Intelligent current regulation and voltage regulation control method and system

Abstract

The invention discloses an intelligent current regulation and voltage regulation control method and system. The method comprises the following steps: acquiring real-time sensing data; a cooperative regulation and control instruction is obtained based on real-time sensing data analysis; and executing current regulation and voltage regulation control based on the coordinated regulation and control instruction. Therefore, pipe network data is transmitted to the cloud server in real time, water demand prediction analysis is carried out, a cooperative regulation instruction is generated to carry out targeted regulation on the valve, the valve is controlled by the cloud server in a unified mode, the edge calculation module is further arranged to achieve local optimization regulation, closed-loop control without manual intervention is achieved, the response is rapid, and the efficiency is high. And the leakage and the operation and maintenance cost are effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to an intelligent flow and voltage regulation control method and system. Background Technology

[0002] In traditional water supply network management, fixed pressure reducing valves and manual butterfly valves are commonly used for local flow and pressure regulation to ensure normal pressure and flow in each section of the pipeline and at the water terminals. However, these methods are mostly manual adjustments made based on the situation after overpressure or pressure reduction occurs in the water supply pipeline. As a result, the response speed is poor. Often, by the time manual intervention is made, sudden situations such as pipeline ruptures or leaks have already occurred, or the specific situation is only known after the emergency is reported, resulting in significant equipment damage and making it difficult to control operation and maintenance costs.

[0003] Therefore, although there are intelligent systems for leakage analysis and assisting manual scheduling, they do not have a closed-loop control system that includes terminal execution devices and cloud-based intelligent scheduling platforms. The actual control process still relies on manual operation, and it has not changed the problem of slow response speed. Summary of the Invention

[0004] The first aspect of this embodiment discloses an intelligent current and voltage regulation control method, specifically including: Acquire real-time sensor data; Based on the analysis of the real-time sensor data, collaborative control commands are obtained; Flow and voltage regulation control is executed based on the aforementioned coordinated control instructions.

[0005] As an optional implementation, the real-time sensing data includes at least the pressure, flow rate, and opening degree corresponding to each valve.

[0006] As an optional implementation, each valve is connected to a cloud server for communication. In addition, any valve can communicate with another valve upstream or downstream of the pipeline.

[0007] As an optional implementation, obtaining the coordinated control command based on the analysis of the real-time sensing data includes: Based on the real-time sensing data, a hydraulic model of the pipeline network is constructed; Deep learning algorithms are used to analyze the pressure / flow changes of the pipeline water conservancy model to predict water demand and pipeline risks in a specific future time period. Using a game theory algorithm, the adjustment weight of each valve is allocated according to the water demand and pipeline risk, and the coordinated control command is generated.

[0008] As an optional implementation, the execution of current and voltage regulation control based on the coordinated control command includes: Based on the aforementioned coordinated control command, an edge algorithm is used to compensate for local disturbances in the valve; To regulate the opening degree or control the opening and closing of the valve; The real-time sensor data of the valve after adjustment is transmitted back to the cloud server.

[0009] As an optional implementation method, several alarm data models are preset; When the real-time sensor data corresponding to any valve fits any alarm data model, an alarm signal is output. The alarm data model is used to indicate pipeline bursts, special water demand, and valve core wear and maintenance.

[0010] The second aspect of this embodiment discloses an intelligent flow and voltage regulation control system, specifically including: Several valve bodies that communicate with the cloud server; The valve body includes a sensing module for measuring real-time sensing data, an execution module for performing valve control, and an edge computing module for performing adaptive optimization. The cloud server includes a digital twin model that constructs a hydraulic model of the pipeline network based on the real-time sensor data, an intelligent scheduling model that generates coordinated control commands based on the hydraulic model of the pipeline network, and an energy efficiency optimization model that performs pipeline network optimization.

[0011] As an optional implementation, the sensing data includes at least the pressure, flow rate, and opening degree corresponding to each valve; The execution module is a servo motor-driven worm gear, used to adjust the opening degree or control the opening and closing of the valve according to the collaborative control command issued by the cloud server.

[0012] As an optional implementation, the edge computing module incorporates an adaptive PID+MPC algorithm to optimize and adjust the collaborative control commands.

[0013] As an optional implementation, the intelligent scheduling model uses a deep learning algorithm to generate coordinated control commands corresponding to the opening degree of each valve. The energy efficiency optimization model is used to combine the pressure distribution in the pipeline hydraulic model, the current leakage data, and the valve power consumption to calculate the optimal pressure distribution and optimize the coordinated control command.

[0014] Compared with the prior art, this embodiment has the following beneficial effects: In this embodiment, pipeline data is transmitted to the cloud server in real time for water demand prediction and analysis, generating collaborative control commands to control the valves in a targeted manner. In addition to being uniformly controlled by the cloud server, the valves also have an edge computing module to achieve local optimization and control, realizing closed-loop control without human intervention. Its response is rapid, effectively reducing leakage and maintenance costs. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this embodiment, the accompanying drawings used in the embodiment will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the workflow of an intelligent current and voltage regulation control method disclosed in this embodiment; Figure 2 This is a schematic diagram of the structure of an intelligent flow and voltage regulation control system disclosed in this embodiment. Detailed Implementation

[0017] The technical solutions in this embodiment will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1 Please see Figure 1 This embodiment discloses an intelligent current and voltage regulation control method, including: 101. Acquire real-time sensor data.

[0019] In this embodiment, the real-time sensing data includes at least the pressure, flow rate, and opening degree corresponding to each valve.

[0020] As an optional implementation, each valve is connected to a cloud server for communication. In addition, any valve can communicate with another valve upstream or downstream of the pipeline.

[0021] Specifically, the system monitors the operating status of each valve and its corresponding pipeline segment, and can immediately detect leaks or other issues without requiring reports from lower levels to higher levels.

[0022] In addition, accurate and real-time first-hand data can be used to analyze the status of the pipeline network and make precise adjustments in real time.

[0023] 102. Obtain coordinated control commands based on real-time sensor data analysis.

[0024] In this embodiment, the cloud server performs unified and coordinated control over all valves in the pipeline network.

[0025] As an optional implementation method, a hydraulic model of the pipeline network is constructed based on real-time sensor data; Deep learning algorithms are used to analyze pressure / flow changes in a pipeline hydraulic model to predict water demand and pipeline risks in a specific future time period. Using a game theory algorithm, the adjustment weight of each valve is allocated according to water demand and pipeline risk, and coordinated control instructions are generated.

[0026] Specifically, the pipeline hydraulic model is used to dynamically simulate the current pipeline status based on continuously updated real-time sensor data. When combined with historical water consumption data, it can effectively predict water demand and pipeline risks in a specific future period.

[0027] For example, for Community A, it can be predicted that it will enter the peak water consumption period between 18:00 and 23:00. Therefore, after 18:00, the opening of the valves in Community A should be increased to increase the water supply, and after 23:00, the opening of the valves in Community A should be decreased to reduce the water supply, so as to avoid overpressure in the pipeline network and leakage.

[0028] 103. Execute flow and voltage regulation control based on coordinated control commands.

[0029] In this embodiment, each valve performs flow and pressure regulation control uniformly under the collaborative control command issued by the cloud server to ensure the overall stable operation of the pipeline network.

[0030] As an optional implementation method, an edge algorithm is used to compensate for local disturbances in the valve based on the coordinated control command; To regulate the opening degree or control the opening and closing of the valve; The real-time sensor data of the valve after adjustment is transmitted back to the cloud server.

[0031] Specifically, taking Community A in step 102 as an example, suppose the valve in Community A receives a coordinated control instruction to reduce the opening at 23:00, but the valve learns through local monitoring at 22:40 that the water pressure in the pipeline is close to overpressure, then the valve opening will be reduced in advance.

[0032] Alternatively, if some buildings in Community A require pipeline maintenance and their terminal gates are closed, the terminal gates will notify the main gate of Community A of the closure status. The main gate will then reduce its valve opening in advance to prevent pipeline pressure from changing over distances and causing leakage.

[0033] Furthermore, the local adjustment operations performed by the gate will be fed back to the cloud server through real-time sensor data, thereby correcting the values ​​of the pipeline hydraulic model in the cloud server.

[0034] Taking Community B as an example, it adopted the intelligent current and voltage regulation control scheme of this embodiment in April 2024, and obtained the following production and sales difference ratio data:

[0035] The first pressure adjustment (0.2MPa from 1:00 to 4:00, 0.26MPa from 4:00 to 1:00) and the second pressure adjustment (0.2MPa from 0:00 to 6:00, 0.25MPa from 6:00 to 24:00) were implemented between April and September. The third pressure adjustment was carried out in October, and the municipal water supply pipe was fully opened to supply water to Community B.

[0036] It is evident that, compared to before the renovation, the production-sales gap in Community B has decreased significantly since April. Although the production-sales gap has increased somewhat after the municipal water supply was fully operational, it is still lower than the data before the renovation.

[0037] In addition, only 6 explosion-damage repair incidents occurred from June to September, a decrease of more than 70% compared to the same period in previous years, which significantly reduced operation and maintenance costs.

[0038] In this embodiment, several alarm data models are preset; When the real-time sensor data corresponding to any valve fits any alarm data model, an alarm signal is output. Among them, the alarm data model is used to indicate pipeline bursts, special water demand, and valve core wear and maintenance.

[0039] Specifically, real-time sensor data can not only inform about pipeline bursts, but also reflect special water demand, such as increasing the water pressure of a section of the pipeline when fire hydrants need emergency water supply; it can also reflect the usage time and wear condition of equipment. For example, if the water pressure or flow data after adjusting a certain valve is abnormal when performing the same control operation, it indicates that the valve is worn and needs to be maintained or replaced.

[0040] In summary, pipeline data is transmitted to the cloud server in real time for water demand prediction and analysis, generating collaborative control commands to control valves in a targeted manner. In addition to being uniformly controlled by the cloud server, the valves also have an edge computing module to achieve local optimization and control, realizing closed-loop control without human intervention. Its response is rapid, effectively reducing leakage and maintenance costs.

[0041] Example 2 Please see Figure 2 This embodiment discloses an intelligent flow and voltage regulation control system, comprising: Several valve bodies that communicate with the cloud server; The valve body includes a sensing module for measuring real-time sensor data, an execution module for performing valve control, and an edge computing module for performing adaptive optimization. The cloud server includes a digital twin model that builds a hydraulic model of the pipeline network based on real-time sensor data, an intelligent scheduling model that generates coordinated control commands based on the hydraulic model of the pipeline network, and an energy efficiency optimization model that performs pipeline network optimization.

[0042] In this embodiment, the sensing data includes at least the pressure, flow rate, and opening degree corresponding to each valve; The execution module is a servo motor-driven worm gear, which is used to adjust the opening degree or control the opening and closing of the valve according to the collaborative control instructions issued by the cloud server.

[0043] In this embodiment, the edge computing module incorporates an adaptive PID+MPC algorithm to optimize and adjust collaborative control commands.

[0044] In this embodiment, the intelligent scheduling model uses a deep learning algorithm to generate coordinated control commands corresponding to the opening degree of each valve. The energy efficiency optimization model is used to combine the pressure distribution in the pipeline hydraulic model, the current leakage data and valve power consumption to calculate the optimal pressure distribution and optimize the coordinated control commands.

Claims

1. A smart current and voltage regulation control method, characterized in that, The method comprises: acquiring real-time sensing data; obtaining a coordinated control instruction based on analysis of the real-time sensing data; performing flow and pressure control based on the coordinated control instruction.

2. The intelligent flow and voltage regulation control method of claim 1, wherein, The method comprises: The real-time sensing data at least includes pressure, flow and opening degree corresponding to each valve.

3. The intelligent flow and voltage regulation control method of claim 2, wherein, The method comprises: Each valve is communicatively connected to a cloud server; and any valve is communicatively connected to another valve upstream or downstream of the pipeline.

4. The intelligent flow and voltage regulation control method of claim 1, wherein, The method comprises: Based on the real-time sensing data, a pipe network hydraulic model is constructed; using a deep learning algorithm to analyze the pressure / flow changes of the pipe network hydraulic model, to predict water demand and pipe network risk in a specific future time period; using a game theory algorithm, according to the water demand and pipe network risk, to allocate the adjustment weight of each valve, to generate the coordinated control instruction.

5. The intelligent flow and voltage regulation control method of claim 3, wherein, The method comprises: Based on the coordinated control instruction, using an edge algorithm to compensate for local disturbance of the valve; performing opening degree adjustment or on-off control on the valve; the real-time sensing data of the valve after adjustment is fed back to the cloud server.

6. The intelligent flow and voltage regulation control method of claim 1, wherein, The method further comprises: presetting a plurality of alarm data models; when the real-time sensing data corresponding to any valve fits any alarm data model, outputting an alarm signal; wherein the alarm data model is used to indicate pipe network explosion, special water demand and valve core wear and maintenance.

7. An intelligent flow and voltage regulation control system, characterized in that, The method comprises: a plurality of valve bodies communicatively connected to a cloud server; The valve body comprises a sensing module for measuring real-time sensing data, an execution module for performing valve control, and an edge computing module for performing adaptive optimization; The cloud server comprises a digital twin model for constructing a pipe network hydraulic model based on the real-time sensing data, an intelligent scheduling model for generating a coordinated control instruction based on the pipe network hydraulic model, and an energy efficiency optimization model for performing pipe network optimization.

8. The intelligent flow and pressure regulating control system of claim 7, wherein, The method comprises: The sensing data at least includes pressure, flow and opening degree corresponding to each valve; The execution module is a turbine worm driven by a servo motor, used to perform opening degree adjustment or on-off control on the valve according to the coordinated control instruction issued by the cloud server.

9. The intelligent flow and pressure regulating control system of claim 8, wherein, The method comprises: The edge computing module is built-in adaptive PID+MPC algorithm, used to optimize the coordinated control instruction.

10. The intelligent flow and pressure regulating control system of claim 7, wherein, The method comprises: The intelligent scheduling model uses a deep learning algorithm to generate a coordinated control instruction corresponding to the opening degree of each valve; The energy efficiency optimization model is used to calculate the optimal pressure distribution by combining the pressure distribution in the pipe network hydraulic model, the current leakage data and the valve power consumption, to optimize the coordinated control instruction.

Images