Intelligent water and electricity monitoring method and system

Abstract

The invention relates to an intelligent hydropower monitoring method and system, and the method comprises the following steps: building a water level change model through obtaining the operation data of a hydropower station in real time, including the water level, the water flow and the reservoir surface area, and predicting the change rate of the water level along with the time. And calculating water level change by using the change rate, constructing a water level spatial distribution model, and outputting spatial distribution of the water level. And constructing a water level prediction model based on spatial distribution, and predicting the water level at the next moment. And constructing a control optimization model, outputting a regulation and control parameter adjustment amount according to the water level at the next moment, and dynamically updating a regulation and control parameter vector. Through the method, the water level of the hydropower station can be kept within the preset target range, fluctuation is reduced, the water level is stabilized, equipment faults and cavitation erosion can be avoided, the service life of equipment is prolonged, the operation stability of the hydropower station is enhanced, and the overall efficiency is improved.

Description

Technical Field

[0001] The present invention relates to a method and system for intelligent hydropower monitoring, belonging to the technical field of water conservancy and hydropower engineering. Background Art

[0002] With the continuous development of water conservancy and hydropower projects, the safe operation and efficient management of hydropower stations have become crucial. The operating state of a hydropower station is affected by various factors, such as water level, water flow rate, reservoir surface area, etc. Traditional hydropower station monitoring methods mainly rely on manual inspections and simple automation devices, and these methods have obvious deficiencies in the accuracy, real-time nature of data collection, as well as the analysis and prediction capabilities for complex operating states. In recent years, with the development of information technology, automation technology, and mathematical modeling technology, intelligent hydropower monitoring technology has gradually emerged, aiming to achieve real-time monitoring, accurate prediction, and optimized regulation of the operating state of hydropower stations through intelligent means, so as to improve the safety, stability, and power generation efficiency of hydropower stations. Most traditional monitoring technologies can only simply monitor and alarm the current operating state, and it is difficult to accurately predict future water level changes, water flow rate changes, etc. This makes it impossible for hydropower stations to take effective countermeasures in advance when facing emergencies, increasing the operating risks.

[0003] The patent document with the patent number "CN119130183A" discloses a water conservancy power generation regulation system and method based on big data analysis. This method determines the target turbine combination by analyzing the efficiency of the turbine combination, and adjusts the downstream water level accordingly to maximize the power generation efficiency. However, in terms of the comprehensive optimization regulation of the overall operating parameters of the hydropower station, it is not flexible and comprehensive enough, lacking an optimization model that comprehensively considers various factors and constraint conditions to guide the regulation. Although the actual inflow runoff curve is obtained through the analysis of meteorological data and historical inflow runoff, and then the target downstream water level is derived, this prediction method is relatively indirect, and mainly focuses on the regulation target of the downstream water level, and the dynamic prediction ability for the internal water level changes of the hydropower station may not be accurate. Summary of the Invention

[0004] In order to solve the problems existing in the above-mentioned prior art, the present invention proposes a method and system for intelligent hydropower monitoring.

[0005] The technical solution of the present invention is as follows:

[0006] On the one hand, the present invention provides an intelligent hydropower monitoring method, including the following steps:

[0007] Obtain the operating data of the hydropower station, where the operating data includes water level, water flow rate, and the surface area of the reservoir;

[0008] Construct a water level change model based on the operating data, and predict the change rate of the water level of the hydropower station over time through the water level change model;

[0009] Obtain the water level change based on the change rate, construct a water level spatial distribution model based on the water level change, and output the spatial distribution of the water level of the hydropower station through the water level spatial distribution model;

[0010] Construct a water level prediction model through the spatial distribution, and the water level prediction model predicts the water level of the hydropower station at the next moment;

[0011] Construct a control optimization model and output the control parameter adjustment amount according to the water level at the next moment, and update the control parameter vector through the control parameter adjustment amount.

[0012] As a preferred embodiment, the water level change model is expressed as:

[0013]

[0014] Wherein, represents the first derivative of the water level H with respect to the time variable t, and Q in (t) represents the water flow rate flowing into the hydropower station at time t, and Q out (t) represents the water flow rate flowing out of the hydropower station at time t, A represents the surface area of the reservoir, and E(t) represents the evaporation rate at time t.

[0015] As a preferred embodiment, the water level spatial distribution model is expressed as:

[0016]

[0017]

[0018] Wherein, represents the first partial derivative of the water level H(x,y,t) at the coordinate (x,y) with respect to the time variable t at time t, D represents the water level diffusion coefficient, and S(x,y,t) represents the water level change at the coordinate (x,y) at time t, represents the second partial derivative of the water level H(x,y,t) at the coordinate (x,y) with respect to the abscissa at time t, represents the second partial derivative of the water level H(x,y,t) at the coordinate (x,y) with respect to the ordinate at time t, and ρ(x,y) represents the spatial weighting function.

[0019] As a preferred embodiment, the water level prediction model is expressed as:

[0020]

[0021] Wherein, represents the predicted water level at time t+Δt, Δt represents the preset time interval, and Q in(τ) represents the water flow rate flowing into the hydropower station at time τ, Q out (τ) represents the water flow rate flowing out of the hydropower station at time τ, E(τ) represents the evaporation rate at time τ, and τ represents the continuously varying time variable in the time interval [t, t + Δt].

[0022] As a preferred embodiment, the control optimization model is expressed as:

[0023]

[0024]

[0025]

[0026] Among them, C represents the cost function, Minimize C represents the minimum cost function, α and β represent preset weights, T represents the preset time period, represents the predicted water level at time t, H target represents the preset target water level, Q target (t) represents the preset target water flow rate at time t, H min , Q out,min represents the lower limit of the preset constraint condition, H max , Q out,max represents the upper limit of the preset constraint condition;

[0027] Solve the control optimization model through the gradient descent algorithm to obtain the optimal adjustment amount Δu(t) of the control parameters.

[0028] As a preferred embodiment, the method for updating the control parameter vector is:

[0029] u(t) = u(t - 1) + Δu(t);

[0030] Among them, u(t) represents the control parameter vector at time t.

[0031] On the other hand, the present invention also provides a smart hydropower monitoring system, including:

[0032] Data acquisition module: Obtain the operation data of the hydropower station, and the operation data includes water level, water flow rate, and the surface area of the reservoir;

[0033] Water level change model module: Construct a water level change model according to the operation data, and predict the change rate of the water level of the hydropower station over time through the water level change model;

[0034] Water level spatial distribution model module: Obtain the water level change based on the change rate, construct a water level spatial distribution model based on the water level change, and output the spatial distribution of the water level of the hydropower station through the water level spatial distribution model;

[0035] Water level prediction model module: Construct a water level prediction model through spatial distribution, and the water level prediction model predicts the water level of the hydropower station at the next moment;

[0036] Control module: Construct a control optimization model and output the control parameter adjustment amount according to the water level at the next moment, and update the control parameter vector through the control parameter adjustment amount.

[0037] The present invention has the following beneficial effects:

[0038] The present invention obtains key operation data such as the water level, water flow, and reservoir surface area of the hydropower station in real time through the data acquisition module, and constructs a water level change model, a water level spatial distribution model, and a water level prediction model based on these data, which can monitor the operation status of the hydropower station in real time. The water level prediction model can predict the water level of the hydropower station at the next moment, enabling managers to understand the change trend of the water level in advance. In the face of emergencies (such as a rapid rise in water level caused by heavy rain), countermeasures can be formulated in advance, such as adjusting the gate opening in advance and arranging flood discharge, to avoid safety accidents caused by excessive water level and effectively reduce the operation risk. The control optimization model outputs the control parameter adjustment amount according to the predicted water level, and solves the optimal control parameter adjustment amount through the gradient descent algorithm, and then updates the control parameter vector. This enables the control parameters of the hydropower station (such as gate opening, turbine speed, etc.) to be dynamically optimized and adjusted according to the real-time operation data and prediction results, ensuring that the hydropower station can maintain the best operation state under different working conditions and improving the power generation efficiency. The water level spatial distribution model can output the spatial distribution of the water level of the hydropower station, enabling managers to comprehensively understand the changes in the water level at different positions. This helps to discover potential uneven distribution problems, such as local abnormal increase or decrease in water level, and take measures to adjust in time to avoid affecting the overall operation stability of the hydropower station due to local water level changes. By dynamically adjusting the control parameters through the control optimization model, the water level of the hydropower station can be maintained within the preset target range, reducing the water level fluctuation. A stable water level helps to maintain the normal operation of the hydropower station, avoid problems such as power generation equipment failure and turbine cavitation caused by large water level fluctuations, extend the service life of the equipment, and enhance the operation stability of the hydropower station. Constructing models based on a large amount of operation data and providing a scientific basis for the operation management of the hydropower station through data analysis and prediction. Managers can formulate more reasonable and effective operation strategies according to the prediction results and optimization suggestions of the system, improve the scientificity and accuracy of decision-making, and enhance the overall management level of the hydropower station. Description of the Drawings

[0039] Figure 1 It is a flowchart of the method implementation of the present invention. Detailed Embodiments

[0040] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0041] It should be understood that the step numbers used in the text are only for convenience of description and do not limit the execution order of the steps.

[0042] It should be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0043] The terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0044] The term "and / or" refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0045] Embodiment 1:

[0046] Refer to Figure 1 , the present invention provides an intelligent hydropower monitoring method, including the following steps:

[0047] Obtain the operation data of the hydropower station, where the operation data includes water level, water flow rate, and the surface area of the reservoir;

[0048] The water level is measured in real time by water level sensors (such as float type water level gauges, pressure type water level gauges, or radar water level gauges) installed at different positions of the reservoir. These sensors are distributed at key positions of the reservoir, such as upstream and downstream of the dam and the flood discharge channel, etc., to ensure comprehensive monitoring of water level changes.

[0049] The measurement of the water flow rate can be achieved by a flowmeter. The flowmeter is installed at positions such as the water inlet, water outlet, and flood discharge channel, etc., for measuring the water flow rate flowing into and out of the hydropower station. The type of the flowmeter can be selected according to actual needs, such as electromagnetic flowmeters, ultrasonic flowmeters, etc., to ensure the accuracy and reliability of the measurement.

[0050] The surface area of the reservoir can be obtained from the reservoir design drawings or dynamically measured through satellite remote sensing technology or unmanned aerial vehicle mapping technology. These technologies can provide high-precision topographic data to help update the surface area of the reservoir in real time. Especially when the water level changes significantly, they can more accurately reflect the actual area of the reservoir.

[0051] Construct a water level change model based on the operating data, and predict the change rate of the water level of the hydropower station over time through the water level change model;

[0052] Obtain the water level change based on the change rate, construct a water level spatial distribution model based on the water level change, and output the spatial distribution of the water level of the hydropower station through the water level spatial distribution model;

[0053] Construct a water level prediction model through the spatial distribution, and the water level prediction model predicts the water level of the hydropower station at the next moment;

[0054] Construct a control optimization model and output the control parameter adjustment amount according to the water level at the next moment, and update the control parameter vector through the control parameter adjustment amount.

[0055] As a preferred embodiment, the water level change model is expressed as:

[0056]

[0057] Where represents the first derivative of the water level H with respect to the time variable t (the change rate of the water level over time), Q in (t) represents the water flow rate flowing into the hydropower station at time t, Q out (t) represents the water flow rate flowing out of the hydropower station at time t, A represents the surface area of the reservoir, E(t) represents the evaporation rate at time t, and data such as temperature, humidity, wind speed, and solar radiation are obtained through a weather station, and the evaporation rate is calculated using an evaporation formula (such as the Penman formula).

[0058] Through this water level change model, the system can calculate the change rate of the water level in real time, providing basic data for subsequent water level prediction and regulation.

[0059] As a preferred embodiment, the water level spatial distribution model is expressed as:

[0060]

[0061]

[0062] Where represents the first partial derivative of the water level H(x, y, t) at the coordinate (x, y) at time t with respect to the time variable t, D represents the water level diffusion coefficient (preset according to experience), and S(x, y, t) represents the water level change at the coordinate (x, y) at time t. Denote the second-order partial derivative of the water level H(x, y, t) at the coordinate (x, y) at time t with respect to the abscissa. Denote the second-order partial derivative of the water level H(x, y, t) at the coordinate (x, y) at time t with respect to the ordinate, and ρ(x, y) represents the spatial weighting function.

[0063] Obtain the water levels H(x, y, t) at several coordinates (x, y) at time t to obtain the spatial distribution of the water level.

[0064]

[0065] Among them, Z(x, y) represents the topographic height of the hydropower station.

[0066] As a preferred implementation manner, the water level prediction model is expressed as:

[0067]

[0068] Among them, Denote the predicted water level at time t + Δt, Δt represents the preset time interval, Q in (τ) represents the water flow rate flowing into the hydropower station at time τ, Q out (τ) represents the water flow rate flowing out of the hydropower station at time τ, E(τ) represents the evaporation rate at time τ, and τ represents the continuously varying time variable in the time interval [t, t + Δt].

[0069] As a preferred implementation manner, the control optimization model is expressed as:

[0070]

[0071]

[0072]

[0073] Among them, C represents the cost function, Minimize C represents the minimum cost function, α and β represent the preset weights, T represents the preset time period, Denote the predicted water level at time t, H target Denote the preset target water level, Q target (t) represents the preset target water flow rate at time t, H min 、Q out,min Denote the lower limit of the preset constraint condition, H max 、Q out,max Denote the upper limit of the preset constraint condition;

[0074] Solve the control optimization model through the gradient descent algorithm to solve the optimal adjustment amount Δu(t) of the control parameter.

[0075] As a preferred embodiment, the method for updating the regulation parameter vector is as follows:

[0076] u(t) = u(t - 1) + Δu(t);

[0077] where u(t) represents the regulation parameter vector at time t (including the gate opening and the turbine speed).

[0078] Embodiment 2:

[0079] The present invention also provides an intelligent hydropower monitoring system, including:

[0080] A data acquisition module: obtaining the operation data of the hydropower station, where the operation data includes the water level, water flow rate, and the surface area of the reservoir;

[0081] A water level change model module: constructing a water level change model based on the operation data, and predicting the change rate of the water level of the hydropower station over time through the water level change model;

[0082] A water level spatial distribution model module: obtaining the water level change according to the change rate, constructing a water level spatial distribution model based on the water level change, and outputting the spatial distribution of the water level of the hydropower station through the water level spatial distribution model;

[0083] A water level prediction model module: constructing a water level prediction model through the spatial distribution, and predicting the water level of the hydropower station at the next moment by the water level prediction model;

[0084] A control module: constructing a control optimization model and outputting a control parameter adjustment amount according to the water level at the next moment, and updating the regulation parameter vector through the control parameter adjustment amount.

[0085] This system is used to implement the method in Embodiment 1, which will not be elaborated here.

[0086] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent the situation where A exists alone, A and B exist simultaneously, or B exists alone. Where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0087] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0088] Those skilled in the art can clearly understand that for the sake of convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0089] In several embodiments provided in this application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0090] The above are only the embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A smart hydropower monitoring method, characterized in that: The following steps are involved: Acquiring operation data of the hydropower station, the operation data including water level, water flow, and surface area of ​​the reservoir; Constructing a water level change model based on the operation data, and predicting the rate of change of the water level of the hydropower station over time through the water level change model; Obtaining water level changes according to the change rate, constructing a water level spatial distribution model based on the water level changes, and outputting the spatial distribution of the water level of the hydropower station through the water level spatial distribution model; A water level prediction model is constructed through spatial distribution, and the water level prediction model predicts the water level of the hydropower station at the next moment; Construct a control optimization model and output the control parameter adjustment amount according to the water level at the next moment, and update the control parameter vector through the control parameter adjustment amount.

2. The smart hydropower monitoring method according to claim 1, characterized in that: The water level change model is expressed as: in, represents the first-order derivative of the water level H with respect to the time variable t, Q in (t) represents the water flow into the hydropower station at time t, Q out (t) represents the water flow out of the hydropower station at time t, A represents the surface area of ​​the reservoir, and E(t) represents the evaporation rate at time t.

3. The smart hydropower monitoring method according to claim 2, characterized in that: The water level spatial distribution model is expressed as: in, represents the first-order partial derivative of the water level H(x,y,t) at the coordinate (x,y) at time t with respect to the time variable t, D represents the water level diffusion coefficient, S(x,y,t) represents the change of the water level at the coordinate (x,y) at time t, It represents the second-order partial derivative of the water level H(x,y,t) at the coordinate (x,y) at time t with respect to the horizontal coordinate. represents the second-order partial derivative of the water level H(x,y,t) at the coordinate (x,y) at time t with respect to the ordinate, and ρ(x,y) represents the spatial weighting function.

4. The smart hydropower monitoring method according to claim 3 is characterized in that: The water level prediction model is expressed as: in, represents the water level predicted at time t+Δt, Δt represents the preset time interval, Q in (τ) represents the water flow into the hydropower station at time τ, Q out (τ) represents the water flow out of the hydropower station at time τ, E(τ) represents the evaporation rate at time τ, and τ represents a time variable that changes continuously in the time interval [t, t+Δt].

5. The smart hydropower monitoring method according to claim 4 is characterized in that: The control optimization model is expressed as: Among them, C represents the cost function, Minimize C represents the minimum cost function, α and β represent the preset weights, and T represents the preset time period. represents the predicted water level at time t, H target Indicates the preset target water level, Q target (t) represents the preset target water flow at time t, H min , Q out,min represents the preset lower limit of the constraint condition, H max , Q out,max Indicates the preset upper limit of the constraint condition; The control optimization model is solved by the gradient descent algorithm to obtain the optimal control parameter adjustment Δu(t).

6. The smart hydropower monitoring method according to claim 5, characterized in that: The control parameter vector update method is: u(t)=u(t-1)+Δu(t); Among them, u(t) represents the control parameter vector at time t.

7. A smart hydropower monitoring system, characterized in that: include: Data acquisition module: acquiring the operation data of the hydropower station, the operation data including water level, water flow and surface area of ​​the reservoir; Water level change model module: constructing a water level change model according to the operation data, and predicting the rate of change of the water level of the hydropower station over time through the water level change model; Water level spatial distribution model module: obtain water level changes according to the change rate, build a water level spatial distribution model based on the water level changes, and output the spatial distribution of the water level of the hydropower station through the water level spatial distribution model; Water level prediction model module: constructs a water level prediction model through spatial distribution, and the water level prediction model predicts the water level of the hydropower station at the next moment; Control module: Build a control optimization model and output the control parameter adjustment according to the water level at the next moment, and update the control parameter vector through the control parameter adjustment.

8. The smart hydropower monitoring system according to claim 7, characterized in that: The water level change model is expressed as: in, represents the first-order derivative of the water level H with respect to the time variable t, Q in (t) represents the water flow into the hydropower station at time t, Q out (t) represents the water flow out of the hydropower station at time t, A represents the surface area of ​​the reservoir, and E(t) represents the evaporation rate at time t.

9. The smart hydropower monitoring system according to claim 8, characterized in that: The water level spatial distribution model is expressed as: in, represents the first-order partial derivative of the water level H(x,y,t) at the coordinate (x,y) at time t with respect to the time variable t, D represents the water level diffusion coefficient, S(x,y,t) represents the change of the water level at the coordinate (x,y) at time t, It represents the second-order partial derivative of the water level H(x,y,t) at the coordinate (x,y) at time t with respect to the horizontal coordinate. represents the second-order partial derivative of the water level H(x,y,t) at the coordinate (x,y) at time t with respect to the ordinate, and ρ(x,y) represents the spatial weighting function.

10. The smart hydropower monitoring system according to claim 9, characterized in that: The water level prediction model is expressed as: in, represents the water level predicted at time t+Δt, Δt represents the preset time interval, Q in (τ) represents the water flow into the hydropower station at time τ, Q out (τ) represents the water flow out of the hydropower station at time τ, E(τ) represents the evaporation rate at time τ, and τ represents a time variable that changes continuously in the time interval [t, t+Δt].

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