Method for predicting amplitude variation of water level of wharf under dam under non-constant flow condition

Abstract

The invention discloses an under-dam wharf water level amplitude prediction method under a non-constant flow condition, and particularly relates to the technical field of water level prediction.The method comprises the steps that according to river topographic data of the under-dam wharf position, flow information is obtained through a hydrological information issuing platform of a hydropower station, and related data influencing the under-dam wharf water level amplitude are collected and preprocessed; comprising the following steps of performing qualitative analysis according to a relationship between related data influencing the water level variation of the wharf under the dam and the water level variation of the wharf, further obtaining a prediction model of the water level variation of the wharf under the dam, and outputting a prediction result so as to facilitate visual analysis and decision-making. The method is easy to realize and use, is suitable for field rapid application, can rapidly respond under a real-time monitoring condition, adapts to dynamic characteristics of water level change, realizes water level amplitude variation prediction of a near-dam wharf, and reduces shipping and wharf operation risks caused by non-constant flow water level fluctuation.

Description

Technical Field

[0001] The present invention relates to the technical field of water level prediction, and in particular to a method for predicting water level fluctuations at a wharf below a dam under non-steady flow conditions. Background Art

[0002] After the hydropower station was built, the unsteady flow generated by daily regulation or flood season discharges caused the downstream river channel near the dam to fluctuate much more than in a natural river channel, including water level, flow velocity, and water surface gradient. This adversely impacted navigation and dock operations. The docks downstream of the hydropower station are crucial for loading and unloading cargo and berthing vessels, and water level fluctuations directly impact their safety and economic efficiency.

[0003] Currently, many hydropower stations use fixed or average flow models to predict water level fluctuations near dam terminals. However, actual flow often exhibits non-constant characteristics. This non-constant discharge causes large fluctuations in the water level downstream of the dam, making it difficult to accurately predict using traditional methods. Existing technologies lack effective computational models and methods to accurately assess the fluctuations in water level at downstream dam terminals under non-constant flow conditions.

[0004] Currently, most water-level prediction technologies are based on methods such as machine learning and hydrological models. However, these methods typically require extensive computing resources, are costly to implement, focus on theoretical analysis, and lack practicality, making them unable to adapt to rapidly changing flow conditions. Some water-level fluctuation prediction methods also fail to comprehensively consider factors or are tailored to specific application conditions. Therefore, a simple, practical, and effective prediction method is urgently needed to quickly and accurately predict water-level fluctuations at dam terminals under unsteady flow conditions, providing reliable support for the safe operation of hydropower stations and terminals. Summary of the Invention

[0005] To this end, the present invention provides a method for predicting water level fluctuations at a wharf downstream of a dam under non-steady flow conditions to solve the problems raised in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solutions: a method for predicting water level fluctuations at a dam wharf under non-steady flow conditions, which collects and pre-processes relevant data affecting the water level fluctuations at the dam wharf based on river channel topography data at the dam wharf location and flow information obtained through a hydrological information release platform at a hydropower station. A qualitative analysis is then performed based on the relationship between the relevant data affecting the water level fluctuations at the dam wharf and the wharf water level fluctuations, thereby obtaining a water level fluctuation prediction model for the dam wharf. The prediction results are then output for visualization and decision-making.

[0007] The water level fluctuation prediction model for the dam downstream wharf is as follows:

[0008]

[0009] Where C is the parameter to be fitted; ΔH is the amplitude of the wharf water level, in m; Q0 is the discharge flow of the hydropower station foundation, in m 3 / s; ΔQ is the amplitude of the downstream flow, unit is m 3 / s; ΔT is the duration of the downstream flow change, in h; g is the acceleration of gravity, which is 9.81 m / s 2 h is the water depth at the wharf, in meters; B is the water surface width at the wharf, in meters; B0 is the water surface width at the initial section, in meters; L is the distance from the wharf to the dam, in kilometers; α is the attenuation coefficient along the way; c is the non-constant flow wave velocity;

[0010] Among them, the along-the-way attenuation coefficient α is an important parameter that describes the attenuation rate of water level fluctuation along the way. Its value is determined by the characteristics of the river channel. The calculation formula of the along-the-way attenuation coefficient α is as follows:

[0011]

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

[0013] f is the dimensionless coefficient of friction, which arises from the friction between the water and the riverbed and banks. The greater the friction, the greater the energy loss of the water flow and the faster the water level amplitude decays. The friction coefficient f is affected by factors such as the roughness of the riverbed and the morphology of the river channel. The commonly used Manning formula can be used to estimate the friction coefficient. The Manning formula is used to estimate the friction coefficient: Where n is the Manning roughness coefficient, which is related to the roughness of the riverbed;

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

[0015] The depth of the river h affects the propagation speed of the water flow and the loss of wave energy. The deeper the river water, the smoother the wave propagation and the slower the decay.

[0016] Preferably, the relevant data affecting the water level fluctuation of the wharf below the dam include the basic discharge flow of the hydropower station Q0, the flow fluctuation amplitude ΔQ, the flow change duration ΔT, the water depth h of the river where the wharf is located, the width B of the river where the wharf is located, the mileage L between the wharf and the dam, and the river roughness n. The preprocessing includes removing outliers and filling missing values ​​to improve data quality.

[0017] Preferably, the qualitative analysis of the relationship between the relevant data affecting the water level fluctuation of the dam wharf is as follows:

[0018] The basic discharge flow Q0 is the initial flow before the hydropower station's peak regulation. When Q0 is large, the existing flow in the river channel is greater and the water level is also higher, which may intensify the impact of flow changes on the fluctuation of the dock water level. The qualitative relationship between the basic discharge flow Q0 of the hydropower station and the fluctuation of the dock water level ΔH is as follows:

[0019] ΔH∝kQ0;

[0020] The downstream flow fluctuation amplitude ΔQ directly reflects the flow fluctuation amplitude during the peak regulation process. The greater the flow fluctuation amplitude, the greater the water level fluctuation amplitude ΔH. Generally, flow changes and water level fluctuations are positively correlated. The qualitative relationship between the downstream flow fluctuation amplitude ΔQ and the dock water level fluctuation amplitude ΔH is as follows:

[0021] ΔH∝kΔQ;

[0022] The duration of the discharge flow change, ΔT, affects the duration of the wave propagation. When ΔT is short, the water level changes are more dramatic and concentrated; when ΔT is long, the water level changes are more gradual. The qualitative relationship between the discharge flow change duration, ΔT, and the wharf water level amplitude, ΔH, is as follows:

[0023]

[0024] The river depth h at the dock reflects the initial water depth. In deeper river sections, flow changes are less sensitive to water level. In shallower sections, flow changes may trigger larger water level fluctuations. The qualitative relationship between the river depth h at the dock and the dock water level fluctuation ΔH is as follows:

[0025]

[0026] The width B of the river channel where the dock is located determines the lateral expansibility of the water body. A wider channel can better disperse flow fluctuations and slow the rate of water level rise; whereas a narrower channel may exacerbate water level fluctuations caused by flow fluctuations. The qualitative relationship between the width B of the river channel where the dock is located and the water level fluctuation amplitude ΔH at the dock is as follows:

[0027]

[0028] The distance L between the wharf and the dam is another key factor. Fluctuations attenuate during propagation, and the farther the wharf is from the dam, the weaker the water level fluctuations. It can generally be assumed that the relationship between the water level fluctuation amplitude ΔH and the distance L between the wharf and the dam is exponential. The qualitative relationship between the distance L between the wharf and the wharf water level fluctuation amplitude ΔH is as follows:

[0029] ΔH∝e -αL ;

[0030] The wave velocity c of an unsteady flow represents the propagation speed in the river channel. When the wave velocity is faster, flow changes are transmitted to the downstream more quickly, but the fluctuation duration is shorter. When the wave velocity is slower, the fluctuation may be transmitted more slowly, but the amplitude of the water level change is more obvious. The qualitative relationship between the wave velocity c of an unsteady flow and the amplitude of the water level change at the dock, ΔH, is as follows:

[0031]

[0032] The present invention has the following advantages:

[0033] The present invention conducts a qualitative analysis based on the relationship between the relevant data affecting the water level fluctuation of the wharf below the dam and the water level fluctuation of the wharf, and then obtains a water level fluctuation prediction model for the wharf below the dam, and outputs the prediction results to facilitate visual analysis and decision-making. The present invention is easy to implement and use, suitable for rapid on-site application, can respond quickly under real-time monitoring conditions, adapt to the dynamic characteristics of water level changes, realize the prediction of water level fluctuations near the dam wharf, and reduce the risks of shipping and wharf operations caused by non-constant flow water level fluctuations. DETAILED DESCRIPTION

[0034] The following describes the implementation of the present invention using specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. Obviously, the embodiments described are only a portion of the present invention, not all of it. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0035] Due to the non-steady discharge flow, the water level below the dam fluctuates greatly, making it difficult to accurately predict using traditional methods. Currently, there is a lack of effective computational models and methods to accurately assess the amplitude of the water level at the dam wharf under non-steady flow conditions. Most water level prediction technologies are based on methods such as machine deep learning and hydrological models. However, these methods usually require a large amount of computing resources, have high application costs, focus on theoretical analysis but lack practicality, and cannot adapt to rapidly changing flow conditions. Some water level fluctuation prediction methods do not consider comprehensive factors or are only targeted at specific application conditions. Therefore, the present invention provides a method for predicting the amplitude of the water level at the dam wharf under non-steady flow conditions. According to the river channel topography data of the dam wharf location and the flow information obtained through the hydrological information release platform of the hydropower station, relevant data affecting the amplitude of the water level at the dam wharf are collected and pre-processed. A qualitative analysis is performed based on the relationship between the relevant data affecting the amplitude of the water level at the dam wharf and the amplitude of the wharf water level, thereby obtaining a prediction model for the amplitude of the water level at the dam wharf, and outputting the prediction results for easy visual analysis and decision-making. This method is simple, practical and effective for quickly and accurately predicting the amplitude of the water level at the dam wharf under non-steady flow conditions, providing reliable support for the safe operation of the hydropower station and the wharf.

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

[0037]

[0038] Where C is the parameter to be fitted; ΔH is the amplitude of the wharf water level, in m; Q0 is the discharge flow of the hydropower station foundation, in m 3 / s; ΔQ is the amplitude of the downstream flow, unit is m 3 / s; ΔT is the duration of the downstream flow change, in h; g is the acceleration of gravity, which is 9.81 m / s 2 h is the water depth at the wharf, in meters; B is the water surface width at the wharf, in meters; B0 is the water surface width at the initial section, in meters; L is the distance from the wharf to the dam, in kilometers; α is the attenuation coefficient along the way; c is the non-constant flow wave velocity;

[0039] The calculation formula of the attenuation coefficient α along the way is as follows:

[0040]

[0041] K is a constant related to the channel type and flow regime, which is obtained by numerical simulation or fitting of measured data; f is the friction coefficient, which is dimensionless and estimated by the Manning formula: Where n is the Manning roughness coefficient, which is related to the roughness of the riverbed; B is the width of the river surface, in meters;

[0042] The relevant data affecting the water level fluctuation of the wharf below the dam include the basic discharge flow of the hydropower station Q0, the flow fluctuation amplitude ΔQ, the flow change duration ΔT, the water depth h of the river where the wharf is located, the width B of the river where the wharf is located, the distance L between the wharf and the dam, and the river roughness n. Preprocessing includes removing outliers and filling missing values ​​to improve data quality.

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

[0044] The qualitative relationship between the hydropower station foundation discharge Q0 and the wharf water level fluctuation ΔH is as follows:

[0045] ΔH∝kQ0;

[0046] The qualitative relationship between the discharge flow variation ΔQ and the wharf water level variation ΔH is as follows:

[0047] ΔH∝kΔQ;

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

[0049]

[0050] The qualitative relationship between the river depth h at the wharf location and the wharf water level fluctuation ΔH is as follows:

[0051]

[0052] The qualitative relationship between the width B of the river where the dock is located and the water level fluctuation amplitude ΔH of the dock is as follows:

[0053]

[0054] The qualitative relationship between the distance L between the wharf and the dam and the wharf water level fluctuation ΔH is as follows:

[0055] ΔH∝e -αL ;

[0056] The qualitative relationship between the unsteady flow wave velocity c and the wharf water level fluctuation ΔH is as follows:

[0057]

[0058] This embodiment provides a method for predicting water level fluctuations at a dam wharf based on a dam wharf water level fluctuation prediction model. Taking the prediction of water level fluctuations at a dam wharf caused by a peak regulation process of a hydropower station as an example, the method specifically includes the following steps:

[0059] S1: Data collection and preprocessing

[0060] Through hydrological monitoring equipment and meteorological stations, key data affecting water level changes near the dam wharf are obtained, including:

[0061] The discharge flow Q0 of a hydropower station foundation, unit: m 3 / s, flow rate variation ΔQ, unit: m 3 / s, flow change duration ΔT, unit s, wharf channel width B, unit m, the water surface width B0 of the wharf closest to the hydropower station in the initial section is selected as 160m, wharf channel water depth h, unit m; the distance from the wharf to the dam is L, unit m, and the channel roughness n is taken as 0.04.

[0062] S2: Use of the water level fluctuation prediction model for the dam wharf

[0063] Substituting the calculation formula of the attenuation coefficient α along the way into the empirical calculation formula of the water level amplitude ΔH, we can obtain:

[0064]

[0065] According to historical data, the values ​​of the unknown coefficients C and K are fitted to be 0.109 and 0.032 respectively.

[0066] Based on the prediction model of the water level fluctuation of the wharf below the dam, the wharf water level fluctuation ΔH is finally obtained, and the formula is as follows:

[0067]

[0068] S3: Prediction of wharf water level fluctuations

[0069] The real-time monitoring of the discharge flow Q0 of a hydropower station foundation, unit m 3 / s, flow rate variation ΔQ, unit: m 3 Substitute the following data into the above formula: / s, flow change duration ΔT, unit h, wharf channel width B, unit m, wharf channel water depth h, unit m, wharf distance from dam L, unit m, channel roughness n, etc. to calculate the change range of the wharf water level below the dam. For example:

[0070] As shown in Table 1, the current discharge flow of a hydropower station is Q0 = 2090m 3 / s, outbound traffic increased by 2850m in 3 hours 3 / s, the water depth of Dahewan Wharf, which is about 4.826 km away from the dam site, is about 11.83 m, and the water surface width is about 195.7 m. Substitute this into the formula:

[0071]

[0072] Calculation shows that the water level fluctuation at Dahewan Wharf is 1.475m, and the measured water level fluctuation at Dahewan Wharf is 1.552m.

[0073] In order to verify the prediction effect of the water level fluctuation prediction model for the dam wharf in this embodiment, the water level fluctuation near the dam wharf under a typical flow change process was calculated and predicted using this formula and compared with the measured value (see Table 2). From the prediction results in Table 2, it can be seen that the absolute error and relative error between the predicted value and the actual value are relatively small, with an average relative error of 6%, a maximum relative error of only 8%, a maximum absolute error of only 0.089m, a root mean square error of only 0.00372, and a Pearson correlation coefficient square R 2 Reached 0.988.

[0074] Table 1 Typical flow rate change process

[0075]

[0076] Table 2 Comparison of calculated and measured values ​​of wharf water level fluctuation during typical flow change process

[0077]

[0078] In summary, the water level fluctuation prediction model for the dam wharf of the present invention can effectively predict the water level fluctuation of the wharf near the dam under certain peak-shaving non-steady 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 does not exceed 0.09m.

[0079] At present, the mainstream deep learning model for water level prediction, for example, a method for predicting the time series of estuary water level based on the improved Informer deep learning framework ([1] The Third Institute of Oceanography, Ministry of Natural Resources. A method for predicting the time series of estuary water level based on the improved Informer deep learning framework: 202411497591.8[P]. 2025-03-04.), has a maximum absolute error of no more than 0.085m between the predicted value and the actual value, indicating that the model error control effect of this embodiment is good, and it has important guiding value for achieving reasonable peak regulation of a hydropower station operation and dispatching center and the safety of ship navigation and terminal operations.

[0080] Although the present invention has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made thereto. Therefore, such modifications and improvements, without departing from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. A method for predicting water level fluctuations at a dam wharf under non-steady flow conditions, based on river channel topography data at the dam wharf and flow information obtained through a hydrological information release platform at a hydropower station, collects and pre-processes relevant data affecting water level fluctuations at the dam wharf. The method is characterized by: Based on the relationship between the relevant data affecting the water level fluctuation of the wharf below the dam and the water level fluctuation of the wharf, a qualitative analysis is conducted to obtain a water level fluctuation prediction model for the wharf below the dam and output the prediction results; The water level fluctuation prediction model for the dam downstream wharf is as follows: Where C is the parameter to be fitted; ΔH is the amplitude of the wharf water level; Q0 is the discharge flow of the hydropower station foundation; ΔQ is the amplitude of the discharge flow; ΔT is the duration of the discharge flow change; g is the acceleration of gravity; h is the water depth of the river where the wharf is located; B is the water surface width at the wharf; B0 is the water surface width of the initial section; L is the distance from the wharf to the dam; α is the attenuation coefficient along the way; c is the non-constant flow wave velocity, non-constant flow propagation velocity The calculation formula of the attenuation coefficient α along the way is as follows: K is a constant related to the channel type and flow regime, which is obtained by numerical simulation or fitting of measured data; f is the friction coefficient, which is dimensionless and estimated by the Manning formula: Among them, n is the Manning roughness coefficient, which is related to the roughness of the riverbed; B is the width of the river surface.

2. The method for predicting water level fluctuations at a dam downstream wharf under unsteady flow conditions according to claim 1, characterized in that: The relevant data affecting the water level fluctuation of the wharf below the dam include the basic discharge flow of the hydropower station Q0, the flow fluctuation amplitude ΔQ, the flow change duration ΔT, the water depth h of the river where the wharf is located, the width B of the river where the wharf is located, the distance L between the wharf and the dam, and the river roughness n. The preprocessing includes removing outliers and filling missing values.

3. The method for predicting water level fluctuations at a dam downstream wharf under unsteady flow conditions according to claim 1, characterized in that: The qualitative analysis of the relationship between the relevant data affecting the water level fluctuation of the dam wharf is as follows: The qualitative relationship between the hydropower station foundation discharge Q0 and the wharf water level fluctuation ΔH is as follows: ΔH∝kQ0; The qualitative relationship between the discharge flow variation ΔQ and the wharf water level variation ΔH is as follows: ΔH∝kΔQ; The qualitative relationship between the duration of the discharge flow change ΔT and the wharf water level change ΔH is as follows: The qualitative relationship between the river depth h at the wharf location and the wharf water level fluctuation ΔH is as follows: The qualitative relationship between the width B of the river where the dock is located and the water level fluctuation amplitude ΔH of the dock is as follows: The qualitative relationship between the distance L between the wharf and the dam and the wharf water level fluctuation ΔH is as follows: ΔH∝e -αL ; The qualitative relationship between the unsteady flow wave velocity c and the wharf water level fluctuation ΔH is as follows:

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