CN111210050A - Model and method for incremental forecast of rainfall incoming water of small and medium-sized hydropower stations in mountainous and hilly types - Google Patents
Model and method for incremental forecast of rainfall incoming water of small and medium-sized hydropower stations in mountainous and hilly types Download PDFInfo
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Abstract
The invention discloses a rainfall incoming water increment forecasting method for small and medium hydropower stations in mountainous and hilly types, which comprises the steps of accessing weather and hydrologic rainfall station actual situation data and rainfall lattice point forecasting products, forecasting the incoming water flow of a small reservoir rainwater collection area through an established watershed hydrologic model, and further forecasting the reservoir capacity increment; the rain amount forecasting method for the rain collecting area of the medium and small reservoirs adopts a fine grid rain amount method to calculate the rain amount of the drainage basin. The method fully considers the unique topographic features and the climatic features of medium and small hydropower stations, optimizes and calibrates the model parameters by adopting an artificial intelligence method based on years of historical meteorological, hydrological and flow observation data, and greatly improves the regional adaptability and the accuracy of the model.
Description
Technical Field
The invention belongs to the technical field of watershed hydrology, and particularly relates to a model and a method for incremental forecast of rainfall incoming water of small and medium-sized hydropower stations in mountainous and hilly types.
Background
The rainfall is the most direct meteorological element influencing the small and medium hydropower stations, and the prediction accuracy has the most direct influence on the flood control safety and the power generation prediction of the small and medium hydropower stations. The prediction of the precipitation at present is mainly based on the prediction of global and regional modes and the correction of mode post-release technology, and mainly comprises the following steps: dynamic multi-mode integration based on early prediction errors, a frequency matching method, ensemble probability prediction, rainfall revision based on terrain, downscaling statistics and the like. The hydropower and the flow are closely related and mainly determined by the rainfall of a watershed surface, and a watershed hydrological model is generally adopted for predicting the flow. In the 60's of the 20 th century, the Stanford model, the first real watershed hydrological model in the world, was born at Stanford university in the United states, and the combination of computer science and classical hydrology was realized. Later, the basin hydrological model enters a vigorous development period, and hundreds of basin hydrological models are developed to the present day all over the world. The system mainly comprises an API model provided by the American weather administration V.T.Sitten, N.H.Crawford, a SWAT model (Soil and Water Association Tool) provided by the American Ministry of agriculture (USDA), a Stanford model provided by R.K.Linsley, a Sakramtorr model (SAC) provided by R.J.C.Bernash and the like, a Water tank model provided by professor of Waring of Japan national center for disaster prevention science, an NAM model provided by the Danish technical university, a Xinanjiang model provided by professor of Jun Zhao of original Dong academy of Water conservancy, and the like.
In the aspect of hydropower prediction, the water supply for power generation is mainly predicted at home and abroad at present, and the predicted water supply is more based on a hydrological model; for the runoff hydropower station, the generated energy is directly contributed by runoff flowing through the hydropower station, and numerical simulation analysis is mainly adopted at present, and the small hydropower stations in regions are taken as an overall research object to carry out mining analysis on the power generation sequence. The hydrological model developed in total in 3 stages: a sprouting stage, a conceptual hydrological model and a distributed hydrological model stage. Distributed hydrological models, represented abroad are SHE models, IHDM models, TOPKAPI models, SWAT models, VIC models, SAC models and the like. Although the application of hydrological models in foreign watersheds has become widespread, it has started relatively late in china. In China, the model is mainly a Xinanjiang model provided by Zhao Renjiang Jun professor of original Dong-Dong water conservancy institute, is suitable for rainfall runoff models applied to moist and semi-moist areas in China, and is widely applied. No matter which hydrological model is used, parameter calibration is needed, and the main methods for parameter calibration at home and abroad at present comprise a genetic algorithm, a particle swarm algorithm, an SCE-UA algorithm and the like. In a reservoir area with complete historical data and timely input of live flow data, an artificial intelligence algorithm can be adopted to directly predict flow or skip flow prediction to directly predict water and electricity output. The traditional small hydropower station power generation capacity prediction mainly combines the mathematical statistical law and the hydrological and meteorological characteristics thereof to carry out cause analysis, such as fuzzy pattern recognition, time sequence analysis and the like. With the development of the technology, the artificial intelligence forecasting method is widely applied, and a novel model is established by coupling hydrological and meteorological information, so that a more scientific method is provided for power generation forecasting.
The current hydrologic field is mainly based on hydrologic dynamic model deduction carried out by hydrologic station precipitation condition and flow condition data installed in a basin range aiming at reservoir water forecast, and the water use decision advance of the reservoir is insufficient due to lack of prediction of precipitation water increment forecast under the condition of future precipitation forecast.
In the meteorological field, the conventional surface rainfall forecast is released based on a rainfall numerical forecast product, or the rainfall level of a station is corrected, the influence of the pad surface under the terrain on rainfall cannot be considered by the method for correcting the rainfall forecast in the small scale range of the medium and small reservoirs, and the method is not enough in the aspect of prediction accuracy.
Disclosure of Invention
In order to solve the problems, the invention provides a model and a method for forecasting the rainfall incoming water increment of small and medium hydropower stations in mountainous and hilly types, which can optimize and rate model parameters by adopting an artificial intelligence method based on years of historical meteorological, hydrological and flow observation data, and greatly improve the regional adaptability and accuracy of the model.
The technical scheme of the invention is as follows: the rainfall incoming water increment forecasting method for the small and medium hydropower stations in mountainous and hilly types comprises the steps of accessing weather and hydrological rainfall station live data and rainfall lattice point forecasting products, forecasting the incoming water flow of a small and medium reservoir rainwater collection area through an established watershed hydrological model, and further forecasting the reservoir capacity increment; the rain amount forecasting method for the rain collecting area of the medium and small reservoirs adopts a fine grid rain amount method to calculate the rain amount of the drainage basin.
Preferably, the specific steps of the watershed surface rainfall calculation are as follows:
s101, grid division: determining the range size of the divided grids, and determining the range of the x and y coordinates of the attention area as follows: (xmin, xmax), (ymin, ymax).
S102, calculating the rainfall of the grid points: firstly, the latest reported rainfall point in four quadrants around the grid point is respectively searched, then the rainfall of the rainfall points in the four quadrants is processed, and the result is the rainfall of the grid point.
S103, calculating the surface rainfall: on the basis of obtaining the rainfall of the grid points, simple bilinear interpolation is carried out once to obtain the rainfall of the grid with higher density, and the rainfall of all grid nodes in the flow domain is directly subjected to arithmetic mean to obtain the average rainfall.
The rainfall inflow incremental forecasting model of the small and medium hydropower stations in mountainous and hilly types is established based on years of historical rainfall, warehousing flow and ex-warehouse flow of a reservoir, and comprises a runoff generating model, a evapotranspiration model, a water diversion source model and a confluence model, wherein 15 parameters are required to be calibrated.
Preferably, the evapotranspiration model is calculated by adopting a three-layer evaporation mode, conversion coefficients of water surface evaporation capacity and drainage basin evapotranspiration capacity are input, parameters of the model are water storage capacity and deep layer evapotranspiration coefficients of an upper layer, a lower layer and a deep layer, and drainage basin evapotranspiration capacity of the upper layer, the lower layer and the deep layer is output.
Preferably, the runoff yield model calculation is obtained according to the full runoff yield theory, when the runoff yield calculation is carried out, the input of the model is rainfall and evaporation, the parameters comprise the average watershed water storage capacity and the parabolic index, and the output is the watershed runoff yield and the average soil water storage capacity at the end of the watershed period.
Preferably, the water diversion source model solves water source division by using a structure of a free water reservoir.
The river network converging means a converging process that water flow continues along the river network after entering the river channel from the slope; the sloping field confluence refers to the gathering process of a water body on a sloping surface, and water flow moves horizontally and vertically; preferably, the confluence model comprises two confluence stages of a slope and a river network.
Preferably, the parameter calibration of the rainfall inflow incremental forecast model adopts a genetic algorithm, and the genetic algorithm comprises the following level elements: parameter coding, initial population setting, fitness function design, genetic operation design and control parameter setting.
Preferably, the water diversion source model considers that the evaporation is consumed in the tension water, and the water quantity of the free water reservoir is all runoff.
Preferably, the parameter calibration of the model of the present invention may adopt a genetic algorithm, and the step of performing parameter calibration by using the genetic algorithm includes:
s201, determining the range of each parameter;
s202, taking a population of randomly generated chromosomes as an initial generation, and randomly taking values of the chromosomes according to a parameter range;
s203, decoding the chromosomes, and calculating the fitness of each chromosome;
s204, selecting operation: distributing the fitness of each chromosome in proportion to determine the selected parts of each chromosome;
s205, exchange operation: multipoint switching may be employed;
s206, mutation operation: performing mutation on the content of a certain bit according to the bit;
s207, generating a next generation population, and selecting chromosomes with high fitness;
s208, repeating the steps S201-S207, and finishing the calculation when an iteration termination condition is met; and decoding the obtained individual with the maximum fitness to obtain a value of the calibration parameter.
Compared with the prior art, the invention has the beneficial effects that:
the method fully considers the unique topographic features and the climatic features of medium and small hydropower stations, optimizes and calibrates the model parameters by adopting an artificial intelligence method based on years of historical meteorological, hydrological and flow observation data, and greatly improves the regional adaptability and the accuracy of the model.
Drawings
FIG. 1 is a general study route of the present invention.
FIG. 2 is a block diagram of a model prediction process according to the present invention.
FIG. 3 is a basic flow chart of the control parameter setting according to the present invention.
Detailed Description
The invention is described in further detail below with reference to specific embodiments and with reference to the following drawings:
example 1
As shown in fig. 1, the method for forecasting the rainfall inflow increment of the small and medium hydropower stations in mountainous and hilly types comprises the steps of accessing weather and hydrologic rainfall station actual situation data and rainfall lattice point forecasting products, forecasting the inflow of water in a small reservoir rain collecting area through an established hydrologic model in the river basin of the Xinanjiang, and further forecasting the reservoir capacity increment; the rain amount forecasting method for the rain collecting area surface of the medium and small reservoirs adopts a fine grid rain amount method to calculate the rain amount of the drainage basin surface. The basic idea of the fine grid rainfall method is to cover a drainage basin surface with a fixed grid with a certain density, calculate the rainfall on each grid node through mathematical operation processing, and the surface rainfall of the drainage basin is the arithmetic average of the rainfall of the grid nodes on the surface. Thus, the calculation of the rainfall of the live area is converted into the grid point-grid point interpolation problem of the rainfall of the measuring station under the condition of not fixing the measuring station, and the rainfall of the forecast area is the grid point-grid point interpolation problem.
The method comprises the following specific steps of calculating the rainfall of the drainage basin surface:
s101, grid division: and determining the range size of the divided grids, wherein the range size comprises the whole drainage basin or the whole area, the range size also needs to be expanded by 1-2 longitudes and latitudes around, and the rainfall of the edge grid points needs to be estimated by calculating the rainfall of peripheral stations. The range of x, y coordinates of the region of interest is determined as: (xmin, xmax), (ymin, ymax), when the density of the grid is designed, 2 stations do not appear in 1 grid, so the maximum grid distance (Δ x, Δ y) is set to be controlled by the density and distribution of the stations.
S102, calculating the rainfall of the grid points: firstly, the nearest reported rainfall point in four quadrants around the grid point is respectively searched, then the rainfall of the rainfall points of the four quadrants is processed by a distance weighting method, and the result is the rainfall of the grid point. When performing the rainfall interpolation calculation on the grid point, the distance from the station to the grid point should be considered. If too far a station rains, the interpolation should be considered invalid. The selection is generally based on the density of the grid and stations, and in most cases, the value of N is selected such that there are no less than 2 stations in each of the 4 quadrants adjacent to 1 grid point. For grid point forecast, the rainfall of the grid points can be obtained by adopting inverse distance weight interpolation.
S103, calculating the surface rainfall: on the basis of obtaining the rainfall of the grid points, simple bilinear interpolation is carried out once to obtain the rainfall of the grid with higher density, and the rainfall of all grid nodes in the flow domain is directly subjected to arithmetic mean to obtain the average rainfall. Since the boundary is irregular, areas that do not fall within the watershed should be theoretically omitted from the boundary.
As shown in fig. 2, the rainfall inflow incremental forecasting model of the small and medium hydropower stations in mountainous and hilly types is established based on years of historical rainfall, warehousing flow and ex-warehouse flow of a reservoir, and is established by referring to a new anjiang model and comprehensively considering initial values such as rainfall of the surface of an input reservoir, evaporation capacity and area of the reservoir, output warehousing flow, upper-layer water storage capacity, lower-layer water storage capacity, basin water storage capacity, free water storage capacity, initial runoff, initial soil runoff and initial groundwater demonstration runoff. The forecasting model comprises a production flow model, a evapotranspiration model, a water diversion source model and a confluence model, and 15 parameters are calibrated.
The evapotranspiration model is calculated by adopting a three-layer evaporation mode, conversion coefficients of water surface evaporation capacity and drainage basin evapotranspiration capacity are input, parameters of the model are water storage capacity and deep layer evapotranspiration coefficients of an upper layer, a lower layer and a deep layer, and drainage basin evapotranspiration capacity of the upper layer, the lower layer and the deep layer is output. Three time-varying parameters are included in the calculation: total basin water storage capacity, evapotranspiration and soil water content. The calculation principle of each layer evapotranspiration is that the upper layer is evaporated according to the evapotranspiration capacity, when the evaporation capacity of the water content of the upper layer is not enough, the residual evapotranspiration capacity is evaporated from the lower layer, the evaporation of the lower layer is in direct proportion to the evapotranspiration capacity and the water content of the lower layer and in inverse proportion to the water storage capacity of the lower layer, and the ratio of the calculated evaporation capacity of the lower layer to the residual evapotranspiration capacity is not less than the deep evapotranspiration coefficient. Otherwise, the insufficient water is supplied by the lower water content, and when the lower water content is insufficient, the deep water content is used for supplying.
The runoff yield model calculation is obtained according to the full runoff yield theory, runoff yield is not generated when the soil humidity does not reach the field water capacity, and all rainfall is absorbed by the soil to become tension water. And when the soil humidity reaches the field water capacity, all rainfall with the evaporation in the same period subtracted produces runoff. The water storage capacities of all points in the watershed are different, and the water storage capacities of all points in the watershed are generalized into a parabola by the three-source model of Xinanjiang. When the runoff yield calculation is carried out, the input of the model is rainfall and evaporation, and parameters comprise the average water storage capacity of a watershed and a parabolic index; the output is the drainage basin yield and the average water storage capacity of the soil at the end of the drainage basin time period.
The water source dividing model solves the water source division by using the structure of a free water storage reservoir. The Xinanjiang model divides a water source into three water source models, and the original structure is replaced by the structure of a free water reservoir so as to solve the problem of water source division and calculate the output flow according to the full-storage output flow model. Firstly, the free water storage capacity is added, and then the water source is divided. The three water sources are a reservoir with one bottom hole forming underground runoff and one side hole forming interflow and ground runoff. The outflow laws of the first two water sources are all according to the outflow of the linear reservoir. Considering the problem of runoff generating area, the free water reservoir only occurs on the runoff generating area, the bottom width of the free water reservoir is changed, runoff generating flow enters the reservoir, namely on the runoff generating area, so that the free water reservoir increases the water storage depth, and when the free water storage depth exceeds the maximum value, the excess part of the free water reservoir becomes ground runoff. The model considers that the evapotranspiration is consumed in the tension water, and the water quantity of the free water reservoir is runoff.
The river basin confluence calculation comprises two confluence stages of a sloping field and a river network. The sloping field confluence refers to the gathering process of water on a sloping surface, and water flow not only moves horizontally, but also moves vertically. On the slope of the drainage basin, the regulation and storage effect of the surface runoff is not large, the subsurface runoff is subjected to large regulation and storage, and the regulation and storage of the interflow runoff is between the two. The river network converging means that water flows enter a river channel from the slope surface and then continue to converge along the river network. In the river network converging stage, converging characteristics are limited by the hydraulics conditions of the river channel, various water sources are consistent, and the river network converging in the Xinanjiang three-water-source model only refers to the process that water bodies on each unit area converge from the inlet of the river channel to the outlet of the unit, but does not include the river network converging stage from the outlet of the unit to the outlet of the basin.
The parameter calibration of the rainfall inflow incremental forecast model adopts a genetic algorithm, the genetic algorithm directly operates the structural object, the derivation and the function continuity limitation do not exist, and the rainfall inflow incremental forecast model has the inherent implicit parallelism and better global optimization capability; by adopting a probabilistic optimization method, the optimized search space can be automatically acquired and guided, the search direction can be adaptively adjusted, and a determined rule is not needed. The genetic algorithm comprises the following elements of the scale: parameter coding, initial population setting, fitness function design, genetic operation design and control parameter setting.
As shown in fig. 3, the step of performing parameter calibration using a genetic algorithm includes:
s201, determining the range of each parameter, wherein the value range of the calibration parameter is shown in Table 1.
TABLE 1
| Parameter(s) | Means of | Value range |
| K | Conversion coefficient of evaporation capacity | 0.1-3 |
| WDM | Deep water storage capacity | 1-100 |
| WUM | Upper water storage capacity | 1-30 |
| WLM | Lower water storage capacity | 1-100 |
| C | Coefficient of deep evapotranspiration | 0.05-50 |
| IMP | Ratio of water-impermeable area to total flow area | 0.005-0.2 |
| B | Water storage capacity curve index | 0.05-2 |
| SM | Watershed free water storage capacity | 2-80 |
| EX | Curve index of water storage capacity of free water | 0.1-3 |
| KSS | Runoff discharge coefficient of soil water | 0.1-0.55 |
| KG | Runoff discharge coefficient of groundwater | 0.7-KSS |
| CS | Coefficient of regression of surface runoff | 0.1-0.9 |
| KKSS | Coefficient of subsidence of runoff of water in soil | 0.1-0.99 |
| KKG | Coefficient of groundwater runoff regression | 0.9-0.999 |
| L | Time of retardation | 0-20 |
Genetic algorithms require the selection of high crossover rates, low mutation rates and appropriate population numbers. There are 4 parameters for GA: psize (chromosome number), Pc (crossover rate), Pm (mutation rate), Tmax (maximum passage number). Wherein the Psize value is 200, the Pc value is 0.8, the Pm value is 0.15, and the Tmax value is 500.
S202, taking a randomly generated chromosome population as an initial generation, and adopting a binary method for individual coding, wherein the number of variables is 15 parameters to be calibrated. The string length m is determined by the number and range of parameters and the precision to be achieved, and 15 bits are taken. Chromosomes take values randomly within the ranges of parameters listed above.
And S203, decoding the chromosomes and calculating the fitness of each chromosome.
S204, selecting operation: the higher the fitness of the chromosome, the more opportunities are selected. The number of copies of each chromosome selection is determined using a round of betting selection to apportion each chromosome fitness.
S205, exchange operation: multipoint switching may be employed.
S206, mutation operation: the mutation operation can avoid local optimization, is carried out according to bits and mutates the content of a certain bit; for binary coding, i.e., 0 to 1 and 1 to 0.
And S207, generating a next generation population, evaluating the fitness of the population again, finding out the optimal chromosome to be compared with the optimal chromosome of the previous generation, and selecting the chromosome with high fitness.
And S208, repeating the steps S201-S207, and finishing the calculation when the iteration termination condition is met. The iteration termination conditions as selected are: first, the largest genetic algebra is reached; secondly, the error is less than 10 percent; and selecting the individual with the maximum fitness, and decoding to obtain the value of the rating parameter.
Claims (10)
1. The rainfall inflow increment forecasting method of the small and medium hydropower stations in mountainous and hilly types is characterized by comprising the steps of accessing weather, hydrological rainfall station actual situation data and rainfall lattice point forecasting products, forecasting inflow water flow of a small reservoir rainwater collection area through an established watershed hydrological model, and further forecasting reservoir capacity increment; the rain amount forecasting method for the rain collecting area of the medium and small reservoirs adopts a fine grid rain amount method to calculate the rain amount of the drainage basin.
2. The method for forecasting the rainfall inflow increment of the small and medium-sized hydropower stations in the mountainous and hilly types according to claim 1, wherein the method for calculating the rainfall of the watershed surface comprises the following specific steps:
s101, grid division: determining the range size of the divided grids, and determining the range of the x and y coordinates of the attention area as follows: (xmin, xmax), (ymin, ymax).
S102, calculating the rainfall of the grid points: firstly, the latest reported rainfall point in four quadrants around the grid point is respectively searched, then the rainfall of the rainfall points in the four quadrants is processed, and the result is the rainfall of the grid point.
S103, calculating the surface rainfall: on the basis of obtaining the rainfall of the grid points, simple bilinear interpolation is carried out once to obtain the rainfall of the grid with higher density, and the rainfall of all grid nodes in the flow domain is directly subjected to arithmetic mean to obtain the average rainfall.
3. The rainfall inflow increment forecasting model of the small and medium hydropower stations in the mountainous and hilly types is characterized in that the rainfall inflow increment forecasting model of the reservoir area is established based on years of historical rainfall, warehousing flow and ex-warehouse flow of a reservoir, and comprises a production flow model, a evapotranspiration model, a water diversion source model and a confluence model.
4. The model of claim 3, wherein the evapotranspiration model is calculated by adopting a three-layer evaporation mode.
5. The model of claim 3, wherein the runoff yield model calculation is based on the flooded runoff yield theory.
6. The model of claim 3, wherein the model of water diversion source is a free water reservoir structure solution for water source division.
7. The model of claim 3, wherein the confluence model comprises two confluence stages of slope and river network.
8. The model of claim 3, wherein the parameter calibration of the model adopts a genetic algorithm, and the genetic algorithm comprises the following level elements: parameter coding, initial population setting, fitness function design, genetic operation design and control parameter setting.
9. The model of claim 6, wherein in the model of water diversion source, the amount of water in free water reservoir is runoff.
10. The model of claim 8, wherein the step of using the genetic algorithm to rate the parameters comprises:
s201, determining the range of each parameter;
s202, taking a population of randomly generated chromosomes as an initial generation, and randomly taking values of the chromosomes according to a parameter range;
s203, decoding the chromosomes, and calculating the fitness of each chromosome;
s204, selecting operation: distributing the fitness of each chromosome in proportion to determine the selected parts of each chromosome;
s205, exchanging operation;
s206, mutation operation;
s207, generating a next generation population;
s208, when the iteration termination condition is met, the calculation is finished; and decoding the obtained individual with the maximum fitness to obtain a value of the calibration parameter.
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| CN116228046A (en) * | 2023-05-09 | 2023-06-06 | 成都信息工程大学 | Mountain area space precipitation estimation method based on satellite remote sensing and geographic data |
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| CN111817321A (en) * | 2020-06-04 | 2020-10-23 | 国网宁夏电力有限公司经济技术研究院 | Pumped storage power station peak regulation capacity analysis method considering precipitation influence |
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| CN112163366A (en) * | 2020-08-14 | 2021-01-01 | 贵州东方世纪科技股份有限公司 | Automatic calibration method for hydrological model parameters in data-free area |
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| CN114137171A (en) * | 2021-11-22 | 2022-03-04 | 中国水利水电科学研究院 | Groundwater salinity analysis method based on hydrogeology and hydrodynamics |
| CN114137171B (en) * | 2021-11-22 | 2022-08-05 | 中国水利水电科学研究院 | Groundwater salinity analysis method based on hydrogeology and hydrodynamics |
| CN116228046A (en) * | 2023-05-09 | 2023-06-06 | 成都信息工程大学 | Mountain area space precipitation estimation method based on satellite remote sensing and geographic data |
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