensive characteristics and the like) in the running process of a group of units with the hydrologic process of the power station and reservoir dispatching control, analyzing by utilizing a neural network acquisition data set, inputting hydrologic processes such as annual incoming water flow of the hydropower station, upstream and downstream water levels and the like into model parameters, dynamically simulating by taking the maximum generated energy as an objective function, simulating the hydrologic overall process, and reselecting (D j ,n j ) m Where (j=1, 2, …, N, m=1, 2, …, N) gives the average power generation amount E over a plurality of years 2 ＝(A 2 ,B 2 )。
Combining a set of invariants in the running process of the unit with the hydrologic process of a power station and reservoir dispatching control by adopting a dynamic simulation method, wherein the dynamic simulation method is to put the invariants in the running process into the hydrologic process of a specific sequence for testing, and finally select 'satisfaction solution' from comprehensive analysis in annual energy generation, unit stability, cavitation erosion performance, unit cost and auxiliary facility cost; finally, analyzing the collection data set by utilizing a neural network, inputting hydrologic processes such as the annual incoming water flow and the upstream and downstream water levels of the hydropower station into parameters of a water turbine model, dynamically simulating the flow (output) of each unit by taking the maximum generated energy as an objective function, so as to fully utilize and re-use the water energySelecting (D) j ,n j ) m To obtain the average power generation quantity E for many years 2 ＝(A 2 ,B 2 )；
In step S6, the calculation model of the power generation amount is:
s7, according to the optimizing solution E 1 = (a, B) and average power generation amount E for many years 2 ＝(A 2 ,B 2 ) Obtaining an optimal solution E by adopting a weighting algorithm * . Optimal solution E * The calculation formula of (2) is as follows:
wherein E is * ＝[(kA 1 +rA 2 ),(kB 1 +rB 2 )]. The method has the advantages that the running condition of the hydropower station is improved, the fluctuation of the water level of the reservoir and the frequent start-stop operation are avoided, no-load flow loss during stop is avoided, the running range of the unit is increased, and the water energy during low flow is effectively utilized.
Aiming at the problems that the calculation result of the traditional hydropower station is sometimes deviated greatly because the calculation of the water energy often depends on experience, the invention provides a hydropower dynamic simulation method, namely, the hydrologic process, the unit characteristics and the scheduling control of the power station are taken as a whole, the real-time power generation amount is calculated according to the efficiency of the real-time working condition, and the average power generation amount for many years is taken as an objective function to obtain the optimal unit parameters, so that the unit operation parameters are determined, and a good effect can be obtained.
According to the invention, an artificial neural network and a genetic algorithm are combined with a water energy calculation model, so that the water energy calculation, unit model selection and actual operation of the hydropower station are realized, mathematical models are built for water energy influence factors (the number of start-up stations, the characteristics of the water turbine, the characteristics of the generator, head loss and the like) of the hydropower station, the hydrologic process parameters of the power station are input, the basic condition of the power station during normal operation power generation is simulated, and the accurate generated energy achievement is calculated through a weighting algorithm in combination with a historical record, so that the model selection operation of the hydropower station is guided, the hydrologic meteorological conditions and the unit power generation characteristics can be effectively combined, the real-time efficiency is simulated, the accuracy is improved, and the condition that the unit characteristics are inconsistent with the hydrologic conditions is avoided.
While the invention has been described in detail in the foregoing general description and specific examples, it will be apparent to those skilled in the art that modifications and improvements can be made thereto. Accordingly, such modifications or improvements may be made without departing from the spirit of the invention and are intended to be within the scope of the invention as claimed.
Claims (6)
1. An algorithm for hydropower station hydropower dynamic simulation, comprising the steps of:
s1, on-line collecting natural working condition parameters of the operation of a hydroelectric generating set, namely head loss H, set reference flow Q and runner diameter D;
s2, analyzing the comprehensive characteristic curve graph of the turbine model runner by utilizing the neural network to acquire data, and forming a database;
s3, acquiring relation curve data and output limit line data reflected in a comprehensive characteristic curve graph of the turbine model runner, and importing the acquired data into the database;
s4, based on the rotation speed n of the water turbine, combining the acquisition parameters in the step S1, establishing an objective function of the unit operation working condition efficiency C taking the unit flow A and the unit rotation speed B as parameters, and optimizing according to the objective function, the requirements of the safety and stability limiting conditions and a database established by a neural network to obtain an optimizing solution E 1 ＝(A,B)；
S5, deriving [ Q ] of each time point of unit history long sequence i (t),H i (t)]And determining the diameter and rotation speed (D j ,n j ) Describing a comprehensive characteristic curve graph of the turbine model runner, and analyzing by utilizing a neural network to collect data;
s6, adopting a dynamic simulation method to ensure that invariant in the running process of a group of units and hydrologic process of a power station and reservoirThe scheduling control is combined, wherein the dynamic simulation method is to put the invariant in the running process into the hydrologic process of a specific sequence to try, and the satisfaction solution is selected from the comprehensive analysis of annual energy generation capacity, unit stability, cavitation performance, unit cost and auxiliary facility cost; the neural network is utilized to collect the data set for analysis, the water flow of hydropower station for years and the hydrologic process of upstream and downstream water level are input into the parameters of the water turbine model, and the flow of each unit is dynamically simulated by taking the maximum generating capacity as an objective function, so that the water energy is fully utilized, and the water energy is reselected (D j ,n j ) m To obtain the average power generation quantity E for many years 2 ＝(A 2 ,B 2 )；
S7, optimizing the requirements of the unit operation working condition efficiency C and the safety and stability limiting conditions and the established neural network database to obtain an optimizing solution E 1 = (a, B) and years average power generation E obtained by analysis using neural network with maximum power generation as objective function 2 ＝(A 2 ,B 2 ) Finally, weighting algorithm is adopted to E 1 And E is 2 Solving to obtain an optimal solution E * 。
2. An algorithm for hydropower station hydropower dynamic simulation according to claim 1, wherein in step S4, the calculation formula of the unit flow a is:
the calculation formula of the unit rotating speed B is as follows:
3. an algorithm for the dynamic simulation of the hydraulic power of a hydroelectric power plant according to claim 2, characterized in that in step S4, the hydraulic power calculation simulation of the hydroelectric power plant uses the following model:
in a water energy calculation simulation model of a hydropower station,
wherein: h 1(t) A water head corresponding to the upstream of the time t is H 2(t) The head corresponding to the downstream of the moment t is the head difference between the upstream and the downstream, V 0 The volume of the initial water of the hydropower station is V (t) is the volume of the water corresponding to the hydropower station at the moment t, beta is the opening degree of the blade, beta max The maximum blade opening eta is the efficiency of the water turbine i For generator efficiency, sigma t Q i Drainage flow rate, Q of all water turbines max Is the maximum overflow of the water turbine, alpha is the opening degree of the guide vane, alpha max Is the maximum opening of the guide vane; d is the water turbine efficiency set.
4. An algorithm for hydropower station hydropower dynamic simulation according to claim 1, wherein in step S5, the calculation model of the output in the hydropower station is as follows:
wherein: p (t) is the maximum output of the water turbine at the moment t, D i Is the diameter of the ith water turbine, H is the water head, alpha is the opening of the guide vane, beta is the opening of the blade, Q i Is the unit flow rate of the ith water turbine, n i The unit rotating speed of the ith water turbine is defined as eta as generator efficiency.
5. An algorithm for hydropower station hydropower dynamic simulation according to claim 1, wherein in step S6, the calculation model of the power generation amount is:
6. an algorithm for hydropower station hydropower dynamic simulation according to claim 1, wherein in step S7, the optimal solution E * The calculation formula of (2) is as follows:
wherein E is * ＝[(kA 1 +rA 2 ),(kB 1 +rB 2 )]。
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117539168A (en) * | 2024-01-09 | 2024-02-09 | 江西江投能源技术研究有限公司 | Hydraulic turbine cavitation diagnosis system and method based on semi-physical simulation |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117539168A (en) * | 2024-01-09 | 2024-02-09 | 江西江投能源技术研究有限公司 | Hydraulic turbine cavitation diagnosis system and method based on semi-physical simulation |
| CN117539168B (en) * | 2024-01-09 | 2024-03-26 | 江西江投能源技术研究有限公司 | Hydraulic turbine cavitation diagnosis system and method based on semi-physical simulation |
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