Self-adaptive intelligent hot rod system for power transmission tower footing in permafrost region

Abstract

The invention discloses a self-adaptive intelligent hot rod system for a power transmission tower footing in a permafrost region, and the system comprises a sensor group, an intelligent control unit, a hot rod body, an energy supply module and a communication module, the monitoring module is used for monitoring frozen soil environment parameters and power transmission tower footing settlement in real time; a data acquisition and processing module and a self-adaptive control algorithm are arranged in the intelligent control unit; the energy supply module is used for providing stable power support for the system and ensuring all-weather stable operation of the system; and the communication module is connected with a remote server through a wireless communication technology and is used for realizing data exchange and communication between the system and a remote monitoring center. By intelligently controlling the working state of the hot rod, the stability of the tower footing is effectively maintained, the influence of frost heaving and thaw collapse on the power transmission tower footing is reduced, and the stability of the power transmission tower in a permafrost region is ensured.

Description

Technical Field

[0001] The invention belongs to the field of transmission tower foundation construction in permafrost areas, relates to a high-efficiency heat-conducting heat rod, and specifically relates to a self-adaptive intelligent heat rod system for transmission tower foundations in permafrost areas. Background Art

[0002] In the prior art, traditional heat rods include low-temperature heat rods and heating heat rods. Low-temperature heat rods use the principle of heat conduction to achieve one-way heat conduction by converting the internal working fluid (such as liquid ammonia) between gas and liquid, so they cannot accurately control temperature regulation. Heating rods mostly use simple mechanical switches or electronic temperature control devices to control the heating process. These methods can achieve temperature regulation to a certain extent, but there are many shortcomings.

[0003] Mechanical heating rods lack precise temperature control mechanisms, which easily lead to large temperature fluctuations and high energy consumption; while electronic temperature-controlled heating rods improve temperature control accuracy, their response speed and adaptability still need to be improved when facing complex and changing environmental conditions. In addition, with the development of Internet of Things technology, users have put forward higher requirements for the intelligence, remote monitoring and adaptability of heating equipment.

[0004] Therefore, it is particularly important to improve the traditional heating rod technology and develop an efficient, controllable, temperature adaptive heating rod technology. The use of high-precision sensors, intelligent control units and high-efficiency heating elements can achieve real-time monitoring of ambient temperature and precise adjustment of heating. Summary of the invention

[0005] The purpose of the present invention is to provide an adaptive intelligent thermal rod system for transmission tower foundations in permafrost areas, which aims to effectively maintain the stability of the tower foundation by intelligently controlling the working state of the thermal rod, reduce the impact of frost heave and thaw settlement on the transmission tower foundation, and ensure the stability of the transmission tower in permafrost areas.

[0006] The objective of the present invention is achieved through the following technical solutions:

[0007] An adaptive intelligent heat rod system for a transmission tower base in permafrost areas, comprising a sensor group, an intelligent control unit, a heat rod body, an energy supply module and a communication module, wherein:

[0008] The sensor group is deployed at appropriate positions of the heat rod body and the transmission tower base to monitor the frozen soil environmental parameters and the settlement of the transmission tower base in real time;

[0009] The sensor group includes a temperature sensor, a humidity sensor and a displacement sensor; the temperature sensor is used to monitor the temperature of the frozen soil and the environment, the humidity sensor is used to monitor the humidity of the frozen soil and the environment, and the displacement sensor is used to monitor the displacement of the soil around the tower foundation and evaluate the stability of the tower foundation;

[0010] The intelligent control unit has a built-in data acquisition and processing module and an adaptive control algorithm; the sensor group transmits the collected data to the data acquisition and processing module, the data acquisition and processing module removes abnormal values ​​from the original data, and the intelligent control unit automatically adjusts the working state of the heat rod body through the adaptive control algorithm according to the processed data, thereby maintaining the frozen soil temperature within a certain range;

[0011] The energy supply module is used to provide stable power support for the system to ensure the system operates stably around the clock;

[0012] The communication module is connected to the remote server through wireless communication technology to realize data exchange and communication between the system and the remote monitoring center.

[0013] A method for using an adaptive intelligent heat rod system for a transmission tower base in permafrost regions comprises the following steps:

[0014] Step 1: Deploy displacement sensors at the corresponding positions of the transmission tower foundation in the permafrost area to monitor the changes in the displacement and settlement of the tower foundation. Deploy temperature sensors and humidity sensors at the corresponding positions outside the heat rod body to collect changes in the temperature and humidity of the external environment and frozen soil. These sensors are wirelessly connected to the intelligent control unit;

[0015] Step 2: The data collected by the sensor group in step 1 is transmitted to the data acquisition and processing module in the intelligent control unit, and abnormal values ​​are eliminated from the original data;

[0016] Step 3: The processed data is processed and predicted using an adaptive control algorithm in the intelligent control unit and intelligently analyzed and controlled. The specific steps are as follows:

[0017] Step 3-1: Parameter estimation

[0018] The recursive least squares (RLS) method is used to estimate the current temperature distribution, moisture state and settlement change of the frozen soil online. The specific steps are as follows:

[0019] (1) There exists a linear model:

[0020] y(k)=x(k)′θ+e(k)

[0021] Among them, y(k) is the observation value of the kth step, x(k) is the input vector of the kth step, θ is the model parameter vector to be estimated, and e(k) is the error term;

[0022] (2) Calculate the gain matrix K(k):

[0023]

[0024] Where, K(k1) is the temperature gain matrix of the kth step, K(k2) is the humidity gain matrix of the kth step, K(k3) is the displacement gain matrix of the kth step, P(k1-1) is the temperature covariance matrix of the k-1th step, P(k2-1) is the humidity covariance matrix of the k-1th step, P(k3-1) is the displacement covariance matrix of the k-1th step, x(k1) is the temperature input vector of the kth step, x(k2) is the humidity input vector of the kth step, and x(k3) is the displacement input vector of the kth step;

[0025] (3) Update parameter estimates

[0026]

[0027] in, is the temperature estimate for the k-1th step, is the humidity estimate at step k-1, is the displacement estimate at step k-1, y(k1) is the temperature observation at step k, y(k2) is the humidity observation at step k, and y(k3) is the displacement observation at step k;

[0028] (4) Update the covariance matrix P(k):

[0029] P(k1)=[IK(k1)x(k1)′]P(k1-1)

[0030] P(k2)=[IK(k2)x(k2)′]P(k2-1)

[0031] P(k3)=[IK(k3)x(k3)′]P(k3-1)

[0032] Where I is the identity matrix, P(k1) is the temperature covariance matrix of the kth step, P(k2) is the humidity covariance matrix of the kth step, and P(k3) is the displacement covariance matrix of the kth step;

[0033] Step 3-2: Control coefficient adjustment

[0034] According to the parameter estimation results and real-time data, Model Predictive Control (MPC) is used to adjust the working state of the heat rod body and the ambient soil temperature to achieve the desired soil temperature:

[0035]

[0036] Subject to T s (k+1)=f(T s(k),u(k),υ(k),λ(k),…)

[0037] Among them, J is the objective function, N is the prediction time step, T s (k) is the predicted value of soil temperature at step k, T target is the target temperature, u(k) is the control input of the kth step, γ is the control cost weight, and f is the dynamic model of the system;

[0038] Step 4: According to the analysis of the data results by the adaptive control algorithm, the frozen soil state is analyzed and predicted in real time, so that the intelligent control unit sends a control instruction to the heat rod body, automatically adjusts the working parameters of the heat rod body, and realizes adaptive control adjustment;

[0039] Step 5: The system transmits data to the remote monitoring platform in real time to view the system operation status, frozen soil status and tower foundation settlement status in real time. The remote platform regularly maintains and upgrades the system to ensure long-term stable operation of the system.

[0040] Compared with the prior art, the present invention has the following advantages:

[0041] 1. The adaptive intelligent thermal rod system for transmission tower foundation in permafrost areas of the present invention can monitor environmental parameters and tower foundation settlement status in real time, and can be used to detect environmental soil parameters.

[0042] 2. The present invention adopts intelligent heat rod technology to effectively alleviate the frost heave and thaw settlement problem of transmission tower foundations in permafrost areas and improve the stability of tower foundations. At the same time, the working mode of the heat rod is controlled by an intelligent algorithm to achieve precise adjustment of the heat dissipation of the heat rod, thereby ensuring the stability of the tower foundation while avoiding unnecessary energy consumption and maximizing energy savings.

[0043] 3. The present invention realizes remote management and maintenance of the system by connecting with the remote server through wireless technology, reduces operation and maintenance costs and time costs, and improves operation and maintenance efficiency.

[0044] 4. The present invention uses intelligent algorithms, adaptive control and nano-thermal rod technology to control the settlement of the transmission tower foundation in permafrost areas and maintain the stability of the transmission tower. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a composition diagram of the adaptive intelligent heat rod system for transmission tower foundation in permafrost areas;

[0046] Figure 2 It is a flow chart of the use of the adaptive intelligent heat rod system for transmission tower foundations in permafrost areas;

[0047] Figure 3 This is a schematic diagram of zigbee wireless transmission. DETAILED DESCRIPTION

[0048] The technical solution of the present invention is further described below in conjunction with the accompanying drawings, but is not limited thereto. Any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention should be included in the protection scope of the present invention.

[0049] The present invention provides an adaptive intelligent heat rod system for transmission tower foundation in permafrost areas, such as Figure 1 As shown, the system is mainly composed of a sensor group, an intelligent control unit, a heat rod body, an energy supply module and a communication module, wherein:

[0050] The sensor group is deployed at appropriate positions of the heat rod body and the transmission tower base to monitor the frozen soil environmental parameters and the settlement changes of the transmission tower base in real time. It mainly includes a temperature sensor, a humidity sensor and a displacement sensor. The temperature sensor is used to monitor the temperature of the frozen soil and the environment. The humidity sensor is used to monitor the humidity of the frozen soil and the environment. The displacement sensor is used to monitor the displacement of the soil around the tower foundation and evaluate the stability of the tower foundation.

[0051] The intelligent control unit is used to receive data collected by the sensor group, and has a built-in data acquisition and processing module and an adaptive control algorithm. The sensor group transmits the collected data to the data acquisition and processing module, and the data acquisition and processing module removes abnormal values ​​from the original data. The intelligent control unit automatically adjusts the working state of the heat rod body through the adaptive control algorithm according to the processed data, thereby maintaining the temperature of the frozen soil within a certain range;

[0052] The heating rod body is a nano-heat rod, and the heating core is made of a nano-alloy material with high thermal conductivity, specifically two bundles of nano-alloy twisted wires of different materials are twisted together to achieve heat conversion, and the heating nano-heat rod is also equipped with a temperature control device for monitoring and controlling the heating temperature;

[0053] The energy supply module can use renewable energy such as solar energy and wind energy as the main energy source, supplemented by batteries for energy storage, to provide stable power support for the system and ensure the system operates stably around the clock;

[0054] The communication module is connected to the remote server through wireless communication technology to realize data exchange and communication between the system and the remote monitoring center;

[0055] The communication module adopts a zigbee wireless communication module, which includes a wireless gateway node and multiple terminal nodes. The number of the multiple terminal nodes is the same as the number of sensors, and the multiple terminal nodes are respectively connected to the temperature sensor, the humidity sensor and the displacement sensor.

[0056] The present invention provides a method for using a tower base adaptive intelligent heat rod system in permafrost areas, such as Figure 2As shown, the method comprises the following steps:

[0057] Step 1: Deploy displacement sensors at the corresponding positions of the transmission tower foundation in the permafrost area to monitor the changes in the displacement and settlement of the tower foundation. Deploy temperature sensors and humidity sensors at the corresponding positions outside the heat rod body to collect changes in the external environment and frozen soil temperature and humidity. These sensors are wirelessly connected to the intelligent control unit.

[0058] In this step, the heat rod body is a nano heat rod, which is a high-efficiency heating device. The core heating material is made of nano alloy material with high thermal conductivity. It is specifically made of two bundles of nano alloy twisted wires of different materials twisted together to achieve heat conversion. The nano heat rod converts electrical energy into molecular vibrations and then radiates heat energy. It has the characteristics of high heating efficiency and stable working temperature.

[0059] Step 2: The data collected by the sensor group in step 1 is transmitted to the data acquisition and processing module in the intelligent control unit, and outliers are removed from the original data. In this project, the accuracy and completeness of the data must be ensured at all times.

[0060] Step 3: The processed data is processed and predicted using an adaptive control algorithm in the intelligent control unit and intelligently analyzed and controlled. The specific steps are as follows:

[0061] Step 3-1: Parameter estimation

[0062] The recursive least squares (RLS) method is used to online estimate the current state of frozen soil (such as temperature distribution, moisture state) and the changes in transmission tower foundation settlement.

[0063] (1) There exists a linear model:

[0064] y(k)=x(k)′θ+e(k)

[0065] Among them, y(k) is the observation value of the kth step, x(k) is the input vector of the kth step, θ is the model parameter vector to be estimated, and e(k) is the error term;

[0066] (2) Calculate the gain matrix K(k):

[0067]

[0068] Where, K(k1) is the temperature gain matrix of the kth step, K(k2) is the humidity gain matrix of the kth step, K(k3) is the displacement gain matrix of the kth step, P(k1-1) is the temperature covariance matrix of the k-1th step, P(k2-1) is the humidity covariance matrix of the k-1th step, P(k3-1) is the displacement covariance matrix of the k-1th step, x(k1) is the temperature input vector of the kth step, x(k2) is the humidity input vector of the kth step, and x(k3) is the displacement input vector of the kth step;

[0069] (3) Update parameter estimates

[0070]

[0071] in, is the temperature estimate for the k-1th step, is the humidity estimate at step k-1, is the displacement estimate at step k-1, y(k1) is the temperature observation at step k, y(k2) is the humidity observation at step k, and y(k3) is the displacement observation at step k;

[0072] (4) Update the covariance matrix P(k):

[0073] P(k1)=[IK(k1)x(k1)′]P(k1-1)

[0074] P(k2)=[IK(k2)x(k2)′]P(k2-1)

[0075] P(k3)=[IK(k3)x(k3)′]P(k3-1)

[0076] Where I is the identity matrix, P(k1) is the temperature covariance matrix of the kth step, P(k2) is the humidity covariance matrix of the kth step, and P(k3) is the displacement covariance matrix of the kth step. Usually set to zero vector, P(0) is a large positive definite matrix, usually set to aI, where is a large number (can be set to 10 4 ), I is the identity matrix.

[0077] Step 3-2: Control coefficient adjustment

[0078] Based on the parameter estimation results and real-time data, a control strategy can be designed to adjust the heating power of the nano-thermal rods to adjust the ambient soil temperature. This system can use Model Predictive Control (MPC) to optimize the control strategy.

[0079] MPC is a model-based control strategy that optimizes control inputs based on the current state of the system and future predictions. In a heat rod system, MPC can be used to adjust the operating state of the heat rod (such as turning it on / off or adjusting the power) to achieve the desired soil temperature.

[0080] In this system, a simple conceptual framework can be adopted, which is expressed as follows:

[0081]

[0082] Subject to T s (k+1)=f(T s (k),u(k),υ(k),λ(k),…)

[0083] Among them, J is the objective function, N is the prediction time step, T s (k) is the predicted value of soil temperature at step k, T target is the target temperature, u(k) is the control input (heat rod power) of the kth step, γ is the control cost weight, and f is the dynamic model of the system.

[0084] Step 4: According to the analysis of the data results by the adaptive control algorithm, the frozen soil state can be analyzed and predicted in real time, so that the intelligent control unit sends control instructions to the nano-thermal rods, automatically adjusts the working parameters of the nano-thermal rods, and realizes adaptive control adjustment.

[0085] Step 5: The system transmits data to the remote monitoring platform in real time, so that the system operation status, frozen soil status and tower foundation settlement status can be viewed in real time. The remote platform can also regularly maintain and upgrade the system to ensure long-term stable operation of the system.

Claims

1. An adaptive intelligent heat rod system for transmission tower foundation in permafrost areas, characterized in that The system includes a sensor group, an intelligent control unit, a heat rod body, an energy supply module and a communication module, wherein: The sensor group is deployed at appropriate positions of the heat rod body and the transmission tower base to monitor the frozen soil environmental parameters and the settlement of the transmission tower base in real time; The intelligent control unit has a built-in data acquisition and processing module and an adaptive control algorithm; the sensor group transmits the collected data to the data acquisition and processing module, the data acquisition and processing module removes abnormal values ​​from the original data, and the intelligent control unit automatically adjusts the working state of the heat rod body through the adaptive control algorithm according to the processed data, thereby maintaining the frozen soil temperature within a certain range; The energy supply module is used to provide stable power support for the system to ensure the system operates stably around the clock; The communication module is connected to the remote server through wireless communication technology to realize data exchange and communication between the system and the remote monitoring center.

2. The adaptive intelligent heat rod system for transmission tower foundation in permafrost region according to claim 1 is characterized in that The heat rod body is a nano heat rod, and the heating core is made of nano alloy material.

3. The adaptive intelligent heat rod system for transmission tower foundation in permafrost region according to claim 2 is characterized in that The heating core is formed by twisting two bundles of nano alloy twisted wires of different materials.

4. The adaptive intelligent heat rod system for transmission tower foundation in permafrost region according to claim 2 is characterized in that The nano heat rod is equipped with a temperature control device for monitoring and controlling the heating temperature.

5. The adaptive intelligent heat rod system for transmission tower foundation in permafrost region according to claim 1 is characterized in that The sensor group includes a temperature sensor, a humidity sensor and a displacement sensor; the temperature sensor is used to monitor the temperature of the frozen soil and the environment, the humidity sensor is used to monitor the humidity of the frozen soil and the environment, and the displacement sensor is used to monitor the displacement of the soil around the tower foundation and evaluate the stability of the tower foundation.

6. The adaptive intelligent heat rod system for transmission tower foundation in permafrost region according to claim 1 is characterized in that The energy supply module uses renewable energy as the main energy source, supplemented by batteries for energy storage.

7. The adaptive intelligent heat rod system for transmission tower foundation in permafrost region according to claim 5 is characterized in that The communication module adopts a zigbee wireless communication module, which includes a wireless gateway node and multiple terminal nodes. The number of the multiple terminal nodes is the same as the number of sensors, and the multiple terminal nodes are respectively connected to the temperature sensor, the humidity sensor and the displacement sensor.

8. A method for using the adaptive intelligent heat rod system for transmission tower foundation in permafrost regions according to any one of claims 1 to 7, characterized in that The method comprises the following steps: Step 1: Deploy displacement sensors at the corresponding positions of the transmission tower foundation in the permafrost area to monitor the changes in the displacement and settlement of the tower foundation. Deploy temperature sensors and humidity sensors at the corresponding positions outside the heat rod body to collect changes in the temperature and humidity of the external environment and frozen soil. These sensors are wirelessly connected to the intelligent control unit; Step 2: The data collected by the sensor group in step 1 is transmitted to the data acquisition and processing module in the intelligent control unit, and abnormal values ​​are eliminated from the original data; Step 3: The processed data is processed and predicted using an adaptive control algorithm in the intelligent control unit and intelligently analyzed and controlled. The specific steps are as follows: Step 3-1: Parameter estimation The recursive least squares method is used to estimate the current temperature distribution, humidity state and settlement change of the frozen soil online. The specific steps are as follows: (1) There exists a linear model: y(k)=x(k)′θ+e(k) Among them, y(k) is the observation value of the kth step, x(k) is the input vector of the kth step, θ is the model parameter vector to be estimated, and e(k) is the error term; (2) Calculate the gain matrix K(k): Where, K(k1) is the temperature gain matrix of the kth step, K(k2) is the humidity gain matrix of the kth step, K(k3) is the displacement gain matrix of the kth step, P(k1-1) is the temperature covariance matrix of the k-1th step, P(k2-1) is the humidity covariance matrix of the k-1th step, P(k3-1) is the displacement covariance matrix of the k-1th step, x(k1) is the temperature input vector of the kth step, x(k2) is the humidity input vector of the kth step, and x(k3) is the displacement input vector of the kth step; (3) Update parameter estimates in, is the temperature estimate for the k-1th step, is the humidity estimate at step k-1, is the displacement estimate at step k-1, y(k1) is the temperature observation at step k, y(k2) is the humidity observation at step k, and y(k3) is the displacement observation at step k; (4) Update the covariance matrix P(k): P(k1)=[IK(k1)x(k1)′]P(k1-1) P(k2)=[IK(k2)x(k2)′]P(k2-1) P(k3)=[IK(k3)x(k3)′]P(k3-1) Where I is the identity matrix, P(k1) is the temperature covariance matrix of the kth step, P(k2) is the humidity covariance matrix of the kth step, and P(k3) is the displacement covariance matrix of the kth step; Step 3-2: Control coefficient adjustment According to the parameter estimation results and real-time data, model predictive control is used to adjust the working state of the heat rod body and the ambient soil temperature to achieve the desired soil temperature: Subject to T s (k+1)=f(T s (k),u(k),μ(k),λ(k),...) Among them, J is the objective function, N is the prediction time step, T s (k) is the predicted value of soil temperature at step k, T target is the target temperature, u(k) is the control input of the kth step, γ is the control cost weight, and f is the dynamic model of the system; Step 4: According to the analysis of the data results by the adaptive control algorithm, the frozen soil state is analyzed and predicted in real time, so that the intelligent control unit sends a control instruction to the heat rod body, automatically adjusts the working parameters of the heat rod body, and realizes adaptive control adjustment; Step 5: The system transmits data to the remote monitoring platform in real time to view the system operation status, frozen soil status and tower foundation settlement status in real time. The remote platform regularly maintains and upgrades the system to ensure long-term stable operation of the system.

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