A power distribution cabinet condensation monitoring and removing control system and power distribution cabinet

Abstract

The present application belongs to the technical field of power equipment safety protection, and relates to a power distribution cabinet condensation monitoring and removing control system and a power distribution cabinet. The present application collects real-time internal and external temperature and humidity and future weather forecast through a multi-source collection module, online identifies heat conduction time constant and constructs surface temperature and dew point temperature prediction sequence through a time sequence deduction module, compares two sequences to mark condensation risk time points through a risk positioning module, and continuously calculates target dehumidification rate according to the remaining time interval and the current humidity and generates a pulse width modulation signal to drive the dehumidification actuator according to the dynamic control module, and simultaneously iterates in a closed loop until the risk is eliminated. The present application solves the problems of dehumidification start lag, rough control mode and lack of effect verification in the prior art, realizes the prospective and accurate removal of condensation risk, the continuous matching of dehumidification power and urgency, and the closed loop verification of control effect, effectively reduces the equipment energy consumption and the number of actions, and guarantees the safe operation of the power distribution cabinet.

Description

Technical Field

[0001] This invention belongs to the field of power equipment safety protection technology, and relates to a condensation monitoring and removal control system for distribution cabinets and the distribution cabinet itself. Background Technology

[0002] The electrical equipment inside the distribution cabinet has high requirements for the operating environment. When the temperature inside the cabinet drops below the dew point, condensation easily forms on the insulation surface, which may lead to a decrease in insulation performance, a shortening of creepage distance, or even short circuits or equipment damage. Therefore, monitoring and controlling condensation removal in distribution cabinets is a crucial aspect of ensuring their safe operation.

[0003] Existing condensation control technologies include passive protection and active dehumidification. Passive protection methods, such as spraying anti-condensation coatings, placing desiccants, and installing drainage holes, suffer from problems such as short moisture absorption saturation cycles and high maintenance frequency in continuously high humidity environments. Active dehumidification methods fall into two categories: one is fixed threshold control, which uses temperature and humidity sensors to start and stop the dehumidification equipment when the internal relative humidity exceeds a set value or the temperature falls below a threshold; the other is a scheme based on AI probability prediction and threshold-level discrete control, which uses a neural network model to map multi-source data into condensation risk probability values ​​for future periods, then compares them with multiple preset thresholds to execute the corresponding level of dehumidification.

[0004] In actual operation, the above two types of active dehumidification methods have the following characteristics: First, the occurrence of condensation is not only related to the current internal temperature and humidity state, but also to the dynamic relationship between the surface temperature of key nodes and the dew point temperature of the internal air. Dehumidification triggered solely by a single parameter such as internal relative humidity, or by only outputting a condensation risk probability value for graded control, without taking into account the delayed impact of external environmental changes on the heat and moisture exchange process inside the cabinet, may result in dehumidification starting after the condensation risk has occurred, or even dehumidification only beginning after condensation has already formed.

[0005] Secondly, in seasons with large temperature fluctuations or in scenarios with alternating day and night, fixed threshold control can easily cause dehumidifiers to start and stop frequently, increasing the number of equipment operations and generating additional energy consumption. Control methods based on probability values ​​and discrete levels can only switch between a limited number of dehumidification levels, making it difficult to continuously and precisely adjust according to the actual remaining time window of risk evolution, which may lead to over- or under-dehumidification in some operating conditions.

[0006] Third, in existing control methods, schemes based on fixed thresholds are difficult to use meteorological forecast information for risk prediction, and often adopt a high-power mode of continuous operation, or perform dehumidification treatment only after condensation occurs. Although AI probability prediction-based schemes introduce forecast information, their output is a single probability value rather than a complete time-series curve, and after determining and implementing the dehumidification scheme, the dehumidification effect is usually not verified in a closed loop, which may result in the phenomenon that dehumidification stops before the risk is eliminated. Summary of the Invention

[0007] In view of this, in order to solve the problems mentioned in the background art, a condensation monitoring and removal control system for power distribution cabinets and a power distribution cabinet are proposed.

[0008] The first aspect of the present invention proposes a condensation monitoring and removal control system for power distribution cabinets, comprising: a multi-source acquisition module: real-time acquisition of the internal air temperature, internal relative humidity, surface temperature of key nodes, external air temperature and external relative humidity of the power distribution cabinet, and acquisition of external temperature forecast sequence and external relative humidity forecast sequence for a future preset time period.

[0009] The time series extrapolation module monitors the slopes of external air temperature and key node surface temperature, which alternately turn to the same direction. It then triggers least squares to identify the heat conduction time constant, performs time-domain delay shift on the external temperature forecast sequence to construct the surface temperature prediction sequence, and couples the internal relative humidity, internal air temperature, and external temperature forecast sequences with the external relative humidity forecast sequence to generate the dew point temperature prediction sequence.

[0010] Risk location module: Compare the two prediction sequences point by point, and mark the moment when the predicted dew point temperature first exceeds the predicted surface temperature as the risk time point.

[0011] Dynamic control module: Based on the time interval between the current moment and the risk time point and the internal relative humidity, calculate the target dehumidification rate, map and generate a pulse width modulation signal to output to the dehumidification actuator, and trigger re-acquisition until the dew point temperature prediction sequence is not higher than the surface temperature prediction sequence throughout the entire time period.

[0012] A second aspect of the present invention provides a power distribution cabinet, comprising: a cabinet body, wherein the key node is provided inside the cabinet body.

[0013] The sensing hardware group includes a first temperature and humidity sensor installed inside the cabinet, a temperature sensor attached to the surface of key nodes, and a second temperature and humidity sensor installed outside the cabinet.

[0014] A communication interface, located on the cabinet, is used to connect to an external meteorological server.

[0015] A dehumidifier actuator is located inside the cabinet and is used to perform dehumidification.

[0016] An edge control unit is located on the cabinet and is electrically connected to the sensing hardware group, the communication interface and the dehumidification actuator. The edge control unit is configured to perform the functions of the timing simulation module, the risk positioning module and the dynamic control module in the power distribution cabinet condensation monitoring and removal control system.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention obtains the external temperature forecast sequence and the external relative humidity forecast sequence through a multi-source acquisition module, and identifies the heat conduction time constant online to construct the surface temperature prediction sequence and the dew point temperature prediction sequence. This solves the problem of dehumidification start-up lag caused by the prior art relying only on a single internal parameter or probability value and not incorporating the influence of the external environment's heat and moisture exchange delay. It realizes forward-looking risk prediction based on future temperature and humidity evolution trends, ensuring that the dehumidification action starts before condensation forms.

[0018] (2) This invention outputs risk time points on a continuous time scale through a risk location module, and the dynamic control module continuously calculates the target duty cycle based on the time interval between the current time and that time point to generate a corresponding pulse width modulation signal. This solves the problems of frequent start-stop of dehumidification equipment under fixed threshold control and the inability of discrete level control to finely adjust according to the remaining time window. It achieves continuous matching between dehumidification power and risk urgency, reducing ineffective operating energy consumption and the number of equipment actions.

[0019] (3) This invention generates a surface temperature prediction sequence and a dew point temperature prediction sequence through a time-series extrapolation module, and continuously triggers re-acquisition and iterative calculation during the output pulse width modulation signal until the dew point temperature prediction sequence is no higher than the surface temperature prediction sequence throughout the entire time period. This solves the problem that existing schemes are difficult to verify using meteorological forecasts in a closed loop and are prone to stopping dehumidification before the risk is eliminated. It realizes rolling prediction of condensation risk and closed-loop verification of effect, ensuring that control is only withdrawn after the risk is eliminated. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram showing the connection of each module of the condensation monitoring and removal control system for a power distribution cabinet in this invention;

[0022] Figure 2 This is a flowchart of the method for obtaining the surface temperature prediction sequence in this invention;

[0023] Figure 3 This is a flowchart of the method for obtaining the dew point temperature prediction sequence in this invention. Detailed Implementation

[0024] 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.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0026] The following description, in conjunction with the accompanying drawings, details the specific solution of the condensation monitoring and removal control system for power distribution cabinets and the power distribution cabinet provided by the present invention.

[0027] Example 1

[0028] Please see Figure 1 As shown, the present invention provides a condensation monitoring and removal control system for power distribution cabinets, including: a multi-source acquisition module, a time-series simulation module, a risk location module, and a dynamic control module. The connection relationships between the modules are as follows: the multi-source acquisition module is connected to the time-series simulation module, the time-series simulation module is connected to the risk location module, the risk location module is connected to the dynamic control module, and the dynamic control module is connected to the multi-source acquisition module.

[0029] Multi-source acquisition module: Real-time acquisition of internal air temperature and relative humidity of the distribution cabinet, surface temperature of key nodes, external air temperature and relative humidity, and acquisition of external temperature forecast sequence and external relative humidity forecast sequence for a preset period of time.

[0030] Considering that condensation judgment requires comparing surface temperature and dew point temperature, it is necessary to collect internal temperature and humidity data as well as the surface temperature of key nodes as a calculation benchmark. At the same time, changes in external environmental temperature and humidity are the main driving force for the evolution of the cabinet's state, so it is necessary to collect data on external temperature and humidity quantification disturbances. More importantly, there is an inherent delay in the response of the dehumidifier and the thermal inertia of the cabinet. Relying solely on current instantaneous data cannot achieve forward-looking intervention. Future weather trends must be incorporated to reserve sufficient time margin for regulation. Therefore, a multi-source acquisition module is used to obtain current environmental and key node state data, as well as future temperature and humidity forecast data.

[0031] In one specific embodiment, firstly, a first temperature and humidity sensor is installed in the lower middle part of the distribution cabinet, in a non-ventilated dead corner, to collect the internal air temperature and relative humidity in real time. This sensor preferably adopts a digital temperature and humidity integrated module, with a sampling period adjustable between 1 second and 10 seconds, and is installed in a position that avoids the air outlet inside the cabinet and directly above high-heat-generating components.

[0032] Then, the surface temperature of key nodes is collected in real time by temperature sensors attached to the surface of key nodes inside the distribution cabinet. These key nodes are one or more combinations of busbar joints, circuit breaker contacts, conductive parts of disconnect switches, or cable termination joints inside the distribution cabinet. In practice, surface-mount platinum resistance temperature sensors are used, and their attachment is achieved by filling insulation with thermally conductive silicone grease and covering with high-temperature resistant tape for auxiliary fixation.

[0033] Subsequently, a second temperature and humidity sensor, installed outside the distribution cabinet, collects real-time data on the external air temperature and relative humidity. This sensor is installed inside a shield on the shaded side of the distribution cabinet's outer shell, away from direct exposure to the exhaust fan, and its type is consistent with the first internal temperature and humidity sensor.

[0034] Finally, the system connects to an external meteorological server via the communication interface configured on the power distribution cabinet to obtain the external temperature forecast sequence and external relative humidity forecast sequence for a preset future time period. As an example, this preset time period is typically set to 2 to 12 hours to ensure that the simulation window fully covers the entire process of condensation risk evolution and dehumidification control taking effect. All sensor data and forecast data obtained from the communication interface are aligned with a unified timestamp and stored in a local cache for subsequent module calls.

[0035] The time series extrapolation module monitors the slopes of external air temperature and key node surface temperature, which alternately turn to the same direction. It then triggers least squares to identify the heat conduction time constant, performs time-domain delay shift on the external temperature forecast sequence to construct the surface temperature prediction sequence, and couples the internal relative humidity, internal air temperature, and external temperature forecast sequences with the external relative humidity forecast sequence to generate the dew point temperature prediction sequence.

[0036] Because external temperature changes in the distribution cabinet are delayed in their transmission to critical internal nodes due to thermal inertia, and the heat conduction time constant dynamically changes with environmental fluctuations, ventilation conditions, and material aging, while the internal dew point temperature evolves in real time due to air exchange and dehumidification, static comparisons cannot accurately predict future condensation risks. Therefore, a time-series extrapolation module is used to identify dynamic heat conduction parameters online, and time-domain shifting and humidity coupling calculations are performed on the forecast sequence to generate two types of prediction sequences for future periods.

[0037] In one specific embodiment, a discretized difference equation for a first-order thermal conduction inertial element is first defined, and the thermal conduction time constant is used as the parameter to be identified. Considering that the response characteristics of the surface temperature of key nodes inside the distribution cabinet to changes in external air temperature are similar to a first-order low-pass filter process, with obvious inertial delay and amplitude attenuation characteristics, the first-order inertial element model can achieve an approximate description of the dynamic behavior of thermal conduction with less computational resources while meeting engineering accuracy. Compared with higher-order models, it is more suitable for embedded real-time identification scenarios.

[0038] Specifically, the discretized difference equation of the first-order heat conduction inertial element is as follows: .

[0039] in, This is a discrete-time index, representing the current sampling time. Indicates the previous sampling time; For a fixed sampling period; That is, the heat conduction time constant to be identified; and These represent the rate of change (slope) of the surface temperature at the critical node at the current time and the previous time, respectively. Let be the slope of the external air temperature at the previous moment. Since first-order linear systems have superposition properties, the above slope recursion relationship can be derived by taking the difference between the two sides of the difference equation of temperature itself, so the slope sequence can be directly modeled.

[0040] This is the decay factor for historical states, representing the proportion of the internal temperature change trend from the previous moment retained at the current moment due to thermal inertia. The response factor is the external input, which characterizes the driving weight of the external temperature change on the rate of change of the internal surface temperature. The sum of the two is 1 to maintain the dynamic equilibrium of the system.

[0041] The continuation component representing internal thermal inertia, i.e., the value of the rate of change of surface temperature at the previous moment retained to the current moment according to the decay factor. The response component representing the external stimulus is the numerical value of the rate of change of external temperature at the previous moment applied to the rate of change of surface temperature at the current moment according to the response factor. The sum of the two is based on the principle of state superposition of a first-order heat conduction system, that is, the rate of change of internal temperature at the current moment is equal to the linear superposition of the decay component maintained by the system's own thermal inertia and the response component driven by the external thermal disturbance.

[0042] The rate of change of the real-time acquired external air temperature sequence between adjacent sampling times is calculated to generate a real-time slope sequence. Specifically, the temperature values ​​of two adjacent sampling points in the external air temperature sequence are calculated by difference, that is, the difference between the temperature value at the next moment and the temperature value at the previous moment is calculated. When the sampling period is fixed, the ratio of this difference value to the fixed sampling period is the rate of change of temperature in that discrete time interval, which is geometrically equivalent to the slope of the line connecting two adjacent sampling points. The rate of change calculated for each adjacent interval is arranged in chronological order to generate a real-time slope sequence.

[0043] In the real-time slope sequence, the change in the slope sign is monitored. Specifically, a pre-judgment window and a continuous judgment threshold are set. When the number of times the slope sign flips between positive and negative values ​​is greater than or equal to a preset flip threshold, such as 5 times, within the most recent preset first number of sampling points (e.g., 10 to 20 sampling points), it is judged as a frequent alternation state.

[0044] When the duration corresponding to the number of sampling points with the same slope appearing consecutively from a certain moment is greater than or equal to the minimum response time of heat conduction in the distribution cabinet, it is determined that the state has switched to continuously maintaining the same slope. When the slope sign changes from a frequently alternating state to a continuously maintaining the same slope, it indicates that the external environment has applied a clear and continuous thermal excitation to the distribution cabinet, and this excitation has overcome the heat conduction delay and triggered an effective internal thermal response process. The starting moment of the state switch is marked as the abrupt trigger point.

[0045] Starting from the mutation trigger point, a data segment with a duration greater than or equal to the product of the maximum expected heat conduction time constant of the power distribution cabinet and a preset multiple is extracted and used as a sliding time window; wherein, the preset multiple can be between 1.5 and 2.0; the maximum expected heat conduction time constant of the power distribution cabinet is obtained through simulation calculation based on thermodynamic parameters such as the specific heat capacity of the cabinet material, wall thickness and internal air volume.

[0046] The input vector is constructed by extracting the external air temperature slope sequence within the sliding time window, and the output vector is constructed by extracting the surface temperature slope sequence of key nodes within the corresponding time period. The discretized difference equation is iteratively solved by the least squares method to obtain the heat conduction time constant under the current environmental conditions.

[0047] Specifically, let The discretized difference equation can be rewritten as: .

[0048] Will As the input vector, As the output vector, it is solved using the least squares method. Subsequently, based on the transformation formula Inverse calculation to obtain the heat conduction time constant .

[0049] The heat conduction time constant is a scalar value with the dimension of time, reflecting the degree of inertial delay in the response of the distribution cabinet from external temperature changes to the surface temperature of critical internal nodes. The larger the value, the more sluggish the cabinet's response to external temperature changes, and the smoother the surface temperature change; the smaller the value, the more sensitive the response.

[0050] Subsequently, when a new mutation trigger point is detected, it indicates that the external ambient temperature has entered a new and sustained trend of change, and the original heat conduction time constant may no longer accurately describe the current thermal response characteristics. Therefore, the historical sliding window is cleared, and the iterative solution process for the parameters to be identified is re-executed starting from the new mutation trigger point. The historical sliding window refers to the data segment containing the old operating conditions, extracted from the previous mutation trigger point.

[0051] Furthermore, based on the obtained heat conduction time constant, a time-series projection of future temperature and humidity conditions is performed. Please refer to [link / reference needed]. Figure 2 As shown, the method for obtaining the surface temperature prediction sequence is as follows: S201, calculate the difference value of adjacent time data in the external temperature prediction sequence, and obtain the lag step number based on the ratio of the obtained heat conduction time constant to the prediction sequence time step size and round it up. Shift the difference value sequence backward by the lag step number to truly reflect the physical delay of external heat penetrating the cabinet to reach the internal node in the time domain.

[0052] S202. Since the difference value represents the temperature increment at each moment, the summation of these values ​​point by point yields the temperature change at each future moment relative to the initial moment. By superimposing this summation value with the initial surface temperature value of the key node collected in real time at the current moment, the surface temperature prediction sequence can be obtained.

[0053] Please see Figure 3 As shown, the method for obtaining the dew point temperature prediction sequence is as follows: S301, firstly, determine the saturated water vapor content of the air at the current internal air temperature, and then use the product of the saturated water vapor content and the current internal relative humidity as the current internal absolute humidity; similarly, determine the corresponding saturated water vapor content point by point according to the predicted temperature at each moment in the external temperature prediction sequence, and multiply it by the predicted external relative humidity value at the corresponding moment to calculate the external absolute humidity sequence point by point.

[0054] S302. Based on the air exchange volume flow rate entering the distribution cabinet per unit time, the product of the air exchange volume flow rate and the time step is taken as the exchange volume. Multiply it by the external absolute humidity at the current step and the internal absolute humidity at the previous moment to obtain the mass of external moisture entering and the mass of internal moisture exiting.

[0055] As one possible implementation, the air exchange volumetric flow rate can be calibrated by injecting a constant flow rate of tracer gas (such as SF6) into the cabinet while the distribution cabinet is closed, and monitoring the concentration decay curve of the tracer gas in the cabinet. Those skilled in the art can understand this as needed, and it will not be described in detail again.

[0056] Simultaneously, the dehumidified moisture mass within the current time step is calculated based on the current actual operating duty cycle and rated dehumidification capacity of the dehumidifier. The dehumidifier is a semiconductor condensing dehumidifier, and the rated dehumidification capacity is the amount of water removed per unit time at 100% duty cycle. The dehumidified moisture mass within the current time step is obtained by multiplying the rated dehumidification capacity, the current actual operating duty cycle, and the time step.

[0057] Subtract the mass of external moisture entering from the mass of internal moisture exiting, and then subtract the mass of dehumidified moisture within the current time step to obtain the net change in moisture mass. Divide the net change in moisture mass by the fixed volume of air inside the distribution cabinet to calculate the change in internal absolute humidity within each time step.

[0058] S303. Starting from the known internal absolute humidity at the current moment, according to the time step order, the internal absolute humidity change calculated at each moment is superimposed on the internal absolute humidity at the previous moment, and the internal absolute humidity at the next moment is gradually updated. This process is repeated until the end of the preset time period in the future to obtain the internal absolute humidity prediction sequence.

[0059] The internal absolute humidity at each moment in the prediction sequence is taken as the target. It is first reduced to the actual water vapor partial pressure according to the gas law. Since the saturated water vapor partial pressure increases monotonically with the increase of temperature, the actual water vapor partial pressure at each moment is taken as the target saturated water vapor partial pressure. By solving the inverse function of the relationship between saturated water vapor partial pressure and temperature, the temperature value corresponding to the target saturated water vapor partial pressure is directly calculated. This temperature value is the dew point temperature at that moment. After point-by-point conversion, the dew point temperature prediction sequence is generated.

[0060] Risk location module: Compare the two prediction sequences point by point, and mark the moment when the predicted dew point temperature first exceeds the predicted surface temperature as the risk time point.

[0061] Given that the fundamental physical condition for condensation to occur is that the air dew point temperature reaches or exceeds the solid surface temperature, in order to intervene in advance, it is necessary to find the earliest occurrence time of this critical state in the prediction sequence, so as to leave a time margin for dehumidification operation.

[0062] Therefore, firstly, the dew point temperature prediction sequence and the surface temperature prediction sequence are synchronously traversed along the time axis starting from the next time step of the current time; within each time step, the difference between the current time step's dew point temperature prediction value and the surface temperature prediction value is calculated.

[0063] When the difference changes from less than or equal to zero to greater than zero, it is determined that the predicted dew point temperature exceeds the predicted surface temperature for the first time. At this time, the critical condition for condensation risk is met. The moment when the difference changes state is marked as the risk time point, and the traversal is stopped immediately.

[0064] If all predicted dew point temperatures are not greater than the corresponding predicted surface temperature within a preset future time period, a risk-free flag will be output and the process will be terminated.

[0065] Dynamic control module: Based on the time interval between the current moment and the risk time point and the internal relative humidity, calculate the target dehumidification rate, map and generate a pulse width modulation signal to output to the dehumidification actuator, and trigger re-acquisition until the dew point temperature prediction sequence is not higher than the surface temperature prediction sequence throughout the entire time period.

[0066] Typically, different types of dehumidifiers exhibit nonlinear dehumidification rates at varying power levels, and the dehumidification process itself alters the humidity inside the cabinet, thus affecting subsequent dew point temperature predictions. Therefore, the control strategy must be able to dynamically calculate the required dehumidification rate based on the urgency of the current risk point and the current humidity level, and continuously monitor the control effect to form a closed-loop iteration.

[0067] In one specific embodiment, firstly, the predicted surface temperature value corresponding to the risk time point is extracted as the critical dew point temperature, and the saturated water vapor content at the critical dew point temperature is obtained as the maximum allowable water vapor content inside the distribution cabinet; the saturated water vapor content at the current internal air temperature is calculated, and the maximum allowable water vapor content is divided by the saturated water vapor content at the current internal air temperature to calculate the safe relative humidity threshold.

[0068] The time interval between the current moment and the risk time point is calculated in real time. The current internal relative humidity is extracted. The difference between the current internal relative humidity and the safe relative humidity threshold is compared with zero and the larger value is taken as the humidity difference to be eliminated. Combined with the saturated water content at the current internal air temperature, the humidity difference to be eliminated is converted into the internal absolute humidity difference at the corresponding temperature. The absolute humidity difference is multiplied by the fixed volume of air inside the distribution cabinet to obtain the mass of moisture to be removed.

[0069] The system determines whether the time interval is greater than a preset minimum control time margin. If it is, the mass of moisture to be removed is divided by the time interval to calculate the target dehumidification rate. If it is less than or equal to the target dehumidification rate, the target dehumidification rate is directly set as the maximum dehumidification rate of the dehumidification actuator. The minimum control time margin is determined by the sum of the dehumidification actuator's start-up response delay and the system communication calculation delay.

[0070] Obtain the maximum dehumidification rate of the dehumidification actuator at full duty cycle, calculate the ratio of the target dehumidification rate to the maximum dehumidification rate, and multiply this ratio by 100% to obtain the target duty cycle value; when the calculated target duty cycle value exceeds 100%, clamp it to 100% output to prevent control signal overflow.

[0071] The target duty cycle value increases continuously as the time interval between the current moment and the risk time point decreases, and decreases continuously as the time interval increases, thereby achieving a continuous match between dehumidification power and the urgency of the risk, avoiding over- or under-dehumidification caused by discrete gear switching.

[0072] Based on the target duty cycle value, a pulse width modulation (PWM) signal with a corresponding duty cycle is generated and output to the dehumidifier. During the output of the PWM signal, considering the dynamic coupling effect between external environmental disturbances and internal dehumidification actions, the multi-source acquisition module is continuously triggered to re-acquire data. The re-acquired data is then sequentially input into the time series extrapolation module and the risk location module for iterative calculation. When the updated dew point temperature prediction sequence is not higher than the updated surface temperature prediction sequence throughout the entire time period, it indicates that the condensation risk has been eliminated, and the output of the PWM signal is stopped, completing the closed-loop control.

[0073] Example 2

[0074] The present invention proposes a power distribution cabinet, comprising: a cabinet body, wherein the key node is provided inside the cabinet body.

[0075] The sensing hardware group includes a first temperature and humidity sensor installed inside the cabinet, a temperature sensor attached to the surface of key nodes, and a second temperature and humidity sensor installed outside the cabinet.

[0076] A communication interface, located on the cabinet, is used to connect to an external meteorological server.

[0077] A dehumidifier actuator is located inside the cabinet and is used to perform dehumidification.

[0078] An edge control unit is located on the cabinet and is electrically connected to the sensing hardware group, the communication interface and the dehumidification actuator. The edge control unit is configured to perform the functions of the timing simulation module, the risk positioning module and the dynamic control module in the power distribution cabinet condensation monitoring and removal control system.

[0079] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0080] Those skilled in the art will recognize that the algorithmic steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0081] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0082] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0083] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A condensation monitoring and removal control system for power distribution cabinets, characterized in that, include: Multi-source acquisition module: Real-time acquisition of internal air temperature and relative humidity of the distribution cabinet, surface temperature of key nodes, external air temperature and relative humidity, and acquisition of external temperature forecast sequence and external relative humidity forecast sequence for a future preset period; Time series extrapolation module: When the slopes of external air temperature and key node surface temperature alternately turn to the same direction, the least squares method is triggered to identify the heat conduction time constant. The external temperature forecast sequence is time-domain delayed and shifted to construct the surface temperature prediction sequence. The internal relative humidity, internal air temperature and external temperature forecast sequence are coupled with the external relative humidity forecast sequence to generate the dew point temperature prediction sequence. Risk location module: Compare the two prediction sequences point by point, and mark the moment when the predicted dew point temperature first exceeds the predicted surface temperature as the risk time point; Dynamic control module: Based on the time interval between the current moment and the risk time point and the internal relative humidity, calculate the target dehumidification rate, map and generate a pulse width modulation signal to output to the dehumidification actuator, and trigger re-acquisition until the dew point temperature prediction sequence is not higher than the surface temperature prediction sequence throughout the entire time period.

2. The condensation monitoring and removal control system for a power distribution cabinet as described in claim 1, characterized in that, The multi-source acquisition module specifically includes: The internal air temperature and relative humidity are collected in real time by a first temperature and humidity sensor installed inside the distribution cabinet, the surface temperature of the key nodes is collected in real time by a temperature sensor attached to the surface of the key nodes inside the distribution cabinet, and the external air temperature and relative humidity are collected in real time by a second temperature and humidity sensor installed outside the distribution cabinet. By connecting to an external meteorological server through a communication interface, the system can obtain the external temperature forecast sequence and the external relative humidity forecast sequence for the next preset period.

3. The condensation monitoring and removal control system for a power distribution cabinet as described in claim 1, characterized in that, The key nodes are one or more combinations of the following: busbar connection points inside the distribution cabinet, circuit breaker contacts, conductive parts of disconnect switches, or cable terminal joints.

4. The condensation monitoring and removal control system for a power distribution cabinet as described in claim 1, characterized in that, The method for obtaining the heat conduction time constant is as follows: A discretized difference equation for the first-order thermal conduction inertial element is defined, with the thermal conduction time constant as the parameter to be identified. Calculate the rate of change of the real-time acquired external air temperature sequence between adjacent sampling times to generate a real-time slope sequence; In the real-time slope sequence, the change state of the slope sign is monitored. When the slope sign changes from a state of frequent alternation to a state of continuous same sign, and the duration of the same sign state is greater than or equal to the minimum response time of heat conduction in the distribution cabinet, the start time of the state switch is marked as the sudden change trigger point. Starting from the point of sudden change, a data segment with a duration greater than or equal to the product of the maximum expected heat conduction time constant of the power distribution cabinet and a preset multiple is extracted and used as a sliding time window. The input vector is constructed by extracting the external air temperature slope sequence within the sliding time window, and the output vector is constructed by extracting the surface temperature slope sequence of key nodes within the corresponding time period. The discretized difference equation is iteratively solved by the least squares method to obtain the heat conduction time constant under the current environmental conditions. When a new mutation trigger point is detected, the historical sliding window is cleared and the iterative solution process for the parameters to be identified is re-executed starting from the new mutation trigger point.

5. The condensation monitoring and removal control system for a power distribution cabinet as described in claim 1, characterized in that, The method for obtaining the surface temperature prediction sequence is as follows: Calculate the difference between adjacent time data in the external temperature forecast sequence, and shift the difference sequence backward by the number of time steps corresponding to the heat conduction time constant; The differential value sequence after translation is accumulated point by point, and the initial surface temperature value of the key node at the current moment is superimposed to construct the surface temperature prediction sequence.

6. The condensation monitoring and removal control system for a power distribution cabinet as described in claim 1, characterized in that, The method for obtaining the dew point temperature prediction sequence is as follows: Extract the current internal absolute humidity corresponding to the internal air temperature and internal relative humidity; Extract the external absolute humidity sequence corresponding to the external temperature forecast sequence and the external relative humidity forecast sequence point by point; Based on the air exchange volume flow rate entering the distribution cabinet per unit time, calculate the mass of external moisture entering with the external air and the mass of internal moisture exiting with the internal air in each time step. Subtract the internal moisture mass from the external moisture mass, and then subtract the dehumidified moisture mass calculated based on the current actual operating status of the dehumidifier within the current time step to obtain the net change in moisture mass. Divide the net change in moisture mass by the fixed volume of air inside the distribution cabinet to calculate the change in internal absolute humidity within each time step. Add the change in internal absolute humidity at the current time step to the internal absolute humidity at the previous time step, update it to the internal absolute humidity at the current time step, and iterate through the external absolute humidity sequence to obtain the predicted internal absolute humidity sequence for the future preset time period. The internal absolute humidity prediction sequence is converted point by point into the corresponding dew point temperature to generate the dew point temperature prediction sequence.

7. The condensation monitoring and removal control system for a power distribution cabinet as described in claim 1, characterized in that, The risk location module is also used for: If all predicted dew point temperatures are not greater than the corresponding predicted surface temperature within a preset future time period, a risk-free flag will be output and the process will be terminated.

8. The condensation monitoring and removal control system for a power distribution cabinet as described in claim 1, characterized in that, The dynamic control module specifically includes: The target dehumidification rate is calculated based on the time interval between the current moment and the risk time point, as well as the internal relative humidity. Obtain the maximum dehumidification rate of the dehumidification actuator at full duty cycle, calculate the ratio of the target dehumidification rate to the maximum dehumidification rate, and multiply the ratio by 100% to obtain the target duty cycle value. Based on the target duty cycle value, a pulse width modulation signal with the corresponding duty cycle is generated and output to the dehumidification actuator; During the output of the pulse width modulation signal, the multi-source acquisition module is continuously triggered to re-acquire data, and the re-acquired data is sequentially input into the time series deduction module and the risk location module for iterative calculation. When the updated dew point temperature prediction sequence is not higher than the updated surface temperature prediction sequence throughout the entire time period, the output of the pulse width modulation signal is stopped.

9. The condensation monitoring and removal control system for a power distribution cabinet as described in claim 8, characterized in that, The method for obtaining the target dehumidification rate is as follows: The system calculates the time interval between the current moment and the risk time point in real time, extracts the current internal relative humidity, and obtains the safe relative humidity threshold based on the current internal air temperature. The difference between the current internal relative humidity and the safe relative humidity threshold is calculated as the humidity difference to be eliminated. The humidity difference to be eliminated is converted into the internal absolute humidity difference at the corresponding temperature, and multiplied by the fixed volume of air inside the distribution cabinet to obtain the mass of moisture to be removed. If the time interval is greater than the preset minimum control time margin, the target dehumidification rate is calculated by dividing the mass of moisture to be removed by the time interval. If the time interval is less than or equal to the target dehumidification rate, the target dehumidification rate is directly set to the maximum dehumidification rate of the dehumidification actuator.

10. A power distribution cabinet, characterized in that, include: The cabinet, with key nodes located inside; The sensing hardware group includes a first temperature and humidity sensor installed inside the cabinet, a temperature sensor attached to the surface of key nodes, and a second temperature and humidity sensor installed outside the cabinet. A communication interface, located on the cabinet, is used to connect to an external meteorological server; A dehumidifier actuator, located inside the cabinet, is used to perform dehumidification. An edge control unit is located on the cabinet and is electrically connected to the sensing hardware group, the communication interface and the dehumidification actuator. The edge control unit is configured to perform the functions of the timing simulation module, the risk positioning module and the dynamic control module in the power distribution cabinet condensation monitoring and removal control system.

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