A method for predicting water level amplitude of a dam downstream wharf under non-constant flow conditions

Abstract

The application discloses a dam-down wharf water level amplitude prediction method under non-constant flow conditions and particularly relates to the technical field of water level prediction. According to the river channel topographic data of the dam-down wharf position, flow information is obtained through a hydroelectric station hydrological information release platform, relevant data influencing the dam-down wharf water level amplitude are collected and preprocessed, qualitative analysis is performed according to the relationship between the relevant data influencing the dam-down wharf water level amplitude and the wharf water level amplitude, and then a dam-down wharf water level amplitude prediction model is obtained, and a prediction result is outputted so as to be convenient for visual analysis and decision-making. The application is easy to implement and use, is suitable for on-site rapid application, can rapidly respond under real-time monitoring conditions, adapts to the dynamic characteristics of water level changes, realizes water level amplitude prediction of the near-dam wharf, and reduces shipping and wharf operation risks caused by non-constant flow water level fluctuations.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water level prediction, in particular to a method for predicting water level amplitude of a wharf downstream of a dam under non-constant flow conditions. BACKGROUND

[0002] After the completion of a hydropower station, the non-constant flow generated under daily regulation or flood discharge conditions causes the water level, flow velocity, water surface slope, and other flow characteristics in the downstream near-dam river channel to have a much larger amplitude than in a natural river channel, which adversely affects ship navigation and wharf operations. The near-dam wharf downstream of a hydropower station is an important facility for loading and unloading cargo and berthing ships, and the water level change directly affects the safety and economic benefits of the wharf.

[0003] Currently, many hydropower stations use fixed flow or average flow models to predict the water level change of the near-dam wharf. However, actual flow often exhibits non-constant characteristics. The non-constant flow causes a large fluctuation amplitude of the water level downstream of the dam, making it difficult to accurately predict using traditional methods. There is a lack of effective calculation models and methods to accurately evaluate the water level amplitude of the near-dam wharf under non-constant flow conditions in the prior art.

[0004] Most current water level prediction techniques are based on machine deep learning and hydrological models, but these methods usually require a large amount of computing resources, have high application costs, and focus on theoretical analysis but lack practicality, making them unsuitable for rapidly changing flow conditions. Some water level fluctuation prediction methods do not consider all factors or are only applicable to specific conditions. Therefore, there is an urgent need for a simple, practical, and effective prediction method to quickly and accurately predict the water level amplitude of the near-dam wharf under non-constant flow conditions, providing reliable support for the safe operation of hydropower stations and wharves. SUMMARY

[0005] To this end, the present application provides a method for predicting the water level amplitude of a near-dam wharf under non-constant flow conditions to solve the problems presented in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solution: a method for predicting the water level amplitude of a near-dam wharf under non-constant flow conditions, according to the river channel topographic data of the near-dam wharf location and the flow information obtained through a hydropower station hydrological information release platform, collecting and preprocessing relevant data that affect the water level amplitude of the near-dam wharf, qualitatively analyzing the relationship between the relevant data that affect the water level amplitude of the near-dam wharf and the water level amplitude of the wharf, and then obtaining a water level amplitude prediction model for the near-dam wharf, outputting the prediction results for visual analysis and decision-making.

[0007] The water level amplitude prediction model for the near-dam wharf is as follows:

[0008] ;

[0009] wherein, is a parameter to be fitted; is the amplitude of water level variation of the wharf, in m; is the discharge of the hydropower station foundation, in m 3 / s; is the amplitude of discharge, in m 3 / s; is the duration of discharge variation, in h; is the acceleration of gravity, taken as 9.81 m / s 2 ; is the river depth at the location of the wharf, in m; is the water surface width at the location of the wharf, in m; is the initial cross-sectional water surface width, in m; is the distance of the wharf from the dam, in km; is the attenuation coefficient along the river; is the wave speed of unsteady flow;

[0010] wherein, the attenuation coefficient along the river is an important parameter describing the rate of attenuation of water level variation along the river, which is determined by the characteristics of the river. The attenuation coefficient along the river is calculated as follows:

[0011] ;

[0012] is a constant related to the type of river and flow regime, fitted by numerical simulation or measured data;

[0013] is the friction coefficient, dimensionless, which is derived from the friction between the water flow and the riverbed and riverbank. The greater the friction, the more energy loss of the water flow, and the faster the attenuation of water level variation. The friction coefficient is affected by the roughness of the riverbed and the morphology of the river. The commonly used Manning formula can be used to estimate the friction coefficient, which is estimated by the Manning formula as follows: ; wherein, is the Manning roughness coefficient, which is related to the roughness of the riverbed;

[0014] The width of the river directly affects the energy distribution of the water flow. The wave energy distribution in a wide river is more dispersed, so the wider the river, the faster the attenuation;

[0015] The river depth affects the propagation speed of the water flow and the loss of wave energy. The deeper the river, the more stable the wave propagation, and the slower the attenuation;

[0016] Preferably, the related data affecting the water level variation of the wharf downstream of the dam include the discharge of the hydropower station foundation , the amplitude of the discharge Duration of traffic changes The depth of the river where the dock is located Width of the river channel where the dock is located Distance from the wharf to the dam River channel roughness Preprocessing includes removing outliers and filling in missing values ​​to improve data quality.

[0017] The preferred qualitative analysis of the relationships between relevant data affecting the fluctuation of water level at the downstream wharf is as follows:

[0018] Basic discharge flow It is the initial flow rate before peak shaving at the hydropower station. When the flow is large, the existing flow in the river channel is greater, and the water level is also higher, which may exacerbate the impact of flow changes on the fluctuation of the wharf water level, and the discharge flow from the foundation of the hydropower station. With the fluctuation of the wharf water level The qualitative relationship between them is as follows:

[0019] ;

[0020] Discharge flow variation This directly reflects the magnitude of flow fluctuations during peak shaving. The greater the flow fluctuation, the greater the water level fluctuation. This will also increase accordingly. Generally, changes in flow rate are positively correlated with changes in water level, and changes in discharge flow rate will also increase. With the fluctuation of the wharf water level The qualitative relationship between them is as follows:

[0021] ;

[0022] Duration of outflow variation The duration of wave propagation is affected. When In shorter periods, water level changes are more drastic and concentrated; when Over longer periods, water level changes are more gradual, and the duration of changes in discharge flow is longer. With the fluctuation of the wharf water level The qualitative relationship between them is as follows:

[0023] ;

[0024] The river where the dock is located is deep This reflects the initial water depth of the river channel. In river sections with greater water depth, changes in flow are less sensitive to changes in water level; in river sections with less water depth, changes in flow can cause larger fluctuations in water level. The river channel depth at the location of the wharf... With the fluctuation of the wharf water level The qualitative relationship between them is as follows:

[0025] ;

[0026] The width of the river where the wharf is located Determines the lateral expansion of the water body. A wider river can better disperse flow changes and slow down the speed of water level rise; while a narrower river may exacerbate water level changes caused by flow fluctuations. The qualitative relationship between the width of the river where the wharf is located and the water level amplitude of the wharf is as follows:

[0027] ;

[0028] The distance from the wharf to the dam Is another key factor. Fluctuations will attenuate during propagation, and the farther the wharf is from the dam, the water level amplitude will gradually weaken. Generally, it can be considered that the relationship between the water level fluctuation amplitude and the distance from the wharf to the dam is exponential decay, and the qualitative relationship between the distance from the wharf to the dam and the water level amplitude of the wharf is as follows:

[0029] ;

[0030] The wave speed of unsteady flow Indicates the propagation speed in the river. When the wave speed is faster, the flow changes are faster to transmit downstream, but the fluctuation time is shorter; when the wave speed is slower, the fluctuation may be slower to transmit, but the water level change amplitude is more obvious. The qualitative relationship between the wave speed of unsteady flow and the water level amplitude of the wharf is as follows:

[0031] .

[0032] The present application has the following advantages:

[0033] The present application qualitatively analyzes the relationship between the relevant data affecting the water level amplitude of the wharf downstream of the dam and the water level amplitude of the wharf, and then obtains a prediction model of the water level amplitude of the wharf downstream of the dam, outputs the prediction result, so as to facilitate visual analysis and decision-making. The present application is easy to implement and use, suitable for on-site rapid application, can quickly respond under real-time monitoring conditions, adapt to the dynamic characteristics of water level changes, realizes the prediction of the water level amplitude of the wharf near the dam, and reduces the risk of shipping and wharf operation caused by unsteady flow water level fluctuations. DETAILED DESCRIPTION

[0034] ​​​​​​​​The following specific embodiments illustrate the embodiments of the present application, and those skilled in the art can easily understand other advantages and effects of the present application from the disclosure of the specification. Obviously, the described embodiments are part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0035] The fluctuation range of the water level downstream of the dam caused by the unsteady flow is large, and it is difficult to accurately predict by traditional methods. At present, there is a lack of effective calculation model and method to accurately evaluate the fluctuation range of the water level downstream of the dam under unsteady flow conditions. Most water level prediction techniques are based on machine deep learning and hydrological model methods, but these methods usually require a large amount of computing resources, have high application cost, and focus on theoretical analysis but lack practicality, and cannot adapt to rapidly changing flow conditions. Some water level fluctuation prediction methods do not consider all factors, or are only applicable to specific application conditions. Therefore, the present application provides a water level fluctuation prediction method for a dock downstream of a dam under unsteady flow conditions. According to the river topographic data of the dock downstream of the dam, and by obtaining the flow information through the hydrological information release platform of the hydropower station, the related data affecting the water level fluctuation of the dock downstream of the dam are collected and preprocessed, the relationship between the related data affecting the water level fluctuation of the dock downstream of the dam and the water level fluctuation of the dock is qualitatively analyzed, and then a water level fluctuation prediction model for the dock downstream of the dam is obtained, and the prediction result is outputted for visual analysis and decision-making. The present method is a simple, practical and effective prediction method for quickly and accurately predicting the water level fluctuation of the dock downstream of the dam under unsteady flow conditions, and provides reliable support for the safe operation of the hydropower station and the dock.

[0036] The water level fluctuation prediction model for the dock downstream of the dam is as follows:

[0037] ;

[0038] In the formula, is a parameter to be fitted; is the water level fluctuation of the dock, unit m; is the discharge of the hydropower station, unit m 3 / s; is the fluctuation range of the discharge, unit m 3 / s; is the change duration of the discharge, unit h; is the acceleration of gravity, 9.81 m / s 2 ; is the river depth at the location of the dock, unit m; is the water surface width at the location of the dock, unit m; is the water surface width of the initial section, unit m; is the distance from the wharf to the dam, in km; is the attenuation coefficient along the river; is the wave speed of unsteady flow;

[0039] wherein the attenuation coefficient along the river The calculation formula is as follows:

[0040] ;

[0041] is a constant related to the type of river and flow regime, fitted by numerical simulation or measured data; is the friction coefficient, dimensionless, estimated by the Manning formula: ; wherein, is the Manning roughness coefficient, related to the roughness of the riverbed;

[0042] The related data affecting the water level amplitude of the wharf downstream of the dam include the discharge of the hydropower station foundation , the flow amplitude , the flow change duration , the water depth of the river where the wharf is located , the width of the river where the wharf is located , the distance from the wharf to the dam , the river roughness , preprocessing includes removing outliers and filling missing values to improve data quality.

[0043] The qualitative analysis of the relationship between the related data affecting the water level amplitude of the wharf downstream of the dam is as follows:

[0044] The qualitative relationship between the discharge of the hydropower station foundation and the water level amplitude of the wharf is as follows:

[0045] ;

[0046] The qualitative relationship between the discharge amplitude and the water level amplitude of the wharf is as follows:

[0047] ;

[0048] The qualitative relationship between the flow change duration and the water level amplitude of the wharf is as follows:

[0049] ;

[0050] The qualitative relationship between the water depth of the river where the wharf is located and the water level amplitude of the wharf is as follows:

[0051] ;

[0052] width of the river channel where the wharf is located and the water level amplitude of the wharf The qualitative relationship between them is as follows:

[0053] ;

[0054] distance between the wharf and the dam and the water level amplitude of the wharf The qualitative relationship between them is as follows:

[0055] ;

[0056] wave velocity of unsteady flow and the water level amplitude of the wharf The qualitative relationship between them is as follows:

[0057] .

[0058] The embodiment provides a dam-down wharf water level amplitude prediction method based on a dam-down wharf water level amplitude prediction model. Taking the prediction of the water level amplitude of a near-dam wharf caused by the peak shaving process of a certain hydropower station as an example, the method specifically comprises the following steps:

[0059] S1: data collection and preprocessing

[0060] Through hydrological monitoring equipment and a weather station, key data affecting the water level change of the near-dam wharf are obtained, mainly including:

[0061] the basic discharge of the certain hydropower station , unit m 3 / s, the flow amplitude , unit m 3 / s, the flow change duration , unit h, the width of the river channel of the wharf , unit m, the initial section selection water surface width of the nearest wharf of the hydropower station 160 m, the water depth of the river channel of the wharf , unit m; the distance between the wharf and the dam , unit m, the river roughness n, which is 0.04.

[0062] S2: use of the dam-down wharf water level amplitude prediction model

[0063] The calculation formula of the attenuation coefficient along the path is substituted into the empirical calculation formula of the water level amplitude to obtain:

[0064] ;

[0065] According to historical data, the values of the undetermined coefficients and are 0.109 and 0.032 respectively.

[0066] Based on the dam downstream wharf water level amplitude prediction model, the final wharf water level amplitude is obtained, and the formula is as follows:

[0067] ;

[0068] S3: Prediction of wharf water level amplitude

[0069] The real-time monitored discharge of a certain hydropower station foundation is , unit m 3 / s, the flow amplitude is , unit m 3 / s, the flow change duration is , unit h, the wharf river width is , unit m, the wharf river depth is , the wharf distance from the dam is , unit m, and the river roughness n is substituted into the above formula to calculate the change amplitude of the dam downstream wharf water level. For example:

[0070] As shown in Table 1, the current discharge of a certain hydropower station foundation is , the discharge increases by 2850 m 3 / s within 3h, the water depth of the Dahewan wharf about 4.826km away from the dam site is about 11.83m, and the water surface width is about 195.7m. The formula is:

[0071] ;

[0072] Through calculation, the water level amplitude of the Dahewan wharf is 1.475m, and the actual measured water level amplitude of the Dahewan wharf is 1.552m.

[0073] In order to verify the prediction effect of the dam downstream wharf water level amplitude prediction model of the embodiment, the formula is used to calculate and predict the water level amplitude of the near-dam wharf under a certain typical flow change process, and compared with the actual value (see Table 2). From the prediction results in Table 2, the absolute error value and the relative error of the predicted value and the actual value are relatively small, the average relative error is 6%, the maximum relative error is only 8%, the maximum absolute error is only 0.089m, the root mean square error is only 0.00372, and the Pearson correlation coefficient square R 2 reaches 0.988.

[0074]

[0075] In summary, the dam downstream wharf water level amplitude prediction model of the present application can effectively predict the water level amplitude of the near-dam wharf under certain peak-shaving non-constant flow conditions. The results show that the water level prediction error is controlled within 10%, and the absolute error between the actual value and the predicted value is not more than 0.09m.

[0076] Currently, the mainstream deep learning model for water level prediction, such as a river mouth water level time series prediction method based on an improved Informer deep learning framework ([1] Natural Resources Department Third Marine Institute. A river mouth water level time series prediction method based on an improved Informer deep learning framework: 202411497591.8[P]. 2025-03-04.), the maximum absolute error between the predicted value and the actual value is not more than 0.085m, which shows that the error control effect of the model of the present embodiment is good, and it has important guiding value for realizing reasonable peak-shaving of a certain hydropower station operation and dispatching center, and safety of ship navigation and wharf operation.

[0077] Although the present application has been fully described in the foregoing description with reference to the accompanying drawings, it is apparent that some modifications and improvements can be made to the present application on the basis of the present application, which will be apparent to those skilled in the art. Therefore, these modifications or improvements made on the basis of not deviating from the spirit of the present application, all belong to the scope of protection required by the present application.

Claims

1. A method for predicting the water level amplitude of a dam downstream wharf under unsteady flow conditions, according to the river terrain data at the dam downstream wharf location, and through the hydrological information release platform of the hydropower station to obtain flow information, collect relevant data affecting the water level amplitude of the dam downstream wharf and preprocess, characterized in that: According to the qualitative analysis of the relationship between the relevant data influencing the water level fluctuation of the downstream wharf and the water level fluctuation of the downstream wharf, a water level fluctuation prediction model of the downstream wharf is obtained, and a prediction result is output. The water level fluctuation prediction model of the downstream wharf is as follows: ； wherein, is a parameter to be fitted; is the amplitude of the water level at the wharf; is the discharge at the foundation of the hydropower station; is the amplitude of the discharge; is the duration of the discharge change; is the acceleration of gravity; is the water depth of the river at the location of the wharf; is the width of the water surface at the location of the wharf; is the initial cross-sectional width of the water surface; is the distance from the wharf to the dam; is the attenuation coefficient along the way; is the non-constant flow wave speed, the non-constant flow propagation speed ; wherein the attenuation coefficient along the path The calculation formula is as follows: ; is a constant related to the river type and flow regime, fitted by numerical simulation or measured data; is the friction coefficient, dimensionless, estimated by the Manning formula: ; wherein, is the Manning roughness coefficient, related to the roughness of the river bed.

2. The method for predicting water level fluctuation of a wharf downstream of a dam under unsteady flow conditions according to claim 1, characterized in that: The related data affecting the water level amplitude of the dam downstream wharf includes the water and electricity station basic discharge flow , flow amplitude , flow change duration , water depth of the river channel where the wharf is located , width of the river channel where the wharf is located , distance between the wharf and the dam , river roughness , and preprocessing includes removing outliers and filling missing values.

3. The method for predicting water level fluctuation of a wharf under a non-constant flow condition according to claim 1, characterized in that: The qualitative analysis of the relationship between the relevant data influencing the water level fluctuation of the downstream wharf is as follows: The qualitative analysis of the relationship between the relevant data influencing the water level fluctuation of the downstream wharf is as follows: Hydropower station foundation discharge flow With the fluctuation of the wharf water level The qualitative relationship between them is as follows: ； Discharge flow variation With the fluctuation of the wharf water level The qualitative relationship between them is as follows: ； Duration of outflow variation With the fluctuation of the wharf water level The qualitative relationship between them is as follows: ； The water depth of the river at the location of the wharf The qualitative relationship between the water level amplitude of the wharf is as follows: ； The width of the river where the wharf is located The qualitative relationship between the wharf water level amplitude is as follows: ； harbo(u)r distance qualitative relation between the harbo(u)r water level range is as follows: ； Unsteady flow wave velocity With the fluctuation of the wharf water level The qualitative relationship between them is as follows: 。

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