An indirect air cooling system temperature precision detection system for a thermal power plant

Abstract

The application discloses an indirect air cooling system temperature precision detection system for a thermal power plant, and belongs to the technical field of thermal detection of the thermal power plant. The system comprises a microcontroller and a multi-source interference and operation parameter real-time acquisition in communication with the microcontroller, environment disturbance quantity calculation and temperature measuring point self-correction, thermoelectric potential disturbance component stripping and sampling window adaptation, condensation rate and thermoelectric potential drift linkage offsetting calibration, temperature data verification and closed-loop correction, an Internet of Things remote transmission module, and a software and hardware cooperative fault tolerance module. The application realizes multi-dimensional precision calibration of temperature parameters, improves the effectiveness of sampling data and the environmental adaptability of the system, guarantees the continuity and reliability of temperature measurement, and can iteratively optimize the temperature measurement precision along with the running time, while supporting remote operation and maintenance, and meeting the industrial demand of high-precision temperature measurement of the indirect air cooling system of the thermal power plant.

Description

Technical Field

[0001] This invention relates to the field of thermal power plant thermal testing technology, and in particular to a precise temperature detection system for indirect air-cooled systems in thermal power plants. Background Technology

[0002] In the operation of large thermal power plants, the indirect air-cooling system is the core of the cooling system. The accuracy of monitoring the heat dissipation pipeline and ambient temperature directly affects the economy and safety of the unit. In the existing technology, the temperature detection of the air-cooling system of thermal power plants mostly uses thermocouples or resistance temperature detectors as the core acquisition elements. After the acquired temperature signal is amplified and processed, it is transmitted to the controller to complete the temperature calculation. Some systems also have the function of remotely transmitting temperature data to the monitoring platform through the Internet of Things to realize remote viewing.

[0003] Existing technologies have many significant drawbacks in practical applications:

[0004] First, traditional detection methods only perform basic amplification processing on the original thermoelectric potential signal of the thermocouple, without considering the impact of environmental factors such as complex crosswinds in the air-cooled island, thermal interference from adjacent pipelines, and changes in heat dissipation flux on the outer wall of the pipeline on the temperature measurement. They also do not remove disturbance components such as ambient temperature drift and on-site electromagnetic interference in the thermoelectric potential signal, resulting in the temperature measurement results being affected by multi-source interference and having significant deviations.

[0005] Secondly, the existing system lacks a calibration mechanism for the coupled steady-state deviation caused by the fluctuation of steam condensation rate and the drift of thermocouple thermoelectric potential. It cannot cancel such deviations in real time, resulting in insufficient steady-state accuracy of temperature detection and significant accuracy decay after long-term operation.

[0006] Furthermore, most systems use fixed sampling windows, which cannot adapt to the different characteristics of thermoelectric potential signals under rated and variable operating conditions, resulting in low validity of sampling data and further affecting the accuracy of temperature measurement. In addition, existing technologies generally lack dynamic correction mechanisms for temperature measurement point positions, and deviations in the installation position of temperature measuring elements will amplify temperature measurement errors. At the same time, the lack of a sound hardware fault-tolerant design makes the system prone to temperature measurement interruptions when facing communication failures, data anomalies, or hardware damage. This results in insufficient adaptability and reliability, making it difficult to meet the industrial needs of thermal power plants for high-precision, long-term stable temperature measurement. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a precise temperature detection system for indirect air-cooled systems in thermal power plants.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: a precise temperature detection system for an indirect air-cooled system in a thermal power plant includes a microcontroller and a multi-source interference and real-time operating parameter acquisition module, an environmental disturbance calculation and temperature measurement point self-calibration module, a thermoelectric potential disturbance component stripping and sampling window adaptation module, a condensation rate and thermoelectric potential drift linkage cancellation calibration module, a temperature data verification and closed-loop correction module, and an Internet of Things remote transmission module.

[0009] The multi-source interference and real-time operating parameter acquisition module is used to collect environmental interference parameters, thermoelectric potential related parameters, and steady-state deviation related parameters of the indirect air-cooled system of the thermal power plant and transmit them to the microcontroller.

[0010] The environmental disturbance calculation and temperature measurement point self-calibration module is used to quantify and calculate the environmental disturbance parameters to obtain the actual temperature gradient parameters of the pipeline, and to perform dynamic calibration of the temperature measurement point position and dynamic matching of the thermal insulation sleeve.

[0011] The thermoelectric potential perturbation component stripping and sampling window adaptation module is used to strip the perturbation component from the thermoelectric potential related parameters to obtain the pure thermoelectric potential change gradient parameter, and dynamically adjust the sampling window specification according to the parameter.

[0012] The condensation rate and thermoelectric potential drift linkage cancellation calibration module is used to perform correlation calculation on the steady-state deviation related parameters to obtain the coupled steady-state total deviation, and to achieve temperature parameter calibration through reverse cancellation operation.

[0013] The temperature data verification and closed-loop correction module is used to verify the consistency of the calibrated temperature parameters and to build a correction parameter library to achieve closed-loop optimization of the system's operational parameters.

[0014] The IoT remote transmission module is used to remotely transmit the collected raw parameters, calibrated temperature data and system operating status data to the remote monitoring and management platform, and to receive control commands from the remote monitoring and management platform.

[0015] As a further aspect of the present invention, the environmental interference parameters collected by the multi-source interference and real-time operating parameter acquisition module include crosswind speed parameters, heat dissipation flux parameters of the outer wall of the pipeline, and thermal interference parameters of adjacent pipelines.

[0016] The thermoelectric potential related parameters include the original total thermoelectric potential signal parameters, the ambient temperature drift disturbance parameters, and the on-site electromagnetic interference disturbance parameters.

[0017] The steady-state deviation-related parameters include steam condensation rate parameters and thermocouple thermoelectric potential drift parameters.

[0018] The IoT remote transmission module uses an IoT communication protocol to achieve bidirectional data transmission. The IoT communication protocol includes one of NB-IoT, 5G or LoRa. The remote monitoring and management platform can realize real-time display of temperature data, abnormal alarms and remote configuration of system parameters.

[0019] As a further embodiment of the present invention, the environmental disturbance calculation and temperature measurement point self-calibration module includes an environmental disturbance calculation unit, a temperature measurement point drive calibration unit, and an isolation sleeve matching unit;

[0020] The environmental disturbance calculation unit is used to calculate the comprehensive environmental disturbance by weighted summation of the crosswind speed parameter and the heat dissipation flux parameter of the outer wall of the pipeline, and then calculate the difference between the comprehensive environmental disturbance and the thermal interference parameter of the adjacent pipeline, and combine the environmental temperature correction coefficient to obtain the actual temperature gradient parameter of the pipeline.

[0021] The temperature measuring point drive correction unit is used to generate correction commands to drive the temperature measuring element to dynamically adjust its position based on the offset between the actual temperature gradient parameters of the pipeline and the original detected temperature parameters.

[0022] The isolation sleeve matching unit is used to match the corresponding specification of the heat insulation isolation sleeve to wrap and isolate the temperature measuring element according to the value of the comprehensive environmental disturbance.

[0023] As a further embodiment of the present invention, the condensation rate and thermoelectric potential drift linkage cancellation calibration module includes a coupling deviation calculation unit, a reverse cancellation calibration unit, and a threshold verification unit.

[0024] The coupling deviation calculation unit is used to perform nonlinear correlation calculations on the steam condensation rate parameter and the thermocouple thermoelectric potential drift parameter to obtain the steady-state total deviation formed by their coupling.

[0025] The reverse cancellation calibration unit is used to perform reverse cancellation calculation between the steady-state total deviation and the pre-set temperature steady-state calculation reference parameter to obtain the temperature parameter after preliminary calibration.

[0026] The threshold verification unit is used to compare and verify the temperature parameters after preliminary calibration with the preset temperature steady-state qualified threshold. If the threshold requirement is not met, the coupling deviation calculation unit and the reverse cancellation calibration unit are driven to recalculate until the temperature parameters that meet the threshold requirement are obtained.

[0027] As a further aspect of the present invention, in the thermoelectric potential perturbation component stripping and sampling window adaptation module, the pure thermoelectric potential change gradient parameter is obtained by formula:

[0028] ,

[0029] in, These are the parameters of the original total thermoelectric potential signal. These are parameters related to ambient temperature drift disturbance. These are the electromagnetic interference parameters at the site. This is the electromagnetic shielding correction factor;

[0030] The dynamic adjustment of the sampling window adopts a threshold design based on different operating conditions. The reference width of the sampling window is 200ms for the rated operating condition and 500ms for the variable operating condition. The threshold for the fluctuation of the thermoelectric potential change gradient is 0.8mV / ℃ for the rated operating condition and 0.4mV / ℃ for the variable operating condition. If the fluctuation amplitude of the pure thermoelectric potential change gradient parameter exceeds the preset threshold, the expansion ratio of the sampling window is increased according to the ratio of the fluctuation amplitude to the threshold. If it is within the threshold range, the sampling window is shrunk back to the reference width.

[0031] As a further embodiment of the present invention, the temperature data verification and closed-loop correction module includes a consistency verification unit, a correction parameter library unit, and a parameter optimization unit;

[0032] The consistency verification unit is used to calculate the difference between the calibrated temperature parameters and the actual temperature gradient parameters of the pipeline. If the difference exceeds the preset verification threshold, the preceding module's calculation process is traced back and the relevant calculation parameters are adjusted.

[0033] The correction parameter library unit is a structured parameter library based on three-dimensional working condition division. It divides the working conditions according to three dimensions: steam condensation rate, ambient temperature, and crosswind speed, and stores the steady-state deviation, temperature difference, and optimized calculation parameters of the corresponding working conditions.

[0034] The parameter optimization unit uses an incremental learning algorithm combined with gradient descent to iteratively optimize the operational parameters in the correction parameter library based on effective deviation data of similar working conditions, and realizes dynamic parameter retrieval for working conditions.

[0035] As a further aspect of the present invention, the multi-source interference and real-time acquisition module for operating parameters adopts a graded sampling frequency to acquire parameters, with the sampling frequency of environmental interference parameters being 1Hz, the sampling frequency of thermoelectric potential-related parameters being 10Hz, and the sampling frequency of steady-state deviation-related parameters being 2Hz.

[0036] The module achieves unified synchronous triggering and acquisition through the high-precision crystal oscillator clock of the microcontroller. The acquired data is transmitted to the microcontroller via RS-485 bus, and the microcontroller has a built-in ring buffer to store the acquired data. The buffer refreshes the data according to the first-in-first-out rule, and the refresh frequency is 100ms.

[0037] As a further embodiment of the present invention, the temperature measuring point drive correction unit includes a stepper motor, a precision linear electric slide, and a position feedback component. The stepper motor is a two-phase hybrid stepper motor with a step angle of 1.8°, and is equipped with a microstepping driver to achieve a microstep of 0.09°.

[0038] The effective stroke of the precision linear electric slide is 200mm, the positioning accuracy is ±0.1mm, and the movement range is ±100mm axially and ±50mm radially along the steam condensation pipe.

[0039] The position feedback component is a grating ruler, which is used to provide real-time feedback on the actual position of the temperature measuring element to the microcontroller to achieve closed-loop position correction. The electric slide is equipped with a dual limit protection structure with mechanical limit and photoelectric limit at both ends.

[0040] As a further aspect of the present invention, the heat insulation sleeve is made of a composite material of aluminum silicate fiber and aerogel, with a temperature resistance range of -40℃ to 600℃ and a thermal conductivity of ≤0.02W / (m・K). It is also classified into three grades according to the overall environmental disturbance: Grade 1, 20mm thick, suitable for overall environmental disturbances ≤50W / ㎡; Grade 2, 40mm thick, suitable for overall environmental disturbances of 50W / ㎡ to 100W / ㎡; and Grade 3, 60mm thick, suitable for overall environmental disturbances >100W / ㎡.

[0041] The heat insulation sleeve is a ring structure that matches the temperature measuring element. It adopts a half-splitting design and is fixed by 304 stainless steel quick-change buckles. The inner side is attached with a high-temperature resistant silicone sealing gasket, and the outer side is wrapped with a waterproof and oil-proof polytetrafluoroethylene layer.

[0042] As a further embodiment of the present invention, the system is also provided with a hardware and software collaborative fault tolerance module, which is communicatively connected to the microcontroller. The hardware and software collaborative fault tolerance module includes a communication redundancy unit, an abnormal data processing unit, and a hardware fault diagnosis unit.

[0043] The communication redundancy unit adopts a dual-protocol design of Modbus-RTU main protocol and CAN bus backup protocol. When the main protocol data packet loss rate is ≥5%, it automatically switches to the backup protocol.

[0044] The abnormal data processing unit is used to filter out collected data that exceeds the reasonable range for industrial applications and replace invalid data with the average value of previous valid data.

[0045] The hardware fault diagnosis unit is used to collect the operating current and voltage parameters of each hardware in real time. When the parameters exceed the rated range, it is determined to be a hardware fault. The power supply of the faulty hardware is cut off and the backup temperature measuring element is activated. At the same time, the fault alarm signal is sent to the remote monitoring and management platform through the Internet of Things remote transmission module.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0047] In this invention, by setting up an environmental disturbance calculation and temperature measurement point self-calibration module, a thermoelectric potential disturbance component stripping and sampling window adaptation module, and a condensation rate and thermoelectric potential drift linkage cancellation calibration module, the coupling deviations of environmental interference, thermoelectric potential disturbance, and condensation rate and thermoelectric potential drift are processed in layers, thereby realizing multi-dimensional accurate calibration of temperature parameters, effectively improving the accuracy and steady-state of temperature detection, and making the detection results more consistent with the actual temperature of the pipeline.

[0048] By setting up a thermoelectric potential disturbance component stripping and sampling window adaptation module, the sampling window size is dynamically adjusted using a threshold design based on different operating conditions. When the thermoelectric potential signal fluctuation exceeds the threshold, the sampling window is expanded to ensure the integrity of the sampled data. Within the threshold range, it shrinks back to the reference width to improve sampling efficiency. This achieves precise adaptation between the sampling window and the system operating conditions, and significantly improves the effectiveness of the sampled data.

[0049] By setting up an environmental disturbance calculation and temperature measurement point self-calibration module, the position of the temperature measurement point can be dynamically adjusted according to the actual temperature gradient parameters of the pipeline, and a heat insulation sleeve of appropriate specifications can be matched to improve the system's adaptability to environmental changes. At the same time, a software and hardware collaborative fault tolerance module is set up. Through dual communication protocols, abnormal data processing, hardware fault diagnosis and backup component startup, the impact of communication failure, data abnormality and hardware failure on temperature measurement is effectively avoided, which greatly improves the reliability of system operation.

[0050] By setting up a multi-source interference and real-time operational parameter acquisition module with graded sampling frequencies and unified synchronous triggering, the acquisition needs of different types of parameters and data synchronization are taken into account. The setting of a ring buffer enables efficient storage and refreshing of acquired data. The temperature data verification and closed-loop correction module constructs a correction parameter library based on three-dimensional operating condition division, and combines incremental learning algorithms and gradient descent methods to achieve iterative optimization of calculation parameters, so that the temperature measurement accuracy of the system can be continuously improved over time. The IoT remote transmission module supports multiple communication protocols such as NB-IoT, 5G, and LoRa, realizing bidirectional data transmission. The remote monitoring and management platform can complete real-time display of temperature data, abnormal alarms, and remote configuration of system parameters, improving the convenience of remote operation and maintenance of the system. Attached Figure Description

[0051] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0053] like Figure 1 As shown, a precise temperature detection system for an indirect air-cooled system in a thermal power plant includes:

[0054] It includes a microcontroller and a multi-source interference and real-time operating parameter acquisition module that communicates with the microcontroller, an environmental disturbance calculation and temperature measurement point self-calibration module, a thermoelectric potential disturbance component stripping and sampling window adaptation module, a condensation rate and thermoelectric potential drift linkage cancellation calibration module, a temperature data verification and closed-loop correction module, and an IoT remote transmission module.

[0055] In this embodiment, the STM32H743IIK6 microcontroller is selected as the system control core, undertaking the functions of data reception, module driving, operation coordination, and instruction issuance, meeting the needs of high-speed data interaction and complex operation of multiple modules. Each module communicates with the microcontroller through RS-485 bus, which has strong anti-interference ability and is suitable for the industrial environment of thermal power plants.

[0056] The multi-source interference and real-time operating parameter acquisition module is used to collect environmental interference parameters, thermoelectric potential related parameters, and steady-state deviation related parameters of the indirect air-cooled system of thermal power plants and transmit them to the microcontroller.

[0057] The multi-source interference and real-time operating parameter acquisition module collects parameters through various sensors. Environmental interference parameters are acquired using wind speed sensors, heat flow sensors, and thermocouple arrays to collect crosswind speed, heat dissipation flux through the outer wall of the pipeline, and thermal interference parameters from adjacent pipelines, respectively. Thermoelectric potential-related parameters are acquired through thermocouples to collect the original total thermoelectric potential signal, temperature sensors to collect environmental temperature drift disturbance parameters, and electromagnetic interference detectors to collect on-site electromagnetic interference disturbance parameters. Steady-state deviation-related parameters are calculated using a combination of flow and temperature sensors to determine the steam condensation rate, and a thermocouple signal analysis module to collect thermocouple thermoelectric potential drift parameters. All sensor outputs are connected to the microcontroller's signal acquisition interface. The acquired analog signals are converted to digital signals by the microcontroller's internal AD conversion module and stored in a designated storage area of ​​the microcontroller. Simultaneously, they are transmitted in real-time to the microcontroller's processing core, providing a data foundation for subsequent module calculations.

[0058] The environmental disturbance calculation and temperature measurement point self-calibration module is used to quantify environmental disturbance parameters to obtain the actual temperature gradient parameters of the pipeline, and to perform dynamic calibration of temperature measurement point positions and dynamic matching of thermal insulation sleeves.

[0059] The environmental disturbance calculation and temperature measurement point self-calibration module first receives environmental interference parameters transmitted from the multi-source interference and real-time operating parameter acquisition module. It then quantifies these parameters using a built-in calculation program to eliminate the impact of environmental interference on pipeline temperature detection, obtaining the true pipeline temperature gradient parameters that accurately reflect the pipeline temperature distribution. Based on these parameters, it determines whether the current temperature measurement point is within the effective acquisition area of ​​the pipeline temperature gradient. If there is an offset, a drive signal is generated to control the temperature measurement element for dynamic position correction, ensuring the temperature measurement point is always positioned for accurate pipeline temperature acquisition. Simultaneously, according to the magnitude of the environmental disturbance, a heat-insulating sleeve of the corresponding specification is used to wrap the temperature measurement element, reducing heat conduction through the element and further lowering measurement errors.

[0060] The thermoelectric potential perturbation component stripping and sampling window adaptation module is used to strip the perturbation component from the thermoelectric potential related parameters to obtain the pure thermoelectric potential change gradient parameter, and dynamically adjust the sampling window specification according to the parameter.

[0061] The thermoelectric potential disturbance component stripping and sampling window adaptation module receives thermoelectric potential-related parameters transmitted by the multi-source interference and real-time operating parameter acquisition module. Through the built-in signal processing algorithm, it strips out multiple invalid components such as ambient temperature drift disturbance and on-site electromagnetic interference disturbance from the original total thermoelectric potential signal, retaining only the pure thermoelectric potential change gradient parameter related to pipeline temperature changes. At the same time, it has a built-in operating condition judgment and sampling window adjustment program. Based on the fluctuation characteristics of the pure thermoelectric potential change gradient parameter, it judges the current operating condition of the system and then dynamically adjusts the width of the sampling window and the sampling frequency specification to adapt the sampling window to the change characteristics of the thermoelectric potential signal. The technical effect of this module is to achieve accurate stripping of thermoelectric potential disturbance components, obtain pure thermoelectric potential parameters that can truly reflect pipeline temperature changes, and improve sampling efficiency while ensuring the integrity of the sampling data through dynamic adaptation of the sampling window, thus greatly improving the effectiveness of thermoelectric potential sampling data.

[0062] The condensation rate and thermoelectric potential drift linkage cancellation calibration module is used to calculate the coupled steady-state total deviation by correlating the relevant parameters of steady-state deviation, and to achieve temperature parameter calibration through reverse cancellation operation;

[0063] The condensation rate and thermoelectric potential drift linkage cancellation calibration module receives steady-state deviation parameters transmitted from the multi-source interference and real-time operating parameter acquisition module. Through the built-in nonlinear correlation calculation program, it performs coupled calculation on the steam condensation rate parameter and the thermocouple thermoelectric potential drift parameter to obtain the coupled steady-state total deviation formed by the two. Then, the deviation is substituted into the pre-set calibration formula, and through the reverse cancellation operation, the coupled steady-state total deviation is subtracted from the detected temperature parameter to achieve temperature parameter calibration.

[0064] The temperature data verification and closed-loop correction module is used to verify the consistency of the calibrated temperature parameters and build a correction parameter library to achieve closed-loop optimization of the system's operational parameters.

[0065] The temperature data verification and closed-loop correction module receives calibrated temperature parameters from the condensation rate and thermoelectric potential drift linkage cancellation calibration module, and simultaneously receives the actual pipeline temperature gradient parameters from the environmental disturbance calculation and temperature measurement point self-calibration module. It compares the consistency of these two parameters using a built-in verification program to determine the validity of the calibrated temperature parameters. If a deviation exists, it traces the calculation process of the preceding modules, adjusts the relevant calculation parameters, and stores the deviation data and optimized calculation parameters under this condition in the built-in correction parameter library. Simultaneously, through a built-in optimization algorithm, it iteratively optimizes the overall system calculation parameters based on the data in the correction parameter library, achieving closed-loop correction of the calculation parameters.

[0066] The IoT remote transmission module is used to remotely transmit the collected raw parameters, calibrated temperature data, and system operating status data to the remote monitoring and management platform, and to receive control commands from the remote monitoring and management platform.

[0067] The IoT remote transmission module first receives the raw parameters, calibrated temperature data, and system operating status data transmitted by the microcontroller. After encapsulating these data according to a pre-set communication protocol, it remotely transmits them to the remote monitoring and management platform for maintenance personnel to view in real time. At the same time, it receives control commands issued by the remote monitoring and management platform, parses the commands, and transmits them to the microcontroller, which then drives each module to complete the corresponding parameter configuration and operation.

[0068] In this embodiment, each module is connected to the microcontroller via wired communication. The communication interface is an RS-485 bus, which has the characteristics of strong anti-interference capability and long transmission distance. It is suitable for the industrial environment of thermal power plants and ensures the stability and reliability of data transmission between the modules and the microcontroller.

[0069] The environmental interference parameters collected by the multi-source interference and real-time operation parameter acquisition module include crosswind speed parameters, heat dissipation flux parameters of the outer wall of the pipeline, and thermal interference parameters of adjacent pipelines.

[0070] Thermoelectric potential related parameters include the original total thermoelectric potential signal parameters, ambient temperature drift disturbance parameters, and on-site electromagnetic interference disturbance parameters;

[0071] Steady-state deviation parameters include steam condensation rate parameters and thermocouple thermoelectric potential drift parameters;

[0072] This embodiment specifies the types of parameters to be collected and the transmission via the Internet of Things. The motivation for limiting the collection parameters to three types is that crosswind speed, heat dissipation flux of the outer wall of the pipeline, and thermal interference from adjacent pipelines are the core environmental interference factors affecting the temperature distribution of the pipeline. Temperature drift and electromagnetic interference are the core invalid components of the thermoelectric potential signal. Steam condensation rate and thermoelectric potential drift are the core parameters that form the coupled steady-state deviation. Accurate collection of these parameters can enable targeted processing of various interferences.

[0073] In this embodiment, the crosswind speed parameter is collected using an FS4003 wind speed sensor with a measurement range of 0-30 m / s and a measurement accuracy of ±0.1 m / s. The sensor is installed on the windward and leeward sides of the air-cooled pipe array, and the average value is taken as the crosswind speed parameter.

[0074] The heat dissipation flux parameters of the outer wall of the pipeline are collected using an HFM-215 heat flux sensor with a measurement range of 0-2000W / ㎡ and a measurement accuracy of ±2%. The sensor is directly attached to the outer wall of the air-cooled pipeline to collect the heat exchange flux between the pipeline and the environment.

[0075] The thermal interference parameters of adjacent pipelines are collected using a K-type thermocouple array. The thermocouple array is installed in the gap between adjacent pipelines to collect the thermal radiation temperature of adjacent pipelines. The thermal interference parameters of adjacent pipelines are calculated by combining the thermal radiation formula.

[0076] The original thermoelectric potential total signal parameters were acquired using an S-type thermocouple with a measurement range of 0-1300℃ and a thermoelectric potential measurement accuracy of ±0.01mV. The thermocouple was directly inserted into the temperature measurement point of the air-cooled pipeline. The ambient temperature drift disturbance parameters were acquired using a PT1000 platinum resistance temperature sensor with a measurement range of -50℃-200℃ and a measurement accuracy of ±0.1℃. The sensor was installed at the junction box of the thermocouple to collect changes in ambient temperature.

[0077] The electromagnetic interference parameters on site were collected using an EM500 electromagnetic interference detector with a measurement range of 10kHz-3GHz and a measurement accuracy of ±1dBμV. The detector was installed near industrial equipment around the temperature sensing element to collect the intensity and frequency of electromagnetic interference on site and convert them into corresponding thermoelectric potential disturbance parameters.

[0078] The steam condensation rate parameter was obtained by collecting steam flow rate data using an LWGY model turbine flow sensor, and then calculating it using a heat balance formula in conjunction with the inlet and outlet temperatures of the air-cooled pipeline. The flow rate measurement accuracy was ±0.5%.

[0079] The thermocouple thermoelectric potential drift parameter is obtained by long-term monitoring of the output signal of the S-type thermocouple and calculating the change in its thermoelectric potential at a fixed temperature.

[0080] The IoT remote transmission module uses IoT communication protocols to achieve bidirectional data transmission. The IoT communication protocols include one of NB-IoT, 5G or LoRa. The remote monitoring and management platform can realize real-time display of temperature data, abnormal alarms and remote configuration of system parameters.

[0081] The motivation for using one of the following communication protocols—NB-IoT, 5G, or LoRa—for the IoT remote transmission module is that air-cooling systems in thermal power plants are mostly located on the periphery of the plant area, and network coverage varies in different areas. NB-IoT features low power consumption and wide coverage, making it suitable for long-distance, small-data-volume transmission; 5G features high bandwidth and low latency, making it suitable for large-data-volume, real-time transmission; and LoRa features strong anti-interference capabilities and long transmission distances, making it suitable for the complex environment of industrial sites. Providing multiple communication protocol options allows the system to adapt to different plant site environments.

[0082] In this embodiment, based on the actual network coverage of the thermal power plant, the 5G communication protocol is selected, and the MH5000-31 5G module is used. This module supports SA / NSA dual mode and the transmission rate can reach up to 2.5Gbps, ensuring the real-time performance and stability of data transmission.

[0083] The remote monitoring and management platform is built on a B / S architecture and deployed on the central control room server of the thermal power plant. Maintenance personnel can access the platform through a browser. The platform displays temperature data in real time, visualizes the calibrated temperature data transmitted by the IoT remote transmission module according to time series and pipeline number, and generates temperature change curves and pipeline temperature distribution heat maps.

[0084] The abnormal alarm method is to preset the normal threshold range for temperature detection. When the calibrated temperature data exceeds the threshold range or the system operation status data display module malfunctions, the platform will provide abnormal reminders through audible and visual alarms, SMS alarms, and platform pop-up alarms.

[0085] The system parameters are configured remotely by the operation and maintenance personnel entering the parameters to be adjusted, such as sampling frequency, calibration threshold, and sampling window reference width, etc., on the platform's parameter configuration interface. The platform encapsulates the configuration instructions and sends them to the microcontroller through the IoT remote transmission module. The microcontroller adjusts the operation parameters and working mode of each module according to the instructions.

[0086] The environmental disturbance calculation and temperature measurement point self-calibration module includes an environmental disturbance calculation unit, a temperature measurement point drive calibration unit, and an isolation sleeve matching unit;

[0087] This embodiment specifies the internal structure and working principle of each unit of the environmental disturbance calculation and temperature measurement point self-calibration module. The quantitative calculation of environmental disturbance, the driving calibration of temperature measurement point position, and the matching of heat insulation sleeve are three interrelated but independent working links. Dividing them into independent functional units can make the module's workflow clearer, the function of each unit more specialized, and improve the module's calculation and execution efficiency.

[0088] The environmental disturbance calculation unit is used to calculate the comprehensive environmental disturbance by weighted summation of crosswind speed parameters and heat dissipation flux parameters of the outer wall of the pipeline, then calculate the difference between the comprehensive environmental disturbance and the thermal interference parameters of adjacent pipelines, and combine the environmental temperature correction coefficient to obtain the actual temperature gradient parameters of the pipeline.

[0089] The environmental disturbance calculation unit first receives crosswind speed parameters, pipe outer wall heat dissipation flux parameters, adjacent pipe thermal interference parameters, and environmental temperature correction coefficients transmitted by the multi-source interference and real-time operating parameter acquisition module. The environmental temperature correction coefficients are obtained from the ambient temperature using a lookup table method, and are applied when the ambient temperature is between -20℃ and 0℃. At 0℃-20℃, At 20℃-40℃, At 40℃-60℃, Then, the weighted summation is performed according to the formula to obtain the comprehensive environmental disturbance amount, which is:

[0090] ,

[0091] in, This represents the comprehensive environmental disturbance quantity. This is the crosswind speed weighting coefficient, with a value of 0.3. The weighting coefficient for heat dissipation flux through the outer wall of the duct is 0.7. This weighting coefficient was obtained through numerous industrial field tests and can accurately reflect the contribution of crosswind speed and heat dissipation flux through the outer wall of the duct to environmental disturbances. This refers to the heat dissipation flux parameters of the outer wall of the pipeline. Crosswind speed parameters, These are the thermal interference parameters between adjacent pipelines. The ambient temperature correction factor is used; then the difference between the comprehensive environmental disturbance and the thermal interference parameters of adjacent pipelines is calculated. Finally, according to the formula The actual temperature gradient parameters of the pipeline were calculated. The unit of this parameter is ℃ / m, which reflects the rate of temperature change of the air-cooled pipeline along the axial and radial directions.

[0092] The temperature measurement point drive correction unit is used to generate correction commands based on the offset between the actual temperature gradient parameters of the pipeline and the original detected temperature parameters, and drive the temperature measuring element to make dynamic position adjustments.

[0093] The temperature measurement point drive correction unit first receives the actual pipeline temperature gradient parameters transmitted by the environmental disturbance calculation unit and the raw detected temperature parameters transmitted by the multi-source interference and operating parameter real-time acquisition module; then it calculates the offset between the two, using the following formula:

[0094] ,

[0095] in, The original measured temperature parameters, These are the actual temperature gradient parameters of the pipeline. This is the offset. The distance from the temperature measuring point to the pipeline reference point is preset with an offset threshold of 0.5℃. When the temperature exceeds 0.5℃, it is determined that there is a deviation in the current temperature measurement point position. A correction command is generated based on the direction and magnitude of the deviation. The correction command includes the moving direction, moving distance, and moving speed of the temperature measuring element. After receiving the correction command, the drive chip outputs a drive signal to the execution component to control the temperature measuring element to dynamically adjust its position along the axial and radial directions of the pipeline. At the same time, the position feedback component collects the actual position of the temperature measuring element in real time and transmits the position data to the microcontroller to form a closed-loop position control, ensuring that the temperature measuring element moves accurately to the target position.

[0096] The isolation sleeve matching unit is used to match the corresponding specification of the thermal insulation isolation sleeve to wrap and isolate the temperature measuring element according to the value of the comprehensive environmental disturbance.

[0097] The isolation sleeve matching unit first receives the comprehensive environmental disturbance quantity transmitted by the environmental disturbance quantity calculation unit, presets the threshold range of the comprehensive environmental disturbance quantity, and classifies the specification level of the heat insulation isolation sleeve according to the threshold range; the data processing chip judges the comprehensive environmental disturbance quantity and determines the specification of the isolation sleeve that needs to be matched; then it generates control commands to drive the isolation sleeve storage and replacement components to move the corresponding specification of the heat insulation isolation sleeve to the temperature measuring element, and completes the wrapping and isolation of the temperature measuring element;

[0098] The condensation rate and thermoelectric potential drift linkage calibration module includes a coupling deviation calculation unit, a reverse cancellation calibration unit, and a threshold verification unit.

[0099] This embodiment specifies the internal structure and working principle of each unit of the condensation rate and thermoelectric potential drift linkage cancellation calibration module. The calculation of coupling deviation, the reverse cancellation calibration of temperature parameters, and the threshold verification of calibration results are the three key links of temperature steady-state calibration. Separating them into independent units can realize the professional processing of each link, ensure the accuracy and effectiveness of calibration results, and avoid the output of invalid calibration data.

[0100] The coupling deviation calculation unit is used to perform nonlinear correlation calculations on the steam condensation rate parameter and the thermocouple thermoelectric potential drift parameter to obtain the steady-state total deviation formed by their coupling.

[0101] The coupling deviation calculation unit first receives the steam condensation rate parameters and thermocouple thermoelectric potential drift parameters transmitted by the multi-source interference and real-time operating parameter acquisition module; then, it substitutes these two parameters into a pre-set nonlinear correlation calculation formula:

[0102] ,

[0103] in, This represents the total steady-state deviation of the coupling (unit: °C). These are nonlinear fitting coefficients, obtained through extensive industrial field experiments and data fitting, specifically set to a=0.02, b=0.15, c=2.5, and d=0.05. This is a parameter for the steam condensation rate (unit: t / h). The thermocouple thermoelectric potential drift parameter (unit: mV) is given by this formula, which accurately reflects the coupling relationship between the steam condensation rate and the thermoelectric potential drift.

[0104] The reverse cancellation calibration unit is used to perform reverse cancellation calculations between the total steady-state deviation and the pre-set temperature steady-state calculation reference parameters to obtain the temperature parameters after preliminary calibration.

[0105] The reverse cancellation calibration unit first receives the total steady-state deviation of the coupling from the coupling deviation calculation unit, and the steady-state temperature calculation reference parameter transmitted by the microcontroller. This reference parameter is the detection temperature of the thermocouple under standard operating conditions, obtained through laboratory calibration. Then, reverse cancellation calculation is performed using the formula to obtain the pre-calibrated temperature parameter:

[0106] ,

[0107] in, This represents the total steady-state deviation of the coupling. These are the reference parameters for steady-state temperature calculations. These are the temperature parameters after preliminary calibration; the principle of reverse cancellation is to take the total deviation of the coupled steady state as the error value, subtract this error value from the reference temperature, and obtain a calibration value that is closer to the actual temperature of the pipeline.

[0108] The threshold verification unit is used to compare the temperature parameters after preliminary calibration with the preset temperature steady-state qualified threshold. If the threshold requirement is not met, the coupling deviation calculation unit and the reverse cancellation calibration unit are driven to recalculate until the temperature parameters that meet the threshold requirement are obtained.

[0109] The threshold verification unit first receives the preliminary calibrated temperature parameters transmitted by the reverse cancellation calibration unit. and the temperature steady-state pass threshold transmitted by the microcontroller This threshold is set according to the operating requirements of the indirect air-cooling system of the thermal power plant, and is usually ±0.3℃ of the design temperature; subsequently... Compare and verify with the threshold, if ≤ ≤ If the temperature parameters after preliminary calibration are deemed to meet the requirements, they are transmitted to the temperature data verification and closed-loop correction module; if < or > If the temperature parameters after the initial calibration are not met, a recalculation instruction is generated to drive the coupling deviation calculation unit and the reverse cancellation calibration unit to re-acquire parameters, perform calculations and calibrations until temperature parameters that meet the threshold requirements are obtained.

[0110] In the module for stripping thermoelectric potential perturbation components and adapting the sampling window, the gradient parameter of the pure thermoelectric potential change is obtained through the formula:

[0111] ,

[0112] in, These are the parameters of the original total thermoelectric potential signal. These are parameters related to ambient temperature drift disturbance. These are the electromagnetic interference parameters at the site. This is the electromagnetic shielding correction factor;

[0113] This embodiment specifies the calculation formula for the removal of thermoelectric potential disturbance components and the rules for dynamic adjustment of the sampling window. The motivation for clarifying the calculation formula and adjustment rules is to achieve accurate removal of thermoelectric potential disturbance components through standardized formulas and to achieve accurate adaptation of the sampling window through threshold design for different working conditions, so as to ensure the standardization and repeatability of the module's operation.

[0114] In this embodiment, the electromagnetic shielding correction coefficient k is determined based on the electromagnetic shielding level of the temperature sensing element. When the temperature sensing element adopts Level 1 electromagnetic shielding... =1.0, when using secondary electromagnetic shielding =0.7, for level 3 electromagnetic shielding =0.4, the temperature sensing element in this embodiment adopts two-stage electromagnetic shielding. Take 0.7; The total raw thermoelectric potential signal acquired by the S-type thermocouple is expressed in mV. The thermoelectric potential disturbance converted from ambient temperature drift is collected by the PT1000 platinum resistance thermometer, and the unit is mV. The thermoelectric potential disturbance of electromagnetic interference converted by the electromagnetic interference detector is expressed in mV. A specific calculation example is shown below: when... =10.5mV, =0.2mV, When =0.3mV, =10.5-0.2-0.3×0.7=10.09mV;

[0115] The technical effect of this formula is that it achieves accurate separation of ambient temperature drift and on-site electromagnetic interference components in the thermoelectric potential, and obtains pure thermoelectric potential change gradient parameters that can truly reflect the temperature change of the pipeline, providing an accurate signal basis for subsequent temperature calculations.

[0116] The dynamic adjustment of the sampling window adopts a threshold design based on different operating conditions. The reference width of the sampling window is 200ms for the rated operating condition and 500ms for the variable operating condition. The threshold for the fluctuation of the thermoelectric potential change gradient is 0.8mV / ℃ for the rated operating condition and 0.4mV / ℃ for the variable operating condition. If the fluctuation amplitude of the pure thermoelectric potential change gradient parameter exceeds the preset threshold, the expansion ratio of the sampling window will be increased according to the ratio of the fluctuation amplitude to the threshold. If it is within the threshold range, the sampling window will be shrunk back to the reference width.

[0117] The dynamic adjustment of the sampling window is first determined by the microcontroller based on the system's operating condition. When the system's steam load is between 90% and 110% of the design load, it is determined to be under rated operating condition, with the sampling window reference width set to 200 ms and the thermoelectric potential change gradient fluctuation threshold set to 0.8 mV / ℃. When the system's steam load exceeds 90%-110% of the design load, it is determined to be under variable operating condition, with the sampling window reference width set to 500 ms and the thermoelectric potential change gradient fluctuation threshold set to 0.4 mV / ℃.

[0118] The sampling window is dynamically adjusted by first calculating the fluctuation amplitude of the gradient parameter of the pure thermoelectric potential change. That is, the difference between the maximum and minimum values ​​of the gradient of the pure thermoelectric potential change per unit time; subsequently, Compare with the fluctuation threshold of the corresponding operating condition, if If the value is less than or equal to the threshold, the sampling window will be reduced to the reference width for the corresponding operating condition; if... If the threshold is greater than the threshold, then follow the formula. = / Threshold calculation sampling window expansion ratio The actual width of the sampling window = reference width × ,in The maximum value is set to 3 to avoid excessively wide sampling windows that lead to low sampling efficiency. For example, under rated operating conditions... =1.0mV / ℃, threshold =0.8mV / ℃, then =1.0 / 0.8=1.25, actual sampling window width = 200ms × 1.25 = 250ms; under varying operating conditions, =0.3mV / ℃, threshold =0.4mV / ℃, then the sampling window shrinks back to the reference width of 500ms;

[0119] The temperature data verification and closed-loop correction module includes a consistency verification unit, a correction parameter library unit, and a parameter optimization unit;

[0120] This embodiment specifies the internal structure and working principle of each unit of the temperature data verification and closed-loop correction module. The consistency verification of the calibrated temperature data, the storage of operating parameters, and the iterative optimization of the system operation parameters are the three core links to achieve closed-loop self-optimization of the system. Separating them into independent units can make the function of each link more specialized and improve the optimization efficiency and accuracy of the module.

[0121] The consistency verification unit is used to calculate the difference between the calibrated temperature parameters and the actual temperature gradient parameters of the pipeline. If the difference exceeds the preset verification threshold, the previous module's calculation process is traced back and the relevant calculation parameters are adjusted.

[0122] The consistency verification unit first receives the calibrated temperature parameters transmitted by the condensation rate and thermoelectric potential drift linkage cancellation calibration module, as well as the actual pipeline temperature gradient parameters transmitted by the environmental disturbance calculation and temperature measurement point self-calibration module; then, it calculates the theoretical temperature at the temperature measurement point based on the actual pipeline temperature gradient parameters, using the following formula:

[0123] ,

[0124] in, The theoretical temperature at the temperature measurement point. These are the actual temperature gradient parameters of the pipeline. The distance from the temperature measuring point to the reference point. Reference point temperature;

[0125] The difference between the calibrated temperature parameter and the theoretical temperature is calculated using the following formula:

[0126] ,

[0127] in, The difference between the calibrated temperature parameter and the theoretical temperature. For the calibrated temperature parameters, This is the theoretical temperature at the temperature measurement point; the pre-set verification threshold is 0.4℃. If the temperature is ≤0.4℃, the calibrated temperature parameter is considered valid and transmitted to the IoT remote transmission module; if... If the temperature is greater than 0.4℃, the calibrated temperature parameter is deemed invalid. The preceding module's calculation process traceability program is initiated, sequentially checking the calculation parameters and collected data of the condensation rate and thermoelectric potential drift linkage cancellation calibration module, the thermoelectric potential disturbance component stripping and sampling window adaptation module, and the environmental disturbance quantity calculation and temperature measurement point self-calibration module. The cause of the deviation is located, and relevant calculation parameters are adjusted accordingly, such as the fitting coefficient of the coupling deviation calculation, the correction coefficient of the thermoelectric potential stripping, and the weighting coefficient of the environmental disturbance quantity calculation. After adjustment, the preceding module is driven to recalculate and recompute.

[0128] The correction parameter library unit is a structured parameter library based on three-dimensional working condition division. It divides the working conditions according to three dimensions: steam condensation rate, ambient temperature, and crosswind speed, and stores the steady-state deviation, temperature difference, and optimized calculation parameters of the corresponding working conditions.

[0129] The parameter library unit first defines the operating condition range in three dimensions: steam condensation rate is divided into four intervals: 0-50t / h, 50-100t / h, 100-150t / h, and above 150t / h; ambient temperature is divided into four intervals: -20℃-0℃, 0℃-20℃, 20℃-40℃, and 40℃-60℃; and crosswind speed is divided into four intervals: 0-5m / s, 5-10m / s, 10-15m / s, and above 15m / s. This three-dimensional division results in 4×4×4=64 operating condition intervals. Then, an independent parameter storage area is established for each operating condition interval to store the coupled steady-state total deviation, temperature difference, and optimized calculation parameters such as weighting coefficients, fitting coefficients, and correction coefficients. Simultaneously, a data update program is set up so that when the system operates under a certain operating condition and generates new valid deviation data, the data is promptly stored in the corresponding operating condition's parameter storage area.

[0130] The parameter optimization unit uses an incremental learning algorithm combined with gradient descent to iteratively optimize the operational parameters in the correction parameter library based on effective deviation data of similar working conditions, and realizes dynamic parameter retrieval for working conditions.

[0131] The parameter optimization unit first receives the valid deviation data of the same working condition transmitted by the consistency verification unit and the deviation data and calculation parameters already stored under the working condition transmitted by the correction parameter library unit; then it uses an incremental learning algorithm to learn the new valid deviation data and integrate it into the existing data model, avoiding the tedious process of retraining the model in traditional algorithms.

[0132] Then, the gradient descent method is used, with the minimization of temperature difference as the objective function, to iteratively optimize the operational parameters in the correction parameter library. By continuously adjusting the values ​​of the operational parameters, the objective function value gradually converges to the minimum value, and the optimized operational parameters are obtained.

[0133] Finally, the optimized calculation parameters are stored in the corresponding working condition area of ​​the correction parameter library unit, and the dynamic parameter retrieval under working condition is realized. That is, when the system runs to a certain working condition, the optimized calculation parameters under that working condition are automatically retrieved from the correction parameter library for use by each module.

[0134] The multi-source interference and real-time operation parameter acquisition module uses a graded sampling frequency to acquire parameters. The sampling frequency for environmental interference parameters is 1Hz, the sampling frequency for thermoelectric potential related parameters is 10Hz, and the sampling frequency for steady-state deviation related parameters is 2Hz.

[0135] This embodiment specifies the sampling frequency, acquisition triggering method, data transmission method, and storage method of the multi-source interference and real-time operating parameter acquisition module. Different types of parameters have different change characteristics. The graded sampling frequency can take into account both acquisition accuracy and system resource consumption. Unified synchronous triggering acquisition can ensure the time consistency of data. RS-485 bus transmission can ensure the stability of data transmission. Ring buffer storage can realize efficient storage and refresh of acquired data.

[0136] In this embodiment, the sampling frequency is implemented as follows: the microcontroller sets the corresponding sampling trigger time for each type of parameter through the built-in timer. Environmental interference parameters are sampled once every 1000ms, thermoelectric potential related parameters are sampled once every 100ms, and steady-state deviation related parameters are sampled once every 500ms. Each sensor completes parameter acquisition according to the trigger signal.

[0137] The unified synchronous trigger acquisition method is as follows: the microcontroller has a built-in high-precision crystal clock with a frequency of 80MHz and an accuracy of ±10ppm. This crystal clock provides a unified time reference for the entire acquisition module. The sampling trigger signals of all sensors are generated by this crystal clock, ensuring that each sensor completes parameter acquisition at the same time node.

[0138] The output terminals of each sensor in the multi-source interference and real-time acquisition module for operating parameters are all connected to an RS-485 bus transceiver. The transceiver is a MAX485 model, which features strong anti-interference capability and long transmission distance. The RS-485 bus transmission rate is set to 9600bps. After the acquired data is converted into differential signals by the transceiver, it is transmitted to the RS-485 interface of the microcontroller via the RS-485 bus. The microcontroller parses and processes the received data.

[0139] The microcontroller's built-in circular buffer is implemented by allocating a 1024KB storage area in the microcontroller's on-chip SRAM as a circular buffer. The buffer adopts a first-in-first-out storage rule, storing the collected data sequentially into the storage units of the buffer according to the time order. When the buffer is full, the newly collected data automatically overwrites the oldest stored data. The refresh frequency of the buffer is set to 100ms, that is, the stored data in the buffer is updated once every 100ms.

[0140] The temperature measuring point drive correction unit includes a stepper motor, a precision linear electric slide, and a position feedback component. The stepper motor is a two-phase hybrid stepper motor with a step angle of 1.8°, and is equipped with a microstepping driver to achieve a microstep of 0.09°.

[0141] This embodiment specifies the hardware composition, parameters, and functions of the temperature measuring point drive correction unit. It is necessary to ensure the accuracy, stability, and safety of the temperature measuring element position adjustment. A two-phase hybrid stepper motor with a microstepping driver can achieve micro-step drive to ensure the accuracy of the adjustment; a precision linear electric slide can achieve multi-directional precise movement of the temperature measuring element; a grating ruler can achieve precise position feedback to form a closed-loop control; and a dual limit protection structure can prevent the electric slide from overtravel and ensure the safety of hardware operation.

[0142] The stepper motor is a two-phase hybrid stepper motor, model 42BYGH34, with a step angle of 1.8°. The matching microstepping driver is model TB6600, with a microstepping factor set to 20, achieving microstepping drive of 0.09°. The stepper motor works by receiving pulse signals from the temperature sensing point drive correction unit's drive chip. The frequency of the pulse signals controls the stepper motor's speed, and the number of pulse signals controls the stepper motor's rotation angle. The output shaft of the stepper motor is connected to the transmission mechanism of the precision linear electric slide, converting the rotational motion into linear motion and driving the electric slide to move. The technical effect of this hardware is to achieve precise microstepping drive for adjusting the position of the temperature sensing element, ensuring the accuracy and stability of the position adjustment.

[0143] The precision linear electric slide stage uses a ball screw type linear slide stage with an effective stroke of 200mm, a positioning accuracy of ±0.1mm, and a movement range set at ±100mm axially and ±50mm radially along the steam condensing pipe. The precision linear electric slide stage operates by using a stepper motor to drive the ball screw to rotate. The ball screw drives the slide block in a linear motion. The temperature sensing element is fixed on the slide block and moves with it, achieving axial and radial adjustment of the temperature sensing point position. This hardware technology achieves precise linear movement of the temperature sensing element in the axial and radial directions of the pipe, meeting the movement requirements for dynamic correction of the temperature sensing point position and ensuring the accuracy of position adjustment.

[0144] The position feedback component uses a KA300 grating ruler, with a measuring range of 0-200mm and a measuring accuracy of ±0.005mm. The main scale of the grating ruler is mounted on the guide rail of a precision linear electric slide, and the reading head is mounted on the slider. The grating ruler works as follows: when the slider moves, the reading head reads the grating signal from the main scale, converts the position signal into an electrical signal, and transmits it to the microcontroller. The microcontroller compares the actual position with the target position. If there is a deviation, it generates a compensation pulse signal to drive the stepper motor to continue adjusting until the actual position matches the target position, thus achieving closed-loop position correction. The technical effect of this hardware is to achieve accurate and real-time feedback of the actual position of the temperature measuring element, forming a closed-loop position control, further improving the accuracy of the temperature measuring point position adjustment, and ensuring that the temperature measuring point is always in the accurate acquisition position.

[0145] A dual limit protection structure, consisting of mechanical and photoelectric limits, is installed at both ends of the electric slide table. The mechanical limits utilize metal blocks installed at the extreme positions of the slide table's guide rails. The photoelectric limits employ diffuse reflection photoelectric sensors (model E3Z-D61) installed 5mm inside the mechanical limits. The working principle is as follows: when the slider moves close to its extreme position, the photoelectric limit is triggered first. The photoelectric sensor outputs a low-level signal to the microcontroller, which immediately cuts off the stepper motor's drive power, stopping the slide table's movement. If the photoelectric limit fails, and the slider continues to move, the mechanical limit will be triggered. The metal blocks directly block the slider's movement, preventing the slide table from overtravel. This structure provides dual limit protection for the electric slide table, effectively preventing hardware damage caused by overtravel and improving the safety and reliability of the system hardware.

[0146] The thermal insulation sleeve is made of a composite material of aluminum silicate fiber and aerogel, with a temperature resistance range of -40℃ to 600℃ and a thermal conductivity of ≤0.02W / (m・K). It is divided into three specifications according to the comprehensive environmental disturbance: Grade 1, 20mm thick, suitable for comprehensive environmental disturbance ≤50W / ㎡; Grade 2, 40mm thick, suitable for comprehensive environmental disturbance 50W / ㎡~100W / ㎡; Grade 3, 60mm thick, suitable for comprehensive environmental disturbance >100W / ㎡.

[0147] This embodiment specifies the material, parameters, specifications, and structural design of the thermal insulation sleeve. The temperature measurement environment of the indirect air-cooled system in thermal power plants has a large temperature variation range and is contaminated by water vapor and oil. Moreover, the requirements for thermal insulation performance vary under different environmental disturbances. The composite material of aluminum silicate fiber and aerogel can ensure excellent thermal insulation performance and wide temperature adaptability. The graded specifications can achieve precise matching of thermal insulation performance with environmental disturbance. The ring splicing structure can ensure the fit with the temperature measuring element. The quick-change buckle and protective layer can improve the convenience of installation and environmental adaptability.

[0148] The thermal insulation sleeve is made of a composite material of aluminum silicate fiber and aerogel, with aerogel as the core insulation layer and aluminum silicate fiber as the outer support layer. The composite material has a temperature resistance range of -40℃ to 600℃ and a thermal conductivity of ≤0.02W / (m・K). The reason for choosing this composite material is that aerogel is currently the solid material with the best thermal insulation performance. It has an extremely low thermal conductivity and can effectively block the conduction of ambient heat. However, aerogel has poor mechanical properties and is easily broken. Aluminum silicate fiber has good high temperature resistance and mechanical support. The combination of the two can achieve a balance between thermal insulation performance and structural strength. The temperature resistance range of -40℃ to 600℃ covers the full operating temperature of the indirect air-cooling system of thermal power plants. The thermal conductivity of ≤0.02W / (m・K) can minimize the heat conduction of the temperature sensing element through environmental interference and ensure the accuracy of temperature measurement.

[0149] Based on the comprehensive environmental disturbance, the thermal insulation sleeves are divided into three specifications: Level 1 (20mm thickness) is suitable for comprehensive environmental disturbances ≤ 50W / ㎡; Level 2 (40mm thickness) is suitable for 50W / ㎡~100W / ㎡; and Level 3 (60mm thickness) is suitable for >100W / ㎡. This graded design is based on extensive industrial field tests. The smaller the comprehensive environmental disturbance, the lower the demand for thermal insulation performance, and a thinner insulation sleeve can meet the requirements. Using a thicker insulation sleeve would increase the installation load on the temperature sensing element and cause material waste. The larger the comprehensive environmental disturbance, the higher the demand for thermal insulation performance, requiring an increase in the insulation sleeve thickness to improve the thermal insulation effect. This graded design can achieve a precise match between thermal insulation performance and environmental disturbance, and achieve rational resource utilization while ensuring thermal insulation effect. In this embodiment, the increase in insulation sleeve thickness is achieved by layering aerogel. For every 20mm increase in thickness, an aerogel thermal insulation layer is added to the inner layer to ensure that the thermal insulation performance improves synchronously with the thickness.

[0150] The heat insulation sleeve is designed as a ring structure to match the temperature sensing element. The inner diameter is determined according to the outer diameter of the temperature sensing element, and the gap is controlled within 0.5mm. It adopts a half-splitting design with a concave-convex interlocking structure at the splice. It is fixed by three 304 stainless steel quick-change buckles, evenly distributed around the circumference of the insulation sleeve. The purpose of the ring structure is to ensure that the insulation sleeve fits snugly around the entire circumference of the temperature sensing element, avoiding air gaps caused by loose fit. Air gaps will cause heat convection and reduce the heat insulation effect. The 0.5mm gap ensures a good fit and facilitates installation. The purpose of the half-splitting design and quick-change buckles is that the temperature sensing elements in thermal power plants are mostly installed online and cannot be disassembled. The splicing design allows for online wrapping and installation of the temperature sensing element. The 304 stainless steel quick-change buckles are corrosion-resistant, high-strength, and easy to operate, allowing for quick installation and removal of the insulation sleeve, facilitating later maintenance and specification changes.

[0151] A 2mm thick high-temperature resistant silicone gasket is attached to the inside of the isolation sleeve, with a temperature resistance range of -40℃ to 200℃. A 0.5mm thick polytetrafluoroethylene (PTFE) layer is wrapped around the outside. The purpose of the high-temperature resistant silicone gasket is to fill the tiny gap between the isolation sleeve and the temperature sensing element, further improving the fit and sealing. Simultaneously, the silicone's good elasticity can buffer the pressure exerted on the temperature sensing element during installation, preventing damage. The purpose of the PTFE layer is that the temperature measurement environment in thermal power plants contains contaminants such as cooling water, oil, and dust. PTFE's excellent waterproof, oil-proof, and corrosion-resistant properties effectively protect the composite material of the isolation sleeve, preventing contamination and corrosion, and extending the service life of the isolation sleeve.

[0152] The system is also equipped with a hardware and software collaborative fault tolerance module, which communicates with the microcontroller. The hardware and software collaborative fault tolerance module includes a communication redundancy unit, an abnormal data processing unit, and a hardware fault diagnosis unit.

[0153] This embodiment adds a hardware and software collaborative fault tolerance module and specifies its internal structure and working principle. The industrial site of thermal power plant has many complex working conditions such as strong electromagnetic interference and equipment vibration, which can easily lead to system communication failure, abnormal data acquisition, and hardware equipment failure. Existing technologies lack effective fault tolerance mechanisms. After a failure occurs, it will directly lead to temperature measurement interruption or data distortion. The hardware and software collaborative fault tolerance module can realize the fault-tolerant operation of the system through communication redundancy, abnormal data processing, and hardware fault diagnosis, so as to ensure the continuity and reliability of the temperature measurement system.

[0154] The communication redundancy unit adopts a dual-protocol design with Modbus-RTU main protocol and CAN bus backup protocol. When the main protocol data packet loss rate is ≥5%, it automatically switches to the backup protocol.

[0155] The communication redundancy unit uses Modbus-RTU as the primary communication protocol. This protocol is the most commonly used serial communication protocol in industrial environments, featuring strong compatibility and stable transmission, and is suitable for short-distance data transmission between modules and microcontrollers. CAN bus is selected as the backup communication protocol. This protocol features strong anti-interference capabilities, high transmission rates, and support for multiple master stations, making it suitable for communication in environments with strong electromagnetic interference. The unit's built-in packet loss rate detection program continuously monitors the number of data packets sent and received by the primary protocol, calculating the real-time packet loss rate using the following formula:

[0156] ;

[0157] The packet loss rate threshold is preset to 5%. When the real-time packet loss rate is ≥5% and the duration exceeds 3 seconds, the main protocol communication is determined to be faulty. The protocol switching program automatically cuts off the main protocol communication link and starts the CAN bus backup protocol to achieve seamless switching of the communication protocol. When the main protocol packet loss rate recovers to below 5% and the duration exceeds 10 seconds, it automatically switches back to the Modbus-RTU main protocol.

[0158] The abnormal data processing unit is used to filter out collected data that exceeds the reasonable range for industrial applications and replace invalid data with the average value of previous valid data.

[0159] The abnormal data processing unit first pre-sets reasonable industrial ranges for each type of acquired parameter. These ranges are determined by the design parameters and on-site operating data of the indirect air-cooled system in the thermal power plant. For example, the reasonable range for crosswind speed is 0-30 m / s, the reasonable range for heat dissipation flux on the outer wall of the pipe is 0-2000 W / m², and the reasonable range for the original thermoelectric potential total signal is 0-15 mV. The unit receives data from multi-source interference and real-time operating parameter acquisition modules in real time, comparing each data point with its corresponding reasonable range. If the data is within the reasonable range, it is considered valid and directly transmitted to the microcontroller. If the data exceeds the reasonable range, it is considered invalid, and a filtering program is initiated to remove it. Simultaneously, a data completion program is initiated, retrieving the previous 5 valid acquisition data points for that parameter and calculating the average value using the formula:

[0160] ;

[0161] The calculated average value is used as the alternative data and transmitted to the microcontroller.

[0162] In this embodiment, the basis for averaging the first 5 valid data is that the average of multiple valid data can effectively offset the influence of random interference, is closer to the actual parameter value, and the sample size of the 5 data is moderate, which takes into account both the accuracy of data completion and the efficiency of operation.

[0163] The hardware fault diagnosis unit is used to collect the operating current and voltage parameters of each hardware in real time. When the parameters exceed the rated range, it is determined to be a hardware fault. The power supply of the faulty hardware is cut off and the backup temperature measuring element is activated. At the same time, the fault alarm signal is sent to the remote monitoring and management platform through the Internet of Things remote transmission module.

[0164] The hardware fault diagnosis unit first pre-sets the rated operating current and voltage range for each hardware device. This range is determined based on the hardware's product parameters; for example, an S-type thermocouple has an operating voltage of 5V and an operating current ≤1mA, while a two-phase hybrid stepper motor has an operating voltage of 24V and an operating current of 1-3A. The current and voltage acquisition chip, through current and voltage transformers, collects the operating current and voltage parameters of each hardware device in real time and transmits them to the hardware status judgment program. The program compares the real-time collected current and voltage parameters with the rated range. If the parameters are within the rated range, the hardware is considered to be working normally; if the parameters exceed the rated range and remain so, the hardware is considered to be operating normally. If the fault persists for more than 5 seconds, it is determined to be a hardware failure. A control signal is immediately generated and transmitted to the relay control module, which cuts off the power supply to the faulty hardware to prevent the fault from escalating. At the same time, the backup temperature sensing element is activated. The backup temperature sensing element is a redundant design with the same specifications and installation location as the main temperature sensing element. After activation, it immediately takes over from the main temperature sensing element to complete parameter acquisition and temperature detection. Simultaneously, the fault alarm module generates a fault signal, which includes the number of the faulty hardware, the fault type, and the time of the fault occurrence. This signal is sent to the remote monitoring and management platform via the IoT remote transmission module. Upon receiving the signal, the platform immediately issues an audible and visual alarm and a pop-up window to remind maintenance personnel to handle the situation promptly.

[0165] In this embodiment, each unit of the hardware and software collaborative fault-tolerant module communicates with the microcontroller via an internal bus at a rate of 1 Mbps. This ensures the real-time performance of multiple operations, including fault detection, protocol switching, and data completion. The working status of each unit is fed back to the microcontroller in real time. The microcontroller then transmits the fault-tolerant operating status of the module to a remote monitoring and management platform, thereby enabling remote monitoring of the system's fault-tolerant operation.

[0166] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A precise temperature detection system for an indirect air-cooled system in a thermal power plant, characterized in that: It includes a microcontroller and a multi-source interference and real-time operating parameter acquisition module, an environmental disturbance calculation and temperature measurement point self-calibration module, a thermoelectric potential disturbance component stripping and sampling window adaptation module, a condensation rate and thermoelectric potential drift linkage cancellation calibration module, a temperature data verification and closed-loop correction module, and an Internet of Things remote transmission module. The multi-source interference and real-time operating parameter acquisition module is used to collect environmental interference parameters, thermoelectric potential related parameters, and steady-state deviation related parameters of the indirect air-cooled system of the thermal power plant and transmit them to the microcontroller. The environmental disturbance calculation and temperature measurement point self-calibration module is used to quantify and calculate the environmental disturbance parameters to obtain the actual temperature gradient parameters of the pipeline, and to perform dynamic calibration of the temperature measurement point position and dynamic matching of the thermal insulation sleeve. The thermoelectric potential perturbation component stripping and sampling window adaptation module is used to strip the perturbation component from the thermoelectric potential related parameters to obtain the pure thermoelectric potential change gradient parameter, and dynamically adjust the sampling window specification according to the parameter. The condensation rate and thermoelectric potential drift linkage cancellation calibration module is used to perform correlation calculation on the steady-state deviation related parameters to obtain the coupled steady-state total deviation, and to achieve temperature parameter calibration through reverse cancellation operation. The temperature data verification and closed-loop correction module is used to verify the consistency of the calibrated temperature parameters and to build a correction parameter library to achieve closed-loop optimization of the system's operational parameters. The IoT remote transmission module is used to remotely transmit the collected raw parameters, calibrated temperature data and system operating status data to the remote monitoring and management platform, and to receive control commands from the remote monitoring and management platform.

2. The precise temperature detection system for indirect air-cooled systems in thermal power plants according to claim 1, characterized in that: The environmental interference parameters collected by the multi-source interference and real-time operating parameter acquisition module include crosswind speed parameters, heat dissipation flux parameters of the outer wall of the pipeline, and thermal interference parameters of adjacent pipelines. The thermoelectric potential related parameters include the original total thermoelectric potential signal parameters, the ambient temperature drift disturbance parameters, and the on-site electromagnetic interference disturbance parameters. The steady-state deviation-related parameters include steam condensation rate parameters and thermocouple thermoelectric potential drift parameters. The IoT remote transmission module uses an IoT communication protocol to achieve bidirectional data transmission. The IoT communication protocol includes one of NB-IoT, 5G or LoRa. The remote monitoring and management platform can realize real-time display of temperature data, abnormal alarms and remote configuration of system parameters.

3. The precise temperature detection system for indirect air-cooled systems in thermal power plants according to claim 1, characterized in that: The environmental disturbance calculation and temperature measurement point self-calibration module includes an environmental disturbance calculation unit, a temperature measurement point drive calibration unit, and an isolation sleeve matching unit. The environmental disturbance calculation unit is used to calculate the comprehensive environmental disturbance by weighted summation of the crosswind speed parameter and the heat dissipation flux parameter of the outer wall of the pipeline, and then calculate the difference between the comprehensive environmental disturbance and the thermal interference parameter of the adjacent pipeline, and combine the environmental temperature correction coefficient to obtain the actual temperature gradient parameter of the pipeline. The temperature measuring point drive correction unit is used to generate correction commands to drive the temperature measuring element to dynamically adjust its position based on the offset between the actual temperature gradient parameters of the pipeline and the original detected temperature parameters. The isolation sleeve matching unit is used to match the corresponding specification of the heat insulation isolation sleeve to wrap and isolate the temperature measuring element according to the value of the comprehensive environmental disturbance.

4. The precise temperature detection system for an indirect air-cooled system in a thermal power plant according to claim 1, characterized in that: The condensation rate and thermoelectric potential drift linkage calibration module includes a coupling deviation calculation unit, a reverse calibration unit, and a threshold verification unit. The coupling deviation calculation unit is used to perform nonlinear correlation calculations on the steam condensation rate parameter and the thermocouple thermoelectric potential drift parameter to obtain the steady-state total deviation formed by their coupling. The reverse cancellation calibration unit is used to perform reverse cancellation calculation between the steady-state total deviation and the pre-set temperature steady-state calculation reference parameter to obtain the temperature parameter after preliminary calibration. The threshold verification unit is used to compare and verify the temperature parameters after preliminary calibration with the preset temperature steady-state qualified threshold. If the threshold requirement is not met, the coupling deviation calculation unit and the reverse cancellation calibration unit are driven to recalculate until the temperature parameters that meet the threshold requirement are obtained.

5. The precise temperature detection system for indirect air-cooled systems in thermal power plants according to claim 1, characterized in that: In the thermoelectric potential perturbation component stripping and sampling window adaptation module, the pure thermoelectric potential change gradient parameter is obtained through the formula: ， in, These are the parameters of the original total thermoelectric potential signal. These are parameters related to ambient temperature drift disturbance. These are the electromagnetic interference parameters at the site. This is the electromagnetic shielding correction factor; The dynamic adjustment of the sampling window adopts a threshold design based on different operating conditions. The reference width of the sampling window is 200ms for the rated operating condition and 500ms for the variable operating condition. The threshold for the fluctuation of the thermoelectric potential change gradient is 0.8mV / ℃ for the rated operating condition and 0.4mV / ℃ for the variable operating condition. If the fluctuation amplitude of the pure thermoelectric potential change gradient parameter exceeds the preset threshold, the expansion ratio of the sampling window is increased according to the ratio of the fluctuation amplitude to the threshold. If it is within the threshold range, the sampling window is shrunk back to the reference width.

6. The precise temperature detection system for an indirect air-cooled system in a thermal power plant according to claim 1, characterized in that: The temperature data verification and closed-loop correction module includes a consistency verification unit, a correction parameter library unit, and a parameter optimization unit. The consistency verification unit is used to calculate the difference between the calibrated temperature parameters and the actual temperature gradient parameters of the pipeline. If the difference exceeds the preset verification threshold, the preceding module's calculation process is traced back and the relevant calculation parameters are adjusted. The correction parameter library unit is a structured parameter library based on three-dimensional working condition division. It divides the working conditions according to three dimensions: steam condensation rate, ambient temperature, and crosswind speed, and stores the steady-state deviation, temperature difference, and optimized calculation parameters of the corresponding working conditions. The parameter optimization unit uses an incremental learning algorithm combined with gradient descent to iteratively optimize the operational parameters in the correction parameter library based on effective deviation data of similar working conditions, and realizes dynamic parameter retrieval for working conditions.

7. The precise temperature detection system for an indirect air-cooled system in a thermal power plant according to claim 2, characterized in that: The multi-source interference and real-time operating parameter acquisition module uses a graded sampling frequency to acquire parameters. The sampling frequency for environmental interference parameters is 1Hz, the sampling frequency for thermoelectric potential related parameters is 10Hz, and the sampling frequency for steady-state deviation related parameters is 2Hz. The module achieves unified synchronous triggering and acquisition through the high-precision crystal oscillator clock of the microcontroller. The acquired data is transmitted to the microcontroller via RS-485 bus, and the microcontroller has a built-in ring buffer to store the acquired data. The buffer refreshes the data according to the first-in-first-out rule, and the refresh frequency is 100ms.

8. The precise temperature detection system for an indirect air-cooled system in a thermal power plant according to claim 3, characterized in that: The temperature measuring point drive correction unit includes a stepper motor, a precision linear electric slide, and a position feedback component. The stepper motor is a two-phase hybrid stepper motor with a step angle of 1.8°, and is equipped with a microstepping driver to achieve a microstep of 0.09°. The effective stroke of the precision linear electric slide is 200mm, the positioning accuracy is ±0.1mm, and the movement range is ±100mm axially and ±50mm radially along the steam condensation pipe. The position feedback component is a grating ruler, which is used to provide real-time feedback on the actual position of the temperature measuring element to the microcontroller to achieve closed-loop position correction. The electric slide is equipped with a dual limit protection structure with mechanical limit and photoelectric limit at both ends.

9. The precise temperature detection system for an indirect air-cooled system in a thermal power plant according to claim 3, characterized in that: The heat insulation sleeve is made of a composite material of aluminum silicate fiber and aerogel, with a temperature resistance range of -40℃ to 600℃ and a thermal conductivity of ≤0.02W / (m・K). It is divided into three specifications according to the comprehensive environmental disturbance: Grade 1, 20mm thick, suitable for comprehensive environmental disturbance ≤50W / ㎡; Grade 2, 40mm thick, suitable for comprehensive environmental disturbance 50W / ㎡~100W / ㎡; Grade 3, 60mm thick, suitable for comprehensive environmental disturbance >100W / ㎡. The heat insulation sleeve is a ring structure that matches the temperature measuring element. It adopts a half-splitting design and is fixed by 304 stainless steel quick-change buckles. The inner side is attached with a high-temperature resistant silicone sealing gasket, and the outer side is wrapped with a waterproof and oil-proof polytetrafluoroethylene layer.

10. The precise temperature detection system for an indirect air-cooled system in a thermal power plant according to claim 1, characterized in that: The system is also equipped with a hardware and software collaborative fault tolerance module, which is connected to the microcontroller. The hardware and software collaborative fault tolerance module includes a communication redundancy unit, an abnormal data processing unit, and a hardware fault diagnosis unit. The communication redundancy unit adopts a dual-protocol design of Modbus-RTU main protocol and CAN bus backup protocol. When the main protocol data packet loss rate is ≥5%, it automatically switches to the backup protocol. The abnormal data processing unit is used to filter out collected data that exceeds the reasonable range for industrial applications and replace invalid data with the average value of previous valid data. The hardware fault diagnosis unit is used to collect the operating current and voltage parameters of each hardware in real time. When the parameters exceed the rated range, it is determined to be a hardware fault. The power supply of the faulty hardware is cut off and the backup temperature measuring element is activated. At the same time, the fault alarm signal is sent to the remote monitoring and management platform through the Internet of Things remote transmission module.

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