An energy-saving control method and device for a power distribution room anti-condensation dehumidification device

Abstract

This invention relates to the field of power environment monitoring and intelligent control technology, and discloses an energy-saving control method and device for a power distribution room anti-condensation dehumidification device. The method includes acquiring real-time environmental parameters collected by a sensor array; performing dew point physical model calculations to obtain a condensation safety margin index; substituting the condensation safety margin index and relative humidity into a graded control strategy table to determine the target operating mode; deriving the basic pulse width modulation duty cycle based on the condensation safety margin index and its rate of change; and performing peak load constraint correction on the basic pulse width modulation duty cycle based on the real-time total power load to generate the final execution control command to generate control signals for driving component operation. This invention enables precise decision-making for anti-condensation, energy-saving control, and power distribution load safety constraints.

Description

Technical Field

[0001] This invention relates to the field of power environment monitoring and intelligent control technology, and in particular to an energy-saving control method and device for a power distribution room anti-condensation dehumidification device. Background Technology

[0002] Currently, as a critical terminal node in the power system, the stability of the internal environment of the substation directly affects the operational safety and service life of electrical equipment. Since substations are often located in basements or on the ground floor, they are highly susceptible to humidity due to seasonal changes, rainfall, and temperature fluctuations. When the ambient humidity is too high or the surface temperature of the equipment is below the air dew point, condensation will form on the insulation surface of the electrical equipment, leading to serious accidents such as decreased insulation strength, flashover, and even short-circuit tripping. Therefore, efficient and reliable anti-condensation control is a necessary means to ensure the safety of the power grid.

[0003] In existing technologies, environmental regulation typically employs a process control system comprised of temperature and humidity sensors, a controller, and dehumidification and heating equipment. Its control logic primarily relies on preset fixed environmental parameter thresholds (e.g., triggering the dehumidifier when the monitored relative humidity (RH) exceeds 80%; triggering the heater when the ambient temperature is below 5°C). While this control method can alleviate high humidity conditions to some extent, it is essentially a one-size-fits-all passive response mechanism based on air conditions. However, this existing technology neglects the physical nature of condensation formation. According to thermodynamic principles, whether condensation occurs depends on the difference between the surface temperature of an object and the dew point temperature of the air, not just the relative humidity. In actual operating conditions, if the surface temperature of the equipment drops sharply due to a sudden cooling of the environment, condensation may still occur even if the relative humidity has not reached the alarm threshold; conversely, in certain high-humidity but safe-temperature conditions, operating the dehumidifier at full power would result in significant energy waste.

[0004] Therefore, existing technologies suffer from delayed anti-condensation response and low energy efficiency. Summary of the Invention

[0005] This invention provides an energy-saving control method and device for a power distribution room anti-condensation dehumidification device, in order to solve the problems of delayed anti-condensation response and low energy consumption efficiency in the prior art.

[0006] In a first aspect, the present invention provides an energy-saving control method for a power distribution room anti-condensation dehumidification device, executed by a controller, comprising:

[0007] The sensor arrays deployed in different physical zones of the power distribution room collect real-time environmental parameters for each zone, including ambient temperature, relative humidity, and surface temperature of key equipment.

[0008] The dew point physical model is used to calculate the current dew point temperature based on the ambient temperature and relative humidity. The difference between the surface temperature of the key equipment and the current dew point temperature is calculated to obtain the condensation safety margin index, which reflects the degree of condensation risk.

[0009] The condensation safety margin index and the relative humidity are substituted into a preset graded control strategy table for matching and querying to determine the target working mode of the current zone. The target working mode includes at least one of the following: standby monitoring mode, ventilation and dehumidification mode only, far-infrared surface heating mode, and emergency strong dehumidification mode.

[0010] If the target working mode is the far-infrared surface heating mode, then based on the condensation safety margin index and the rate of change of the condensation safety margin index, fuzzy PID logic operation is performed to derive the basic pulse width modulation duty cycle value for the far-infrared heating component.

[0011] The real-time total power load of the power distribution room is obtained, and the peak load constraint correction is performed on the basic pulse width modulation duty cycle value based on the real-time total power load to generate the final execution control command.

[0012] Based on the final execution control command, the far-infrared heating components and ventilation components of the corresponding partition are driven to operate, and real-time current feedback data after operation is collected to verify the execution status.

[0013] Secondly, the present invention provides an energy-saving control device for a power distribution room anti-condensation dehumidification device, configured in a controller, the energy-saving control device for the power distribution room anti-condensation dehumidification device comprising:

[0014] The zoned environmental monitoring module is used to acquire real-time environmental parameters of each zone collected by sensor arrays deployed in different physical zones of the power distribution room. The real-time environmental parameters include ambient temperature, relative humidity, and surface temperature of key equipment.

[0015] The condensation risk assessment module is used to perform dew point physical model calculations on the ambient temperature and relative humidity to obtain the current dew point temperature, and to calculate the difference between the surface temperature of the key equipment and the current dew point temperature to obtain a condensation safety margin index that reflects the degree of condensation risk.

[0016] The working mode decision module is used to substitute the condensation safety margin index and the relative humidity into a preset graded control strategy table for matching and querying, and determine the target working mode of the current zone. The target working mode includes at least one of the following: standby monitoring mode, ventilation and dehumidification mode only, far-infrared surface heating mode, and emergency strong dehumidification mode.

[0017] The fuzzy PID calculation module is used to perform fuzzy PID logic calculation based on the condensation safety margin index and the rate of change of the condensation safety margin index if the target working mode is the far-infrared surface heating mode, and derive the basic pulse width modulation duty cycle value for the far-infrared heating component.

[0018] The load constraint correction module is used to obtain the real-time total power load of the power distribution room, perform peak load constraint correction on the basic pulse width modulation duty cycle value based on the real-time total power load, and generate the final execution control command.

[0019] The closed-loop drive verification module is used to drive the far-infrared heating components and ventilation components of the corresponding partition to operate based on the final execution control command, and to collect real-time current feedback data after operation to verify the execution status.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] (1) This invention, by constructing a dew point safety margin assessment model based on physical mechanisms, breaks through the limitations of traditional control methods that rely solely on a single environmental humidity threshold, achieving precise decision-making and on-demand activation for condensation prevention. This invention abandons the crude approach of relying solely on relative humidity for control, instead using the Magnus thermodynamic model to calculate the current dew point temperature in real time and combining it with the surface temperature of key equipment to calculate the condensation safety margin. Through this derivation process, it can accurately identify pseudo-demand conditions where high humidity exists but the equipment surface temperature is still high (no risk of condensation), thus avoiding ineffective heating; it can also identify hidden dangers where low humidity exists but the equipment surface cools rapidly (risk of condensation). This judgment logic based on physical essence eliminates condensation at its source while minimizing unnecessary equipment operating time, significantly improving energy efficiency.

[0022] (2) This invention solves the problems of large lag and nonlinearity in the environmental control of power distribution rooms by integrating a hierarchical control strategy with a fuzzy PID control algorithm, achieving smooth and stable control and deep energy saving. Firstly, this invention divides the operating conditions into multiple levels such as standby, ventilation, heating, and emergency response based on safety margins, and matches the optimal energy consumption strategy for different operating conditions (e.g., prioritizing ventilation and dehumidification, and only turning on infrared heating when necessary). Furthermore, in the heating mode, the fuzzy PID algorithm dynamically calculates the pulse width modulation duty cycle based on the deviation and rate of change of the safety margin. This process avoids the large temperature fluctuations and frequent start-stop oscillations caused by traditional on-off control, and can maintain the equipment surface temperature slightly above the dew point with minimal power output, thus extending the service life of the actuator and ensuring the constant temperature accuracy of environmental control, achieving excellent energy-saving effects.

[0023] (3) This invention eliminates the impact risk of dehumidification device operation on power grid distribution load by introducing peak load constraint correction and closed-loop current feedback mechanism, thereby improving operational safety and maintenance reliability. Before issuing control commands, this invention pre-calculates the superposition value of the new power and the current total load, and automatically performs peak shaving correction according to the safety capacity of the power distribution room to prevent the power distribution room from tripping due to the simultaneous full-power start-up of multiple devices; at the same time, by collecting real-time current feedback data after execution, the execution status of physical actions can be verified in real time (such as identifying load circuit breakage or relay sticking). This closed-loop verification mechanism makes up for the blind spot in traditional open-loop control where the execution is considered successful as soon as the command is issued, effectively ensuring the power safety of the power distribution room and significantly reducing the maintenance cost of manual inspection and troubleshooting. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the energy-saving control method of the anti-condensation dehumidification device for power distribution rooms provided in the first embodiment of the present invention;

[0025] Figure 2 This is a schematic diagram of the energy-saving control device structure of the power distribution room anti-condensation dehumidification device provided in the second embodiment of the present invention. Detailed Implementation

[0026] 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.

[0027] Reference Figure 1 The first embodiment of the present invention provides an energy-saving control method for a power distribution room anti-condensation dehumidification device, which is executed by a controller and includes the following steps:

[0028] S11. Obtain real-time environmental parameters of each zone collected by sensor arrays deployed in different physical zones of the power distribution room. The real-time environmental parameters include ambient temperature, relative humidity, and surface temperature of key equipment.

[0029] S12. Perform dew point physical model calculation on the ambient temperature and relative humidity to obtain the current dew point temperature, and calculate the difference between the surface temperature of the key equipment and the current dew point temperature to obtain a condensation safety margin index that reflects the degree of condensation risk.

[0030] S13. Substitute the condensation safety margin index and the relative humidity into the preset graded control strategy table for matching and querying to determine the target working mode of the current zone. The target working mode includes at least one of the following: standby monitoring mode, ventilation and dehumidification mode only, far-infrared surface heating mode, and emergency strong dehumidification mode.

[0031] S14. If the target working mode is the far-infrared surface heating mode, then based on the condensation safety margin index and the rate of change of the condensation safety margin index, fuzzy PID logic operation is performed to derive the basic pulse width modulation duty cycle value for the far-infrared heating component.

[0032] S15. Obtain the real-time total power load of the power distribution room, and perform peak load constraint correction on the basic pulse width modulation duty cycle value based on the real-time total power load to generate the final execution control command.

[0033] S16. Based on the final execution control command, drive the far-infrared heating component and ventilation component of the corresponding partition to operate, and collect real-time current feedback data after operation to verify the execution status.

[0034] In step S11, real-time environmental parameters of each physical zone in the power distribution room are acquired from sensor arrays deployed in different zones. These real-time environmental parameters include ambient temperature, relative humidity, and surface temperature of key equipment.

[0035] The sensor array acquires the original sensor data sequence of each zone at a preset sampling frequency. The original sensor data sequence includes multiple sets of temperature readings, humidity readings and infrared temperature readings with timestamps.

[0036] Outlier detection is performed on the original sensing data sequence to remove noisy data points that exceed the preset physical range or gradient change threshold, thereby generating an effective monitoring sequence.

[0037] The effective monitoring sequence is smoothed in the time domain by using a moving average filtering algorithm, and the arithmetic mean within the moving window is calculated to obtain the ambient temperature, relative humidity and surface temperature of key equipment.

[0038] In one implementation, this embodiment utilizes sensor arrays deployed in different physical zones of a power distribution room to perform environmental perception tasks. The process first involves acquiring raw sensor data sequences for each zone using the sensor array at a preset sampling frequency. Then, outlier detection is performed on the sequences to remove hard errors and non-physical jumps. Next, a moving average filtering algorithm is used for temporal smoothing to improve the signal-to-noise ratio. Finally, the processed data is associated and mapped with physical location identifiers.

[0039] In one implementation, this embodiment acquires the original sensing data sequence of each zone through the sensor array at a preset sampling frequency. The sensor array consists of several composite sensing nodes that integrate temperature and humidity sensing units (e.g., digital sensors based on capacitive humidity sensors and bandgap temperature sensors) and non-contact infrared temperature measurement units.

[0040] It should be noted that the method for determining the preset sampling frequency in this embodiment is based on Shannon's sampling theorem and the thermal inertia characteristics of the power distribution room environment. Specifically, considering that the changes in ambient temperature and humidity caused by air convection in the power distribution room are usually low-frequency signals with a cutoff frequency far below 1Hz, but in order to capture the transient temperature rise caused by partial discharge or poor contact in the switchgear, this embodiment sets the sampling frequency to 1Hz. This value is set based on the following: firstly, a sampling rate of 1Hz meets the requirement of distortion-free reconstruction at more than twice the highest effective frequency of the signal; secondly, this frequency is sufficient to accumulate enough sample points within the system control cycle to support subsequent digital filtering processing, while avoiding excessive data throughput redundancy. In this embodiment, a high-precision hardware clock is read at each acquisition moment, and a microsecond-level timestamp is added to each set of readings.

[0041] In one implementation, this embodiment performs outlier detection on the original sensor data sequence. For the preset physical range, this embodiment determines it based on the limiting parameters in the sensor hardware specifications and the safety regulations for power distribution room operation. For example, the ambient temperature range is set to -40 to +85 degrees Celsius; readings exceeding this range are judged as sensor malfunctions and discarded. For the preset gradient change threshold, this embodiment adopts a strategy combining default presets and dynamic updates to solve the cold start problem during the initial system operation.

[0042] Specifically, during the initial system installation or in the absence of historical data accumulation, this embodiment initializes the gradient change threshold to an industry-standard default value, such as 0.5 degrees Celsius per second. As the system operates and data accumulates, this embodiment periodically calculates the probability density distribution of the rate of change of adjacent sampling points under historical operating conditions and selects the 99.9th percentile of this distribution as the updated gradient change threshold. This dynamic update mechanism ensures that the threshold can adapt to the actual thermal characteristics of the power distribution room, statistically eliminating most non-physical jumps caused by electromagnetic interference, while retaining a very small number of real rapid temperature change signals.

[0043] In one implementation, this embodiment uses a moving average filtering algorithm to smooth the effective monitoring sequence in the time domain. It should be noted that the sliding window size is determined using a method based on on-site noise spectrum analysis. Considering the complexity of the electromagnetic environment in the power distribution room, this embodiment controls the high-power HVAC equipment in the power distribution room to shut down during equipment commissioning or system downtime to create an unloaded static environment, and continuously collects sensor data for at least 60 seconds as analysis samples. The purpose of collecting long data is to ensure sufficient frequency resolution for subsequent spectrum analysis. Subsequently, a fast Fourier transform is performed on the sample data to analyze the main peak frequency of the noise spectrum. This embodiment sets the sliding window duration to an integer multiple of the main period of the noise, maximizing the cancellation of periodic interference. In this embodiment, combined with a sampling frequency of 1Hz, the sliding window size is set to 5 sampling points, i.e., a 5-second window. This window size effectively smooths random white noise, improving the signal-to-noise ratio by a factor of √5, while ensuring that the system's response delay to real-world environmental changes is controlled within 2.5 seconds, meeting the real-time requirements of the power distribution room dehumidification control.

[0044] In one implementation, this embodiment associates and maps the ambient temperature, relative humidity, and surface temperature of the key equipment with corresponding zone physical location identifiers to generate the zone environmental state dataset. The key equipment specifically includes, but is not limited to, electrical facilities sensitive to condensation or prone to heat generation, such as copper busbar contacts, cable terminations, circuit breaker contacts, and transformer casings within high-voltage switchgear. This embodiment maintains a hash mapping table in memory, where the key is a preset zone physical location identifier (e.g., Zone_A_Transformer_No1), corresponding to the GIS spatial coordinates or electrical topology location of the power distribution room; and the value is a smoothed combination of environmental parameters calculated above. Through this structured key-value pair storage, the zone environmental state dataset is constructed, providing a spatially defined input source for subsequent dew point calculations and pattern decisions.

[0045] In step S12, a dew point physical model is used to calculate the ambient temperature and relative humidity to obtain the current dew point temperature. Then, based on the difference between the surface temperature of the critical equipment and the current dew point temperature, a condensation safety margin index reflecting the degree of condensation risk is calculated, including:

[0046] The ambient temperature and relative humidity are used as input parameters and substituted into a preset thermodynamic calculation model. Logarithmic and rational fractional nonlinear operations are performed to output the current dew point temperature.

[0047] Calculate the difference between the surface temperature of the key equipment and the current dew point temperature, and mark the difference as the condensation safety margin index.

[0048] In one implementation, this embodiment invokes a pre-set thermodynamic calculation model based on saturated water vapor pressure characteristics, substituting the ambient temperature and relative humidity as input parameters into the model. By performing nonlinear operations involving logarithms and rational fractions, the current dew point temperature is output. Subsequently, this embodiment calculates the algebraic difference between the surface temperature of the critical equipment and the current dew point temperature, and marks this difference as a condensation safety margin index.

[0049] In one implementation, the thermodynamic calculation model used in this embodiment is a modified version based on the Magnus-Tetens formula. Conventional thermodynamic calculation models typically establish an exponential function relationship between saturated vapor pressure and temperature, while the improved model in this application further transforms it into a dew point inverse solution logic that is easier for digital processors to compute. The specific calculation process is as follows: The temperature factor is calculated by multiplying the first thermodynamic parameter by the current ambient temperature, and then dividing the product by the sum of the second thermodynamic parameter and the current ambient temperature; the humidity factor is calculated by taking the natural logarithm of the relative humidity (after normalization); the temperature factor and humidity factor are added to obtain an intermediate variable; the second thermodynamic parameter is multiplied by the intermediate variable, and the product is divided by the difference between the first thermodynamic parameter and the intermediate variable to obtain the final dew point temperature. This model describes the physical properties of a pure substance in the coexistence of two phases using a preset atmospheric thermodynamic constant.

[0050] It should be noted that, to ensure the model's calculation accuracy within the common operating conditions of power distribution rooms (i.e., -45°C to 60°C), this embodiment sets fixed physical constants according to the World Meteorological Organization's "Guide to Meteorological Instruments and Methods of Observation," specifically including the reference constant characterizing the saturated water vapor pressure at zero degrees Celsius, denoted as [reference needed]. The first thermodynamic parameter, with a value of 6.112 hPa, related to latent heat and gas properties, is denoted as... Given a liquid water surface value of 17.62 and a second thermodynamic parameter, denoted as... The value for the liquid water surface is 243.12 degrees Celsius. These constants are stored in the controller's non-volatile memory space as a fixed reference for physical calculations and are not lost when power is off.

[0051] In one implementation, this embodiment performs a specific dew point physical model calculation. This embodiment reads the smoothed ambient temperature (denoted as ) from S11. (unit: degrees Celsius) and relative humidity (denoted as ) (The value is expressed as a percentage), and the calculation is performed using the floating-point unit inside the controller. The calculation process first constructs an intermediate function relationship containing the natural logarithm and rational fraction, and then solves for the dew point temperature value under the current air conditions (denoted as...). The specific function operation logic is shown in the following formula:

[0052]

[0053] in, Represents ambient temperature. Represents relative humidity. and The aforementioned thermodynamic parameters are used. This formula corresponds to the technical limitations of performing nonlinear operations of logarithms and rational fractions, and can map the current temperature and humidity state to a precise physical critical temperature value, that is, the temperature point at which water vapor in the air reaches saturation and begins to condense into liquid water.

[0054] In one implementation, this embodiment calculates the difference between the equipment surface temperature and the current dew point temperature to obtain a condensation safety margin index reflecting the degree of condensation risk. In this embodiment, the key equipment surface temperature collected in S11 is used as the minuend, and the calculated current dew point temperature is used as the subtrahend. An algebraic subtraction operation is performed, and the resulting difference is the condensation safety margin index. This index has a clear physical indication. If the index is positive and large, it indicates that the equipment surface temperature is much higher than the dew point, and there is no risk of condensation. If the index approaches zero or is negative, it indicates that the equipment surface temperature is close to or lower than the ambient dew point, and condensation is about to occur or has already occurred. By introducing this index, this embodiment can effectively eliminate the hidden danger of localized condensation caused by excessively low temperatures of local equipment in the power distribution room (such as busbars after shutdown and cooling), overcoming the limitations of traditional methods that rely solely on a single threshold of ambient humidity for judgment.

[0055] In step S13, the condensation safety margin index and the relative humidity are substituted into a preset graded control strategy table for matching and querying to determine the target working mode of the current zone. The target working mode includes at least one of the following: standby monitoring mode, ventilation-only dehumidification mode, far-infrared surface heating mode, and emergency strong dehumidification mode.

[0056] The preset graded control strategy table is invoked. The graded control strategy table stores a two-dimensional mapping matrix consisting of a preset condensation safety margin range and a preset relative humidity range. Each matrix unit is associated with a unique working mode label.

[0057] The condensation safety margin index is compared with the preset condensation critical threshold, and the relative humidity is compared with the preset high humidity alarm threshold.

[0058] If the condensation safety margin index is less than the preset condensation critical threshold and the relative humidity is greater than the preset high humidity alarm threshold, then according to the two-dimensional mapping matrix, the target working mode is locked as the emergency strong dehumidification mode.

[0059] If the condensation safety margin index is less than the condensation critical threshold, and the relative humidity is less than or equal to the high humidity alarm threshold, then according to the two-dimensional mapping matrix, the target working mode is locked as the far-infrared surface heating mode.

[0060] If the condensation safety margin index is greater than or equal to the condensation critical threshold, and the relative humidity is greater than the high humidity alarm threshold, then according to the two-dimensional mapping matrix, the target working mode is locked as the ventilation and dehumidification mode only.

[0061] If the condensation safety margin index is greater than or equal to the condensation critical threshold, and the relative humidity is less than or equal to the high humidity alarm threshold, then according to the two-dimensional mapping matrix, the target working mode is locked as the standby monitoring mode, and a shutdown command is generated for the far-infrared heating component and the ventilation component.

[0062] In one implementation, a preset hierarchical control strategy table stored in the controller's non-volatile memory (such as EEPROM or Flash) is invoked. This strategy table is logically constructed as a two-dimensional mapping matrix, or exists in the form of multiple nested conditional judgment logic. This matrix uses the condensation safety margin index as the vertical axis variable and the relative humidity as the horizontal axis variable, dividing the operating conditions of the power distribution room into four independent logical quadrants. Each quadrant is uniquely associated with a predefined target operating mode.

[0063] Specifically, the graded control strategy table is constructed using a two-dimensional interval division method. The vertical axis represents the condensation safety margin index, divided into danger zones and safe zones with a preset condensation critical threshold as the dividing point; the horizontal axis represents relative humidity, divided into high humidity zones and normal humidity zones with a preset high humidity alarm threshold as the dividing point; the two axes intersect to form four logical quadrants, which are respectively associated with emergency dehumidification mode, far-infrared surface heating mode, ventilation-only dehumidification mode, and standby monitoring mode; this division is based on thermodynamic principles and power equipment operation procedures to ensure that the optimal energy consumption strategy is adopted under different operating conditions.

[0064] In one implementation, before performing a matching query, this embodiment first loads two key physical judgment criteria: a preset condensation threshold and a preset high humidity alarm threshold.

[0065] It is particularly important to explain in detail the method for determining the condensation critical threshold. In this embodiment, the sensor comprehensive error superposition method is used for calibration. Considering the measurement accuracy of industrial-grade temperature sensors (typically...), The transmission error when converting dew point from relative humidity (degrees Celsius) and humidity is significant. If the threshold is set to 0 degrees Celsius, a negative measurement deviation can easily lead to an accident where condensation has actually occurred but has not been detected, even under critical conditions. Therefore, this embodiment sets the threshold to four to five times the absolute value of the sensor's maximum permissible negative deviation, for example, to 2.0 to 2.5 degrees Celsius. This value acts as a physical safety firewall, ensuring that this embodiment intervenes before condensation actually occurs, i.e., before the safety margin reaches zero. Regarding the method for determining the high humidity alarm threshold, this embodiment is based on power industry standards (such as DL / T related standards) and the moisture absorption characteristic curve of the insulating material. It is usually set to 80% to 85% RH. When the air humidity exceeds this value, even if condensation has not yet occurred, the air insulation strength will decrease significantly, and creepage phenomena are easily triggered.

[0066] In one implementation, this embodiment executes specific numerical comparison and mode locking logic. This embodiment reads the condensation safety margin index calculated in S12 and the relative humidity obtained in S11, and compares them with the two thresholds mentioned above. If the condensation safety margin index is less than the condensation critical threshold, it indicates a risk of condensation; and if the relative humidity is greater than the high humidity alarm threshold, it indicates an extremely humid environment. This embodiment determines that the current operating condition is the worst, and based on the two-dimensional mapping matrix, locks the target operating mode as the emergency strong dehumidification mode. In this mode, this embodiment will invoke the maximum power strategy. If the condensation safety margin index is less than the condensation critical threshold, but the relative humidity is less than or equal to the high humidity alarm threshold, it indicates that the air quality is acceptable, but the equipment surface is too cold. This embodiment locks the target operating mode as the far-infrared surface heating mode. This strategy increases the safety margin by precisely heating the equipment surface temperature, rather than blindly dehumidifying. If the condensation safety margin index is greater than or equal to the condensation critical threshold, but the relative humidity is greater than the high humidity alarm threshold, this embodiment locks the target operating mode as the ventilation-only dehumidification mode. This strategy reduces humidity by replacing air, while also saving energy.

[0067] In one implementation, this embodiment performs the determination and processing of the fourth case (safe and energy-saving operating condition). If the condensation safety margin index is greater than or equal to the condensation critical threshold, and the relative humidity is less than or equal to the high humidity alarm threshold, this embodiment determines that the current environment is in an ideal safe state. At this time, this embodiment locks the target operating mode to standby monitoring mode and immediately generates a shutdown command for the far-infrared heating component and the ventilation component. Specifically, the far-infrared heating component is configured as a silicon carbide ceramic radiation heater or a PTC thermistor heater installed at the bottom or on the wall of the switch cabinet; the ventilation component is specifically configured as an explosion-proof axial flow fan or a louvered exhaust fan installed on the top of the power distribution room. This command forces all actuators to stop operating by cutting off the relay or setting the drive signal to zero, thereby avoiding energy waste caused by excessive dehumidification and realizing closed-loop energy control.

[0068] In step S14, if the target operating mode is the far-infrared surface heating mode, then fuzzy PID logic calculation is performed based on the condensation safety margin index and the rate of change of the condensation safety margin index to derive the basic pulse width modulation duty cycle value for the far-infrared heating component, including:

[0069] The system deviation value is obtained by calculating the difference between the preset ideal safety margin constant and the condensation safety margin index.

[0070] The deviation change rate is obtained by performing a time-based differential operation on the condensation safety margin index.

[0071] Using a preset Gaussian membership function, the system deviation value and the deviation change rate are mapped into fuzzy state vectors respectively;

[0072] The fuzzy state vector is input into a pre-constructed fuzzy inference matrix for fuzzy inference synthesis operation, and the corresponding fuzzy control decision variable is output.

[0073] The centroid method is used to defuzzify the fuzzy control decision variables to obtain the control response coefficients, and the control response coefficients are linearly quantized into the basic pulse width modulation duty cycle value.

[0074] In one implementation, the target operating mode determined in S13 is first logically branched to determine the control strategy for the far-infrared heating component. Specifically, if S13 is locked to the far-infrared surface heating mode, the device determines that a precise temperature control request is involved and then activates the fuzzy PID logic operation module; if locked to the emergency dehumidification mode, in order to contain the risk of condensation as quickly as possible, the device directly sets the basic pulse width modulation duty cycle value to a preset hardware-allowed maximum value (e.g., 100%); it should be noted that if locked to the standby monitoring mode or the ventilation-only dehumidification mode, since no heating component intervention is required at this time, the device forcibly initializes the basic pulse width modulation duty cycle value to zero. This explicit initialization step ensures that the subsequent S15 has a clear zero-value input when performing load calculations, preventing logical errors caused by undefined dependent variables.

[0075] In one implementation, when the device enters the fuzzy PID logic operation flow, it first performs preprocessing of the input variables. This embodiment calculates the algebraic difference between the preset ideal safety margin constant and the currently calculated real-time condensation safety margin index to obtain the system deviation value (denoted as...). Simultaneously, the rate of change of the condensation safety margin index at the current moment relative to the previous sampling moment is calculated using the discrete difference method, yielding the deviation change rate (denoted as ). ).

[0076] It should be noted in detail that the ideal safety margin constant is determined using the safety buffer boundary method in this embodiment. This constant represents the optimal temperature difference target for the device's desired surface temperature to be higher than the dew point temperature. If set too low (e.g., 0.5 degrees), the device is prone to frequent start-ups and shutdowns due to minor environmental disturbances; if set too high (e.g., 10 degrees), it leads to unnecessary continuous heating energy consumption. This embodiment comprehensively considers the typical temperature fluctuation range caused by air convection in the power distribution room (typically...). The constant is set to 3.0 to 5.0 degrees Celsius to account for the measurement uncertainty of the sensor (degrees Celsius). This value serves as the steady-state control target for the device, ensuring that the device surface is always within a thermodynamically safe zone.

[0077] In one implementation, this embodiment performs fuzzification processing, using a preset Gaussian membership function to fuzzify the system deviation values. and the rate of change of the deviation The mapping is done as a fuzzy state vector. This embodiment first defines the fuzzy universe of discourse, mapping the range of physical quantities to a standard interval (e.g., [-3, +3]), and pre-defines several linguistic variables within this interval, including "negative large (NB)", "negative small (NS)", "zero (ZO)", "positive small (PS)", and "positive large (PB)". Unlike traditional trigonometric functions, this embodiment uses a Gaussian function as the membership function, utilizing its smoothing properties to simulate the nonlinear characteristics of human temperature perception. The device will display the real-time... and The numerical values ​​are substituted into the Gaussian function expressions corresponding to each linguistic variable to calculate the membership degree (0 to 1.0) of each fuzzy set, thereby generating a fuzzy state vector that reflects the membership degree of the current state.

[0078] It should be noted that the center value and width parameters of the Gaussian membership function are determined based on experience in controlling the temperature and humidity of the power distribution room. The center value of the function corresponds to the typical physical value of each linguistic variable, such as a large negative value corresponding to a large negative deviation and a large positive value corresponding to a large positive deviation. The width value is set to ensure that adjacent linguistic variables overlap by about one-third at the boundary of the domain of discourse, thus ensuring a smooth transition in fuzzy inference. This parameter combination has been verified through multiple rounds of field tests and can achieve the best balance between control sensitivity and anti-interference.

[0079] In one implementation, this embodiment inputs the fuzzy state vector into a pre-constructed fuzzy inference matrix for fuzzy inference synthesis. To ensure the reproducibility of the technical solution, the fuzzy inference matrix is ​​constructed as a 5×5 two-dimensional spatial rule table, with its row indices corresponding to system deviations. The language variable, with column indexes corresponding to the rate of change of deviation. The linguistic variables. The construction of this matrix follows the negative feedback control principle of eliminating deviations and damping vibration suppression, specifically including the following three types of logical rules.

[0080] The first type is the strong correction rule, which applies when the system deviation... With rate of change When the signs are the same and the absolute values ​​are large, for example For Zhengda PB and A PB ratio of 0.5 indicates a very high safety margin that is still increasing; or For negative NB and A negative value of NB indicates that the risk of condensation is extremely high and is still worsening. The maximum control quantity with the opposite sign of the matrix output and input, such as a negative value of NB or a positive value of PB, can suppress the current trend to the greatest extent.

[0081] The second category is the steady-state maintenance rule, which applies when the system deviation... With rate of change When all values ​​are zero (ZO) or close to zero, the matrix outputs zero (ZO) or a small control value to maintain the current thermal equilibrium state of the device.

[0082] The third category is overshoot suppression rules, which apply when the system deviation... With rate of change When the signs are different (e.g.) For Zhengda PB, but A negative NB indicates that although the current situation is safe, the device is rapidly approaching a danger zone. The matrix outputs a moderate-amplitude reverse control quantity to slow down in advance and prevent the device from overshooting or oscillating.

[0083] This embodiment uses the Mamdani inference method, which matches the input fuzzy state vector with the matrix constructed by the above logic through fuzzy minimum operation and maximum-minimum synthesis rule, and outputs a synthesized fuzzy control decision variable.

[0084] In one implementation, this embodiment uses the centroid method to defuzzify the fuzzy control decision variables. The device calculates the centroid abscissa of the geometric region enclosed by the fuzzy set, and this coordinate value is the normalized control response coefficient. Subsequently, the device performs a linear quantization operation, multiplying the coefficient by the full-scale value of the pulse width modulation controller, for example, 255 for an 8-bit precision pulse width modulation generator; or directly mapping it to a duty cycle of 0-100%. The resulting product is determined as the basic pulse width modulation duty cycle value. Through this complete fuzzy PID calculation chain, the device can achieve a faster response and smaller overshoot than traditional PID in the face of the complex and variable thermal environment of a power distribution room, ensuring the flexibility and precision of the control process.

[0085] In step S15, the real-time total power load of the power distribution room is obtained, and the peak load constraint correction is performed on the basic pulse width modulation duty cycle value based on the real-time total power load to generate the final execution control command, including:

[0086] The current real-time total power load of the power distribution room is read through power monitoring instruments;

[0087] Based on the basic pulse width modulation duty cycle value and the preset rated power of the far-infrared heating component, calculate the expected additional power corresponding to the current heating request;

[0088] The predicted total load is obtained by adding the real-time total power load to the expected additional power.

[0089] The predicted total load is compared with the preset safe load threshold for the power distribution room.

[0090] If the predicted total load is less than or equal to the preset safe load threshold of the power distribution room, then the basic pulse width modulation duty cycle value is directly encapsulated into the final execution control command.

[0091] If the predicted total load is greater than the preset safe load threshold for the power distribution room, then the power reduction coefficient is calculated based on the difference between the preset safe load threshold for the power distribution room and the remaining available power between the real-time total power load.

[0092] The base pulse width modulation duty cycle value is derating and multiplied using the power reduction factor to obtain the corrected pulse width modulation duty cycle value, which is then encapsulated into the final execution control command.

[0093] In one implementation, this embodiment reads the current real-time total power load of the power distribution room through a power monitoring instrument. The device periodically accesses the intelligent power meter at the incoming cabinet of the power distribution room via an RS485 communication bus or power line carrier communication (PLC) interface using Modbus-RTU or DL / T 645 protocol. The device reads the current three-phase total active power (in kilowatts), which objectively reflects the real-time total energy consumption of lighting, HVAC, maintenance equipment, and other fixed loads in the power distribution room at the current moment; simultaneously, the device synchronously reads the current real-time power factor value from the instrument.

[0094] In one implementation, this embodiment calculates the expected additional power corresponding to the current request based on the basic pulse width modulation duty cycle value and the preset rated power of the execution component. This embodiment first reads the nominal rated power of the far-infrared heating component (e.g., 2000W) and the nominal rated power of the ventilation component (e.g., 300W) from the hardware configuration file. This embodiment reads the basic pulse width modulation duty cycle value output in S14. It should be noted that, to prevent dimensional errors caused by different definitions of underlying hardware registers, this embodiment first performs a normalization check. If the basic pulse width modulation duty cycle value is stored in integer form (e.g., an 8-bit value from 0 to 255), it is divided by the full-scale value (e.g., 255) to convert it to a floating-point scaling factor between 0 and 1.0. Next, a power calculation is performed, multiplying the nominal rated power of the heating component by the normalized proportional coefficient to obtain the expected additional power of the heating circuit. It is then determined whether the current target operating mode requires the ventilation component to switch from a stopped state to an operating state. If so, the nominal rated power of the ventilation component is included in the additional power; otherwise, it is counted as zero. The expected additional power of the heating circuit is added to the power of the ventilation component, and the result is determined as the expected additional power.

[0095] In one implementation, this embodiment performs an addition operation on the real-time total power load and the expected new power to obtain the predicted total load, and compares the predicted total load with the preset safe load threshold of the power distribution room.

[0096] It is particularly important to explain in detail the method for determining the safe load threshold of the power distribution room. In this embodiment, the threshold is dynamically set based on the rated capacity and real-time operating status of the power distribution transformer. Specifically, the device reads the rated apparent power (kVA) of the transformer, multiplies it by the read real-time power factor to obtain the current rated active power limit, and then multiplies it by a preset safety margin coefficient, which is set to 0.85 to 0.90 in this embodiment. The final product is determined as the safe load threshold of the power distribution room. The basis for setting this margin coefficient is to reserve a buffer space of 10% to 15% for the transformer to cope with the inrush current and harmonic losses when nonlinear loads are started, and to prevent accelerated equipment aging due to full-load operation.

[0097] In one implementation, this embodiment executes specific constraint correction and power reduction factor calculation logic. The device first calculates the algebraic difference between the preset safe load threshold of the power distribution room and the real-time total power load, marking this difference as the remaining available power difference. Based on this, the device performs graded judgment and safety response according to the positive or negative attribute of the remaining available power difference and the expected magnitude of the additional power.

[0098] In the first scenario, the power grid is already overloaded. If the calculated difference in remaining available power is less than or equal to zero, it indicates that the current base load has reached or exceeded the transformer's safety threshold. In this case, the device forcibly sets the power reduction factor to zero, prohibiting any new heating load.

[0099] It is important to note that in this situation, if the device detects that the current target operating mode is emergency dehumidification mode, this indicates a severe conflict between the extremely high humidity requiring powerful dehumidification and the overloaded power grid preventing power supply. To avoid condensation accidents caused by forced shutdown, the device immediately triggers a power grid overload dehumidification interlock alarm signal while setting the reduction coefficient to zero. This is done via local audible and visual alarms or remote SMS notifications to prompt maintenance personnel for manual intervention, such as cutting off unnecessary loads.

[0100] In the second scenario, if the remaining available power difference is greater than zero but less than the projected additional power, it indicates that while the power grid has a surplus, it is insufficient to support full-power heating. In this case, the device performs a division operation, dividing the remaining available power difference by the projected additional power. The quotient is the power reduction factor. This factor is a decimal between 0 and 1.0, representing the maximum allowable heating ratio that the device can provide without exceeding limits.

[0101] In the third scenario, there is sufficient margin. If the difference in remaining available power is greater than the expected additional power, it indicates that the power supply is ample. In this case, the device directly sets the power reduction factor to 1.0, allowing full power operation as originally planned.

[0102] In one implementation, this embodiment uses the power reduction coefficient to perform a derating multiplication correction on the base pulse width modulation duty cycle value to generate the final execution control command. Specifically, the device multiplies the base pulse width modulation duty cycle value (a normalized floating-point number) by the power reduction coefficient to obtain the corrected pulse width modulation duty cycle value. The device encapsulates this corrected value (which necessarily satisfies grid load constraints) into a standard control command packet (e.g., requantizes it into a hexadecimal data frame) and sends it to the driver layer as the final execution control command. Through this correction, the device achieves adaptive flexible control that operates only according to available power, completely solving the safety hazard of tripping caused by blind startup of traditional equipment.

[0103] In step S16, based on the final execution control command, the far-infrared heating components and ventilation components of the corresponding partition are driven to operate, and real-time current feedback data after operation is collected to verify the execution status, including:

[0104] The final execution control command is subjected to signal decoding operation to separate the pulse width modulation parameters for the far-infrared heating component and the start / stop switch parameters for the ventilation component.

[0105] The pulse width modulation parameters are used to generate a corresponding pulse width modulation drive signal, and the start / stop switch parameters are used to generate an IO level control signal;

[0106] The pulse width modulation drive signal is transmitted to the solid-state power switch unit connected to the far-infrared heating component, and the IO level control signal is transmitted to the relay control unit connected to the ventilation component, physically driving the corresponding component to operate;

[0107] The real-time operating current of the heating circuit is collected synchronously through a current transformer.

[0108] Based on the pulse width modulation parameters and the preset load rated current, the theoretical expected current is calculated, and the deviation between the real-time operating current and the theoretical expected current is compared.

[0109] If the result of the deviation comparison calculation is greater than the preset fault tolerance threshold, an abnormal alarm signal is generated.

[0110] If the result of the deviation comparison operation is less than or equal to the fault tolerance threshold, a normal confirmation signal is generated.

[0111] In one implementation, this embodiment performs signal decoding on the final executed control command. This embodiment receives the command data packet encapsulated from S15 and uses preset communication protocol parsing rules, such as JSON key-value pair parsing or bit-field mask extraction, to separate the command into two independent control parameters: pulse width modulation parameters for the far-infrared heating component (i.e., the corrected duty cycle value) and start / stop switch parameters for the ventilation component (Boolean values, 0 for stop, 1 for start). Subsequently, this embodiment utilizes the timer / counter resources within the microcontroller (MCU) to generate a corresponding pulse width modulation drive signal based on the pulse width modulation parameters.

[0112] It should be noted that, in order to accommodate the large thermal inertia of the heating component and avoid high-frequency harmonic pollution to the power grid, the pulse width modulation signal generated in this embodiment adopts a low-frequency mode, for example, with a period set to 2 to 5 seconds. That is, if the duty cycle is 50%, then within a 2-second period, a high level is output for 1 second and a low level for 1 second. At the same time, the level toggling of the GPIO port is directly controlled using the start / stop switch parameters to generate IO level control signals.

[0113] In one implementation, this embodiment performs a physical driving action. This embodiment transmits the pulse width modulation drive signal to a solid-state power switch unit connected to the far-infrared heating component. This unit integrates a zero-crossing trigger circuit, which only turns on or off at the zero-crossing point of the AC voltage, thereby physically eliminating electromagnetic interference and arcing at the switching moment. Simultaneously, this embodiment transmits the IO level control signal to a relay control unit, such as an electromagnetic relay or contactor, connected to the ventilation component to drive the on / off switching of the fan circuit.

[0114] In one implementation, this embodiment utilizes a current transformer pre-connected in series in the power supply circuit of the far-infrared heating component to synchronously acquire the real-time operating current of the heating circuit. It should be specifically noted that, since the heating component operates intermittently under the control of a pulse width modulation signal, this embodiment employs a periodic integral averaging method for data acquisition in order to obtain a current reading that reflects the equivalent power.

[0115] Specifically, current transformers, such as Hall effect current sensors or precision transformers, continuously sample the heating circuit current at a sampling rate much higher than the pulse width modulation frequency (e.g., 1 kHz). Within each complete pulse width modulation cycle, the device performs a summation operation on the absolute or effective current values ​​of all sampled points and divides the sum by the total number of sampled points to obtain the smoothed real-time operating current. This processing method effectively avoids the logical error of misreading the current as zero (open circuit fault) because the sampling time happens to fall during the pulse width modulation off-period.

[0116] In one implementation, this embodiment performs a deviation comparison operation. The device reads the load rated current stored in the hardware configuration file, which is the nominal current when the heater is running at full power. Then, it calculates the theoretical expected current based on a linear proportional relationship. Specifically, the calculation logic is to multiply the load rated current by the pulse width modulation parameter (i.e., the duty cycle ratio), and the product is the theoretical expected current. Next, the device performs a difference operation, calculates the algebraic difference between the real-time operating current and the theoretical expected current, and takes the absolute value of the difference as the result of the deviation comparison operation.

[0117] In one implementation, this embodiment generates a status signal based on a preset fault tolerance threshold. Regarding the method for determining the fault tolerance threshold, in order to balance the proportional error during high-power operation and the noise floor interference during low-power (or standby) operation, this embodiment adopts a composite setting strategy that takes the larger value of the proportional tolerance and the absolute noise floor.

[0118] Specifically, firstly, the proportional tolerance value is calculated. Considering that industrial power grids typically allow for a certain range of voltage fluctuations (e.g., ±10%), and that the heating resistor experiences resistance drift with temperature changes, this embodiment multiplies the theoretically expected current by a preset tolerance coefficient (e.g., 0.15 to 0.20). Secondly, a preset minimum current quiescent threshold (e.g., 0.2A) is introduced. This value is set based on the zero-point drift parameters of the current transformer and the line induced noise level, and is used to shield false current readings in standby mode. Finally, the device compares the proportional tolerance value with the minimum current quiescent threshold, and selects the larger value as the final fault tolerance threshold.

[0119] It should be noted that the logical judgment process is as follows: If the result of the deviation comparison calculation (i.e., the absolute value of the current error) is greater than the fault tolerance threshold, it indicates that a physical abnormality has occurred at the execution end. For example, if the real-time current is much less than the expected current, a heating element meltdown or SSR open circuit fault may have occurred; if the real-time current is much greater than the expected current, an SSR breakdown short circuit fault may have occurred. In this case, the device immediately generates an execution abnormality alarm signal and triggers an audible and visual alarm or remote push notification. If the result is less than or equal to the fault tolerance threshold, it is determined that the physical execution state is highly consistent with the logical instruction, and the device generates a normal execution confirmation signal, completing the closed-loop verification of this control cycle.

[0120] Reference Figure 2 The second embodiment of the present invention provides an energy-saving control device for a power distribution room anti-condensation dehumidification device, which is configured in a controller. The energy-saving control device for the power distribution room anti-condensation dehumidification device includes:

[0121] The zoned environmental monitoring module is used to acquire real-time environmental parameters of each zone collected by sensor arrays deployed in different physical zones of the power distribution room. The real-time environmental parameters include ambient temperature, relative humidity, and surface temperature of key equipment.

[0122] The condensation risk assessment module is used to perform dew point physical model calculations on the ambient temperature and relative humidity to obtain the current dew point temperature, and to calculate the difference between the surface temperature of the key equipment and the current dew point temperature to obtain a condensation safety margin index that reflects the degree of condensation risk.

[0123] The working mode decision module is used to substitute the condensation safety margin index and the relative humidity into a preset graded control strategy table for matching and querying, and determine the target working mode of the current zone. The target working mode includes at least one of the following: standby monitoring mode, ventilation and dehumidification mode only, far-infrared surface heating mode, and emergency strong dehumidification mode.

[0124] The fuzzy PID calculation module is used to perform fuzzy PID logic calculation based on the condensation safety margin index and the rate of change of the condensation safety margin index if the target working mode is the far-infrared surface heating mode, and derive the basic pulse width modulation duty cycle value for the far-infrared heating component.

[0125] The load constraint correction module is used to obtain the real-time total power load of the power distribution room, perform peak load constraint correction on the basic pulse width modulation duty cycle value based on the real-time total power load, and generate the final execution control command.

[0126] The closed-loop drive verification module is used to drive the far-infrared heating components and ventilation components of the corresponding partition to operate based on the final execution control command, and to collect real-time current feedback data after operation to verify the execution status.

[0127] It should be noted that the energy-saving control device for the anti-condensation dehumidification device of the power distribution room provided in the embodiments of the present invention is used to execute all the process steps of the energy-saving control method for the anti-condensation dehumidification device of the power distribution room in the above embodiments. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0128] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0129] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. An energy-saving control method for a dehumidification device for power distribution rooms to prevent condensation, characterized in that, Executed by the controller, including: The sensor arrays deployed in different physical zones of the power distribution room collect real-time environmental parameters for each zone, including ambient temperature, relative humidity, and surface temperature of key equipment. The dew point physical model is used to calculate the current dew point temperature based on the ambient temperature and relative humidity. The difference between the surface temperature of the key equipment and the current dew point temperature is calculated to obtain the condensation safety margin index. The condensation safety margin index and the relative humidity are substituted into a preset graded control strategy table for matching and querying to determine the target working mode of the current zone. The target working mode includes at least one of the following: standby monitoring mode, ventilation and dehumidification mode only, far-infrared surface heating mode, and emergency strong dehumidification mode. If the target working mode is the far-infrared surface heating mode, then based on the condensation safety margin index and the rate of change of the condensation safety margin index, fuzzy PID logic operation is performed to derive the basic pulse width modulation duty cycle value for the far-infrared heating component. The real-time total power load of the power distribution room is obtained, and the peak load constraint correction is performed on the basic pulse width modulation duty cycle value based on the real-time total power load to generate the final execution control command. Based on the final execution control command, the far-infrared heating components and ventilation components of the corresponding partition are driven to operate, and real-time current feedback data after operation is collected to verify the execution status.

2. The energy-saving control method for the anti-condensation and dehumidification device in the power distribution room according to claim 1, characterized in that, The system acquires real-time environmental parameters for each physical zone of the power distribution room, collected by sensor arrays deployed in different zones. These real-time environmental parameters include ambient temperature, relative humidity, and surface temperature of key equipment. The sensor array acquires the original sensor data sequence of each zone at a preset sampling frequency. The original sensor data sequence includes multiple sets of temperature readings, humidity readings and infrared temperature readings with timestamps. Outlier detection is performed on the original sensing data sequence to remove noisy data points that exceed the preset physical range or gradient change threshold, thereby generating an effective monitoring sequence. The effective monitoring sequence is smoothed in the time domain by using a moving average filtering algorithm, and the arithmetic mean within the moving window is calculated to obtain the ambient temperature, relative humidity and surface temperature of key equipment.

3. The energy-saving control method for the anti-condensation dehumidification device in the power distribution room according to claim 1, characterized in that, A dew point physical model is used to calculate the current dew point temperature based on the ambient temperature and relative humidity. Then, the difference between the surface temperature of the critical equipment and the current dew point temperature is calculated to obtain a condensation safety margin index reflecting the degree of condensation risk, including: The ambient temperature and relative humidity are used as input parameters and substituted into a preset thermodynamic calculation model. Logarithmic and rational fractional nonlinear operations are performed to output the current dew point temperature. Calculate the difference between the surface temperature of the key equipment and the current dew point temperature, and mark the difference as the condensation safety margin index.

4. The energy-saving control method for the anti-condensation dehumidification device in the power distribution room according to claim 1, characterized in that, The condensation safety margin index and the relative humidity are substituted into a preset graded control strategy table for matching and querying to determine the target operating mode of the current zone. The target operating mode includes at least one of the following: standby monitoring mode, ventilation-only dehumidification mode, far-infrared surface heating mode, and emergency strong dehumidification mode. The preset graded control strategy table is invoked. The graded control strategy table stores a two-dimensional mapping matrix consisting of a preset condensation safety margin range and a preset relative humidity range. Each matrix unit is associated with a unique working mode label. If the condensation safety margin index is less than the preset condensation critical threshold and the relative humidity is greater than the preset high humidity alarm threshold, then according to the two-dimensional mapping matrix, the target working mode is locked as the emergency strong dehumidification mode. If the condensation safety margin index is less than the condensation critical threshold, and the relative humidity is less than or equal to the high humidity alarm threshold, then according to the two-dimensional mapping matrix, the target working mode is locked as the far-infrared surface heating mode. If the condensation safety margin index is greater than or equal to the condensation critical threshold, and the relative humidity is greater than the high humidity alarm threshold, then according to the two-dimensional mapping matrix, the target working mode is locked as the ventilation and dehumidification mode only. If the condensation safety margin index is greater than or equal to the condensation critical threshold, and the relative humidity is less than or equal to the high humidity alarm threshold, then according to the two-dimensional mapping matrix, the target working mode is locked as the standby monitoring mode, and a shutdown command is generated for the far-infrared heating component and the ventilation component.

5. The energy-saving control method for the anti-condensation dehumidification device in the power distribution room according to claim 1, characterized in that, If the target operating mode is the far-infrared surface heating mode, then based on the condensation safety margin index and the rate of change of the condensation safety margin index, fuzzy PID logic calculation is performed to derive the basic pulse width modulation duty cycle value for the far-infrared heating component, including: The system deviation value is obtained by calculating the difference between the preset ideal safety margin constant and the condensation safety margin index. The deviation change rate is obtained by performing a time-based differential operation on the condensation safety margin index. Using a preset Gaussian membership function, the system deviation value and the deviation change rate are mapped into fuzzy state vectors respectively; The fuzzy state vector is input into a pre-constructed fuzzy inference matrix for fuzzy inference synthesis operation, and the corresponding fuzzy control decision variable is output. The centroid method is used to defuzzify the fuzzy control decision variables to obtain the control response coefficients, and the control response coefficients are linearly quantized into the basic pulse width modulation duty cycle value.

6. The energy-saving control method for the anti-condensation dehumidification device in the power distribution room according to claim 1, characterized in that, Obtain the real-time total power load of the power distribution room, perform peak load constraint correction on the basic pulse width modulation duty cycle value based on the real-time total power load, and generate the final execution control command, including: The current real-time total power load of the power distribution room is read through power monitoring instruments; Based on the basic pulse width modulation duty cycle value and the preset rated power of the far-infrared heating component, calculate the expected additional power corresponding to the current heating request; The predicted total load is obtained by adding the real-time total power load to the expected additional power. If the predicted total load is less than or equal to the preset safe load threshold of the power distribution room, then the basic pulse width modulation duty cycle value is directly encapsulated into the final execution control command. If the predicted total load is greater than the preset safe load threshold for the power distribution room, then the power reduction coefficient is calculated based on the difference between the preset safe load threshold for the power distribution room and the remaining available power between the real-time total power load. The base pulse width modulation duty cycle value is derating and multiplied using the power reduction factor to obtain the corrected pulse width modulation duty cycle value, which is then encapsulated into the final execution control command.

7. The energy-saving control method for the anti-condensation dehumidification device in the power distribution room according to claim 1, characterized in that, Based on the final execution control command, the far-infrared heating and ventilation components of the corresponding zones are driven to operate, and real-time current feedback data after operation is collected to verify the execution status, including: The final execution control command is subjected to signal decoding operation to separate the pulse width modulation parameters for the far-infrared heating component and the start / stop switch parameters for the ventilation component. The pulse width modulation parameters are used to generate a corresponding pulse width modulation drive signal, and the start / stop switch parameters are used to generate an IO level control signal; The pulse width modulation drive signal is transmitted to the solid-state power switch unit connected to the far-infrared heating component, and the IO level control signal is transmitted to the relay control unit connected to the ventilation component, physically driving the corresponding component to operate; The real-time operating current of the heating circuit is collected synchronously through a current transformer. Based on the pulse width modulation parameters and the preset load rated current, the theoretical expected current is calculated, and the deviation between the real-time operating current and the theoretical expected current is compared. If the result of the deviation comparison calculation is greater than the preset fault tolerance threshold, an abnormal alarm signal is generated. If the result of the deviation comparison operation is less than or equal to the fault tolerance threshold, a normal confirmation signal is generated.

8. The energy-saving control method for the anti-condensation dehumidification device in the power distribution room according to claim 1, characterized in that, The far-infrared heating component is a silicon carbide ceramic radiation heater or a PTC thermistor heater.

9. The energy-saving control method for the anti-condensation dehumidification device in the power distribution room according to claim 1, characterized in that, The ventilation component is an explosion-proof axial flow fan or a louvered exhaust fan.

10. An energy-saving control device for a power distribution room anti-condensation dehumidification device, characterized in that, The energy-saving control device of the power distribution room anti-condensation dehumidification device, configured in the controller, includes: The zoned environmental monitoring module is used to acquire real-time environmental parameters of each zone collected by sensor arrays deployed in different physical zones of the power distribution room. The real-time environmental parameters include ambient temperature, relative humidity, and surface temperature of key equipment. The condensation risk assessment module is used to perform dew point physical model calculations on the ambient temperature and relative humidity to obtain the current dew point temperature, and to calculate the difference between the surface temperature of the key equipment and the current dew point temperature to obtain the condensation safety margin index. The working mode decision module is used to substitute the condensation safety margin index and the relative humidity into a preset graded control strategy table for matching and querying, and determine the target working mode of the current zone. The target working mode includes at least one of the following: standby monitoring mode, ventilation and dehumidification mode only, far-infrared surface heating mode, and emergency strong dehumidification mode. The fuzzy PID calculation module is used to perform fuzzy PID logic calculation based on the condensation safety margin index and the rate of change of the condensation safety margin index if the target working mode is the far-infrared surface heating mode, and derive the basic pulse width modulation duty cycle value for the far-infrared heating component. The load constraint correction module is used to obtain the real-time total power load of the power distribution room, perform peak load constraint correction on the basic pulse width modulation duty cycle value based on the real-time total power load, and generate the final execution control command. The closed-loop drive verification module is used to drive the far-infrared heating components and ventilation components of the corresponding partition to operate based on the final execution control command, and to collect real-time current feedback data after operation to verify the execution status.

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