Condensation risk intelligent identification method

Abstract

The invention discloses an intelligent condensation risk identification method, relates to the technical field of high-voltage switch cabinets, and solves the problems that a traditional method has obvious limitation that most dehumidification devices are passively triggered and lack the condensation risk pre-judgment ability. Three-level early warning is set based on a difference value, and the change trend is analyzed in combination with an 1h traceability period curve; the risk of subsequent 5 minutes is accurately predicted, a closed loop of real-time monitoring, trend pre-judgment and early warning is realized, and equipment faults caused by condensation are avoided; the temperature change trend is locked through comparison of the duration of the climbing / descending section, the prediction curve fitting the reality is generated, the prediction result can be continuously and dynamically adjusted according to real-time data, the method is suitable for a high-voltage switch cabinet scene with environment parameter fluctuation, and a timely and feasible coping basis is provided for operation and maintenance personnel.

Description

Technical Field

[0001] This invention relates to the field of high-voltage switchgear technology, specifically to an intelligent identification method for condensation risk. Background Technology

[0002] As a key piece of equipment in the power system, high-voltage switchgear is prone to failure due to factors such as high humidity, temperature rise and electrostatic dust in its operating environment. Among these, condensation-induced insulation degradation, short circuits and even switchgear explosions account for a relatively high proportion of accidents. The physical essence of condensation is the phase change process in which water vapor in the air condenses into liquid water on a cold surface. Its occurrence depends on the relative relationship between ambient temperature and dew point temperature. When the ambient temperature drops below the dew point temperature, water vapor in the air reaches saturation and begins to condense. The calculation model for dew point temperature needs to be derived from thermodynamic equations, taking into account both ambient temperature and relative humidity, reflecting the critical temperature value at which condensation may occur under the current humidity conditions. The enclosed structure of high-voltage switchgear restricts heat and moisture exchange between the inside and outside of the cabinet. The temperature difference between the inside and outside of the cabinet exacerbates the formation of localized cold surfaces, while an increased rate of humidity change shortens the time to reach saturation. Insufficient airflow reduces the water vapor diffusion rate. These factors collectively constitute the key environmental parameters affecting condensation inside the cabinet.

[0003] Condensation can cause multiple hazards: on the one hand, it reduces the dielectric strength of insulating materials, making them prone to insulation faults such as creepage and flashover, and in severe cases, short circuit accidents; on the other hand, it accelerates the corrosion of metal parts, affects the mechanical transmission performance and conductivity reliability of switches and contacts, shortens the service life of equipment, and increases operation and maintenance costs and the risk of power outages.

[0004] Currently, the industry relies heavily on traditional methods for condensation protection in switchgear, such as installing dehumidifiers, conducting regular manual inspections, or using fixed threshold alarms. However, traditional methods have significant limitations: dehumidifiers are mostly passively triggered, lacking the ability to predict condensation risks, and often only activate after condensation has occurred, resulting in insufficient timeliness of protection; manual inspections are limited by cycles, making it difficult to capture dynamic changes in environmental parameters in real time, and easily overlooking potential risks; fixed threshold alarms can only determine the current state, without considering the changing trends of temperature and humidity, and cannot provide early warnings of condensation hazards in subsequent cycles, making it difficult for maintenance personnel to take targeted measures in advance.

[0005] Therefore, there is an urgent need for an intelligent identification method that can monitor environmental parameters in real time, accurately deduce core physical quantities, and dynamically predict condensation trends, so as to solve the pain points of traditional protection methods such as "passive response, insufficient prediction, and limited accuracy" and provide technical support for the proactive operation and safe operation of high-voltage switchgear. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an intelligent method for identifying condensation risks, which solves the problem that traditional methods have significant limitations: dehumidification devices are mostly passively triggered and lack the ability to predict condensation risks.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent identification of condensation risk, comprising the following steps: Step 1: Monitor the ambient temperature and relative humidity of the high-voltage switchgear in real time, and derive the saturated water vapor partial pressure, actual water vapor partial pressure, and dew point temperature in real time based on the monitored real-time parameters. Step 2: Conduct an initial assessment of the real-time monitored ambient temperature and the derived dew point temperature. If there are no abnormalities, generate parameter change curves for ambient temperature and dew point temperature, confirm the predicted trend from the parameter change curves, and then predict whether there is a risk of condensation in the subsequent period based on the predicted trend, and carry out early warning processing.

[0008] Preferably, in step one, the derivation process of the saturated water vapor partial pressure includes: The real-time monitored ambient temperature is calibrated as T. a And adopt: T a，k =T a +273.15, confirm ambient temperature T a The associated Kelvin temperature T a，k ; When T a When the temperature is ≥0℃, the following should be used: Confirm the saturated water vapor partial pressure e s Where 23.8326, 3816.44, and 46.13 are engineering correction coefficients; ln is the natural logarithm, and the output is e. s The unit is kPa; When T a When <0℃, use: ; Preferably, in step one, the derivation process regarding the actual water vapor partial pressure includes: The monitored relative humidity is labeled as RH, and the following method is used: Confirm the actual water vapor partial pressure e a ; Preferably, in step one, the derivation process regarding the dew point temperature includes: If e a >0.6116 kPa, representing T d >0℃, use: Then use: T d =T d，K -273.15, and then round the value to positive. If ea =0.6116kPa, then directly calibrate the current time T. d =0℃; If e a <0.6116 kPa represents T d <0℃, use: Then use: T d =T d，K -273.15, and then the value is negative.

[0009] Preferably, in step two, the specific method for initially assessing the real-time monitored ambient temperature and the derived dew point temperature is as follows: The T monitored at the current moment a With the derived T d Conduct early warning assessment: If (T) a -T d If the temperature is ≥3℃, then proceed with the subsequent prediction process; if the temperature is <3℃, then proceed with the subsequent prediction process. a -T d If the temperature is less than 3℃, a warning signal will be generated and displayed directly. ... a -T d If the temperature is ≤0℃, a condensation risk signal will be generated and displayed directly. The specific method for confirming the predicted trend from the parameter change curve is as follows: Based on the current moment, a set of source tracing cycles is identified, and the monitored ambient temperature and associated dew point temperature within the source tracing cycle are determined. Based on the time relationship, ambient temperature change curves and dew point temperature change curves associated with the source tracing cycle are generated. The initial point of the change curve is associated with the initial moment of the source tracing cycle, and the end point is associated with the current moment. The trend of change between adjacent moments is determined from the ambient temperature change curve. The ambient temperature at the next moment within the adjacent moment is defined as T1 and the ambient temperature at the previous moment is defined as T2. The trend of change is determined by the formula: trend of change = T1 - T2. The trend of change between adjacent moments in the corresponding time period is then determined. The minimum and maximum values ​​are selected from the several sets of trends of change as the environmental characteristic intervals associated with the corresponding change curves. The same processing method is used to determine the trend of change of dew point temperature change curve between adjacent moments, and the dew point characteristic intervals associated with the dew point temperature change curve are locked. Starting from the end point of the ambient temperature change curve, identify the associated curves 10 minutes backward. Within these curves, identify the 10-minute change status: Record the curve segments with a change trend > 0 as the climbing segment, and record the climbing duration as P1. Record the curve segments with a change trend < 0 as the descending segment, and record the descending duration as P2. Do not mark the curve segments with a change trend = 0. If P1 < P2, select the minimum value of the environmental characteristic interval as the predicted trend. If P1 > P2, select the maximum value of the environmental characteristic interval as the predicted trend. If P1 = P2, again identify the curve segments 10 minutes backward until the predicted trend is determined. For the dew point temperature change curve, the prediction trend of the dew point temperature change curve is locked by adopting the same determination method as the prediction trend of the ambient temperature change curve. The specific method for predicting whether there is a risk of condensation in subsequent periods is as follows: Based on the generated ambient temperature change curve and dew point temperature change curve, predict whether there is a risk of condensation in the next 5 minutes. Based on the predicted trend confirmed by the corresponding change curve, a subsequent prediction curve associated with the corresponding change curve is generated. The change trend of the prediction curve is consistent with the predicted trend, and the initial point of the prediction curve is the same as the end point of the corresponding change curve. Within the prediction curves associated with the ambient temperature change curve and the dew point temperature change curve, lock the associated ambient predicted temperature YTa at the same moment. p and dew point prediction temperature YTd p Where p represents different times within the next 5 minutes, and two sets of temperature values ​​YTa are identified at different times. p and YTd p Does it satisfy: (YTa) p -YTd p If the temperature is ≤0℃, a condensation risk prediction signal will be generated and displayed directly. If the temperature is not ≤0℃, continuous monitoring will be performed, and real-time prediction will be made based on the real-time monitoring process.

[0010] This invention provides a method for intelligent identification of condensation risk. Compared with existing technologies, it has the following advantages: This invention calculates the partial pressure of saturated water vapor by using an engineering correction coefficient fitting formula in different temperature ranges, and derives the actual partial pressure and dew point temperature by combining relative humidity, ensuring that the calculation error in the core range such as 0-60℃ is <5%, providing accurate physical parameter support for risk assessment. Based on (T) a -T dThe differential value is set with three levels of early warning (normal / early warning / condensation risk), and the trend of change is analyzed by combining the 1-hour traceability cycle curve. The risk in the next 5 minutes is accurately predicted, realizing a closed loop of "real-time monitoring - trend prediction - early warning" to avoid equipment failure caused by condensation. By comparing the duration of the rise / fall phases, the temperature change trend is locked in, and a realistic prediction curve is generated. The prediction results can be continuously and dynamically adjusted according to real-time data. It is suitable for high-voltage switchgear scenarios with fluctuating environmental parameters, providing maintenance personnel with timely and feasible response basis. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0012] 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.

[0013] First Embodiment Please see Figure 1 This application provides a method for intelligent identification of condensation risk, comprising the following steps: Step 1: Monitor the ambient temperature and relative humidity of the high-voltage switchgear in real time, and derive the saturated water vapor partial pressure, actual water vapor partial pressure, and dew point temperature in real time based on the monitored real-time parameters. Step 2: Conduct an initial assessment of the real-time monitored ambient temperature and the derived dew point temperature. If there are no abnormalities, generate parameter change curves for ambient temperature and dew point temperature, confirm the trend of change from the parameter change curves, predict whether there is a risk of condensation in the subsequent period, and carry out early warning processing.

[0014] Second Embodiment As a further embodiment of the first embodiment, this embodiment mainly focuses on the real-time derivation process of saturated water vapor partial pressure, actual water vapor partial pressure and dew point temperature, and prioritizes the derivation of parameters based on the monitored ambient temperature.

[0015] Specifically, the derivation process regarding the partial pressure of saturated water vapor in step one includes: The real-time monitored ambient temperature is calibrated as T. a And adopt: T a，k =T a +273.15, confirm ambient temperature T a The associated Kelvin temperature Ta，k Here, the temperature is converted from "relative zero" (°C) to "absolute zero" (K) to ensure the physical consistency of the temperature parameters in the formula and avoid calculation errors caused by zero-point differences. For example, if the currently monitored ambient temperature is 25°C, then T... a，k =25 + 273.15 = 298.15K; The saturated water vapor partial pressure is "the maximum water vapor pressure that air can hold at the current temperature," and it needs to be determined based on temperature (T). a Is it greater than 0℃, when T a When the temperature is ≥0℃, the following should be used: Confirm the saturated water vapor partial pressure e s Where 23.8326, 3816.44, and 46.13 are engineering correction coefficients, derived from fitting a large amount of experimental data to ensure that the calculation error is <5% within the 0-60℃ range; ln is the natural logarithm, and the output is e. s The unit is kPa; When T a When <0℃, use: e s Directly reflects the air's "water-holding capacity"—the higher the temperature, the higher the water content. s The larger the value (e.g., es = 5.90 kPa at 25℃, e at 30℃) the greater the value. s =4.24 kPa), providing a benchmark for subsequent determination of whether water vapor is saturated.

[0016] Specifically, the derivation process regarding the actual water vapor partial pressure in step one includes: The monitored relative humidity is labeled as RH, and the following method is used: Confirm the actual water vapor partial pressure e a Actual water vapor partial pressure is the "actual water vapor pressure in the current air," and relative humidity (RH) is the "percentage of actual partial pressure to saturation partial pressure." Therefore, this formula can be used to convert humidity in percentage form into physical pressure, facilitating subsequent dew point temperature calculations. RH = 60%, e s =5.90 kPa, where e a =3.54 kPa.

[0017] Specifically, the derivation process for the dew point temperature in step one includes: Dew point temperature T d The essence is to "make the actual water vapor partition e" a The temperature at which saturation is reached is "0°C". The saturated partial pressure of water vapor at 0°C is a constant, 0.6116 kPa. Based on this, the temperature (T) can be assessed by comparing the actual partial pressure of water vapor with the constant value. d Positive and negative relationships: If e a>0.6116 kPa, if e a The saturation partial pressure at 0℃ indicates that "e" a "The saturation temperature Td must be higher than 0℃", representing T d >0℃ (water phase dew point), use: Then use: T d =T d，K -273.15, and then round the value to positive. If e a =0.6116kPa, then directly calibrate the current time T. d =0℃; If e a <0.6116 kPa represents T d <0℃ (ice phase dew point / frost point), using: Then use: T d =T d，K -273.15, and then the value is negative.

[0018] Second Embodiment As a further embodiment of the first embodiment, this embodiment mainly focuses on the early warning process of condensation risk, and generates corresponding analysis signals for display based on the real-time analysis process; The specific methods for confirming the predicted trend are as follows: The T monitored at the current moment a With the derived T d Conduct early warning assessment: If (T) a -T d If the temperature is ≥3℃, then proceed with the subsequent prediction process; if the temperature is <3℃, then proceed with the subsequent prediction process. a -T d If the temperature is less than 3℃, a warning signal will be generated and displayed directly. ... a -T d If the temperature is ≤0℃, a condensation risk signal will be generated and displayed directly. The associated prediction processes include: Based on the current moment, a set of source tracing cycles is identified, which is generally 1 hour. The ambient temperature and the associated dew point temperature monitored within the source tracing cycle are determined. Based on the time relationship, the ambient temperature change curve and the dew point temperature change curve associated with the source tracing cycle are generated. The initial point of the change curve is associated with the initial moment of the source tracing cycle, and the end point is associated with the current moment (that is, a process of tracing forward). The trend of change between adjacent moments is determined from the ambient temperature change curve. The ambient temperature at the next moment within the adjacent moment is defined as T1 and the ambient temperature at the previous moment is defined as T2. The trend of change is determined by the formula: trend of change = T1 - T2. The trend of change between adjacent moments in the corresponding time period is then determined. The minimum and maximum values ​​are selected from the several sets of trends of change as the environmental characteristic intervals associated with the corresponding change curves. The same processing method is used to determine the trend of change of dew point temperature change curve between adjacent moments, and the dew point characteristic intervals associated with the dew point temperature change curve are locked. Starting from the end point of the ambient temperature change curve, identify the associated curves 10 minutes backward. Within these curves, identify the 10-minute change status: Record the curve segments with a change trend > 0 as the climbing segment, and record the climbing duration as P1. Record the curve segments with a change trend < 0 as the descending segment, and record the descending duration as P2. Do not mark the curve segments with a change trend = 0. If P1 < P2, select the minimum value of the environmental characteristic interval as the predicted trend. If P1 > P2, select the maximum value of the environmental characteristic interval as the predicted trend. If P1 = P2, again identify the curve segments 10 minutes backward until the predicted trend is determined. For the dew point temperature change curve, the prediction trend of the dew point temperature change curve is locked by adopting the same determination method as the prediction trend of the ambient temperature change curve.

[0019] The specific method for predicting whether there is a risk of condensation in subsequent periods is as follows: Based on the generated ambient temperature change curve and dew point temperature change curve, predict whether there is a risk of condensation in the next 5 minutes. Based on the predicted trend confirmed by the corresponding change curve, a subsequent prediction curve associated with the corresponding change curve is generated. The change trend of the prediction curve is consistent with the predicted trend, and the initial point of the prediction curve is the same as the end point of the corresponding change curve. Within the prediction curves associated with the ambient temperature change curve and the dew point temperature change curve, lock the associated ambient predicted temperature YTa at the same moment. p and dew point prediction temperature YTd p Where p represents different times within the next 5 minutes, and two sets of temperature values ​​YTa are identified at different times. p and YTd p Does it satisfy: (YTa) p -YTd p If the temperature is ≤0℃, a condensation risk prediction signal will be generated and displayed directly. If the temperature is not ≤0℃, continuous monitoring will be performed. Based on the real-time monitoring process, real-time prediction will be made to facilitate relevant personnel to take timely measures to deal with the distribution cabinet.

[0020] Specifically, in the corresponding prediction and processing process, when there are abnormalities in the temperature parameters associated with the corresponding prediction time, relevant personnel need to adjust the relevant operating parameters of the distribution cabinet to avoid major abnormalities in the corresponding power distribution process. In this way, the abnormal relationship between temperature values ​​can be comprehensively evaluated to avoid the risk of condensation in the distribution cabinet.

[0021] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0022] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for intelligent identification of condensation risk, characterized in that, Includes the following steps: Step 1: Monitor the ambient temperature and relative humidity of the high-voltage switchgear in real time, and derive the saturated water vapor partial pressure, actual water vapor partial pressure, and dew point temperature in real time based on the monitored real-time parameters. Step 2: Conduct an initial assessment of the real-time monitored ambient temperature and the derived dew point temperature. If there are no abnormalities, generate parameter change curves for ambient temperature and dew point temperature, confirm the predicted trend from the parameter change curves, and then predict whether there is a risk of condensation in the subsequent period based on the predicted trend, and carry out early warning processing.

2. The intelligent identification method for condensation risk according to claim 1, characterized in that, In step one, the derivation process of the saturated water vapor partial pressure includes: The real-time monitored ambient temperature is calibrated as T. a And adopt: T a，k =T a +273.15, confirm ambient temperature T a The associated Kelvin temperature T a，k ; When T a When the temperature is ≥0℃, the following should be used: Confirm the saturated water vapor partial pressure e s Where 23.8326, 3816.44, and 46.13 are engineering correction coefficients; ln is the natural logarithm, and the output is e. s The unit is kPa; When T a When <0℃, use: .

3. The intelligent identification method for condensation risk according to claim 2, characterized in that, In step one, the derivation process regarding the actual water vapor partial pressure includes: The monitored relative humidity is labeled as RH, and the following method is used: Confirm the actual water vapor partial pressure e a .

4. The intelligent identification method for condensation risk according to claim 3, characterized in that, In step one, the derivation process regarding the dew point temperature includes: If e a >0.6116 kPa, representing T d >0℃, use: Then use: T d =T d，K -273.15, and then round the value to positive. If e a =0.6116kPa, then directly calibrate the current time T. d =0℃; If e a <0.6116 kPa represents T d <0℃, use: Then use: T d =T d，K -273.15, and then the value is negative.

5. The intelligent identification method for condensation risk according to claim 1, characterized in that, In step two, the specific method for initially assessing the real-time monitored ambient temperature and the derived dew point temperature is as follows: The T monitored at the current moment a With the derived T d Conduct early warning assessment: If (T) a -T d If the temperature is ≥3℃, then proceed with the subsequent prediction process; if the temperature is <3℃, then proceed with the subsequent prediction process. a -T d If the temperature is less than 3℃, a warning signal will be generated and displayed directly. ... a -T d If the temperature is ≤0℃, a condensation risk signal will be generated and displayed directly.

6. The intelligent identification method for condensation risk according to claim 5, characterized in that, In step two, the specific method for confirming the predicted trend from the parameter change curve is as follows: Based on the current moment, a set of source tracing cycles is identified, and the monitored ambient temperature and associated dew point temperature within the source tracing cycle are determined. Based on the time relationship, ambient temperature change curves and dew point temperature change curves associated with the source tracing cycle are generated. The initial point of the change curve is associated with the initial moment of the source tracing cycle, and the end point is associated with the current moment. The trend of change between adjacent moments is determined from the ambient temperature change curve. The ambient temperature at the next moment within the adjacent moment is defined as T1 and the ambient temperature at the previous moment is defined as T2. The trend of change is determined by the formula: trend of change = T1 - T2. The trend of change between adjacent moments in the corresponding time period is then determined. The minimum and maximum values ​​are selected from the several sets of trends of change as the environmental characteristic intervals associated with the corresponding change curves. The same processing method is used to determine the trend of change of dew point temperature change curve between adjacent moments, and the dew point characteristic intervals associated with the dew point temperature change curve are locked. Starting from the end point of the ambient temperature change curve, identify the associated curves 10 minutes backward. Within these curves, identify the 10-minute change status: Record the curve segments with a change trend > 0 as the climbing segment, and record the climbing duration as P1. Record the curve segments with a change trend < 0 as the descending segment, and record the descending duration as P2. Do not mark the curve segments with a change trend = 0. If P1 < P2, select the minimum value of the environmental characteristic interval as the predicted trend. If P1 > P2, select the maximum value of the environmental characteristic interval as the predicted trend. If P1 = P2, again identify the curve segments 10 minutes backward until the predicted trend is determined. For the dew point temperature change curve, the prediction trend of the dew point temperature change curve is locked by adopting the same determination method as the prediction trend of the ambient temperature change curve.

7. The intelligent identification method for condensation risk according to claim 6, characterized in that, In step two, the specific method for predicting whether there is a risk of condensation in subsequent cycles is as follows: Based on the generated ambient temperature change curve and dew point temperature change curve, predict whether there is a risk of condensation in the next 5 minutes. Based on the predicted trend confirmed by the corresponding change curve, a subsequent prediction curve associated with the corresponding change curve is generated. The change trend of the prediction curve is consistent with the predicted trend, and the initial point of the prediction curve is the same as the end point of the corresponding change curve. Within the prediction curves associated with the ambient temperature change curve and the dew point temperature change curve, lock the associated ambient predicted temperature YTa at the same moment. p and dew point prediction temperature YTd p Where p represents different times within the next 5 minutes, and two sets of temperature values ​​YTa are identified at different times. p and YTd p Does it satisfy: (YTa) p -YTd p If the temperature is ≤0℃, a condensation risk prediction signal will be generated and displayed directly. If the temperature is not ≤0℃, continuous monitoring will be performed, and real-time prediction will be made based on the real-time monitoring process.

Images