CN117371990B - Hydropower plant tool intelligent management platform based on Internet of things - Google Patents
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Abstract
The invention discloses an intelligent management platform for hydropower plant tools based on the Internet of things, and relates to the technical field of hydropower plant diversion tool management. Through monitoring of the turbulence state, problems caused by turbulence can be found out and timely dealt with. The real-time monitoring and early warning function enables a hydropower plant to take necessary maintenance measures before a problem occurs, reduces the risk of equipment failure, reduces the downtime, and improves the reliability and the continuity of production. By establishing the digital twin tool model, the platform can compare the consistency of the model and actual data in real time, discover abnormal conditions and trigger an alarm. This helps to improve the accuracy of the model and further reduces potential problems caused by inconsistent model and actual data.
Description
Technical Field
The invention relates to the technical field of hydropower plant diversion tool management, in particular to an intelligent hydropower plant tool management platform based on the Internet of things.
Background
Hydropower plants, as an important representative of renewable energy sources, play an important role in energy production. However, monitoring and maintenance of hydroelectric power plant equipment tools has been a critical challenge because they are often located in remote, harsh environments, are complex to equipment, have long operational cycles, are costly to repair once a problem arises, and can negatively impact the environment.
The internet of things, which is collectively referred to as the "internet of things", is a technical concept that connects various physical objects, devices, machines, and other items to the internet so that they can communicate with each other, collect data, and interact with humans. The basic idea of the internet of things is to connect objects in the physical world with the digital world through means of wireless communication, sensor technology, cloud computing and the like, so that the intelligent, automatic and remote control is realized.
Traditional hydropower plant tool management typically relies on periodic inspection and manual data logging, which results in a lack of real-time knowledge of tool status. Without real-time monitoring, potential problems cannot be found and dealt with in time. In particular, in hydropower plants, diversion tools play an important role in the hydropower plant and they are used to direct water flow into the turbine so that the turbine can rotate and drive the generator to produce electricity. However, in the drainage process of the water turbine, the water flow cannot be stably guided to the runner of the water turbine due to damage, cracks and blockage of the diversion equipment, so that the water flow is not smooth, and the problem of energy production decline of the water turbine is indirectly caused.
Disclosure of Invention
(One) solving the technical problems
Aiming at the defects of the prior art, the invention provides the intelligent management platform for the hydropower plant tool based on the Internet of things, and the intelligent management platform can timely detect whether the guide tool is affected by blockage, cracks or other abnormal conditions by monitoring the state of the guide tool, including key parameters such as flow rate, pressure, temperature and the like. Through monitoring of the turbulence state, problems caused by turbulence can be found out and timely dealt with. The real-time monitoring and early warning function enables a hydropower plant to take necessary maintenance measures before a problem occurs, reduces the risk of equipment failure, reduces the downtime, and improves the reliability and the continuity of production. By establishing the digital twin tool model, the platform can compare the consistency of the model and actual data in real time, discover abnormal conditions and trigger an alarm. This helps to improve the accuracy of the model and further reduces potential problems caused by inconsistent model and actual data.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: the intelligent management platform of the hydropower plant tool based on the Internet of things comprises a first acquisition unit, a management unit, a summarizing unit, a first processing unit, an alarm unit, a second acquisition unit and a second processing unit;
In the running process of hydroelectric power generation, continuously monitoring each water flow guiding tool in real time by the first acquisition unit, acquiring guiding tool data, and establishing a first data set; establishing a corresponding digital twin tool model and a corresponding water turbine region model by a management unit, inputting a first data set into the digital twin tool model, and marking the guiding position of a guiding tool in the water turbine region;
Continuously acquiring water flow data guided by a plurality of guiding tools along a time axis by the summarizing unit, analyzing the water flow data by a first processing unit to acquire a standard deviation sigma of the water flow data, and if the standard deviation sigma is lower than a preset first threshold Q1 of water flow along with the continuous convergence of the current water flow data, sending out first early warning information by the alarm unit;
When the standard deviation sigma of water flow data is lower than a preset first threshold value Q1 of water flow, acquiring water flow data of an inlet and an outlet of the diversion tool by a second acquisition unit, performing dimensionless processing on the water flow data by a second processing unit, and calculating to acquire a turbulence distribution coefficient WL, wherein the turbulence distribution coefficient WL is calculated and generated by the following formula:
Wherein Re is expressed as a fluid flow state coefficient, specifically calculated by Reynolds number, L is expressed as a path length of the diversion tool, P is expressed as a density of water, V is expressed as a water flow speed, U is expressed as a viscosity of water, D 1 is expressed as a correction constant, re_C is expressed as a fluid flow state coefficient critical value; the meaning of the formula is that the calculated turbulence distribution coefficient WL value is a negative value, which indicates that the flow is in a laminar state; the turbulence distribution coefficient WL value is positive, indicating that the flow is in a turbulent state;
when the turbulence distribution coefficient WL value of a plurality of diversion tools is positive and exceeds a second threshold value Q2, second alarm information is generated, and corresponding strategy scheme processing is carried out by a strategy unit according to the second alarm information, wherein the strategy scheme comprises the steps of automatically closing the diversion tools, cleaning plugs and repairing cracks.
Preferably, the first collecting unit is used for installing a flow rate sensor, a pressure sensor and a temperature sensor device on each water flow guiding tool so as to collect guiding tool data in real time;
the pilot tool data includes the flow rate of the water flow through the pilot tool, the inlet and outlet pressures, and the temperature of the water flow, and a first data set is established.
Preferably, historical operation data, a hydraulic model and a water turbine design drawing are collected and used as basic data of a digital twin tool model and a water turbine area model, and the position of a guiding tool is marked in the water turbine area model and corresponds to the data of an actual guiding tool;
The first data set is continuously updated to the digital twin tool model, the difference between the data in the digital twin tool model and the actual data is monitored, and if abnormality or inconsistency is found, an alarm is triggered.
Preferably, the summarizing unit is in charge of continuously acquiring water flow data guided by a plurality of guiding tools and summarizing the data according to a time axis;
the first processing unit analyzes the collected water flow data to calculate the standard deviation of the water flow data; the standard deviation is a statistical index for measuring the degree of data dispersion in a data set, and the larger the standard deviation is, the higher the fluctuation of the data is;
the set of water flow data, noted { x1, x2, x3, & gt, xn }, was set and the standard deviation was calculated as follows:
First, the average μ of the set of water flow data is calculated:
N represents the number of data points, i.e., how many data points the set of water flow data includes;
Next, for each data point xi, the difference from the average μ is calculated, and the sum of squared differences is calculated, and the variance σ 2 is calculated from the following equation:
finally, the standard deviation sigma is calculated and obtained:
continuously monitoring whether current water flow data continue to be imported or not, comparing the current water flow data with a standard deviation sigma calculated before after new water flow data are imported, checking whether the amplitude value of the standard deviation sigma is lower than a first threshold value Q1 of preset water flow, if the amplitude value of the standard deviation sigma is lower than the first threshold value Q1, indicating that the water flow is unstable due to the blocking of the water flow, triggering first alarm information, and sending the first alarm information to an operator or a system administrator through alarm sound by an alarm unit.
Preferably, the second collecting unit is responsible for collecting water flow data, wherein the water flow data comprises the path length L of the diversion tool, the density P of water, the water flow speed V and the water flow viscosity U, and the path length L of the diversion tool, the density P of water, the water flow speed V and the water flow viscosity U are subjected to dimensionless treatment;
setting a critical value Re_C of a fluid flow state coefficient Re, and correspondingly setting according to a load value of the water turbine;
Comparing the calculated turbulence distribution coefficients WL, if the turbulence distribution coefficients WL of a plurality of diversion tools are positive values and exceed a second threshold Q2, indicating that the flow is in a turbulence state and the turbulence distribution coefficients WL are abnormal, generating second alarm information, and performing remarkable marking in a digital twin tool model and a water turbine region model;
the strategy unit formulates a corresponding strategy scheme according to the second alarm information so as to cope with abnormal conditions of turbulence distribution coefficients, and the strategy comprises the following steps:
Automatically closing the affected diversion tool to prevent further damage;
clearing the blockage, and if the turbulence distribution coefficient is abnormal, the blockage is caused by the blockage of the diversion tool;
Repairing the crack if the turbulence distribution coefficient is abnormal due to the crack or damage on the diversion tool;
Generating second alarm information, and sending an alarm notice to the operation and maintenance personnel by the alarm unit.
Preferably, the system further comprises a third acquisition unit and a third processing unit, wherein before the water flow enters the water turbine, sediment sensors are arranged in the front position of the diversion tool and in the reservoir, the condition of floaters and sediments in the water flow is monitored in real time, and a second data set is established;
the third processing unit is used for carrying out dimensionless processing on the second data set, calculating to obtain a sediment water quality coefficient Cd,
The sediment water quality coefficient Cd is generated by the following formula:
Where C represents the sediment content data obtained from the sediment sensor, cmin represents the minimum value of the sediment content data, i.e., the minimum value in the second data set, and Cmax represents the maximum value of the sediment content data, i.e., the maximum value in the second data set; d 2 is denoted as a correction constant; the meaning of the formula is: the sediment water quality coefficient Cd is generally used to represent the water quality condition of sediment in water; the value of Cd is between 0 and 1, where 0 indicates no sediment in the water and 1 indicates the maximum concentration of sediment in the water.
Preferably, the third processing unit is configured to compare the obtained sediment water quality coefficient Cd with a third threshold Q3, and if the sediment water quality coefficient Cd is higher than the third threshold Q3, trigger third alarm information, and send the third alarm information to the operation and maintenance personnel by the alarm unit (5).
Preferably, the policy unit formulates a corresponding policy scheme according to the third alarm information, including:
replacing a filter in the reservoir to remove sediment;
immediately stopping the machine, and after cleaning the deposit in the water reservoir, the diversion tool and other water flow paths, restarting the tool to normally operate;
The speed regulator is used for increasing the water flow speed to help wash away sediment deposition;
The performance of the precipitate treatment plant is optimized.
Preferably, the hydraulic turbine control system further comprises a remote control unit for allowing a remote operator to remotely control the hydropower plant equipment, including remotely starting, stopping, adjusting the operation of the hydraulic turbine and the diversion tool, and remotely accessing real-time data and alarm information.
Preferably, the system further comprises a maintenance unit for tracking according to the strategy schemes of the first alarm information, the second alarm information and the third alarm information, and performing spare part management on the current completion degree and the maintenance log.
(III) beneficial effects
The invention provides an intelligent management platform for hydropower plant tools based on the Internet of things. The beneficial effects are as follows:
(1) This hydropower plant instrument intelligent management platform based on thing networking, the platform carries out real-time supervision to the direction instrument through first collection unit, can acquire the data of direction instrument in real time. When the water flow is blocked or unstable, the platform can timely detect and generate first alarm information to warn an operator or a system administrator. This helps to discover potential problems early and reduces the risk of equipment failure. By early warning, the hydropower plant can take appropriate maintenance measures before the problem becomes serious. This may reduce urgency and cost of maintenance while reducing downtime and improving availability and efficiency of the apparatus.
(2) The intelligent management platform for the hydropower plant tool based on the Internet of things is used for monitoring the turbulence state of water flow, and when the turbulence distribution coefficient WL is abnormal and exceeds a threshold value, second alarm information is generated. This helps to find flow guide tool anomalies such as plugging or damage, and turbulence-induced problems. Timely action can prevent further damage to the equipment. The second alarm information triggers a strategy unit, and corresponding strategy schemes are formulated according to the nature of the problem, wherein the strategy schemes comprise automatic closing of a diversion tool, cleaning of plugs, repairing of cracks and the like. Such intelligent process management is helpful for optimizing maintenance flow, and improves maintainability and reliability of the device.
(3) The intelligent management platform for the hydropower plant tool based on the Internet of things is characterized in that a third acquisition unit and a third processing unit are used for monitoring and evaluating the content of sediment in water and generating third alarm information. This helps detect water quality problems such as abnormally elevated sediment concentrations, which can lead to equipment plugging and performance degradation. The third alarm information triggers a strategy unit to make a strategy scheme for solving the water quality problem, such as filter replacement, shutdown treatment, water flow speed improvement and the like. This helps to maintain water quality and environmental health in the hydropower plant.
(4) According to the hydropower plant tool intelligent management platform based on the Internet of things, the platform uses historical operation data and a model to establish a digital twin tool model, so that real-time digital mirroring of equipment states is realized. The method is helpful for finding the inconsistency between the model and the actual data in advance, triggering an alarm and repairing the problem in time, and improves the accuracy of the model. The intelligent management platform for the hydropower plant tool effectively improves the monitoring, maintenance and management efficiency of hydropower plant equipment through the internet of things technology and intelligent processing management, reduces the risk of potential problems, reduces the maintenance cost and improves the reliability and the production efficiency of the equipment.
Drawings
FIG. 1 is a block diagram and flow diagram of a hydropower plant tool intelligent management platform based on the Internet of things;
In the figure: 1. a first acquisition unit; 2. a management unit; 3. a summarizing unit; 4. a first processing unit; 5. an alarm unit; 6. a second acquisition unit; 7. a second processing unit; 8. a third acquisition unit; 9. a third processing unit; 10. a maintenance unit; 11. a remote control unit; 12. and a policy unit.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Hydropower plants, as an important representative of renewable energy sources, play an important role in energy production. However, monitoring and maintenance of hydroelectric power plant equipment tools has been a critical challenge because they are often located in remote, harsh environments, are complex to equipment, have long operational cycles, are costly to repair once a problem arises, and can negatively impact the environment.
The internet of things, which is collectively referred to as the "internet of things", is a technical concept that connects various physical objects, devices, machines, and other items to the internet so that they can communicate with each other, collect data, and interact with humans. The basic idea of the internet of things is to connect objects in the physical world with the digital world through means of wireless communication, sensor technology, cloud computing and the like, so that the intelligent, automatic and remote control is realized.
Traditional hydropower plant tool management typically relies on periodic inspection and manual data logging, which results in a lack of real-time knowledge of tool status. Without real-time monitoring, potential problems cannot be found and dealt with in time. In particular, in hydropower plants, diversion tools play an important role in the hydropower plant and they are used to direct water flow into the turbine so that the turbine can rotate and drive the generator to produce electricity. However, in the drainage process of the water turbine, the water flow cannot be stably guided to the runner of the water turbine due to damage, cracks and blockage of the diversion equipment, so that the water flow is not smooth, and the problem of energy production decline of the water turbine is indirectly caused.
Example 1
The invention provides an intelligent management platform of a hydropower plant tool based on the Internet of things, referring to FIG. 1, which comprises a first acquisition unit 1, a management unit 2, a summarizing unit 3, a first processing unit 4, an alarm unit 5, a second acquisition unit 6 and a second processing unit 7;
In the running process of hydroelectric power generation, continuously monitoring each water flow guiding tool in real time by the first acquisition unit 1, acquiring guiding tool data, and establishing a first data set; establishing a corresponding digital twin tool model and a water turbine region model by the management unit 2, inputting a first data set into the digital twin tool model, and marking the guiding position of the guiding tool in the water turbine region;
Continuously acquiring water flow data guided by a plurality of guiding tools along a time axis by the summarizing unit 3, analyzing the water flow data by a first processing unit 4 to acquire a standard deviation sigma of the water flow data, and if the standard deviation sigma is lower than a preset first threshold value Q1 of water flow along with the continuous convergence of the current water flow data, sending out first early warning information by an alarm unit 5;
When the standard deviation sigma of the water flow data is lower than a preset first threshold value Q1 of the water flow, the second acquisition unit 6 acquires water flow data of an inlet and an outlet of the diversion tool, the second processing unit 7 performs dimensionless processing on the water flow data, and then a turbulence distribution coefficient WL is calculated and acquired, wherein the turbulence distribution coefficient WL is calculated and generated through the following formula:
Wherein Re is expressed as a fluid flow state coefficient, specifically calculated by Reynolds number, L is expressed as a path length of the diversion tool, P is expressed as a density of water, V is expressed as a water flow speed, U is expressed as a viscosity of water, D 1 is expressed as a correction constant, re_C is expressed as a fluid flow state coefficient critical value; the meaning of the formula is that the calculated turbulence distribution coefficient WL value is a negative value, which indicates that the flow is in a laminar state; the turbulence distribution coefficient WL value is positive, indicating that the flow is in a turbulent state;
when the turbulence distribution coefficient WL value of the plurality of diversion tools is positive and exceeds the second threshold value Q2, second alarm information is generated, and corresponding strategy scheme processing is performed by the strategy unit 12 according to the second alarm information, including automatically closing the diversion tools, cleaning plugs and repairing cracks.
In this embodiment, through first collection unit, this platform can carry out real-time supervision to every rivers direction instrument, acquires the data of direction instrument in real time, including parameters such as velocity of flow, pressure and temperature. Thus, the management staff of the hydropower plant is promoted to know the equipment state at any time, and measures are taken in time.
And the platform establishes a digital twin tool model and a water turbine regional model through the management unit, and the actual data corresponds to the model data. This helps to more accurately analyze and predict device performance and to discover anomalies in time.
The platform analyzes the water flow data in real time through the summarizing unit and the first processing unit, calculates standard deviation, and triggers first early warning information if water flow abnormality is found. This helps to find water flow problems in time, reducing potential losses.
The second acquisition unit and the second processing unit calculate and monitor turbulence distribution coefficients of the diversion tool in real time, and once the turbulence distribution coefficients WL are abnormal, the platform generates second alarm information, so that damage caused by water flow turbulence and energy production reduction are prevented.
The strategy unit formulates a corresponding strategy scheme according to the alarm information, and the strategy scheme comprises the steps of automatically closing the diversion tool, cleaning the blockage, repairing the crack and the like, so that the problem is solved in a targeted manner, and the maintenance cost is reduced.
The intelligent management platform of the hydropower plant tool based on the Internet of things effectively challenges monitoring and maintaining hydropower plant equipment through the real-time monitoring, data analysis and intelligent early warning system, improves the reliability and efficiency of the equipment operation, reduces the maintenance cost, and is beneficial to improving the sustainable development and environmental protection of the hydropower plant.
Example 2
This embodiment is explained in embodiment 1, referring to fig. 1, specifically, the first collecting unit 1 is configured to install a flow rate sensor, a pressure sensor, and a temperature sensor device on each water flow guiding tool to collect guiding tool data in real time;
the pilot tool data includes the flow rate of the water flow through the pilot tool, the inlet and outlet pressures, and the temperature of the water flow, and a first data set is established.
In this embodiment, the pilot tool data includes various parameters such as flow rate, inlet and outlet pressures, and water flow temperature. Such comprehensive data collection facilitates a comprehensive understanding of the operation of the steering tool, including the physical nature of the water flow and the efficiency of the steering tool. By acquiring and recording these data, the first acquisition unit 1 is able to build a first data set, which contains the history data of the guiding means. This is valuable for subsequent analysis, modeling and prediction. With the help of real-time data, operators can optimize and adjust the performance of the guiding tool so as to ensure smooth guiding of water flow and improve the energy production efficiency of the hydropower plant. Because of the real-time collection of the data, operators can monitor the state of the guiding tool remotely at any time without having to be in the spot. This improves convenience and efficiency of management.
Example 3
This embodiment is explained in embodiment 1, please refer to fig. 1, specifically, collecting historical operation data, hydraulic model and hydraulic turbine design drawing as basic data of a digital twin tool model and a hydraulic turbine region model, in which the position of a guiding tool is marked and corresponding to the data of an actual guiding tool;
The first data set is continuously updated to the digital twin tool model, the difference between the data in the digital twin tool model and the actual data is monitored, and if abnormality or inconsistency is found, an alarm is triggered.
In this embodiment, a digital twin tool model and a turbine regional model are created by collecting historical operating data, a hydraulic model and a turbine design drawing. This provides a simulated and analyzed environment for the system that accurately simulates actual hydroelectric power plant tools and turbine areas in the digital world. The first data set is continuously used for updating the digital twin tool model to ensure that the model is consistent with the actual situation. As the performance and status of hydroelectric power plant tools may change over time. By updating the model in real time, the state of the actual tool can be reflected more accurately. The digital twin model is compared to the actual data and if an anomaly or inconsistency between the model and the actual data is found, the system triggers an alarm. This helps to find potential problems in time and take action to reduce the risk of equipment failure and production interruption. By simulating and analyzing the digital twin model, the life and performance decline trend of the guiding tool can be predicted, thereby making a more effective maintenance plan and reducing unnecessary downtime and maintenance cost.
Example 4
In this embodiment, as explained in embodiment 1, referring to fig. 1, specifically, the summarizing unit 3 is responsible for continuously obtaining water flow data of the diversion of the plurality of diversion tools, and summarizing the data according to a time axis;
The first processing unit 4 analyzes the summarized water flow data to calculate the standard deviation of the water flow data; the standard deviation is a statistical index for measuring the degree of data dispersion in a data set, and the larger the standard deviation is, the higher the fluctuation of the data is;
the set of water flow data, noted { x1, x2, x3, & gt, xn }, was set and the standard deviation was calculated as follows:
First, the average μ of the set of water flow data is calculated:
N represents the number of data points, i.e., how many data points the set of water flow data includes;
Next, for each data point xi, the difference from the average μ is calculated, and the sum of squared differences is calculated, and the variance σ 2 is calculated from the following equation:
finally, the standard deviation sigma is calculated and obtained:
Continuously monitoring whether current water flow data continue to be imported or not, comparing the current water flow data with a standard deviation sigma calculated before after new water flow data are imported, checking whether the amplitude value of the standard deviation sigma is lower than a first threshold value Q1 of preset water flow, if the amplitude value of the standard deviation sigma is lower than the first threshold value Q1, indicating that the water flow is unstable due to the blocking of the water flow, triggering first alarm information, and sending the first alarm information to an operator or a system administrator through alarm sound by an alarm unit 5.
In this embodiment, the summarizing unit 3 is responsible for continuously acquiring water flow data of the diversion tool, and summarizing the water flow data according to a time axis. This ensures real-time monitoring of the water flow, enabling the system to obtain information on the performance of the diversion tool in time. The first processing unit 4 analyzes the summarized water flow data and calculates the standard deviation of the set of data. The standard deviation is an index for measuring the data distribution in the data set, and can reflect the discrete degree of the data. By calculating the standard deviation, whether the water flow of the diversion tool is stable or not and whether an abnormal condition exists or not can be known. The standard deviation calculation mode can help the system to continuously monitor whether the current water flow data is continuously imported or not. If the new water flow data shows a significant change from the previously calculated standard deviation, in particular if the standard deviation is below a preset first threshold value Q1 of the water flow, the system will trigger a first alarm message. This helps to find problems such as blocked water flow in time to reduce potential equipment failure and production interruption. Once the first alarm information is triggered, the alarm unit 5 sends the first alarm information to an operator or a system administrator in a sound or other manner. In this way, the relevant personnel can take steps to deal with the problem quickly, reducing possible losses. Through real-time monitoring and standard deviation analysis, the system can better know the water flow state, is favorable for improving the production reliability and efficiency of hydropower plant tools, and reduces unnecessary downtime and maintenance cost.
Example 5
In this embodiment, as explained in embodiment 1, referring to fig. 1, specifically, the second acquisition unit 6 is responsible for acquiring water flow data, where the water flow data includes a path length L of the diversion tool, a water density P, a water velocity V, and a water viscosity U, and performing dimensionless processing on the path length L of the diversion tool, the water density P, the water velocity V, and the water viscosity U;
setting a critical value Re_C of a fluid flow state coefficient Re, and correspondingly setting according to a load value of the water turbine;
Comparing the calculated turbulence distribution coefficients WL, if the turbulence distribution coefficients WL of a plurality of diversion tools are positive values and exceed a second threshold Q2, indicating that the flow is in a turbulence state and the turbulence distribution coefficients WL are abnormal, generating second alarm information, and performing remarkable marking in a digital twin tool model and a water turbine region model;
the policy unit 12 formulates a corresponding policy scheme according to the second alarm information to cope with abnormal conditions of turbulence distribution coefficients, including:
Automatically closing the affected diversion tool to prevent further damage;
clearing the blockage, and if the turbulence distribution coefficient is abnormal, the blockage is caused by the blockage of the diversion tool;
Repairing the crack if the turbulence distribution coefficient is abnormal due to the crack or damage on the diversion tool;
Second alarm information is generated and an alarm notification is sent to the operation and maintenance personnel by the alarm unit 5.
In this embodiment, the second acquisition unit 6 is responsible for acquiring important data related to the flow of the water flow, including the path length of the diversion tool, the density of the water, the water flow velocity and the water flow viscosity. These data are key parameters for assessing water flow conditions and diversion tool performance. Dimensionless treatment of path length, water density, water flow velocity and water flow viscosity helps normalize these parameters for better comparability and analytical value. This helps to accurately assess the water flow conditions and allow comparison and analysis of data under different conditions. By calculating the turbulence distribution coefficient WL, the system can better understand the turbulence state of the water flow. The turbulence distribution coefficient is an important index for evaluating whether the water flow is in a turbulence state, and if the turbulence distribution coefficient WL value is positive and exceeds a second threshold value Q2, the system triggers a second alarm message. Marking the position of the diversion tool with abnormal turbulence distribution coefficient in the digital twin tool model is beneficial to establishing an accurate digital twin tool model. This enables the system to better simulate and predict the performance of the diversion tool and identify potential problems ahead of time. The policy unit 12 formulates a corresponding policy scheme based on the second alarm information. These strategies include automatic shut-down of the affected diversion tools, cleaning of plugs, repair of cracks, etc., aimed at rapidly solving the problem of abnormal turbulence distribution coefficients, thereby reducing potential damage to hydropower plant equipment. The alarm unit 5 sends an alarm notice of the second alarm information to the operation and maintenance personnel, so that the related personnel can timely take action to treat abnormal conditions of the turbulence distribution coefficient, and possible equipment faults and production interruption are reduced.
Example 6
This embodiment is explained in embodiment 1, referring to fig. 1, and specifically further includes a third collecting unit 8 and a third processing unit 9, where before the water flow enters the water turbine, sediment sensors are installed in the front position of the diversion tool and in the water reservoir, and the condition of the floaters and the sediment in the water flow is monitored in real time, so as to establish a second data set;
the third processing unit 9 is configured to perform dimensionless processing on the second data set, and calculate to obtain a sediment water quality coefficient Cd, where the sediment water quality coefficient Cd is generated by the following formula:
Where C represents the sediment content data obtained from the sediment sensor, cmin represents the minimum value of the sediment content data, i.e., the minimum value in the second data set, and Cmax represents the maximum value of the sediment content data, i.e., the maximum value in the second data set; d 2 is denoted as a correction constant; the meaning of the formula is: the sediment water quality coefficient Cd is generally used to represent the water quality condition of sediment in water; the value of Cd is between 0 and 1, where 0 indicates no sediment in the water and 1 indicates the maximum concentration of sediment in the water.
Specifically, the third processing unit 9 is configured to compare the obtained sediment water quality coefficient Cd with a third threshold Q3, trigger third alarm information if the sediment water quality coefficient Cd is higher than the third threshold Q3, and send the third alarm information to the operation and maintenance personnel through the alarm unit 5.
Specifically, the policy unit 12 formulates a corresponding policy scheme according to the third alarm information, including:
replacing a filter in the reservoir to remove sediment;
immediately stopping the machine, and after cleaning the deposit in the water reservoir, the diversion tool and other water flow paths, restarting the tool to normally operate;
The speed regulator is used for increasing the water flow speed to help wash away sediment deposition;
The performance of the precipitate treatment plant is optimized.
In this embodiment, the system is able to monitor in real time the condition of floats and sediments in the water flow by installing sediment sensors in the forward position of the diversion tool and in the reservoir. This helps to identify and address potential sediment problems early. The data collected by the third acquisition unit 8 and the sensor are used to create a second data set comprising information on the sediment content. This dataset is critical for assessing water flow quality and sediment water quality coefficients. The third processing unit 9 calculates and generates a sediment water quality coefficient Cd using the data acquired by the sediment sensor. This coefficient is typically used to represent the concentration of sediment in water and the water quality, with Cd values between 0 and 1. By comparing the Cd value with the third threshold Q3, the system is able to evaluate the quality condition of the sediment in water. If Cd is higher than Q3, the system can trigger third alarm information, and the water quality is indicated to have abnormal conditions. The policy unit 12 will formulate a corresponding policy scheme based on the third alarm information. These strategies include replacing filters in the reservoir, immediately stopping the process, using a governor to increase the water flow rate to flush away sediment, optimizing sediment treatment facilities, etc. These measures aim at rapidly solving the water quality problem, maintaining the equipment and ensuring the production safety.
Example 7
This embodiment is explained in embodiment 1, referring to fig. 1, and specifically further includes a remote control unit 11 for allowing a remote operator to remotely control hydropower plant equipment, including remotely starting, stopping, adjusting operation of the hydraulic turbine and the diversion tool, and remotely accessing real-time data and alarm information.
Specifically, the system further comprises a maintenance unit 10 for tracking according to the policy schemes of the first alarm information, the second alarm information and the third alarm information, and performing spare part management on the current completion degree and the maintenance log.
In this embodiment, a remote operator is allowed to remotely control hydropower plant equipment via the internet, including starting, stopping, adjusting operations of the hydraulic turbine, diversion tools, and the like. The function enables operators not to be in the spot, and flexibility and convenience of operation are improved. The maintenance unit tracks according to the strategy schemes of the first, second and third alarm information. This helps ensure that appropriate measures are taken to solve the equipment problem and track the execution of each policy. The maintenance unit may perform spare part management according to the maintenance log and the policy scheme. The device can identify parts which need to be replaced or maintained, and ensure the timely supply of spare parts so as to reduce maintenance downtime.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. Hydropower plant instrument intelligent management platform based on thing networking, its characterized in that: the system comprises a first acquisition unit (1), a management unit (2), a summarizing unit (3), a first processing unit (4), an alarm unit (5), a second acquisition unit (6), a second processing unit (7), a third acquisition unit (8), a third processing unit (9), a remote control unit (11) and a strategy unit (12);
In the running process of hydroelectric power generation, continuously monitoring each water flow guiding tool in real time by the first acquisition unit (1), acquiring guiding tool data, and establishing a first data set; establishing a corresponding digital twin tool model and a water turbine regional model by a management unit (2), inputting a first data set into the digital twin tool model, and marking the guiding position of a guiding tool in the water turbine regional model;
continuously acquiring water flow data guided by a plurality of guiding tools along a time axis by the summarizing unit (3), analyzing the water flow data by the first processing unit (4), acquiring standard deviation sigma of the summarized water flow data, and if the standard deviation sigma is lower than a preset first threshold Q1 of water flow along with the continuous convergence of the current water flow data, sending out first early warning information by the alarm unit (5);
when the standard deviation sigma of water flow data is lower than a preset first threshold value Q1 of water flow, a second acquisition unit (6) acquires water flow data of an inlet and an outlet of the diversion tool, a second processing unit (7) performs dimensionless processing on the water flow data, and then a turbulence distribution coefficient WL is calculated and obtained, wherein the turbulence distribution coefficient WL is calculated and generated through the following formula:
Wherein Re is expressed as a fluid flow state coefficient, specifically calculated by Reynolds number, L is expressed as a path length of the diversion tool, p is expressed as a density of water, V is expressed as a water flow speed, U is expressed as a viscosity of water, D 1 is expressed as a correction constant, re_C is expressed as a fluid flow state coefficient critical value; the meaning of the formula is that the calculated turbulence distribution coefficient WL value is a negative value, which indicates that the flow is in a laminar state; the turbulence distribution coefficient WL value is positive, indicating that the flow is in a turbulent state;
When the turbulence distribution coefficient WL value of a plurality of diversion tools is positive and exceeds a second threshold value Q2, generating second alarm information, and performing corresponding strategy scheme treatment by a strategy unit (12) according to the second alarm information, wherein the strategy scheme comprises the steps of automatically closing the diversion tools, cleaning plugs and repairing cracks;
The second acquisition unit (6) is responsible for acquiring water flow data, wherein the water flow data comprise the path length L of the diversion tool, the density p of water, the water flow speed V and the water flow viscosity U, and the path length L of the diversion tool, the density p of water, the water flow speed V and the water flow viscosity U are subjected to dimensionless treatment;
setting a critical value Re_C of a fluid flow state coefficient Re, and correspondingly setting according to a load value of the water turbine;
Comparing the calculated turbulence distribution coefficients WL, if the turbulence distribution coefficients WL of a plurality of diversion tools are positive values and exceed a second threshold Q2, indicating that the flow is in a turbulence state and the turbulence distribution coefficients WL are abnormal, generating second alarm information, and performing remarkable marking in a digital twin tool model and a water turbine region model;
A corresponding strategy scheme is formulated by a strategy unit (12) according to the second alarm information so as to cope with abnormal conditions of turbulence distribution coefficients, and the strategy comprises the following steps:
automatically closing the affected diversion tool;
clearing the blockage, and if the turbulence distribution coefficient is abnormal, the blockage is caused by the blockage of the diversion tool;
Repairing the crack if the turbulence distribution coefficient is abnormal due to the crack or damage on the diversion tool;
generating second alarm information, and sending an alarm notification to an operation and maintenance person by an alarm unit (5);
The third acquisition unit (8) is used for installing sediment sensors in the front position of the diversion tool and the reservoir before the water flow enters the water turbine, monitoring the condition of floaters and sediment in the water flow in real time, and establishing a second data set;
The third processing unit (9) is used for performing dimensionless processing on the second data set, and then calculating to obtain a sediment water quality coefficient Cd, wherein the sediment water quality coefficient Cd is generated by the following formula:
Where C represents the sediment content data obtained from the sediment sensor, cmin represents the minimum value of the sediment content data, i.e., the minimum value in the second data set, and Cmax represents the maximum value of the sediment content data, i.e., the maximum value in the second data set; d 2 is denoted as a correction constant; the meaning of the formula is: the sediment water quality coefficient Cd is used for representing the water quality condition of sediment in water; the value of Cd is between 0 and 1, where 0 indicates no precipitate in the water and 1 indicates the maximum concentration of precipitate in the water;
The third processing unit (9) is used for comparing the acquired sediment water quality coefficient Cd with a third threshold value Q3, triggering third alarm information if the sediment water quality coefficient Cd is higher than the third threshold value Q3, and sending the third alarm information to operation and maintenance personnel by the alarm unit (5);
the strategy unit (12) formulates a corresponding strategy scheme according to the third alarm information, and comprises the following steps:
replacing a filter in the reservoir to remove sediment;
immediately stopping the machine, and after cleaning the deposit in the water reservoir, the diversion tool and other water flow paths, restarting the tool to normally operate;
The speed regulator is used for increasing the water flow speed to help wash away sediment deposition;
optimizing the performance of the precipitate treatment facility;
The remote control unit (11) is used for allowing a remote operator to remotely control hydropower plant equipment, and comprises remote starting, stopping, adjusting the operation of the water turbine and the diversion tool, and remotely accessing real-time data and alarm information.
2. The internet of things-based hydropower plant tool intelligent management platform of claim 1, wherein: the first acquisition unit (1) is used for installing a flow rate sensor, a pressure sensor and a temperature sensor on each water flow guiding tool so as to acquire guiding tool data in real time;
the pilot tool data includes the flow rate of the water flow through the pilot tool, the inlet and outlet pressures, and the temperature of the water flow, and a first data set is established.
3. The internet of things-based hydropower plant tool intelligent management platform of claim 1, wherein: collecting historical operation data, a hydraulic model and a water turbine design drawing, and taking the historical operation data, the hydraulic model and the water turbine design drawing as basic data of a digital twin tool model and a water turbine area model, marking the position of a guiding tool in the water turbine area model, and corresponding the position of the guiding tool to the data of an actual guiding tool;
The first data set is continuously updated to the digital twin tool model, the difference between the data in the digital twin tool model and the actual data is monitored, and if abnormality or inconsistency is found, an alarm is triggered.
4. The internet of things-based hydropower plant tool intelligent management platform of claim 1, wherein: the summarizing unit (3) is responsible for continuously acquiring water flow data guided by a plurality of guiding tools and summarizing the data according to a time axis;
The first processing unit (4) analyzes the summarized water flow data to calculate the standard deviation of the summarized water flow data;
The total water flow data were set and noted as { x1, x2, x3 }, xn }, with the standard deviation calculated as follows:
First, the average μ of the summarized water flow data is calculated:
n represents the number of data points, i.e., how many data points the summarized water flow data includes;
Next, for each data point xi, the difference from the average μ is calculated, and the sum of squared differences is calculated, and the variance σ 2 is calculated from the following equation:
finally, the standard deviation sigma is calculated and obtained:
σ＝√(σ2)
continuously monitoring whether current water flow data continue to be imported or not, comparing the current water flow data with a standard deviation sigma calculated before after new water flow data are imported, checking whether the amplitude value of the standard deviation sigma is lower than a first threshold value Q1 of preset water flow, if the amplitude value of the standard deviation sigma is lower than the first threshold value Q1, indicating that the water flow is unstable due to the blocking of the water flow, triggering first alarm information, and sending the first alarm information to an operator or a system administrator through alarm sound by an alarm unit (5).
5. The internet of things-based hydropower plant tool intelligent management platform of claim 1, wherein: the system further comprises a maintenance unit (10) for tracking according to the strategy schemes of the first alarm information, the second alarm information and the third alarm information, and performing spare part management on the current completion degree and the maintenance log.
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