Integrated management and control device for thermal power plant internet-of-things equipment

Abstract

The utility model relates to the technical field of thermal power plant management and control, and particularly discloses an integrated management and control device for thermal power plant Internet of Things equipment, which is characterized in that an industrial computer is connected with a temperature sensor through a cable, and is connected with a pressure sensor through a cable; the industrial computer is connected with a vibration sensor through a cable, the industrial computer is connected with a flow sensor through a cable, and the industrial computer is connected with a flue gas component sensor through a cable; a temperature sensor, a pressure sensor, a vibration sensor, a flow sensor and a flue gas component sensor in each area accurately measure physical parameters and environmental indexes of the area at any time, the sensors in each area transmit data to an industrial computer along a cable, the industrial computer uniformly processes the data, and the data are stored in a database. And the change of each data and the influence of the change of each region on other regions are monitored, so that the effect of comprehensively evaluating the health state of the equipment is achieved.

Description

Technical Field

[0001] This utility model relates to the field of thermal power plant control technology, and in particular to an integrated control device for IoT equipment in thermal power plants. Background Technology

[0002] During the operation of thermal power plants, efficient management and control of various IoT devices is crucial to ensuring the stable, safe, and economical operation of the power generation system. In practical applications, integrated management and control devices for IoT devices in thermal power plants typically rely on the following technologies:

[0003] 1. Monitoring mechanism: Utilizes various types of sensors, such as temperature sensors, pressure sensors, vibration sensors, etc., to monitor the operating parameters of IoT devices in real time;

[0004] 2. Control mechanism: With the help of an automated control system, such as a distributed control system (DCS) or a programmable logic controller (PLC), the IoT devices are precisely controlled based on the data fed back by the monitoring mechanism;

[0005] 3. Data Analysis and Management Organization: Employing big data analytics and management software, the organization conducts in-depth analysis and management of the massive amounts of data collected by the monitoring agency.

[0006] In existing thermal power plant power generation systems, the control of various components is relatively decentralized, and traditional single-point monitoring is insufficient to comprehensively assess the health status of equipment. Utility Model Content

[0007] To address the shortcomings of existing technologies, this utility model provides an integrated management and control device for IoT equipment in thermal power plants, which solves the technical problem that the management and control of various mechanisms in existing thermal power plant power generation systems are relatively decentralized, and traditional single-point monitoring is difficult to comprehensively assess the health status of equipment.

[0008] To achieve the above objectives, this utility model provides the following technical solution:

[0009] An integrated control device for IoT equipment in a thermal power plant includes an industrial computer, which is connected to a temperature sensor via a cable, a pressure sensor via a cable, a vibration sensor via a cable, a flow sensor via a cable, and a flue gas composition sensor via a cable.

[0010] Preferably, multiple temperature sensors are installed in the boiler, steam turbine, generator, and flue gas treatment equipment, respectively.

[0011] Preferably, multiple pressure sensors are installed on the boiler, steam turbine, pipeline and reactor respectively.

[0012] Preferably, multiple vibration sensors are installed on the steam turbine, engine, fan, and gearbox, respectively.

[0013] Preferably, multiple flow sensors are installed in the boiler, steam system, pipeline transportation system, water treatment equipment, and waste gas treatment equipment, respectively.

[0014] Preferably, multiple flue gas composition sensors are installed on the inlet and outlet ends of the waste gas treatment equipment.

[0015] Preferably, the industrial computer is connected to an edge memory via a cable.

[0016] Preferably, the industrial computer is connected to a cloud server via Ethernet.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] I. During operation, temperature sensors, pressure sensors, vibration sensors, flow sensors, and flue gas composition sensors, distributed in various areas such as boilers, steam turbines, generators, flue gas treatment equipment, pipelines, reactors, fans, gearboxes, steam systems, water treatment equipment, and waste gas treatment equipment, continuously and accurately measure the physical parameters and environmental indicators of their respective areas. The data from each sensor in each area will be transmitted along cables to an industrial computer, which will then process the data uniformly, monitor changes in each data point, and analyze the impact of changes in each area on other areas, in order to achieve a comprehensive assessment of the equipment's health status.

[0019] Second, the industrial computer sends the processed data to the cloud server via Ethernet. The cloud server, with its powerful computing capabilities and storage resources, undertakes more complex data processing tasks. In the cloud, a dedicated AI algorithm model is deployed. After receiving equipment operation data from the thermal power plant, the model predicts the overall operating status of the thermal power plant and the future operating trends of key equipment, so as to carry out maintenance in advance. Attached Figure Description

[0020] The above description is only an overview of the technical solution of this utility model. In order to better understand the technical means of this utility model and to implement it in accordance with the contents of the specification, the preferred embodiments of this utility model are described in detail below with reference to the accompanying drawings.

[0021] Figure 1 This is a schematic diagram of the connection structure of this utility model;

[0022] Figure 2 This is a structural diagram of the industrial computer of this utility model;

[0023] Figure 3This is a structural diagram of the flow sensor of this utility model;

[0024] Figure 4 This is a structural diagram of the flue gas composition sensor of this utility model.

[0025] Legend: 1. Industrial computer; 2. Temperature sensor; 3. Pressure sensor; 4. Vibration sensor; 5. Flow sensor; 6. Flue gas composition sensor; 7. Edge memory; 8. Cloud server. Detailed Implementation

[0026] This application provides an integrated management and control device for IoT equipment in thermal power plants. This effectively solves the technical problem of the dispersed management of various components in existing thermal power plant power generation systems, making it difficult for traditional single-point monitoring to comprehensively assess the health status of equipment. During operation, temperature sensors, pressure sensors, vibration sensors, flow sensors, and flue gas composition sensors distributed across various areas, including boilers, turbines, generators, flue gas treatment equipment, pipelines, reactors, fans, gearboxes, steam systems, water treatment equipment, and waste gas treatment equipment, continuously and accurately measure the physical parameters and environmental indicators of their respective areas. The data from each sensor is transmitted via cable to an industrial computer, which processes the data uniformly, monitors changes in each data point, and analyzes the impact of changes in one area on other areas to achieve a comprehensive assessment of equipment health. The industrial computer then sends the processed data to a cloud server via Ethernet. The cloud server, with its powerful computing capabilities and storage resources, undertakes more complex data processing tasks. A dedicated AI algorithm model is deployed in the cloud. This model receives equipment operation data from the thermal power plant and predicts the overall operating status of the plant and the future operating trends of key equipment, enabling proactive maintenance. Example

[0027] like Figure 1 As shown, the technical solution in this application embodiment effectively solves the technical problem that the control of various mechanisms in the existing thermal power plant power generation system is relatively decentralized, and traditional single-point monitoring is difficult to comprehensively assess the health status of equipment. The overall idea is as follows:

[0028] To address the problems existing in the prior art, this utility model provides an integrated control device for IoT equipment in thermal power plants, including an industrial computer 1. The industrial computer 1 is connected to a temperature sensor 2 via a cable, a pressure sensor 3 via a cable, a vibration sensor 4 via a cable, a flow sensor 5 via a cable, and a flue gas composition sensor 6 via a cable.

[0029] Multiple temperature sensors 2 are installed on the boiler, steam turbine, generator and flue gas treatment equipment respectively; multiple pressure sensors 3 are installed on the boiler, steam turbine, pipeline and reactor respectively; and multiple vibration sensors 4 are installed on the steam turbine, engine, fan and gearbox respectively.

[0030] Multiple flow sensors 5 are installed in the boiler, steam system, pipeline transportation system, water treatment equipment and exhaust gas treatment equipment respectively. Multiple flue gas composition sensors 6 are installed at the inlet and outlet of the exhaust gas treatment equipment respectively. The industrial computer 1 is connected to the edge storage 7 via cable. The industrial computer 1 is connected to the cloud server 8 via Ethernet.

[0031] Industrial Computer 1: As the core processing unit of the entire control device, it is connected to various sensors via cables to receive data collected by temperature sensor 2, pressure sensor 3, vibration sensor 4, flow sensor 5, and flue gas composition sensor 6. It then processes this data in a unified manner, monitoring changes in each data point and analyzing the impact of data changes in each area on other areas to comprehensively assess the health status of the equipment. In addition, Industrial Computer 1 is also responsible for sending the processed data to cloud server 8 via Ethernet and connecting to edge storage 7 to back up the data and provide actual measurement results for AI algorithms for comparative analysis.

[0032] Temperature sensor 2: Multiple temperature sensors 2 are installed in key parts such as boilers, steam turbines, generators and flue gas treatment equipment. Their function is to measure the physical parameter of temperature in the area where the equipment is located in real time and accurately. These measurement data are important basis for evaluating the equipment's operating status, thermal efficiency and whether there are potential faults such as overheating. The data is then transmitted to industrial computer 1 for further processing.

[0033] Pressure sensor 3: Installed in boilers, steam turbines, pipelines and reactors, etc., to measure the pressure values ​​in these areas. Pressure parameters play a key role in ensuring the safe operation of equipment and judging the stability of system operation. The measurement data is also transmitted to industrial computer 1 to provide data support for comprehensive evaluation of equipment status.

[0034] Vibration sensor 4: It is placed on equipment such as steam turbine, engine, fan and gearbox. Its main function is to detect the vibration during the operation of the equipment. The vibration status of the equipment can reflect a variety of potential faults such as imbalance, wear and looseness. The data collected by the sensor is transmitted to industrial computer 1 to assist in the assessment of the health status of the equipment.

[0035] Flow sensor 5: Installed in relevant locations such as boilers, steam systems, pipeline transportation systems, water treatment equipment and waste gas treatment equipment, etc., to measure the flow rate of fluids (liquid and gas) in each system and equipment. The flow data is crucial for understanding system operating efficiency, energy consumption and process stability, and is transmitted to industrial computer 1 as part of the overall data analysis.

[0036] Flue gas composition sensor 6: Installed at the inlet and outlet of the exhaust gas treatment equipment respectively, its function is to monitor the composition of the gas entering and leaving the exhaust gas treatment equipment in real time, and detect the content of pollutants such as sulfur dioxide, nitrogen oxides, and particulate matter. These data are of great significance for evaluating the exhaust gas treatment effect, environmental compliance, and the environmental impact of equipment operation. They are also transmitted to industrial computer 1 for processing.

[0037] Edge memory 7: Connected to industrial computer 1 via cable, it stores the actual results measured by each sensor. After the AI ​​algorithm makes a prediction on the operating status of the thermal power plant, it provides the AI ​​algorithm with actual data as the basis for comparative analysis, helps the AI ​​algorithm find the difference between the prediction result and the actual measurement result, and then optimizes the algorithm.

[0038] Cloud Server 8: Connected to Industrial Computer 1 via Ethernet, it utilizes its powerful computing capabilities and storage resources to receive processed data sent by Industrial Computer 1 and undertakes more complex data processing tasks. It deploys a dedicated AI algorithm model in the cloud, which predicts the overall operating status of the thermal power plant and the future operating trends of key equipment based on the received thermal power plant equipment operation data, so as to arrange equipment maintenance in advance and ensure the stable operation of the thermal power plant.

[0039] Working principle:

[0040] The first step involves the use of temperature sensors 2, pressure sensors 3, vibration sensors 4, flow sensors 5, and flue gas composition sensors 6, distributed across various areas including boilers, steam turbines, generators, flue gas treatment equipment, pipelines, reactors, fans, gearboxes, steam systems, water treatment equipment, and waste gas treatment equipment. These sensors continuously and accurately measure the physical parameters and environmental indicators of their respective areas. The data from each sensor is transmitted via cable to industrial computer 1, where it is processed to monitor changes in each area and the impact of these changes on other areas, thus achieving a comprehensive assessment of the equipment's health status.

[0041] In the second step, the industrial computer 1 sends the processed data to the cloud server 8 via Ethernet. With its powerful computing capabilities and storage resources, the cloud server 8 undertakes more complex data processing tasks. In the cloud, a dedicated AI algorithm model is deployed. After receiving equipment operation data from the thermal power plant, the model predicts the overall operation status of the thermal power plant and the future operation trend of each key piece of equipment so as to carry out maintenance in advance.

[0042] Third, after completing the operational status prediction, the AI ​​algorithm will compare and analyze the prediction results with the actual results measured by each sensor in the edge memory 7. If there are differences between the prediction results and the measurement results, the AI ​​algorithm will conduct in-depth mining and analysis of these differences. Through the backpropagation algorithm, the AI ​​model will adjust its own parameters and optimize the algorithm's operating logic, thereby continuously improving the accuracy of the prediction.

[0043] Finally, it should be noted that the above embodiments are merely examples for clearly illustrating the present invention and are not intended to limit the implementation. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the protection scope of this invention.

Claims

1. A device for integrated management and control of Internet of Things equipment in a thermal power plant, comprising an industrial computer (1), characterized in that, The industrial computer (1) is connected to a temperature sensor (2) via a cable, a pressure sensor (3) via a cable, a vibration sensor (4) via a cable, a flow sensor (5) via a cable, and a flue gas composition sensor (6) via a cable.

2. The device integrated management and control device for power plant Internet of Things according to claim 1, wherein, Multiple temperature sensors (2) are respectively installed in the boiler, steam turbine, generator and flue gas treatment equipment.

3. The device integrated management and control device for power plant Internet of Things according to claim 1, characterized in that, Multiple pressure sensors (3) are respectively installed on the boiler, steam turbine, pipeline and reactor.

4. The device integrated management and control device for power plant Internet of Things according to claim 1, characterized in that, Multiple vibration sensors (4) are respectively installed on the steam turbine, engine, fan and gearbox.

5. The integrated control device for IoT equipment in a thermal power plant as described in claim 1, characterized in that, Multiple flow sensors (5) are respectively installed in boilers, steam systems, pipeline transportation systems, water treatment equipment and waste gas treatment equipment.

6. The device integrated management and control device for power plant Internet of Things of claim 1, wherein, Multiple flue gas composition sensors (6) are respectively installed on the inlet and outlet of the waste gas treatment equipment.

7. The device integrated management and control device for power plant Internet of Things according to claim 1, characterized in that, The industrial computer (1) is connected to an edge memory (7) via a cable. 8.The device integrated management and control device of a thermal power plant of claim 1, wherein, The industrial computer (1) is connected to a cloud server (8) via Ethernet.

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