Intelligent adaptive heating system for medium voltage switchgear

Abstract

This application relates to the field of switchgear heating control, specifically disclosing an intelligent adaptive heating system for medium-voltage switchgear. This system actively captures and quantifies the rate of change in the external environment by introducing environmental trend analysis, forming temperature and humidity risk slopes representing the future risk of condensation. Based on this, a target temperature matching the rate of risk change is dynamically generated. When the external environment is stable, the system uses a smaller safety margin to save energy; however, when the system detects a rapid change in the environment towards a direction conducive to condensation, it proactively and significantly increases the safety margin, starting and increasing heating power in advance, thereby effectively offsetting the thermal inertia of the equipment and the response delay of the heating system. This solves the shortcomings of existing technologies in responding to sudden environmental changes and the risk of condensation, while also considering the energy-saving requirements under stable operating conditions.

Description

Technical Field

[0001] This application relates to the field of switchgear heating control, and more specifically, to an intelligent adaptive heating system for medium-voltage switchgear. Background Technology

[0002] Medium-voltage switchgear plays a crucial role in power systems, performing core functions such as switching, closing, protection, and control. Its operational stability and reliability are vital to the safety of the entire power grid. In actual operating environments, especially in areas with high humidity or large diurnal temperature variations, the temperature and humidity inside the switchgear fluctuate. When the surface temperature of the equipment (especially insulators) is lower than or close to the dew point temperature of the surrounding air, condensation occurs on their surfaces. The adhesion of water droplets drastically reduces the insulation performance of the equipment, easily leading to serious faults such as surface discharge, flashover, and even phase-to-phase short circuits, posing a significant threat to equipment and personnel safety. Therefore, equipping medium-voltage switchgear with an effective heating system to proactively raise the surface temperature of critical components and prevent condensation has become a necessary technical means to ensure its safe operation.

[0003] Currently, the industry commonly uses dew point control-based heating solutions. This solution calculates the current dew point temperature by monitoring the temperature and humidity inside the switchgear in real time, and then controls the heater operation to ensure that the surface temperature of critical insulating components inside the cabinet is always above the dew point temperature by a fixed safety margin, such as 3°C or 5°C. However, this control strategy using a fixed safety margin has inherent drawbacks. On the one hand, during periods of stable external environment and low condensation risk, this fixed margin often appears too conservative, leading to unnecessary start-up or prolonged operation of the heating system, resulting in significant energy waste. On the other hand, when the external environment undergoes drastic changes, such as a cold wave causing a sudden drop in temperature or a surge in humidity during the rainy season, the fixed safety margin proves insufficient. Due to the significant thermal inertia of the equipment and metal components inside the switchgear, their temperature changes much slower than the changes in air temperature and humidity. When severe weather causes a rapid rise in the dew point temperature inside the cabinet, the fixed, relatively small safety margin may be breached instantly. Traditional control logic only responds passively at this point. In addition, there is a response delay in the heater itself from startup to the effective transfer of heat to the equipment surface. During this critical reaction window, the equipment surface temperature has already fallen below the dew point temperature, and condensation has already formed, thus creating a serious safety hazard.

[0004] Therefore, this static control method ignores the rate of environmental change and the lag in system response, lacks the ability to predict and dynamically adjust for condensation risks, and is difficult to achieve an ideal balance between energy saving and ultimate safety. Summary of the Invention

[0005] To address the aforementioned technical problems, this application is proposed. According to this application, an intelligent adaptive heating system for medium-voltage switchgear includes: a switchgear multi-dimensional status data acquisition module for acquiring the internal temperature, internal humidity, surface temperature of key insulators, external temperature, and external humidity of the switchgear; an environmental trend analysis module for performing environmental trend analysis based on time-series data of external temperature and external humidity to obtain temperature risk slope and humidity risk slope; a target temperature generation module for making dynamic safety margin and target temperature decisions based on the internal temperature, internal humidity, temperature risk slope, and humidity risk slope to obtain a dynamic target temperature; a heating power determination module for performing adaptive PID power control on the dynamic target temperature and the surface temperature of key insulators to obtain a heating power output percentage; and a power modulation heating module for performing power modulation and heating execution based on the heating power output percentage.

[0006] Compared with existing technologies, this application provides an intelligent adaptive heating system for medium-voltage switchgear. This system employs an adaptive heating control strategy with forward-looking predictive capabilities to replace the traditional passive fixed safety margin method. Specifically, the system no longer focuses solely on the current temperature and humidity inside the switchgear; instead, it actively captures and quantifies the rate of change of the external environment (such as outside temperature and humidity) by introducing environmental trend analysis, forming temperature and humidity risk slopes representing future condensation risks. Based on this, the system can dynamically generate a target temperature that matches the rate of risk change. When the external environment is stable, the system uses a smaller safety margin to save energy; however, when it detects a rapid change in the environment towards a direction conducive to condensation (such as a sudden drop in temperature or a surge in humidity), the system proactively and significantly increases the safety margin, initiating and increasing heating power in advance, thereby effectively offsetting the thermal inertia of the equipment and the response delay of the heating system. This control mode, which transforms passive response into proactive prevention, fundamentally solves the shortcomings of existing technologies in responding to sudden environmental changes and the risk of condensation, while also considering energy-saving requirements under stable operating conditions. Attached Figure Description

[0007] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0008] Figure 1 This is a block diagram of an intelligent adaptive heating system for medium-voltage switchgear according to an embodiment of this application.

[0009] Figure 2This is a schematic diagram of the data flow of an intelligent adaptive heating system for medium-voltage switchgear according to an embodiment of this application.

[0010] Figure 3 This is a block diagram of the environmental trend analysis module in an intelligent adaptive heating system for medium-voltage switchgear according to an embodiment of this application.

[0011] Figure 4 This is a block diagram of a target temperature generation module in an intelligent adaptive heating system for medium-voltage switchgear according to an embodiment of this application.

[0012] Figure 5 This is a block diagram of a heating power determination module in an intelligent adaptive heating system for medium-voltage switchgear according to an embodiment of this application. Detailed Implementation

[0013] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0014] This application is made in response to the problems existing in the aforementioned prior art. Figure 1 This is a block diagram of an intelligent adaptive heating system for medium-voltage switchgear according to an embodiment of this application. Figure 2 This is a schematic diagram of data flow in an intelligent adaptive heating system for medium-voltage switchgear according to an embodiment of this application. Specifically, as shown... Figure 1 and Figure 2 As shown, the intelligent adaptive heating system 100 for medium-voltage switchgear according to an embodiment of this application includes: a switchgear multi-dimensional status data acquisition module 110, used to acquire the internal temperature, internal humidity, surface temperature of key insulators, external temperature, and external humidity of the switchgear; an environmental trend analysis module 120, used to perform environmental trend analysis based on the time series data of external temperature and external humidity to obtain the temperature risk slope and humidity risk slope; a target temperature generation module 130, used to make dynamic safety margin and target temperature decisions based on the internal temperature, internal humidity, temperature risk slope, and humidity risk slope to obtain the dynamic target temperature; a heating power determination module 140, used to perform adaptive PID power control on the dynamic target temperature and the surface temperature of key insulators to obtain the heating power output percentage; and a power modulation heating module 150, used to perform power modulation and heating execution based on the heating power output percentage.

[0015] Specifically, the switchgear multi-dimensional status data acquisition module 110 is used to acquire the internal temperature, internal humidity, key insulator surface temperature, external temperature, and external humidity of the switchgear. It is understood that, to overcome the shortcomings of existing technologies that rely solely on the internal status and cannot predict risks, this application proposes a forward-looking control strategy. This strategy is based on comprehensively sensing the multi-dimensional status inside and outside the switchgear. Therefore, acquiring the internal temperature, internal humidity, key insulator surface temperature, external temperature, and external humidity provides a complete data foundation for subsequent analysis and decision-making: the internal temperature and humidity are used to accurately calculate the current condensation benchmark, i.e., the dew point temperature; the time series data of the external temperature and humidity are used to quantify environmental change trends, thereby predicting future condensation risks; and the key insulator surface temperature, as the most directly controlled object, is the real-time feedback for achieving accurate temperature rise in closed-loop control. By integrating these five dimensions of data, heating control is transformed from a passive, delayed response to an active, predictive adjustment.

[0016] In a feasible technical solution, the multi-dimensional status data acquisition module 110 of the switchgear processes data as follows: The core function of this module is to periodically and accurately collect five core parameters reflecting the internal and external thermal and humidity environment and the status of key components of the switchgear. These parameters are: internal temperature (the macroscopic temperature of the air inside the switchgear); internal humidity (the relative humidity of the air inside the switchgear); surface temperature of key insulators (the actual temperature of the surface of the insulators most prone to condensation); external temperature (the air temperature of the surrounding environment where the switchgear is located); and external humidity (the relative humidity of the surrounding environment).

[0017] To acquire the aforementioned parameters, the switchgear multi-dimensional status data acquisition module 110 integrates multiple sets of sensors and corresponding data processing circuits. Specifically, inside the switchgear, a high-precision digital temperature and humidity sensor is installed in a location that represents the overall air environment inside the cabinet and is far from the heater and vents. This sensor simultaneously outputs digital signals for both the cabinet's internal temperature and humidity. Simultaneously, one or more phase busbar support insulators located at a lower position inside the cabinet and most susceptible to the influence of humid and cold air are selected as key monitoring points. One or more patch-type platinum resistance temperature sensors are tightly attached to their surfaces to measure the surface temperature of the key insulators in real time. Outside the switchgear, in a sheltered location unaffected by direct sunlight and rain, another digital temperature and humidity sensor is installed to measure the external temperature and humidity. The signal lines of all sensors are connected to the data acquisition unit within the module.

[0018] The data acquisition process operates on a preset sampling period, which can be set to 1 minute depending on the severity of changes in the on-site environment. Within one sampling period, the module's control unit sequentially sends acquisition commands to each sensor. The sensors convert the measured physical quantities into electrical signals, which are then processed by the module's internal signal conditioning circuit and analog-to-digital converter to become standardized digital quantities. Subsequently, the module combines these five digital quantities with the current timestamp information to form a complete data frame. This data frame is the output of the switchgear multi-dimensional status data acquisition module 110 within one sampling period. For example, at a certain time t1, the module performs one acquisition: the temperature and humidity sensor installed inside the cabinet measures the cabinet temperature as 15.2 degrees Celsius and the humidity as 85.0%; the platinum resistance sensor installed on the insulator surface measures the surface temperature of the critical insulator as 14.8 degrees Celsius; and the temperature and humidity sensor installed outside the cabinet measures the outside temperature as 13.5 degrees Celsius and the outside humidity as 92.0%. After integrating this data, the module generates and outputs a data frame, the content of which can be represented as: {timestamp:t1, cabinet internal temperature:15.2, cabinet internal humidity:85.0, critical insulator surface temperature:14.8, cabinet external temperature:13.5, cabinet external humidity:92.0}.

[0019] Specifically, the environmental trend analysis module 120 is used to perform environmental trend analysis based on time-series data of external temperature and humidity to obtain the temperature risk slope and humidity risk slope. Correspondingly, existing heating control strategies based on fixed safety margins are essentially a delayed response mechanism, intervening only after the condensation risk occurs—that is, after the difference between the equipment surface temperature and the dew point temperature narrows. This passive mode cannot cope with the delayed condensation threat caused by drastic environmental changes, especially when the equipment has significant thermal inertia. To achieve truly proactive protection, the control logic needs to shift from focusing on the current state to predicting future trends. Therefore, introducing environmental trend analysis based on time-series data of external temperature and humidity quantifies the speed of external environmental deterioration, transforming invisible risk changes into specific, calculable indicators, namely the temperature risk slope and humidity risk slope. This allows control decisions to be based on future predictions and proactive intervention, fundamentally solving the problem of delayed response.

[0020] In a feasible technical solution Figure 3 This is a block diagram of the environmental trend analysis module in an intelligent adaptive heating system for medium-voltage switchgear according to an embodiment of this application. Figure 3As shown, the environmental trend analysis module 120 includes: a linear regression unit 121 for external parameters, used to perform linear regression on the time series data of external temperature and external humidity to obtain the rate of change of external temperature and the rate of change of external humidity; and a temperature and humidity slope calculation unit 122, used to extract the unidirectional risk slope of the rate of change of external temperature and the rate of change of external humidity to obtain the temperature risk slope and the humidity risk slope.

[0021] In the above technical solution, the environmental trend analysis module 120 processes data as follows: First, the environmental trend analysis module 120 maintains two fixed-length time-series data queues, which are used to store the outdoor temperature and outdoor humidity data for the most recent period. The length of these two queues, i.e., the time window size N for regression analysis, is a preset parameter. The setting of N needs to balance response sensitivity and data smoothness: a smaller N value can reflect environmental changes more quickly, but is more susceptible to noise interference from single-point data; a larger N value can provide a smoother and more stable rate of change, but the response to changes is slightly slower. In typical industrial applications, if the data acquisition cycle is 1 minute, N can be set to 10, i.e., trend analysis is performed based on data from the past 10 minutes. When the upstream switchgear multidimensional status data acquisition module 110 outputs a new data frame {timestamp: t1, ..., outdoor temperature: 13.5, outdoor humidity: 92.0} at time t1, the environmental trend analysis module 120 stores the outdoor temperature value of 13.5 and the outdoor humidity value of 92.0 at the end of their respective time-series queues. If the queue is full, the oldest data point at the head of the queue is removed to maintain the queue length as N. For example, at time t1, the outdoor temperature queue stores 10 data points from t1-9 minutes to time t1, and their values ​​may be [14.5, 14.4, 14.3, 14.1, 14.0, 13.9, 13.7, 13.6, 13.5, 13.5] (unit: degrees Celsius); the outdoor humidity queue stores the corresponding 10 humidity data points, and their values ​​may be [90.0, 90.2, 90.5, 90.8, 91.0, 91.3, 91.6, 91.8, 91.9, 92.0] (unit: %).

[0022] Next, the external parameter linear regression unit 121 begins operation. This unit performs univariate linear regression analysis on the data in the two time series queues mentioned above. The goal of linear regression is to find a best-fitting straight line to describe the trend of the dependent variable (temperature or humidity) with respect to the independent variable (time), and the slope of this line represents the average rate of change within the current time window. To achieve this goal, this unit uses the least squares method for calculation, the core idea of ​​which is to find a straight line that minimizes the sum of the squares of the vertical distances (i.e., residuals) from all data points to this line. Specifically, in the calculation process for the external temperature queue, this unit treats the 10 data points in the time series as a set of points in a coordinate system. The independent variable is the time index, which can be simplified to the sequence [0,1,2,...,9], representing the earliest to the latest time point; the dependent variable is the corresponding external temperature value sequence [14.5,14.4,...,13.5]. Calculating the slope, i.e., the rate of change of the external temperature, involves the following steps: First, calculate the average of the time index sequence and the temperature value sequence; then, calculate the deviation of each time index from the average, and the deviation of each temperature value from the average; next, multiply these two deviations for each pair of corresponding points, and sum all the multiplications; finally, calculate the square of the deviation of each time index from the average, and sum all the squares. The final slope is the result of dividing the previous sum (the sum of the products of deviations) by the next sum (the sum of the squares of the time index deviations). Taking the external temperature as an example, based on the above 10 data points, the rate of change of the external temperature calculated by linear regression might be -0.1 (unit: °C / minute). This value precisely indicates that the external temperature has been experiencing a steeper downward trend over the past 10 minutes, decreasing by an average of 0.10 degrees Celsius per minute. Similarly, for the external humidity, the linear regression unit 121 of the external parameters performs the same calculation process on the external humidity queue, and the change rate of external humidity may be +0.2 (unit: % / minute), indicating that the humidity is rising sharply at a faster rate of 0.20 percentage points per minute.

[0023] Subsequently, the temperature and humidity slope calculation unit 122 receives these two rate of change values. The function of this unit is to perform unidirectional risk slope extraction, that is, to focus only on those directions of change that increase the risk of condensation. The increase in condensation risk is related to a decrease in temperature and an increase in humidity. Therefore, the unit calculates according to the following formula: In a feasible technical solution, the temperature and humidity slope calculation unit 122 is used to: extract the unidirectional risk slope of the rate of change of external temperature and the rate of change of external humidity using the following formula: ; ;in, The rate of change of the external temperature of the cabinet. The rate of change of humidity outside the cabinet. To obtain the maximum value, For temperature risk slope, This represents the humidity risk slope. Through asymmetric processing, it refines bidirectional environmental change rate data into a unidirectional, non-negative risk growth rate indicator. Specifically, for temperature, it uses the rate of change of external temperature... Taking negative values ​​can reverse the downward trend in temperature that could increase risk (at this point) Converting a negative value to a positive value will reduce the risk of an upward trend in temperature (at this time). (If positive, convert to a negative value.) Then combine... This function can effectively filter out risk-free operating conditions where temperature rises, extracting and quantifying only the rate of temperature decrease. Regarding humidity, due to the increase in humidity ( A positive value directly corresponds to increased risk, so there is no need to take a negative value. The function also ensures that a positive risk slope is output only when humidity shows an upward trend, ignoring the favorable situation of decreasing humidity. Substituting the output of the linear regression unit 121 for the external parameters into the formula: For temperature, due to the rate of change of external temperature... A value of -0.1, which is negative, indicates that the temperature is decreasing, increasing the risk of condensation. Therefore, it is calculated as: Temperature Risk Slope. = =0.1. This positive value of 0.1 quantifies the rate of risk increase due to a decrease in temperature. Regarding humidity, due to the rate of change in humidity outside the cabinet... A value of +0.2 is positive, indicating that humidity is rising, which also increases the risk of condensation. Therefore, it is calculated as: Humidity Risk Slope. =0.2. This positive value of 0.2 quantifies the rate of risk increase resulting from rising humidity. If a certain rate of change does not increase the risk, such as a rise in temperature, If the value is positive, the corresponding risk slope will be calculated as 0. These two unitless numerical values ​​precisely quantify the severity of the current external environment changing in an unfavorable direction.

[0024] Specifically, the target temperature generation module 130 is used to make dynamic safety margin and target temperature decisions based on the cabinet temperature, cabinet humidity, temperature risk slope, and humidity risk slope to obtain a dynamic target temperature. It should be understood that traditional heating control schemes, due to their fixed safety margins, have static and blind control targets when facing dynamically changing environments, leading to a trade-off between energy saving and safety. Simply quantifying the risk rate of environmental change is insufficient to form effective control actions; this abstract risk prediction needs to be transformed into a specific and executable control target. Therefore, making dynamic safety margin and target temperature decisions involves constructing a self-adjusting control target. That is, by accurately calculating the current condensation baseline (dew point temperature) and superimposing a dynamically changing safety margin positively correlated with the future risk level, a forward-looking dynamic target temperature is generated, providing a smart setpoint for subsequent power control that ensures both safety and energy efficiency.

[0025] In a feasible technical solution Figure 4 This is a block diagram of a target temperature generation module in an intelligent adaptive heating system for medium-voltage switchgear according to an embodiment of this application. Figure 4 As shown, the target temperature generation module 130 includes: a dew point temperature calculation unit 131, used to calculate the dew point temperature based on the cabinet temperature and cabinet humidity; a dynamic safety margin calculation unit 132, used to calculate the dynamic safety margin based on the temperature risk slope and the humidity risk slope; and a dynamic target temperature calculation unit 133, used to use the dew point temperature as a reference and superimpose the dynamic safety margin on the reference to obtain the dynamic target temperature.

[0026] In the above technical solution, the target temperature generation module 130 processes the following: First, the dew point temperature calculation unit 131 begins calculation. The function of this unit is to accurately calculate the critical temperature at which water vapor begins to condense into liquid water, i.e., the dew point temperature, based on the actual air conditions inside the switchgear. This is fundamental for determining the anti-condensation heating benchmark. In a feasible technical solution, the dew point temperature calculation unit 131 is used to calculate the dew point temperature using the following formula: ; ;in, The humidity inside the cabinet, The temperature inside the cabinet. and The preset physical constants are 17.67 and 243.5, respectively. This is the dew point temperature. This unit receives the cabinet internal temperature obtained in this application. =15.2 and humidity inside the cabinet =85.0, and perform the calculation: In the above formula, It is the natural logarithm function. and These are experimentally verified physical constants, and their preset values ​​are as follows: =17.67 and =243.5, to ensure calculation accuracy within a common ambient temperature range. Substitute the input data into the calculation: ≈0.8757, =(243.5×0.8757) / (17.67-0.8757)≈12.70 degrees Celsius. This value indicates that under the current cabinet environment, condensation will occur on the surface of any object with a temperature below 12.70 degrees Celsius.

[0027] Next, the dynamic safety margin calculation unit 132 begins operation. This unit calculates a dynamically adjusted safety margin based on the risk trends among various feasible technical solutions for environmental changes. This unit receives the temperature risk slope from the environmental trend analysis module 120. =0.1 and humidity risk slope Using 0.2 as input and applying the formula, in a feasible technical solution, the dynamic safety margin calculation unit 132 is used to: calculate the dynamic safety margin based on the temperature risk slope and the humidity risk slope using the following formula, which is: ;in, Basic safety margin, and These are the temperature risk weighting coefficient and the humidity risk weighting coefficient, respectively. and For temperature risk slope and humidity risk slope, Let be the system thermal response time constant. This is a dynamic safety margin. The formula consists of two parts: a fixed basic safety margin... And a dynamic increment that changes with risk. The basic safety margin is based on a completely stable environment, i.e. =0 and When the value is 0, it represents a minimum protection value set to account for factors such as sensor errors and uneven temperature distribution within the cabinet. This value is preset based on safety regulations and experience; for example, it can be set to [value missing]. =2.0 degrees Celsius. The thermal response time constant of the switchgear, measured in minutes, characterizes the time required from heater activation to an effective response in the surface temperature of critical insulators. It is a key physical quantity reflecting the thermal inertia of the equipment. This value needs to be determined through offline experimental testing or online identification of a specific model of switchgear. For example, for a medium-sized switchgear, this value can be set to... =15 minutes. and These are risk conversion factors with clearly defined physical units. Their function is to convert their respective risk change rates (units are ℃ / minute and %RH / minute, respectively) into an equivalent instantaneous temperature compensation requirement (unit: ℃). Weights used to adjust for the risk of temperature drop. The weights used to adjust for the risk of rising humidity. The settings of these two factors can be adjusted according to the climate characteristics of the switchgear's location. In this embodiment, a temperature risk slope is considered. =0.1℃ / minute, humidity risk slope For scenarios with relatively drastic environmental changes (e.g., 0.2% / minute), a temperature risk conversion factor can be set. =1.0°C / (°C / minute), Humidity Risk Conversion Factor =0.5°C / (%RH / minute). Substitute the input data and preset parameters of this application into the calculation: =2.0 + 15 × (1.0 × 0.1 + 0.5 × 0.2) = 5 degrees Celsius. This value is significantly higher than the base value of 2.0 degrees Celsius. It accurately quantifies the risk of the current environmental deterioration trend and generates a reasonable and effective forward-looking protection quantity that matches it and is sufficient to offset the system response delay.

[0028] Specifically, in constructing a dynamic safety margin, simply adding the rates representing temperature risk (unit: °C / min) and humidity risk (unit: %min) using a fixed weighting coefficient presents two fundamental drawbacks. First, directly adding two variables with inconsistent physical dimensions results in a risk assessment model lacking clear physical meaning. Second, the impact of humidity on condensation risk is highly nonlinear. In hot and humid environments, a small increase in humidity can lead to a sharp rise in dew point temperature, while its impact is much smaller in dry and cold environments. A fixed weighting coefficient cannot capture this dynamic relationship, potentially leading to insufficient compensation at critical moments or overcompensation during stable periods. Therefore, this application transforms the humidity risk weighting coefficient from a static empirical value into a dynamic parameter with clear physical meaning that can adaptively adjust according to the current environmental conditions by dynamically quantifying the humidity risk weight based on cabinet humidity, cabinet temperature, and dew point temperature. This gives the entire risk assessment model physical interpretability and environmental adaptability.

[0029] Preferably, in a feasible technical solution, the determination of the humidity risk weight coefficient includes: dynamic physical quantification of the humidity risk weight based on the humidity inside the cabinet, the temperature inside the cabinet, and the dew point temperature to obtain the humidity risk weight coefficient.

[0030] In detail, to achieve dynamic physical quantification of the humidity risk weighting coefficient, the dynamic safety margin calculation unit in this embodiment of the invention further includes a process for determining the humidity risk weighting coefficient. This process first requires unifying all risk sources to a metric dimension with clear physical meaning. In this scenario, the most crucial physical quantity is the dew point temperature. Therefore, all external environmental change rates, whether temperature or humidity changes, should be converted into their rate of influence on the dew point temperature, with the unit unified as degrees Celsius per minute.

[0031] First, regarding temperature risk, it primarily affects the rate of decrease in equipment surface temperature, thus narrowing the safe temperature difference. Therefore, it can be approximated that the rate of change of the external temperature directly constitutes the rate of threat to the safe temperature difference, which can itself be considered as the risk rate, measured in °C / minute. As for the humidity risk slope, it primarily affects the rate of increase of the dew point temperature itself. Therefore, a conversion factor needs to be found to convert the rate of change of humidity, measured in %RH / minute, into the rate of change of dew point temperature, measured in °C / minute. This conversion factor is the humidity risk weighting coefficient to be dynamically determined in this step, and its physical meaning is the partial derivative of dew point temperature with respect to the humidity inside the cabinet.

[0032] Since the formula for calculating dew point temperature is not directly linearly related to the humidity inside the cabinet, but rather uses an intermediate variable... Therefore, based on the chain rule in calculus, the partial derivative of dew point temperature with respect to humidity inside the cabinet can be solved in the following form: The solution process consists of two sub-steps: First, based on the calculation formula for dew point temperature... Find its relationship with respect to intermediate variables. The partial derivatives of . In this formula, and The preset values ​​for the physical constants are 17.67 and 243.5. After differentiation, we get: Secondly, based on intermediate variables Calculation formula The question asks about the humidity inside the cabinet. The partial derivative. During the process of finding the partial derivative, the temperature inside the cabinet... Treated as a constant, therefore includes The derivative of the term is zero. Taking the derivative, we get: Finally, substituting the results of the two partial derivatives above into the chain rule formula, we can obtain the dynamic calculation expression for the humidity risk weight coefficient: Through the above improvements, the controller no longer uses a fixed [function / mechanism] in each calculation cycle. The value is not based on the actual temperature inside the cabinet, but rather on the real-time collected temperature. Humidity inside the cabinet First calculate the intermediate variables Then, the current value is calculated in real time using the above formula. Value, then use this dynamic Value to calculate dynamic safety margin The new weighting coefficients are determined in this way. It has a clear physical meaning: at the current temperature, a 1% change in humidity will cause a change in dew point temperature by how many degrees Celsius, thus giving the entire risk assessment model physical interpretability. More importantly, the controller can adjust its operation based on the current environmental conditions (as determined by...). and (Jointly determined) The most suitable humidity risk weight is dynamically calculated. For example, in a hot and humid environment, the calculated... The value will naturally increase, making the heating control more sensitive and aggressive in response to humidity changes; while in dry and cold environments, The value will naturally decrease, thus avoiding unnecessary overreaction and achieving better energy-saving effect while ensuring safety.

[0033] Finally, the dynamic target temperature calculation unit 133 performs the final synthesis. This unit receives the output from the dew point temperature calculation unit 131. =12.70 degrees Celsius, and the output of the dynamic safety margin calculation unit 132. =5 degrees Celsius. The calculation uses the current dew point temperature as a reference, adding a dynamic safety margin to obtain the final control target: Dynamic target temperature = + =12.70 + 5 = 17.7 degrees Celsius.

[0034] Specifically, the heating power determination module 140 is used to perform adaptive PID power control on the dynamic target temperature and the surface temperature of the critical insulator to obtain the heating power output percentage. It is understandable that after determining a dynamic and forward-looking target temperature, the core challenge of control becomes how to accurately, stably, and efficiently drive the heater so that the actual temperature of the critical insulator can quickly and smoothly approach and maintain that target value. If simple on / off control is used, the temperature will inevitably fluctuate drastically around the target value, resulting in severe overshoot and lag, wasting energy and potentially causing thermal stress on the equipment due to frequent temperature fluctuations. Therefore, introducing adaptive PID power control on the dynamic target temperature and the surface temperature of the critical insulator can establish a precise closed-loop feedback regulation mechanism. This allows for the dynamic calculation of the most suitable heating power based on the current error magnitude, the trend of accumulated error, and the rate of error change, thereby translating the forward-looking target into smooth and precise execution, ensuring stable maintenance of a safe temperature under various disturbances.

[0035] In a feasible technical solution Figure 5 This is a block diagram of a heating power determination module in an intelligent adaptive heating system for medium-voltage switchgear according to an embodiment of this application. Figure 5 As shown, the heating power determination module 140 includes: a temperature error calculation unit 141, used to calculate the temperature error between the dynamic target temperature and the surface temperature of the key insulator; a PID control unit 142, used to input the temperature error into the PID controller to obtain the PID calculation result; and a heating power percentage output unit 143, used to limit and convert the output of the PID calculation result to obtain the heating power output percentage.

[0036] In the above technical solution, the heating power determination module 140 processes the following: First, the temperature error calculation unit 141 performs its calculation task. The function of this unit is to calculate the deviation between the control target and the actual state in real time; this deviation is the basis for all subsequent control actions. The calculation formula is: Temperature Error = Dynamic Target Temperature - Critical Insulator Surface Temperature. Substituting the input data at time t1 of this application: Temperature Error = 17.7 - 14.8 = 2.9 degrees Celsius. This positive value of 2.9 degrees Celsius indicates that the actual temperature of the insulator is currently lower than the expected target temperature, and heating is required. The temperature error calculation unit 141 outputs this error value of 2.9 and transmits it to the PID control unit 142.

[0037] Next, the PID control unit 142 begins operation. This unit is the core of adaptive power control, employing the classic proportional-integral-derivative (PI-DE) control algorithm. This algorithm comprehensively considers current, past, and future error information to generate a smooth and precise control input. The output of the PID controller is obtained by a weighted sum of three parts: 1. Proportional (P) term: proportional to the current temperature error. The larger the error, the larger the output of the proportional term, designed to respond quickly to deviations. 2. Integral (I) term: accumulates the error over a period of time. It is used to eliminate static errors caused by various factors (such as uneven heat dissipation, slight environmental changes) that proportional control cannot completely eliminate, ensuring that the target temperature is ultimately reached accurately. 3. Derivative (D) term: reflects the rate of change of the error. When the temperature rapidly approaches the target, the error decreases quickly, and the derivative term generates a counterforce to prevent the temperature from overshooting the target value due to inertia (i.e., overshoot), acting as a brake and stabilizing agent. The three key parameters of the PID controller—proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd—need to be pre-tuned based on the specific thermodynamic characteristics of the switchgear (such as heating power, cabinet volume, and heat dissipation performance). A commonly used tuning method is the empirical method or the Ziegler-Nichols tuning method, which involves testing in actual or simulation environments to find the optimal parameter combination that achieves fast, stable, and overshoot-free control. For example, after tuning, the parameters in this embodiment can be set as: Kp=12.0, Ki=0.5, Kd=5.0. At time t1, the PID control unit 142 receives a temperature error of 2.9. It calculates the PID output based on this error, the internally maintained integral error value, and the previous error value. For example, at this time, the internally maintained integral error value is 25.0, while the temperature error in the previous control cycle was 4.5. The PID calculation result = Kp × (current error) + Ki × (accumulated error integral) + Kd × (current error - previous cycle error) = 12.0 × 2.9 + 0.5 × 25.0 + 5.0 × (2.9 - 4.5) = 39.3. The output of the PID control unit 142 is this original PID calculation result of 39.3.

[0038] Finally, the heating power percentage output unit 143 performs final processing on the PID calculation results. This unit converts the theoretically calculated value output by the PID controller, which may exceed actual physical limitations, into a standardized, directly executable power percentage. This processing step is crucial to ensuring the control logic can be correctly executed by the physical device, because the PID algorithm, as a purely mathematical operation, can theoretically output values ​​far exceeding the actual power range of 0% to 100% that the heater can achieve when faced with large errors or disturbances. Therefore, this unit first applies strict output limiting constraints to the received PID calculation results. This constraint logic ensures the physical feasibility of the output command: any calculated value below 0, which physically corresponds to an invalid command of reverse heating or cooling, will be forcibly set to 0, explicitly indicating that the heater should be completely off; correspondingly, any calculated value exceeding 100, representing a power demand exceeding the device's capacity, will be saturated to 100, instructing the heater to operate at its maximum rated power. In this example, the calculation result received from the PID control unit is 39.3. This value falls exactly within the effective operating range of 0 to 100, so no clamping or saturation operation is required, and its value remains unchanged after the limiting circuit. After this validity verification and constraint, the value 39.3 is directly defined and formatted as the final heating power command, the value itself representing the percentage of power. Therefore, the final output of the heating power percentage output unit 143 is a clear control quantity: heating power output percentage = 39.3%.

[0039] Specifically, the power modulation heating module 150 is used for power modulation and heating execution based on the percentage of heating power output. That is, after the PID controller outputs a precise heating power value, the control command remains at the digital calculation level. However, the heating actuator (such as the heater) itself is usually a switching device, which can only operate in a fully on or fully off state and cannot directly execute a percentage-based analog power command. Simply and crudely converting this command into on or off would lose all the precision of the PID control, degrading the entire control to a simple hysteresis or on / off control, failing to achieve stable temperature regulation. Therefore, the final heating power output percentage is used for power modulation and heating execution to convert the calculated continuous and precise control quantity into the average output power of the heater on a macroscopic time scale through high-frequency switching technology, thereby faithfully reproducing the precise command in the digital domain into the physical world.

[0040] In a feasible technical solution, the power modulation heating module 150 processes the process as follows: at time t1 in the aforementioned embodiment, the power modulation heating module 150 receives the input as: heating power output percentage = 39.3%.

[0041] This module primarily consists of a microcontroller and a power switching element. The power switching element is preferably a solid-state relay (SSR) because it has no mechanical contacts, offers fast switching speed, long lifespan, and no electric arc, making it ideal for power modulation applications requiring frequent switching. The microcontroller receives the percentage of heating power output and generates corresponding control signals to drive the solid-state relay.

[0042] This module employs Pulse Width Modulation (PWM) technology to achieve continuous adjustment of heating power. The core of PWM technology lies in a preset fixed period T_pwm. Within this period, the average power received by the load (heater) is controlled by adjusting the proportion of the power switching element's on-time to the entire period (i.e., the duty cycle). The duty cycle is directly proportional to the percentage of heating power output. Setting the PWM period is a preset parameter that requires trade-offs. A period that is too long will cause perceptible temperature fluctuations within a single period; a period that is too short will place higher demands on the performance of the switching elements. For applications with high thermal inertia, such as switch cabinet heating, a suitable PWM period, such as T_pwm = 10 seconds, ensures smooth temperature control while remaining within the reasonable operating range of the solid-state relay.

[0043] When the power modulation heating module 150 receives the input value of 39.3%, its internal microcontroller immediately performs the following calculations: calculate the on-time T_on = T_pwm × (heating power output percentage / 100) = 10 seconds × (39.3 / 100) = 3.93 seconds, and calculate the off-time T_off = T_pwm - T_on = 10 seconds - 3.93 seconds = 6.07 seconds.

[0044] After the calculation is completed, the microcontroller generates a square wave control signal with a period of 10 seconds and outputs it to the control input terminal of the solid-state relay. The logic of this square wave signal is as follows: At the beginning of each 10-second cycle, the microcontroller sets the control signal to a high level. This high-level signal triggers the optocoupler inside the solid-state relay, turning it on. After the solid-state relay turns on, the AC mains power is fully applied to both ends of the heater, and the heater starts to work at 100% of its rated power, generating heat. This high-level state lasts for 3.93 seconds. At the moment the 3.93 seconds end, the microcontroller flips the control signal to a low level. Upon receiving the low-level signal, the solid-state relay turns off, cutting off the power supply to the heater. The heater stops working. This low-level state lasts for 6.07 seconds until the end of this 10-second cycle. When the 10-second cycle ends, the next cycle begins, and the above process repeats. In this way, although the heater only has two states at the microscopic level—fully on and fully off—the average heat power it transfers to the switch cabinet within the macroscopic 10-second cycle is precisely equivalent to 39.3% of its rated power. This achieves the goal of accurately executing an analog power command (39.3%) through digital switching.

[0045] This modulation and heating process is continuously adjusted as the percentage of heating power output from the upstream module is updated in real time. For example, in the next control cycle, as heating continues, the surface temperature of the critical insulator gradually increases, thereby reducing the error between it and the dynamic target temperature. In response, the newly calculated percentage of heating power output will also be adjusted accordingly, possibly smoothly decreasing from 39.3% to a new value such as 36.5%, thus achieving stable temperature approach and fine-tuning.

[0046] In summary, the intelligent adaptive heating system 100 for medium-voltage switchgear based on the embodiments of this application is explained. It constructs an adaptive heating control strategy with forward-looking predictive capabilities to replace the traditional passive fixed safety margin method. That is, the system no longer only focuses on the current temperature and humidity state inside the switchgear, but actively captures and quantifies the rate of change of the external environment (such as outside temperature and humidity) by introducing environmental trend analysis, forming temperature risk slope and humidity risk slope representing the future condensation risk. Based on this, the system can dynamically generate a target temperature that matches the rate of risk change. When the external environment is stable, the system uses a smaller safety margin to save energy; however, when it detects a rapid change in the environment towards a direction prone to condensation (such as a sudden drop in temperature or a surge in humidity), the system will proactively and significantly increase the safety margin, starting and increasing the power of heating in advance, thereby effectively offsetting the thermal inertia of the equipment and the response delay of the heating system. This control mode, which transforms passive response into active prevention, fundamentally solves the shortcomings of existing technologies in responding to sudden environmental changes and the risk of condensation, while also taking into account the energy-saving requirements under stable operating conditions.

[0047] Various implementations of this disclosure have been described above. The foregoing description is exemplary and not exhaustive. Furthermore, it is not limited to the disclosed implementations, and many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations.

Claims

1. An intelligent adaptive heating system for a medium voltage switchgear, characterized in that, The method comprises the following steps: A switch cabinet multi-dimensional state data acquisition module is used to acquire the temperature inside the cabinet, the humidity inside the cabinet, the surface temperature of key insulators, the temperature outside the cabinet, and the humidity outside the cabinet. An environmental trend analysis module is used to perform environmental trend analysis based on the time series data of the temperature outside the cabinet and the time series data of the humidity outside the cabinet to obtain a temperature risk slope and a humidity risk slope. A target temperature generation module is used to perform dynamic safety margin and target temperature decision-making based on the temperature inside the cabinet, the humidity inside the cabinet, the temperature risk slope, and the humidity risk slope to obtain a dynamic target temperature. A heating power determination module is used to perform adaptive PID power control on the dynamic target temperature and the surface temperature of key insulators to obtain a heating power output percentage. A power modulation and heating module is used to perform power modulation and heating based on the heating power output percentage. The target temperature generation module comprises a dynamic safety margin calculation unit configured to calculate a dynamic safety margin based on the temperature risk slope and the humidity risk slope. The dynamic safety margin calculation unit first performs dynamic physical quantification of humidity risk weight based on the cabinet humidity, the cabinet temperature and the dew point temperature to obtain a humidity risk weight coefficient when calculating the dynamic safety margin The calculation of the humidity risk weight coefficient is performed based on the partial derivative of the dew point temperature to the cabinet humidity, and the calculation formula is: ; wherein, the cabinet humidity is h, an intermediate variable is h, and are preset physical constants; The dynamic safety margin calculation unit calculates the dynamic safety margin based on the humidity risk weight coefficient The dynamic safety margin is calculated as follows : ; wherein is a base safety margin, and are a temperature risk weight coefficient and a humidity risk weight coefficient, respectively, and are a temperature risk slope and a humidity risk slope, a system thermal response time constant, is the dynamic safety margin.

2. The intelligent adaptive heating system for medium voltage switchgear according to claim 1, characterized in that, The environmental trend analysis module comprises: An outdoor parameter linear regression unit is configured to perform linear regression on the time series data of the temperature outside the cabinet and the time series data of the humidity outside the cabinet to obtain a temperature change rate outside the cabinet and a humidity change rate outside the cabinet. A temperature and humidity slope calculation unit is configured to perform one-way risk slope extraction on the temperature change rate outside the cabinet and the humidity change rate outside the cabinet to obtain the temperature risk slope and the humidity risk slope.

3. The intelligent adaptive heating system for medium voltage switchgear according to claim 2, characterized in that, The temperature and humidity slope calculation unit is configured to perform one-way risk slope extraction on the temperature change rate outside the cabinet and the humidity change rate outside the cabinet according to the following formula: ; wherein, is the rate of change of the temperature outside the cabinet, is the rate of change of the humidity outside the cabinet, is the maximum, is the temperature risk slope, is the humidity risk slope.

4. The intelligent adaptive heating system for medium voltage switchgear as claimed in claim 1, wherein, The target temperature generation module comprises: A dew point temperature calculation unit is configured to calculate a dew point temperature based on the temperature inside the cabinet and the humidity inside the cabinet. A dynamic target temperature calculation unit is configured to take the dew point temperature as a reference and superimpose a dynamic safety margin on the reference to obtain the dynamic target temperature.

5. The intelligent adaptive heating system for medium voltage switchgear according to claim 4, characterized in that, The dew point temperature calculation unit is configured to calculate the dew point temperature according to the following formula: ; wherein, is the humidity inside the cabinet, is the temperature inside the cabinet, and are preset physical constants, respectively 17.67 and 243.5, is the dew point temperature.

6. The intelligent adaptive heating system for medium voltage switchgear as claimed in claim 1, wherein, The heating power determination module comprises: A temperature error calculation unit is configured to calculate a temperature error between the dynamic target temperature and the surface temperature of key insulators. A PID control unit is configured to input the temperature error into a PID controller to obtain a PID calculation result. A heating power percentage output unit is configured to output limit and convert the PID calculation result to obtain the heating power output percentage.

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