Moistureproof switch cabinet and control method thereof

Abstract

The invention relates to the technical field of electrical engineering, and discloses a moisture-proof switch cabinet which comprises a switch cabinet body. The temperature and humidity sensor network is deployed in the switch cabinet and used for monitoring humidity and temperature distribution in the switch cabinet; the intelligent controller is in network connection with the temperature and humidity sensor and comprises a humidity dynamic model building module, a target optimization module and a feedback control module; the humidity regulation and control device comprises a dehumidification device and a heating device, is connected with the intelligent controller and is used for realizing humidity regulation according to a regulation and control signal of the intelligent controller; the intelligent controller comprises a variational solution module which is used for calculating optimal control input in real time according to the humidity dynamic model and the optimization objective function. According to the technical scheme of deploying the temperature and humidity sensors at multiple points and monitoring in real time, the temperature and humidity distribution data in the switch cabinet are comprehensively obtained, the technical effect of accurately capturing the humidity dynamic change is achieved, and the monitoring reliability and comprehensiveness are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrical engineering, in particular to a moisture-proof switch cabinet and a control method thereof. BACKGROUND

[0002] With the development of industrial automation and power equipment, the switch cabinet as an important device in the power system, its operation stability is directly related to the safety of power supply. Humidity is one of the important environmental factors affecting the stable operation of the switch cabinet. High humidity can cause the insulation performance to decline, and then cause equipment short circuit, electric leakage and other faults. And low humidity may cause static electricity accumulation, which also poses a threat to the safe operation of the equipment. Therefore, accurate control of the humidity inside the switch cabinet is an important task to ensure its reliable operation.

[0003] In the prior art, the humidity inside the switch cabinet is usually monitored by deploying a temperature and humidity sensor, and the humidity is adjusted in combination with a dehumidifying device or a heating device. Some technical solutions collect humidity data through a single sensor or a small number of sensors, and then directly control the equipment power output according to the humidity value. Although these solutions can adjust the humidity to a certain extent, due to the spatial non-uniformity of the humidity distribution inside the switch cabinet, and the dynamic change of the humidity being affected by the environmental temperature and the penetration of external humidity, the prior art often fails to accurately reflect the overall change of the humidity. In addition, the energy consumption optimization problem of the control device has not been fully solved in the prior art.

[0004] However, the sensor arrangement in the prior art has the problem of insufficient coverage, and it is usually impossible to fully grasp the humidity distribution law inside the switch cabinet, resulting in lagging or poor effect of the control measures. Especially in the case of uneven humidity distribution or large dynamic change, the existing solution cannot accurately capture the subtle changes of the humidity field, directly affecting the reliability and accuracy of the humidity control. Therefore, there is an urgent need for a technical solution that can monitor the dynamic change of the humidity inside the switch cabinet through multiple sensors to solve the problem of insufficient coverage of the humidity monitoring in the prior art, so as to realize more accurate humidity control. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a moisture-proof switch cabinet and a control method thereof, which cannot accurately capture the subtle changes of the humidity field, directly affecting the reliability and accuracy of the humidity control.

[0006] To achieve the above purpose, the present application realizes the following technical scheme: a moisture-proof switch cabinet, comprising: a switch cabinet main body; a temperature and humidity sensor network deployed inside the switch cabinet, for monitoring the humidity and temperature distribution inside the switch cabinet; An intelligent controller connected with the temperature and humidity sensor network, the intelligent controller comprising a humidity dynamic model construction module, a target optimization module and a feedback control module; A humidity regulation device comprising a dehumidification device and a heating device, connected with the intelligent controller, for realizing humidity regulation according to the regulation signal of the intelligent controller.

[0007] Preferably, the intelligent controller comprises a variational solution module for calculating optimal control input in real time according to the humidity dynamic model and the optimization target function, the optimal control input being used to control the operating power of the humidity regulation device.

[0008] Preferably, the intelligent controller is connected with a remote monitoring system through a wireless communication module, for uploading the real-time humidity and temperature data of the switch cabinet to a remote server, and triggering an alarm to notify the operation and maintenance personnel when the humidity is abnormal.

[0009] A moisture-proof switch cabinet control method, comprising the following steps: Step one, deploying multiple temperature and humidity sensors to monitor the humidity and temperature distribution inside the switch cabinet in real time; Step two, establishing a humidity dynamic model, which describes the dynamic evolution law of humidity based on humidity diffusion, heat and humidity coupling and the influence of external humidity source on humidity distribution; Step three, constructing a target function of humidity deviation and energy consumption optimization, the target function taking the minimization of humidity deviation and energy consumption as the optimization target; Step four, solving the target function by using variational optimization method to obtain the optimal control strategy; Step five, designing a nonlinear feedback controller to dynamically adjust the control input according to the humidity deviation, adjust the power output of the dehumidification device or heating device, and realize real-time and accurate humidity control.

[0010] Preferably, the humidity dynamic model comprises a humidity diffusion term for describing the diffusion law of humidity inside the switch cabinet, the humidity diffusion term being constructed based on a humidity diffusion coefficient, and the humidity diffusion coefficient depending on the air flow characteristics and structure inside the switch cabinet.

[0011] Preferably, the humidity dynamic model comprises a heat and humidity coupling term for describing the influence of temperature change on humidity distribution, the heat and humidity coupling term being characterized by a heat and humidity coupling coefficient, and the heat and humidity coupling coefficient depending on the temperature change rate and the material characteristics inside the switch cabinet.

[0012] Preferably, the optimization target function comprises a humidity deviation term and a control energy consumption term, the humidity deviation term being determined by the square of the difference between the actual humidity value and the target humidity value, and the control energy consumption term being determined by the power consumption value of the humidity regulation device.

[0013] Preferably, the variational optimization method is solved by constructing a Lagrangian function and using Euler-Lagrange equation to obtain the optimal control input, which is used to adjust the operating state of the humidity regulation device in real time.

[0014] Preferably, the nonlinear feedback controller specifically combines state feedback and integral control, and the control input includes a real-time humidity deviation feedback signal and an integral signal of the cumulative error of the humidity history.

[0015] Preferably, the step five further comprises seasonal humidity management, dynamically adjusting the target humidity value according to seasonal climate characteristics, setting a lower target humidity value in summer and appropriately increasing the target humidity value in winter.

[0016] The present application provides a moisture-proof switch cabinet and a control method thereof. The following beneficial effects are achieved: 1. The moisture-proof switch cabinet of the present application adopts a technical solution of deploying multiple temperature and humidity sensors and monitoring in real time, and achieves the technical effect of accurately capturing the dynamic changes of humidity by comprehensively obtaining the temperature and humidity distribution data in the switch cabinet. Compared with the technical solution of single sensor arrangement or insufficient coverage in the prior art, the problem of being unable to comprehensively grasp the humidity field change rule is solved, and the reliability and comprehensiveness of the monitoring are effectively improved.

[0017] 2. The moisture-proof switch cabinet of the present application establishes a humidity dynamic model containing humidity diffusion, heat and humidity coupling and external humidity source influence, and achieves the technical effect of comprehensively describing the humidity distribution and change trend of the switch cabinet. Compared with the technical solution of relying only on empirical formula or single humidity change factor in the prior art, the problem of inaccurate model and difficulty in predicting humidity dynamic changes is overcome, and the scientificity and accuracy of humidity control are significantly improved.

[0018] 3. The moisture-proof switch cabinet of the present application constructs a function with humidity deviation and energy consumption optimization as the target, and solves it by combining the variational optimization method, which achieves the technical effect of achieving the best balance between humidity regulation accuracy and energy consumption. Compared with the technical solution of simply relying on manual adjustment or fixed power control in the prior art, the problems of low humidity control accuracy and high energy consumption are solved, and the system operation is more efficient. BRIEF DESCRIPTION OF DRAWINGS

[0019] Fig. 1 FIG. 1 is a schematic diagram of the moisture-proof switch cabinet system of the present application; Fig. 2 FIG. 2 is a schematic diagram of the method flow of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the specification of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0021] Please refer to the drawings attached Figs. 1-2 The embodiments of the present application provide a moisture-proof switch cabinet and a control method thereof, which comprise: Step one: deploying multiple temperature and humidity sensors The dynamic changes of humidity and temperature directly determine the accuracy of the subsequent humidity model construction and affect the real-time performance and reliability of the control system. Therefore, in this step, multiple temperature and humidity sensors are deployed to obtain the environmental data inside the switch cabinet. These data serve as input signals, providing necessary support for the construction of the humidity dynamic model. Especially in complex industrial environments, the deployment of sensors must take into account the dynamic characteristics of the environment and the uniformity of the space.

[0022] Generally, temperature and humidity sensors should have high sensitivity and low delay characteristics to meet the needs of capturing the rapidly changing humidity and temperature inside the switch cabinet. The response time of the sensor is usually controlled within the millisecond range, and its measurement error needs to be less than ±0.5%. Preferably, the sensor adopts a digital output method, which is connected to the controller through a I 2 C or SPI communication interface to avoid interference in the signal transmission process.

[0023] In one possible implementation, the arrangement density of the sensors is optimized according to the internal structure of the switch cabinet, the air flow characteristics, and the heat source distribution. Specifically, the sensors can be deployed at the following positions: Top area: for monitoring the local humidity reduction caused by the rising of hot air.

[0024] Bottom area: to monitor the humidity rise that may be caused by the accumulation of cold air.

[0025] Corner and joint: to capture the humidity fluctuations in the local environment and avoid monitoring blind spots caused by dead angles.

[0026] For example, for a standard switch cabinet with a height of 2 meters and a width of 1.5 meters, 3 sensors can be arranged at the top, middle, and bottom respectively, forming a monitoring network covering the entire cabinet.

[0027] The sensors can be arranged in coordination with the direction of air flow to improve the efficiency of monitoring and the accuracy of data. Specifically, if there is a cooling fan or ventilation system in the switch cabinet, the position of the sensor should avoid the strong air flow area as much as possible to prevent measurement errors caused by rapid air flow disturbance.

[0028] The data collected by the sensors can be uploaded to the intelligent controller through wireless means. Commonly used wireless transmission protocols include LoRa, ZigBee, and Wi-Fi, which can meet the low-power consumption requirements while supporting long-distance communication between sensors and controllers. As an extension, the sensors can also be integrated into an industrial Internet of Things platform to achieve remote data monitoring and recording.

[0029] Specifically, the data collected by the sensors not only includes humidity values and temperature values, but also records timestamps to analyze the dynamic changes of humidity and temperature subsequently. The output signal of the sensor is determined by the following relationship: Wherein: H s H is the humidity value output by the sensor, with a unit of %; H env H is the actual humidity value inside the switch cabinet, with a unit of %; T env T is the actual temperature value inside the switch cabinet, with a unit of Kelvin (K); DH is the gradient change of humidity, used to describe the non-uniformity of humidity distribution, with a unit of %·m -1 . Generally, the humidity gradient change can be obtained by fitting the multi-point data collected by the sensor, for example: Wherein: ΔH is the humidity difference between two adjacent points, with a unit of %; Δx is the distance between adjacent sensors, with a unit of meters (m).

[0030] In this embodiment, the networking mode of the sensors adopts a distributed architecture design. Each sensor independently collects data and transmits it to a local data collection unit, and then uploads it to the intelligent controller through a bus or wireless communication. Preferably, the data collection period of the sensor is set to 1 second to ensure that the system has a high time resolution, while avoiding excessive power consumption caused by frequent collection.

[0031] In some embodiments, the calibration process of the sensor is very important. Specifically, the sensor needs to be strictly calibrated before installation, and the calibration standard is based on the ISO17025 environmental monitoring equipment certification requirements. The sensor may drift during long-term use, so it is recommended to calibrate the sensor every 6 months to ensure the long-term reliability of the measurement data.

[0032] In an extended technical implementation, the sensor system can also monitor other environmental parameters such as air pressure, dew point temperature, and air flow rate to further improve the accurate description of the dynamic changes of humidity inside the switch cabinet. For example, the change of dew point temperature can help predict the risk of moisture condensation, whose calculation formula is: Where: T d is the dew point temperature, in Celsius (℃); A and B are empirical coefficients, usually A = 17.27, B = 237.7.7.

[0033] By monitoring the dew point temperature in real time, it can be determined in advance whether there is a risk of condensation inside the switch cabinet, so that targeted protective measures can be taken.

[0034] Step two: Establishing a humidity dynamic model The humidity dynamic model is used to describe the evolution of humidity inside the switch cabinet over time and space. The model is based on real-time data collected by the temperature and humidity sensor, and by introducing factors such as humidity diffusion, heat and humidity coupling, and external humidity sources, it fully reflects the dynamic characteristics of the humidity changes inside the switch cabinet. The accuracy of the model is directly related to the optimization effect of the subsequent control strategy, and is a key link to achieve precise humidity control.

[0035] Generally, the change of humidity inside the switch cabinet is the result of the comprehensive action of multiple factors, such as moisture diffusion, water vapor conversion caused by temperature change, and infiltration of external moisture, etc. Therefore, the humidity dynamic model introduces partial differential equations and combines actual environmental parameters to construct a mathematical expression describing the humidity distribution and change.

[0036] In this embodiment, in order to accurately depict the dynamic changes of humidity, the humidity dynamic model is based on the following mathematical expression: In the above formula: H(x,t) represents the humidity value at a certain time and a certain position inside the switch cabinet, with a unit of percentage (%). It is the core variable of the humidity field.

[0037] represents the rate of change of humidity over time, with a unit of (%·s -1 ).

[0038] D h is the humidity diffusion coefficient, with a unit of (m 2 / s). This parameter reflects the speed of humidity diffusion in space, which is affected by the air flow characteristics inside the switch cabinet.

[0039] is the spatial second-order gradient of humidity, representing the intensity of spatial variation of humidity field, with unit of (%·m -2 ).

[0040] k is the thermal-hygroscopic coupling coefficient, with unit of (%·K -1 ). This parameter describes the degree of influence of temperature change on humidity.

[0041] is the time rate of temperature change, with unit of (K / s), reflecting the trend of temperature change over time.

[0042] S(x,t) represents the external humidity source term, with unit of (% / s). This term is used to describe the dynamic process of external environmental humidity penetrating into the interior through the structure of the switch cabinet or ventilation system.

[0043] Generally, the humidity diffusion term is an important part of the humidity dynamic model, which is used to describe the process of humidity diffusion from high concentration areas to low concentration areas. In one possible implementation manner, the value of the diffusion coefficient D h may be determined by experiment, or calculated in combination with the geometric structure of the switch cabinet and the air flow speed.

[0044] Specifically, the size of the diffusion term is closely related to the air flow speed in the cabinet, the airtightness of the switch cabinet, and the distribution of internal obstacles.

[0045] The spatial distribution of humidity diffusion can be obtained by fitting the multi-point sensor data. For example, for a linearly distributed humidity field, the gradient can be calculated by the following relationship: wherein: x, y, and z represent the three-dimensional coordinates of space, with unit of meters (m).

[0046] In practical applications, the diffusion process is often non-uniform. For example, in areas with strong air flow, the humidity diffusion speed is faster, while in airtight corners, the humidity diffusion may be inhibited. Therefore, when considering the diffusion coefficient, the model can optimize the humidity field in combination with numerical simulation tools.

[0047] As an option, the thermal-hygroscopic coupling term is used to describe the indirect influence of temperature change on humidity. In one possible implementation manner, the value of the thermal-hygroscopic coupling coefficient k can be calibrated by experiment, or calculated theoretically based on the physical properties of the material. For example, an increase in temperature will cause the saturation vapor pressure of air to increase, thereby reducing the relative humidity. Specifically, the calculation formula of the saturation vapor pressure P s is as follows: wherein: P s P is the saturated vapor pressure, in pascal (Pa).

[0048] A and B are empirical constants, depending on the physical properties of air.

[0049] T is the absolute temperature, in Kelvin (K).

[0050] By analyzing the saturated vapor pressure, the relationship between temperature change and humidity distribution can be further determined, and the heat and humidity coupling coefficient k can be corrected accordingly.

[0051] In one possible implementation, the size of the external humidity source term S(x, t) is influenced by the external environment humidity, the sealing of the switch cabinet, and the rate of change of external humidity. For example, when the switch cabinet is operating in a high humidity environment, the humidity value will increase significantly as the moisture penetrates into the interior through the cabinet structure. In the actual model, the external humidity source term can be estimated by the following formula S(x, t) = λ·ΔH env Wherein: λ is the moisture permeation coefficient, in (% / s), reflecting the moisture permeation ability through the cabinet.

[0052] ΔH env is the difference between the humidity outside the cabinet and the humidity inside the cabinet, in percentage (%).

[0053] Specifically, when the external humidity changes rapidly, the value of the humidity source term will increase rapidly, thereby significantly affecting the humidity dynamics of the switch cabinet.

[0054] Step three: Constructing the objective function of humidity deviation and energy consumption optimization The objective function of humidity deviation and energy consumption optimization is based on the humidity diffusion, heat and humidity coupling, and external humidity source in the humidity dynamic model. The humidity deviation and energy consumption optimization are mathematically quantified and coupled, thereby providing theoretical support for the subsequent optimal control strategy. By constructing the objective function, the best balance point between humidity control accuracy and energy consumption can be found. The reasonable design of the objective function directly affects the overall performance of the control system, and is one of the core links in the control method.

[0055] Generally, the objective function needs to consider the humidity deviation and the energy consumption of the control equipment. The definition of humidity deviation is the difference between the current humidity and the target humidity, and its square value can effectively quantify the size of the deviation; the energy consumption of the control equipment is directly related to the power output of the dehumidification device or the heating device. To optimize both, the objective function minimizes the humidity deviation and minimizes the energy consumption.

[0056] In this embodiment, the mathematical expression of the objective function is as follows: In the formula: J is the objective function, representing the total optimization target of humidity deviation and energy consumption, dimensionless; T represents the time range of optimization, in seconds (s); Ω represents the spatial region inside the switchgear; H(x, t) is the humidity value at a certain time and location inside the switchgear, in percentage (%); H target is the target humidity value, in percentage (%); α is the weight coefficient of humidity deviation, dimensionless, used to control the contribution of humidity deviation to the objective function; P(x, t) is the power output of the control device, in watts (W); β is the weight coefficient of energy consumption, dimensionless, used to control the contribution of energy consumption to the objective function.

[0057] Generally, the humidity deviation term α(H(x, t) - H target ) 2 quantifies the control accuracy of humidity. As an option, this deviation term is expressed in the form of squared error of humidity, to highlight the influence of locations with larger humidity deviation on system performance. Specifically, in the case of uneven humidity distribution, this deviation term can be discretized by multi-point humidity data, for example: Where: N is the number of humidity sensors; H i (t) is the humidity value of the i-th sensor at time t. In this implementation, the weighted processing of multi-point humidity deviation can more accurately describe the influence of humidity distribution on system performance. As a possible implementation, the energy consumption term βP(x, t) 2 is used to describe the influence of control device power output on energy consumption. Specifically, the power P(x, t) of the control device is directly related to the running intensity of the device, for example, the power of the dehumidification device is proportional to the air flow rate and compressor load. The squared term of power output is used to emphasize the significant contribution to the objective function in high energy consumption operating state. In some embodiments, the calculation formula of power output P(x, t) can be further refined, for example: P(x, t) = η · Q(x, t) Where: η is the device efficiency factor, dimensionless, representing the energy utilization efficiency of the device; Q(x, t) is the thermal power output of the device, in watts (W).

[0058] Through the above relationship, the power consumption can be accurately calculated according to the actual device performance parameters.

[0059] Specifically, the values of the weight coefficients a and b in the objective function need to be adjusted according to the actual application scenario. Generally, when the humidity control accuracy requirement is high, the value of a is increased; when the system energy consumption optimization demand is high, the value of b is increased. For example, the switch cabinet running in a humid climate environment should prioritize accurate humidity control, and the value of a can be appropriately increased. As an option, in an application scenario with high energy cost, the energy consumption optimization demand should be prioritized, thereby increasing the weight of b.

[0060] In one possible implementation, the optimization range of the objective function can be further expanded. For example, when the humidity control device supports multi-level power regulation, the value of the power output P(x, t) can be limited to a set of discrete values, thereby converting the optimization problem of the objective function into a discrete optimization problem. In another implementation, the time range T of the objective function can be dynamically adjusted according to the running period of the device, for example, in periods of frequent humidity fluctuations, the optimization time interval is shortened to improve the response speed of the control.

[0061] In this embodiment, through the construction of the objective function, the comprehensive optimization of humidity deviation minimization and energy consumption minimization is realized. The objective function mathematically relates the humidity deviation and energy consumption, providing a clear optimization target and physical constraint condition for the subsequent optimal control strategy. This design not only adapts to complex industrial environments, but also flexibly adjusts the optimization parameters according to actual needs, thereby improving the adaptability and reliability of the humidity control system.

[0062] Step four: solving the objective function using variational optimization method The variational optimization method is used to solve the constructed objective function, thereby obtaining the optimal control strategy. In the foregoing steps, the objective function has quantified the relationship between humidity deviation and energy consumption optimization into mathematical form, and the application of the variational optimization method can effectively find the control input that makes the objective function reach the minimum value. This process guarantees the accuracy of humidity regulation while reducing the energy consumption of device operation.

[0063] Generally, the variational optimization method solves the extreme value of the objective function by constructing the Lagrangian function and combining the Euler-Lagrange equation. This method is rigorous in mathematics and efficient in solving, and is suitable for scenarios in the present invention that require real-time regulation.

[0064] In this embodiment, to solve the objective function, a Lagrangian function L is first constructed, and its expression is as follows: L = a (H(x, t) - H target ) 2+ βP(x, t) 2 In this formula: H(x, t) represents the distribution of humidity in the switchgear, with units of percentage (%); H target is the target humidity value, with units of percentage (%); P(x, t) is the control input (such as the power output of the dehumidification device), with units of watts (W); α and β are the weight coefficients of humidity deviation and energy consumption, respectively, dimensionless. By constructing the Lagrangian function and combining the constraint conditions of the humidity dynamic model: The objective function optimization problem can be converted into a variational optimization problem under the constraint condition.

[0065] Specifically, according to the theory of variational optimization, the minimum value of the objective function satisfies the following Euler-Lagrange equation In this embodiment, the actual solution of this equation is: Where: P(x, t) is the optimal control input, used to adjust the device power in real time; γ is a gain parameter, dimensionless, used to control the strength of the input signal; The definitions of other parameters are consistent with the humidity dynamic model.

[0066] In general, in order to improve the efficiency of optimization solution, the Euler-Lagrange equation can be discretized by combining numerical methods. In some embodiments, the finite difference method is used to discretize the time and space variables, for example: Where: H(i, n) represents the humidity value of discrete node i at time step n; Δt is the time step, with units of seconds (s); w ij is the weight between nodes i and j, related to the distance between nodes.

[0067] Through discretization and solution, the real-time performance of the system can be further improved, so that the control strategy can quickly respond to the dynamic changes of humidity As an option, the solution process of the variational optimization method can be combined with machine learning algorithms to further improve the optimization efficiency in complex environments. For example, in a switchgear operating environment with nonlinear dynamic characteristics, a reinforcement learning method can be used to dynamically adjust the gain parameter γ, thereby improving the adaptability of the control input.

[0068] An online prediction of the humidity dynamic model is performed using a deep learning model, and the control input is adjusted based on the prediction results, thereby achieving higher optimization efficiency.

[0069] The time range T of optimization solution can be dynamically adjusted according to actual needs. For example, in time periods with frequent humidity fluctuations, the optimization time interval can be shortened to improve the response speed of the system; while in time periods with stable humidity changes, the optimization time range can be extended to reduce the computational burden.

[0070] Step five: design a nonlinear feedback controller The controller dynamically adjusts based on the humidity deviation, while introducing integral feedback to eliminate steady-state error, ensuring the accuracy and stability of the system. The design of the nonlinear feedback controller is an important step to convert the theoretical solution into actual control signals, and its implementation directly affects the execution effect of the humidity control system.

[0071] Generally, the nonlinear feedback controller needs to combine the humidity dynamic model and the optimization results of the objective function to adjust the device power output in real time. Through the combination of state feedback and integral control, the control error caused by long-term deviation accumulation can be overcome while quickly responding to humidity deviation In this embodiment, the control law expression of the nonlinear feedback controller is as follows: In this formula: u(t) represents the control input, with units of watts (W), corresponding to the power output of the dehumidification device or heating device; H(t) represents the real-time humidity of the switch cabinet at time t, with units of percentage (%); H target is the target humidity, with units of percentage (%); K is the state feedback gain, dimensionless, used to adjust the feedback strength of humidity deviation; R is the integral gain, dimensionless, used to adjust the compensation strength of integral control on steady-state error; τ represents the integral variable, used to describe the cumulative effect of historical humidity deviation The output u(t) of the controller is directly used as the power input signal of the control device, and by adjusting the power output in real time, the humidity dynamics in the cabinet are changed.

[0072] Generally, the selection of state feedback gain K needs to be debugged in combination with the optimization results of the humidity dynamic model and the objective function.

[0073] The feedback gain can be obtained by simulation analysis to find the optimal value, for example, parameter scanning of K to find the best balance point between system response time and steady-state error.

[0074] The design of the integral gain R needs to consider the long-term trend of humidity changes. For example, in an environment with small humidity fluctuations, the value of R can be reduced to reduce the hysteresis effect of integral control; in the case of severe humidity changes, the value of R should be appropriately increased to enhance the compensation ability for steady-state error.

[0075] In one possible implementation, to enhance the stability of the system, saturation limits can be introduced to the output of the controller, i.e., limiting the maximum and minimum values of u(t) to avoid overloading the device under extreme operating conditions. For example: u min ≤u(t)≤u max where: u min is the minimum allowed value of the control input, usually set to zero; u max is the maximum allowed value of the control input, the specific value is determined according to the power capacity of the device.

[0076] Through saturation limits, damage to the device or excessive energy consumption caused by excessive control input can be effectively prevented.

[0077] Specifically, the implementation of the nonlinear feedback controller can be completed by an embedded control system. In some embodiments, the design of the controller uses a discrete implementation, such as numerical discretization of the integral part: where: u(k) is the control input at discrete time step k; H(k) is the humidity value at time step k; Δt is the time step, with units of seconds (s); i is the discrete time index.

[0078] In this implementation, the discretized controller can adapt to the operating environment of the digital embedded system while reducing computational complexity.

[0079] The nonlinear feedback controller can be added with adaptive functions. For example, by monitoring the parameter changes of the humidity dynamic model in real time, the gain values K and R of the controller are dynamically adjusted.

[0080] The adjustment rules of adaptive gain can be implemented based on fuzzy logic or neural networks. For example, when the detected humidity fluctuation range exceeds the preset threshold, the value of K can be automatically increased to speed up the system response; when the humidity tends to be stable, the value of R can be reduced to reduce the cumulative error of integral control.

[0081] In this embodiment, the design of the nonlinear feedback controller realizes the real-time dynamic response to the humidity deviation in a combination of state feedback and integral control. Through the optimization adjustment of parameters and the extension of adaptive function, the controller can adapt to the changes of complex industrial environment, while ensuring the efficient and stable operation of the humidity control system. Combined with the above variational optimization results, the design of the controller effectively connects the theoretical solution with the actual equipment operation, providing key support for the overall implementation of the switch cabinet humidity control system.

[0082] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are only by way of example and that changes, modifications, substitutions and alterations can be made thereto without departing from the spirit and scope of the application as defined in the following claims, in which:

Claims

1. A moisture-proof switchgear, characterized by The application relates to a switch cabinet humidity control system. The switch cabinet humidity control system comprises the following parts: a switch cabinet main body; a temperature and humidity sensor network arranged in the switch cabinet, which is used for monitoring the humidity and temperature distribution in the switch cabinet; an intelligent controller connected with the temperature and humidity sensor network, wherein the intelligent controller comprises a humidity dynamic model construction module, a target optimization module and a feedback control module; and a humidity control device comprising a dehumidification device and a heating device, which is connected with the intelligent controller and is used for realizing humidity adjustment according to the control signal of the intelligent controller. The intelligent controller comprises a variational solution module, which is used for calculating optimal control input in real time according to a humidity dynamic model and an optimization target function, and the optimal control input is used for controlling the operation power of the humidity control device. The intelligent controller is connected with a remote monitoring system through a wireless communication module, and is used for uploading the real-time humidity and temperature data of the switch cabinet to a remote server and triggering an alarm to notify the operation and maintenance personnel when the humidity is abnormal. The application further discloses a humidity control method of the switch cabinet.

2. The moisture-proof switchgear according to claim 1, characterized in that The method comprises the following steps: step 1, deploying multiple temperature and humidity sensors to monitor the humidity and temperature distribution in the switch cabinet in real time; step 2, establishing a humidity dynamic model, wherein the humidity dynamic model is based on humidity diffusion, heat and humidity coupling and the influence of external humidity sources on humidity distribution, and describes the dynamic evolution law of humidity; step 3, constructing a target function of humidity deviation and energy consumption optimization, wherein the target function takes the minimization of humidity deviation and the minimization of energy consumption as optimization targets; step 4, solving the target function by using a variational optimization method to obtain an optimal control strategy; and step 5, designing a nonlinear feedback controller, adjusting the control input according to the humidity deviation, adjusting the power output of the dehumidification device or the heating device, and realizing real-time and accurate humidity control.

3. The moisture-proof switchgear according to claim 1, characterized in that, The humidity dynamic model comprises a humidity diffusion term, which is used for describing the diffusion law of humidity in the switch cabinet, and the humidity diffusion term is constructed based on a humidity diffusion coefficient, and the humidity diffusion coefficient depends on the air flow characteristics and structure in the switch cabinet.

4. A method of controlling a moisture-proof switchgear cabinet according to claim 1, characterized in that, The humidity dynamic model comprises a heat and humidity coupling term, which is used for describing the influence of temperature change on humidity distribution, and the heat and humidity coupling term is characterized by a heat and humidity coupling coefficient, and the heat and humidity coupling coefficient depends on the temperature change rate and the material characteristics in the switch cabinet. The optimization target function comprises a humidity deviation term and a control energy consumption term, the humidity deviation term is determined by the square of the difference between the actual humidity value and the target humidity value, and the control energy consumption term is determined by the power consumption value of the humidity control device. The variational optimization method solves the Lagrange function and uses the Euler-Lagrange equation to obtain the optimal control input, and the optimal control input is used for adjusting the operation state of the humidity control device in real time. The nonlinear feedback controller specifically combines state feedback and integral control, and the control input comprises a real-time humidity deviation feedback signal and an integral signal of the cumulative error of humidity history. The step 5 further comprises seasonal humidity management, wherein the target humidity value is dynamically adjusted according to the seasonal climate characteristics, the target humidity value in summer is set to be low, and the target humidity value in winter is appropriately increased. ​ 5. The damp-proof switchgear control method according to claim 4, characterized by, ​ 6. The damp-proof switchgear control method according to claim 4, characterized by, ​ 7. The damp-proof switchgear control method according to claim 4, characterized by, ​ 8. The damp-proof switchgear control method according to claim 4, characterized by, ​ 9. The damp-proof switchgear control method according to claim 4, characterized by, ​ 10. The damp-proof switchgear control method according to claim 4, characterized by, ​

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