Method and system for controlling thermal-mechanical stresses in a 10 kv skin heating circuit

Abstract

The application discloses a control method and system for inhibiting thermal mechanical stress of a 10kV skin heating loop, and the method comprises the following steps: limiting the allowable stress required for inhibiting the thermal mechanical stress to the maximum allowable temperature change rate of the loop; constructing a discrete thermodynamic prediction model of the loop, discretizing the heating loop along the space into multiple nodes, establishing a temperature state space equation, and forming a temperature recursive sequence in a prediction time domain; taking the minimum temperature deviation and energy consumption as the target, constructing an optimization model under the constraint of the maximum allowable temperature change rate, and introducing a relaxation variable; in each control cycle, the temperature distribution of the whole loop is obtained, the internal thermal state is estimated, the optimal control sequence that makes the temperature change rate not exceed the allowable upper limit value is obtained through the optimization model, and the heating power is adjusted. The application converts the thermal mechanical stress constraint into the temperature change rate constraint, adopts the model predictive control for rolling optimization closed loop, and inhibits the cumulative damage of the thermal stress from the root.

Description

Technical Field

[0001] This invention relates to the field of skin-collecting heat tracing control system technology, and more specifically, to a control method and system for suppressing thermomechanical stress in a 10kV skin-collecting heat tracing circuit. Background Technology

[0002] Skin effect heat tracing is an important technology for heat tracing long-distance industrial pipelines. It utilizes ferromagnetic pipes with high magnetic permeability as the heating element, combined with skin-effect cables running inside to form a power supply circuit, achieving efficient and uniform heat tracing. With the increasing demand for the construction of long-distance, large-diameter pipelines, 10kV voltage-level skin effect heat tracing technology is gradually becoming the main direction for technological upgrading in the industry due to its advantages such as long single-circuit power supply distance, fewer power supply points, low line loss, and superior overall cost.

[0003] However, 10kV skin-type heat tracing circuits face a prominent problem that urgently needs to be solved in engineering applications: the length of a single 10kV skin-type heat tracing circuit can reach 16 to 30 kilometers, and the skin-type tube is usually made of carbon steel, whose linear thermal expansion coefficient is approximately 12 × 10⁻⁶. -6 / ℃, while the conductor of the Jifu cable is made of copper, whose equivalent coefficient of thermal expansion is approximately 17×10. -6 / ℃. The coefficients of thermal expansion of the two materials differ significantly. In actual operation, the start-up and shutdown of the heating system, load fluctuations, or sudden changes in ambient temperature can cause significant changes in the circuit temperature, resulting in significant thermomechanical stress at constraint points such as the junction box and cable connections. The magnitude of this stress is directly related to the amplitude and rate of temperature change. Long-term, repeated stress can cause fatigue damage to the cable insulation layer, loosening or pull-out of joints, and even insulation breakdown accidents.

[0004] Currently, a simple step-type power switching control is commonly used, which directly switches the power on or off to reach the target temperature without any constraints on the rate of temperature change. This results in a temperature change rate of 3 to 5°C / min during system start-up and shutdown, far exceeding the material's safe tolerance range. This control method cannot effectively suppress the cumulative damage caused by thermomechanical stress, seriously threatening the long-term safe operation of 10kV skin tracing circuits. Especially at higher operating temperatures, the strain difference caused by the difference in thermal expansion of materials increases linearly with temperature. In addition, the problem is exacerbated by the decrease in the elastic modulus and allowable stress of insulating materials at high temperatures.

[0005] Therefore, there is an urgent need for a control method and system that can actively suppress the thermomechanical stress of a 10kV skin heat tracing circuit. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a control method and system for suppressing thermomechanical stress in a 10kV skin heat tracing circuit. By converting thermomechanical stress constraints into temperature change rate constraints, and using model predictive control for rolling optimization closed loop, the cumulative thermal stress damage is suppressed from the root.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A method for controlling thermomechanical stress in a 10kV skin heat tracing circuit includes the following steps:

[0009] Step S1: Based on the difference in thermal expansion coefficients between the skin tube and the skin cable in the heat tracing circuit, the allowable stress limit required to suppress thermomechanical stress is converted into the maximum allowable temperature change rate of the circuit.

[0010] Step S2: Construct a discretized thermodynamic prediction model for the circuit, discretize the heat tracing circuit into multiple nodes along the space, establish the temperature state space equation, and form a temperature recursive sequence in the prediction time domain.

[0011] Step S3: With the goal of minimizing temperature deviation and energy consumption, construct an optimization model under the constraint of the maximum allowable rate of temperature change, and introduce slack variables;

[0012] Step S4: In each control cycle, obtain the temperature distribution of the entire line and estimate the internal thermal state. Optimize the model to obtain the optimal control sequence that ensures the temperature change rate does not exceed the allowable upper limit, and adjust the heating power.

[0013] Furthermore, step S1 includes the following steps:

[0014] Step S11: Calculate the strain difference between the two when the temperature changes, based on the thermal expansion coefficient of the skin tube and the equivalent thermal expansion coefficient of the skin cable.

[0015] Step S12: The strain difference is converted into thermal stress by utilizing the equivalent elastic modulus of the skin cable insulation layer.

[0016] Step S13: Ensure that the thermal stress does not exceed the allowable stress of the material, and then deduce the maximum allowable single temperature change amplitude.

[0017] Step S14: Combining the safety time constant, the maximum allowable single temperature change amplitude is converted into the maximum allowable temperature change rate per unit time.

[0018] Furthermore, step S2 includes the following steps:

[0019] Step S21: Discretize the heat tracing circuit along its length into N nodes, and define the temperature state vector at time k. ;

[0020] Step S22, establish the temperature state space equation:

[0021]

[0022] In the formula, The heating power at time k; Let k be the ambient temperature at time k. This is a time-varying state transition matrix; This is the power input matrix; The time-varying environmental disturbance matrix; Let be the temperature state vector at time k+1;

[0023] Step S23, using the temperature state vector at time k Using the initial value, the temperature recursive sequence at each time point in the prediction time domain is generated iteratively using the temperature state space equation.

[0024] Furthermore, step S3 includes the following steps:

[0025] Step S31: Set the target heat tracing temperature Construct a quadratic objective function:

[0026]

[0027] In the formula, The temperature state vector at the i-th step in the prediction time domain, generated recursively for the temperature state space equation; for Constant heating power; This is the temperature deviation weighting matrix; For control quantity weighting coefficients;

[0028] Step S32, to allow the maximum rate of temperature change As a hard constraint:

[0029]

[0030] In the formula, To control the sampling interval; For the predicted j-th node in Temperature value at any given time; For the predicted j-th node in Temperature value at any given time;

[0031] heating power Perform power limiting:

[0032]

[0033] In the formula, This is the rated maximum heating power;

[0034] Step S33: Introduce nonnegative slack variables And the quadratic objective function is augmented:

[0035]

[0036] In the formula, This is the penalty weighting coefficient; Let be the objective function of the constructed quadratic form.

[0037] Furthermore, step S4 includes the following steps:

[0038] Step S41: Obtain the current temperature distribution across the entire line through distributed temperature measurement, and estimate the internal thermal state using a Kalman filter combined with the temperature state space equation to obtain the initial temperature state vector. ;

[0039] Step S42, using the initial temperature state vector Using the initial values, the online optimization model is solved to obtain the optimal heating power sequence. ;

[0040] Step S43, select the optimal heating power sequence The first element The output is sent to the power supply unit to adjust the current heating power;

[0041] Step S44: Wait until the next sampling time, then return to step S41 to form a rolling closed loop.

[0042] Furthermore, it also includes step S5, performing adaptive correction of model parameters;

[0043] Step S5 includes the following steps:

[0044] Step S51: Rewrite the temperature state space equation in parametric regression form:

[0045]

[0046] In the formula, For regression vectors; The vector of parameters to be corrected; It is the transpose operator;

[0047] Step S52, using an exponential forgetting factor The recursive least squares method updates the parameter estimates of each node online at each sampling time.

[0048] Step S53: When the temperature change rate of the entire system is lower than the preset steady-state threshold, the physical rationality boundary of the parameter update results of each node is checked. After passing the check, the correction parameters are assembled into the updated time-varying state transition matrix and time-varying environmental disturbance matrix, and loaded into the temperature state space equation and the constructed optimization model.

[0049] This invention also provides a control system for suppressing thermomechanical stress in a 10kV skin-collecting heat tracing circuit, comprising a power supply box, a terminal box, a skin-collecting pipe, a skin-collecting cable, a pull box, a skin-collecting connector, and a controller, wherein the controller includes:

[0050] The temperature measurement unit uses distributed optical fibers laid along the entire length of the skin tube to obtain the temperature distribution of the entire line.

[0051] The leakage current monitoring unit is installed on the grounding lead of the skin tube in the power supply box and / or terminal box to collect the leakage current signal to ground in real time.

[0052] An environmental sensor unit is used to collect data on the temperature and humidity inside the chamber and the ambient temperature.

[0053] The control terminal receives data from the temperature measurement unit, leakage current monitoring unit, and environmental sensor unit, has a built-in thermomechanical stress control algorithm, and outputs control commands.

[0054] The power supply unit receives control commands to adjust the heating power of the heat tracing circuit.

[0055] Furthermore, the control terminal includes an insulation condition diagnosis module. The insulation condition diagnosis module receives the leakage current signal and voltage reference signal from the leakage current monitoring unit, calculates the resistive leakage current component in real time through an orthogonal demodulation algorithm, and performs temperature and humidity adaptive compensation in combination with the temperature data from the environmental sensor unit to estimate the comprehensive insulation resistance of the heat tracing circuit online.

[0056] Furthermore, when the insulation condition diagnosis module detects an abnormal leakage current, it combines the temperature data from the temperature measurement unit to identify the temporal correlation and spatial location of the abnormal temperature rise point, thereby locating the weak point in the insulation.

[0057] Furthermore, the skin-featured cable includes, from the inside out, a conductor, a semi-conductive layer, a PFA insulation layer, and a PFA outer sheath.

[0058] The beneficial effects of this invention are:

[0059] 1. This invention transforms the stress constraint caused by the difference in the thermal expansion coefficient of materials into a temperature change rate constraint, and uses it as the core limit for rolling optimization control, so that the temperature change rate during start-up, shutdown and load adjustment is always within a safe range, avoiding cumulative fatigue damage to the insulation layer and joints.

[0060] 2. This invention employs a recursive least squares method with a forgetting factor to correct heat dissipation parameters online using the deviation between measured and predicted temperatures, ensuring that the system always matches environmental heat dissipation conditions during long-term operation.

[0061] 3. This invention combines distributed fiber optic temperature measurement with Kalman filtering to achieve accurate sensing of the thermal state of the entire line, providing complete initial conditions for the accurate execution of temperature rate constraints.

[0062] 4. This invention integrates an insulation condition diagnosis module, which estimates the insulation resistance online through leakage current analysis, and locates weak points in the insulation by correlating temperature measurement data when abnormalities occur, forming a complementary protection with the thermal stress suppression function. Attached Figure Description

[0063] Figure 1 This is a flowchart of a control method for suppressing thermomechanical stress in a 10kV skin heat tracing circuit in this embodiment;

[0064] Figure 2 This is a structural framework diagram of a control system for suppressing thermomechanical stress in a 10kV skin heat tracing circuit in this embodiment;

[0065] Figure 3 This is a schematic diagram of one structure of the skin-feather cable in this embodiment;

[0066] Figure 4 This is a schematic diagram of one structure of the skin connector in this embodiment;

[0067] Figure 5 This is a structural framework diagram of the controller in this embodiment.

[0068] Figure reference numerals: Power supply box 1, Terminal box 2, Skin tube 3, Skin cable 4, Conductor 41, Semiconductor layer 42, Insulation layer 43, PFA outer sheath 44, Pull box 5, Skin connector 6, Cylindrical connector 61, Inner PTFE tape 62, Outer PTFE tape 63, Inner PTFE heat shrink tubing 64, Outer PTFE heat shrink tubing 65, Controller 7, Temperature measurement unit 71, Leakage current monitoring unit 72, Environmental sensor unit 73, Control terminal 74, Insulation status diagnosis module 741, Power supply unit 75. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] Example: A control method for suppressing thermomechanical stress in a 10kV skin heat tracing circuit. This method transforms the thermomechanical stress constraint, which is difficult to measure directly, into a measurable and controllable temperature change rate constraint. It adopts a model predictive control architecture for rolling optimization closed-loop control and embeds an online parameter adaptive correction mechanism to ensure the long-term accuracy of the model.

[0071] like Figure 1 As shown, the method includes the following steps:

[0072] Step S1, thermomechanical stress is converted into temperature change rate: Based on the difference in thermal expansion coefficients between the skin tube 3 and the skin cable 4 in the heat tracing circuit, the allowable stress limit required to suppress thermomechanical stress is converted into the maximum allowable temperature change rate of the circuit.

[0073] This step aims to establish a quantitative relationship between thermomechanical stress caused by differences in material thermal expansion and the rate of change of loop temperature, transforming the stress safety boundary, which is difficult to measure directly, into an upper limit of temperature change that can be directly used by subsequent control algorithms. Specifically:

[0074] Step S11: The skin tube 3 is typically made of carbon steel, while the conductor 41 of the skin cable 4 is made of copper. Because the linear thermal expansion coefficient of the skin tube 3 differs significantly from the equivalent thermal expansion coefficient of the skin cable 4, under the same temperature change, they will produce different amounts of free expansion at the joint constraint point, resulting in a strain difference.

[0075]

[0076] In the formula, The coefficient of thermal expansion of the skin tube 3; The equivalent thermal expansion coefficient of the skin-feather cable 4; This refers to the change in circuit temperature. This is the strain difference. This strain difference is the direct cause of thermomechanical stress at the joint.

[0077] In step S12, since the strain difference cannot be freely released under the constraint of the joint, it will be converted into mechanical stress acting on the insulation layer 43 of the skin cable 4 and the connection of the machine head; by utilizing the equivalent elastic modulus of the stress-weak components in the joint system (such as the PFA insulation layer or the root of the joint), the strain difference can be converted into thermal stress.

[0078]

[0079] Substituting the strain difference expression, we obtain the direct relationship between thermal stress and temperature change:

[0080]

[0081] In the formula, The equivalent elastic modulus was obtained through materials mechanics tests. This is thermal stress.

[0082] Step S13: To ensure the long-term safe operation of the circuit, the thermal stress must not exceed the allowable stress of the insulation material or weak components of the joint. From this, the maximum allowable temperature change amplitude in a single temperature change can be deduced:

[0083]

[0084] In the formula, The allowable stress is the smaller of the allowable stress values ​​of the PFA insulation material or the metal components of the connector in the 4-layer cable. This means that the amplitude of a single temperature change must not exceed this value without causing the material to yield or suffer fatigue damage.

[0085] Step S14: In actual operation, temperature changes are not instantaneous but occur at a certain rate. By introducing a system safety time constant, the maximum allowable temperature change amplitude in step S13 is converted into the maximum allowable temperature change rate per unit time.

[0086]

[0087] In the formula, The safety time constant is the set time from the moment the heating source applies the temperature change to the moment it is completely released in stages. It is an empirical value and can be set comprehensively according to the thermal inertia of the heat tracing system and the response capability of the power supply unit. For example, 10-15 minutes can be set according to the measured temperature rise response curve during on-site commissioning. This serves as a hard constraint upper limit for the rate of temperature change in subsequent model predictive control algorithms.

[0088] Step S2, constructing a discretized thermodynamic prediction model for the loop: constructing a discretized thermodynamic prediction model for the loop, discretizing the heat tracing loop into multiple nodes along the space, establishing a temperature state space equation, and forming a temperature recursive sequence in the prediction time domain.

[0089] This step aims to establish a model capable of recursively calculating the spatiotemporal evolution of temperature in long-distance heat tracing loops, providing a basic model structure for model prediction optimization in step S3 and parameter adaptive correction in step S5. Specifically:

[0090] Step S21: Divide the entire heat tracing loop evenly into N nodes along its length. The node spacing is set according to requirements, such as 100-500 meters. Define the temperature state vector at time k. :

[0091]

[0092] In the formula, for 3D column vector; Let j be the temperature value of the j-th node at time k. .

[0093] Step S22: Taking into account the heat conduction effect between adjacent nodes, the heat loss from each node to the surrounding environment, and the contribution of the power supply to the temperature of each node, the temperature state space equation is established:

[0094]

[0095] In the formula, The heating power at time k; Let k be the ambient temperature at time k. The time-varying state transition matrix has the following dimensions: Its main diagonal elements describe the thermal inertia of each node and the heat dissipation to the environment, while the adjacent sub-diagonal elements describe the heat conduction between adjacent nodes. (Time-varying state transition matrix) The time-varying nature of the heat dissipation coefficient stems from the change in environmental conditions. The power input matrix has the following dimensions: , is used to describe the contribution distribution of unit heating power to the temperature rise of each node. Since the skin tube is a series loop and the heating power is approximately uniformly distributed along the entire line, it can be regarded as a time-invariant matrix. This is a time-varying environmental perturbation matrix, used to describe the impact of environmental temperature changes on the heat dissipation of each node, and is related to the time-varying state transition matrix. Simultaneously affected by the time-varying heat dissipation coefficient; Let be the temperature state vector at time k+1.

[0096] Step S23, using the temperature state vector at time k Using the initial value, the temperature state space equation from step S22 is used to iterate forward to generate a temperature recursion sequence for each time step in the prediction time domain.

[0097] Step S3, construct the rolling optimization model: With the goal of minimizing temperature deviation and energy consumption, construct the optimization model under the constraint of the maximum allowable rate of temperature change, and introduce slack variables.

[0098] This step aims to construct a maximum allowable temperature change rate determined in step S1, with the objectives of minimizing temperature tracking deviation and energy consumption. This problem is centered on core constraints, and slack variables are introduced for optimization. Specifically:

[0099] Step S31: Set the target heat tracing temperature A quadratic objective function is designed to minimize the deviation between the temperature of each node and the target temperature in the prediction time domain, as well as to control energy consumption. :

[0100]

[0101] In the formula, This is the temperature state vector at the i-th step in the prediction time domain generated by the recursive generation of the temperature state space equation in step S23, where i is the step index in the prediction time domain. , Number of steps; for Constant heating power; This is the temperature deviation weight matrix, which is a diagonal matrix. The diagonal elements are used to adjust the penalty weights for temperature deviations at different nodes. This is the weighting coefficient for the control quantity. The larger this coefficient is, the smoother the change in the control quantity and the smaller the power fluctuation.

[0102] Step S32, the maximum allowable temperature change rate determined in step S14. As a hard constraint, the temperature change of any node between adjacent control times is limited, while power upper and lower bounds are applied for power limiting:

[0103]

[0104]

[0105] In the formula, For the predicted j-th node in Temperature value at any given time; For the predicted j-th node in The temperature value at time i=1. That is, the current state estimate. ; To control the sampling interval; This is the system's rated maximum heating power.

[0106] Step S33: Introduce non-negative relaxation variables for each temperature change rate constraint. And for the quadratic objective function in step S31 To expand;

[0107] Constraint relaxation is: , ;

[0108] The augmented objective function is:

[0109] In the formula, These are the slack variables corresponding to the j-th node and the i-th prediction step; The penalty weighting coefficient is chosen to be much larger than the temperature deviation weighting matrix. and control quantity weighting coefficient The value of the element in the middle; Let be the objective function of the constructed quadratic form.

[0110] Step S4: Execute rolling time-domain closed-loop control: In each control cycle, acquire the temperature distribution of the entire line and estimate the internal thermal state. Through optimization model, obtain the optimal control sequence that ensures the temperature change rate does not exceed the allowable upper limit value, and adjust the heating power.

[0111] This step aims to translate the calculation results of the aforementioned three steps into real-time closed-loop control of the system. Through a cycle of "measurement-estimation-optimization-execution-feedback," it achieves the unification of heat tracing temperature regulation and active thermal stress suppression. Specifically:

[0112] Step S41: Using a distributed temperature measurement system laid along the entire length of the skin-feather cable 3, the sampling interval of the distributed temperature measurement system can be set to 5-10 seconds to read the surface temperature distribution of the entire line at the current sampling time k in real time. At the same time, using a Kalman filter, with the temperature state space equation of step S22 as the prediction model and the surface temperature distribution of the entire line as the observation, the optimal recursive estimation is performed on the temperature state inside the skin-feather cable 4 that is difficult to measure directly (such as the internal temperature of the insulation layer 43 and the heat storage temperature at the top of the joint), and the fused complete thermal state of the entire line is output. This serves as the initial state for optimization in step S42.

[0113] Step S42, to obtain the complete thermal state of the entire line. Using the initial values, the optimization model constructed in step S3 is solved online to obtain the optimal heating power sequence for the next P steps starting from the current time k. , .

[0114] Each element in this sequence satisfies the following condition: under this heating strategy, the temperature change rate of each node in the entire line within the next P steps will not exceed the maximum allowable temperature change rate. .

[0115] Step S43, only the optimal heating power sequence The first element The output is sent to the power supply execution unit to adjust the heating power within the current sampling period. The remaining optimal heating power control value... to It will be used as the initial value for the next cycle, but will not be actually executed.

[0116] Step S44, wait until the next sampling time. The new measured temperature distribution and state estimate are used as initial conditions, and steps S41 to S43 are repeated. Through this "rolling forward, continuous feedback" mechanism, each decision of the controller is based on the most realistic current operating state of the system.

[0117] Step S5, perform adaptive model parameter correction: Utilizing the deviation between the measured temperature and the model's predicted temperature, a recursive least squares algorithm with a forgetting factor is used to correct the heat dissipation-related parameters in the prediction model online, ensuring the model remains compatible with the current environmental heat dissipation conditions. Specifically:

[0118] Step S51: Rewrite the temperature state-space equation from step S22 into a parametric regression form:

[0119]

[0120] In the formula, The regression vector includes the current node temperature, neighboring node temperatures, heating power, and ambient temperature. The vector of parameters to be corrected includes inter-node heat conduction and self-heating coefficients, power contribution coefficients, and ambient temperature influence coefficients. This is the transpose operator.

[0121] Step S52, using an exponential forgetting factor The recursive least squares method updates the parameter estimates for each node online at each sampling time:

[0122] Calculate the model prediction error: ;

[0123] Calculate the gain matrix: ;

[0124] Update the parameter estimation vector: ;

[0125] Update the covariance matrix: .

[0126] In the formula, For the j-th node at time... The model prediction error is the difference between the measured temperature and the temperature estimated by the model based on the parameters in the previous step. For the j-th node at time... The actual temperature value obtained from the measurement; To utilize the previous parameter estimates and regression vector The calculated model predicts the temperature; This is the parameter estimation vector for the j-th node at time k; For the j-th node at time... The gain vector determines the degree of response to prediction errors when updating parameters; Estimate the covariance matrix of the parameters of the j-th node at time k, and measure the uncertainty of the current parameter estimate; This is the exponential forgetting factor, with a value range of 0.95 ≤ ≤0.995, The closer the value is to 1, the stronger the algorithm's memory of historical data, resulting in smoother estimations but slower tracking. The smaller the value, the greater the weight the algorithm places on recent data, allowing it to track environmental changes more quickly, but the estimation fluctuations may also increase. The above formulas work together to form a closed-loop recursion, sequentially calculating and updating the parameters.

[0127] Step S53: When the temperature change rate of the entire system is lower than the preset steady-state threshold, perform physical rationality boundary verification on the parameter update results of each node (e.g., the heat dissipation coefficient should not be negative and the conduction coefficient should be within a reasonable order of magnitude). After passing the verification, assemble the correction parameters into the updated time-varying state transition matrix and time-varying environmental disturbance matrix, and load them into the temperature state space equation and the optimization model constructed in step S3.

[0128] This embodiment also provides a control system for suppressing thermomechanical stress in a 10kV skin heat tracing circuit, such as... Figure 2 As shown, the system includes a power supply box 1, a terminal box 2, a skin tube 3, a skin cable 4, a pull box 5, and a skin connector 6.

[0129] like Figure 3 As shown, the skin-featured cable 4 includes, from the inside out, a conductor 41, a semi-conductive layer 42, a PFA insulation layer 43, and a PFA outer sheath 44. The conductor 41 is a stranded conductor (multi-strand copper wire stranded together), with its outer edge arranged in a quincunx pattern, forming a small gap with the PFA insulation layer 43. The semi-conductive layer 42 is disposed between the conductor 41 and the PFA insulation layer 43, using the same material (PFA) as the PFA insulation layer 43, and doped with conductive particles (such as carbon black) to ensure a certain degree of conductivity while maintaining the same temperature resistance and mechanical properties. This makes the semi-conductive layer 42 and the conductor 41 equipotential, eliminating partial discharge at the boundary between the conductor 41 and the PFA insulation layer 43, and preventing electric field concentration that could cause insulation breakdown. The PFA insulation layer 43 and the PFA outer sheath 44 are made of PFA, with a long-term temperature resistance of 260℃, stable dielectric constant, and low dielectric loss.

[0130] The semiconductive layer 42, PFA insulating layer 43 and PFA outer sheath 44 are formed in one step using a multi-layer co-extrusion process. For example, the insulating layer 43 can be divided into 2 or 3 layers, each with a thickness of about 2 mm. Each layer can be independently temperature controlled and cooled to significantly reduce the risks of shrinkage cavities, stress and eccentricity, making the insulation performance more stable.

[0131] like Figure 4As shown, the skin connector 6 adopts a cylindrical connector 61 and is hexagonally crimped, so that the internal conductor 41 can adapt to the shape deformation of the connector after crimping. This ensures the connection strength of the two conductors 41, maximizes the contact area between them, reduces the gap between them, and lowers the grounding resistance. When the connector carries a large current (up to 200 amps), it reduces the power dissipation due to the contact resistance parameter, lowers the temperature rise, and avoids damage or aging of non-metallic materials at high temperatures.

[0132] After crimping, sharp corners and burrs are removed by grinding. The crimped section is then wrapped with inner PTFE tape 62 and outer PTFE tape 63 to form an inner seal. A double-layer PTFE heat shrink tubing (inner PTFE heat shrink tubing 64 and outer PTFE heat shrink tubing 65) is then installed as the outer insulation and seal. The wall thickness of a single layer of PTFE heat shrink tubing after complete shrinkage is not less than 0.6mm. The double-layer structure ensures resistance to insulation test voltages above 20kV. After the skin-feed connector 6 is completed, the entire assembly is placed inside the pull box 5.

[0133] The cable box 5 includes an upper housing and a lower housing, with a fully sealed cavity formed between the upper and lower housings by coating with heat-resistant silicone.

[0134] Power supply box 1 and terminal box 2 are high-voltage increased safety type explosion-proof boxes with an outer shell protection level of not less than IP65. They are equipped with one or two high-voltage porcelain insulators inside, and the center-to-center distance between the two insulators is not less than 160mm.

[0135] The aforementioned control system also includes controller 7, such as Figure 5 As shown, the controller 7 includes a temperature measurement unit 71, a leakage current monitoring unit 72, an environmental sensor unit 73, a control terminal 74, and a power supply unit 75.

[0136] Among them, the temperature measuring unit 71 adopts the temperature measuring optical fiber based on the Brillouin scattering principle, which is bundled and laid along the entire length of the skin tube 3. It is responsible for acquiring the temperature distribution of the entire line in real time, providing the temperature state input of the entire line for the thermomechanical stress control algorithm, providing the measured temperature for the adaptive correction of model parameters, and used for prediction error calculation.

[0137] The leakage current monitoring unit 72 is installed on the grounding lead of the skin tube 3 in the power supply box 1 and / or terminal box 2. It is responsible for collecting the leakage current signal to ground in real time and can be a magnetically modulated zero-sequence current transformer.

[0138] The environmental sensor unit 73 includes a temperature and humidity sensor inside the power supply box 1 and the terminal box 2, and an external ambient temperature sensor, which is responsible for collecting the temperature and humidity inside the box and the ambient temperature.

[0139] The control terminal 74 is responsible for receiving data from the temperature measurement unit 71, the leakage current monitoring unit 72, and the environmental sensor unit 73. It has a built-in thermomechanical stress control algorithm module and a model parameter adaptive correction module. The thermomechanical stress control algorithm model executes the control method of the aforementioned steps S1 to S4 and outputs a smooth heating power command to the power supply unit 75. The model parameter adaptive correction module executes the recursive least squares parameter online correction of the aforementioned step S5. When the steady-state window is triggered, the corrected model parameters are thermally loaded into the prediction model and optimization problem of the thermomechanical stress control algorithm module to achieve adaptive tracking of the model.

[0140] The control terminal 74 also has a built-in insulation condition diagnosis module 741, which receives leakage current signals and voltage reference signals, calculates the resistive leakage current component through quadrature demodulation, performs temperature and humidity adaptive compensation in conjunction with temperature data from the environmental sensor unit 73, estimates the overall insulation resistance of the circuit online, and outputs graded early warning signals. When the leakage current abnormally increases, it automatically correlates with distributed fiber optic temperature measurement data and locates weak points in the insulation by identifying abnormal temperature rise points.

[0141] The power supply unit 75 is responsible for receiving control commands from the control terminal 74 to adjust the heating power of the heat tracing circuit. The power supply unit 75 can use a high-voltage thyristor regulator to continuously adjust the heating power of the heat tracing circuit through phase-shift triggering to achieve smooth power output. At the same time, the high-voltage thyristor regulator has overcurrent and overvoltage protection functions.

[0142] The above description is merely a preferred embodiment of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for controlling thermomechanical stress in a 10kV skin heat tracing circuit, characterized in that, Includes the following steps: Step S1: Based on the difference in thermal expansion coefficients between the skin tube (3) and the skin cable (4) in the heat tracing circuit, the allowable stress limit required to suppress thermomechanical stress is converted into the maximum allowable temperature change rate of the circuit. Step S2: Construct a discretized thermodynamic prediction model for the circuit, discretize the heat tracing circuit into multiple nodes along the space, establish the temperature state space equation, and form a temperature recursive sequence in the prediction time domain. Step S3: With the goal of minimizing temperature deviation and energy consumption, construct an optimization model under the constraint of the maximum allowable rate of temperature change, and introduce slack variables; Step S4: In each control cycle, obtain the temperature distribution of the entire line and estimate the internal thermal state. Optimize the model to obtain the optimal control sequence that ensures the temperature change rate does not exceed the allowable upper limit, and adjust the heating power.

2. The control method for suppressing thermomechanical stress in a 10kV skin heat tracing circuit according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Calculate the strain difference between the two when the temperature changes, based on the thermal expansion coefficient of the skin tube (3) and the equivalent thermal expansion coefficient of the skin cable (4). Step S12: The strain difference is converted into thermal stress by utilizing the equivalent elastic modulus of the insulation layer (43) of the skin-feathered cable (4); Step S13: Ensure that the thermal stress does not exceed the allowable stress of the material, and then deduce the maximum allowable single temperature change amplitude. Step S14: Combining the safety time constant, the maximum allowable single temperature change amplitude is converted into the maximum allowable temperature change rate per unit time.

3. The control method for suppressing thermomechanical stress in a 10kV skin heat tracing circuit according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Discretize the heat tracing circuit along its length into N nodes, and define the temperature state vector at time k. ; Step S22, establish the temperature state space equation: In the formula, The heating power at time k; Let k be the ambient temperature at time k. This is a time-varying state transition matrix; This is the power input matrix; The time-varying environmental disturbance matrix; Let be the temperature state vector at time k+1; Step S23, using the temperature state vector at time k Using the initial value, the temperature recursive sequence at each time point in the prediction time domain is generated iteratively using the temperature state space equation.

4. The control method for suppressing thermomechanical stress in a 10kV skin heat tracing circuit according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Set the target heat tracing temperature Construct a quadratic objective function: In the formula, The temperature state vector at the i-th step in the prediction time domain, generated recursively for the temperature state space equation; for Constant heating power; This is the temperature deviation weighting matrix; For control quantity weighting coefficients; Step S32, to allow the maximum rate of temperature change As a hard constraint: In the formula, To control the sampling interval; For the predicted j-th node in Temperature value at any given time; For the predicted j-th node in Temperature value at any given time; heating power Perform power limiting: In the formula, This is the rated maximum heating power; Step S33: Introduce nonnegative slack variables And the quadratic objective function is augmented: In the formula, This is the penalty weighting coefficient; Let be the objective function of the constructed quadratic form.

5. The control method for suppressing thermomechanical stress in a 10kV skin heat tracing circuit according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Obtain the current temperature distribution across the entire line through distributed temperature measurement, and estimate the internal thermal state using a Kalman filter combined with the temperature state space equation to obtain the initial temperature state vector. ; Step S42, using the initial temperature state vector Using the initial values, the online optimization model is solved to obtain the optimal heating power sequence. ; Step S43, select the optimal heating power sequence The first element The output is sent to the power supply unit to adjust the current heating power; Step S44: Wait until the next sampling time, then return to step S41 to form a rolling closed loop.

6. The control method for suppressing thermomechanical stress in a 10kV skin heat tracing circuit according to claim 1, characterized in that, It also includes step S5, which performs adaptive correction of model parameters; Step S5 includes the following steps: Step S51: Rewrite the temperature state space equation in parametric regression form: In the formula, For regression vectors; The vector of parameters to be corrected; It is the transpose operator; Step S52, using an exponential forgetting factor The recursive least squares method updates the parameter estimates of each node online at each sampling time. Step S53: When the temperature change rate of the entire system is lower than the preset steady-state threshold, the physical rationality boundary of the parameter update results of each node is checked. After passing the check, the correction parameters are assembled into the updated time-varying state transition matrix and time-varying environmental disturbance matrix, and loaded into the temperature state space equation and the constructed optimization model.

7. A control system for suppressing thermomechanical stress in a 10kV skin-collecting heat tracing circuit to implement the method of claim 1, comprising a power supply box (1), a terminal box (2), a skin-collecting pipe (3), a skin-collecting cable (4), a pull box (5), and a skin-collecting connector (6), characterized in that, It also includes a controller (7), which includes: Temperature measurement unit (71) is laid with distributed optical fiber along the entire length of skin tube (3) to obtain the temperature distribution of the entire line; The leakage current monitoring unit (72) is installed on the grounding lead of the skin tube (3) in the power supply box (1) and / or terminal box (2) for real-time acquisition of leakage current signals to ground; An environmental sensor unit (73) is used to collect the temperature and humidity inside the box and the ambient temperature; The control terminal (74) receives data from the temperature measurement unit (71), the leakage current monitoring unit (72), and the environmental sensor unit (73), has a built-in thermomechanical stress control algorithm, and outputs control commands. The power supply unit (75) receives control commands to adjust the heating power of the heat tracing circuit.

8. The control system for suppressing thermomechanical stress in a 10kV skin heat tracing circuit according to claim 7, characterized in that, The control terminal (74) includes an insulation condition diagnosis module (741). The insulation condition diagnosis module (741) receives the leakage current signal and voltage reference signal from the leakage current monitoring unit (72), calculates the resistive leakage current component in real time through an orthogonal demodulation algorithm, and performs temperature and humidity adaptive compensation in combination with the temperature data from the environmental sensor unit (73) to estimate the comprehensive insulation resistance of the heat tracing circuit online.

9. The control system for suppressing thermomechanical stress in a 10kV skin heat tracing circuit according to claim 8, characterized in that, When the insulation condition diagnosis module (741) detects an abnormal leakage current, it combines the temperature data from the temperature measurement unit (71) to identify the temporal correlation and spatial location of the abnormal temperature rise point, thereby locating the weak point in the insulation.

10. The control system for suppressing thermomechanical stress in a 10kV skin heat tracing circuit according to claim 7, characterized in that, The skin-featured cable (4) includes, from the inside out, a conductor (41), a semi-conductive layer (42), a PFA insulation layer (43), and a PFA outer sheath (44).

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