Intelligent heat dissipation method and system for dry-type transformer

Abstract

This invention proposes an intelligent heat dissipation method and system for dry-type transformers. The method includes: collecting the winding temperature, core temperature, load rate, and ambient temperature of the target dry-type transformer to construct a thermal state baseline; calculating and generating a thermal risk assessment result based on the thermal state baseline at the current moment and the previous two sampling periods; comparing the thermal risk assessment result with a benchmark risk value and an enhanced heat dissipation threshold, calculating a target heat dissipation intensity coefficient through a combination of linear adjustment terms and quadratic enhancement terms, and mapping it to a heat dissipation control decision; gradually approximating the target heat dissipation intensity coefficient in the heat dissipation control decision using a first-order inertial recursive method to generate an execution intensity coefficient for the current sampling period, thus completing the heat dissipation control operation. This invention can better solve the problems of traditional threshold control's late intervention, insufficient precision in heat dissipation actions, frequent switching of actuators, and insufficient adaptability to different field conditions.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control of dry-type transformers, and particularly relates to an intelligent heat dissipation method and system for dry-type transformers. Background Technology

[0002] Dry-type transformers, due to their superior flame-retardant properties, ease of maintenance, and suitability for indoor and densely populated environments, are widely used in rail transit, data centers, commercial complexes, industrial plants, and new energy power distribution systems. Compared to oil-immersed transformers, dry-type transformers primarily rely on natural air convection or forced air cooling for heat dissipation. The heat release process of their windings, core, and insulation structures is more susceptible to load changes, inlet air temperature, installation space, cabinet ventilation conditions, and local airflow organization. In practical engineering, existing dry-type transformer heat dissipation control typically relies on measured winding or core temperatures, using preset thresholds to control fan start / stop, speed switching, or auxiliary cooling device operation. Some solutions further incorporate load rate, ambient temperature, or simple temperature rise trends as auxiliary judgment criteria. While these technologies can meet basic heat dissipation protection requirements under relatively stable loads and minimal environmental changes, they are prone to control intervention lag under conditions of rapid load increases, high inlet air temperatures, limited cabinet ventilation, or the gradual formation of localized hot spots. The reason for this is that the windings and insulation structure of dry-type transformers have significant thermal inertia. The current temperature value often lags behind the internal heat accumulation process. Judging whether to start heat dissipation based solely on the current measured temperature is insufficient to reflect the risk of a continuously rising or even accelerating thermal state. Furthermore, existing control methods typically directly link temperature thresholds to heat dissipation actions, lacking a continuous transition mechanism from the overall thermal state to the degree of thermal risk, then to the intensity of heat dissipation and the corresponding action. This results in coarse-grained switching of heat dissipation actions, which may cause frequent fan start-stops or premature high-speed operation during minor thermal disturbances, while failing to introduce enhanced airflow or auxiliary cooling in a timely manner when the thermal risk continues to increase. In actual equipment, fans, airflow guiding components, and auxiliary cooling devices also have different response speeds, energy consumption levels, and mechanical characteristics. If the control command jumps directly to the target action, it may cause impact on the actuator, increased noise, and equipment wear.

[0003] Therefore, although existing technologies have the foundation for temperature acquisition and graded execution, they still lack an intelligent heat dissipation control scheme that can uniformly express the state of winding hot spots, the state of core heat accumulation, the driving effect of load and the environmental heat dissipation conditions, and further combine the continuous change trend of thermal state to generate risk results, and then smoothly transform the risk results into graded heat dissipation actions. Summary of the Invention

[0004] The purpose of this invention is to propose an intelligent heat dissipation method and system for dry-type transformers to solve the above-mentioned problems.

[0005] To achieve the above objectives, a first aspect of the present invention provides an intelligent heat dissipation method for dry-type transformers, the method comprising the following steps: S1. Collect the winding temperature, core temperature, load rate and ambient temperature of the target dry-type transformer to construct a thermal state substrate; S2. Cache the thermal state base during the continuous sampling period, and calculate the first-order difference, second-order difference and trend consistency term based on the thermal state base of the current time and the previous two sampling periods, and calculate and generate thermal risk assessment results in combination with the current thermal state base. S3. Compare the thermal risk assessment results with the benchmark risk value and the enhanced heat dissipation threshold, calculate the target heat dissipation intensity coefficient by combining the linear adjustment term and the quadratic enhancement term, and map the target heat dissipation intensity coefficient into a heat dissipation control decision based on the intensity range in which the target heat dissipation intensity coefficient is located; wherein, the heat dissipation control decision includes the target heat dissipation intensity coefficient, the corresponding target fan speed, the target position of the air guiding component, and the target state of the auxiliary cooling device; S4. The target heat dissipation intensity coefficient in the heat dissipation control decision is gradually approximated by a first-order inertial recursion method to generate the execution intensity coefficient of the current sampling period. The target fan speed, the target position of the air guide component, and the target state of the auxiliary cooling device are adjusted successively according to the interval corresponding to the execution intensity coefficient of the current sampling period to complete the heat dissipation control operation.

[0006] Preferably, S1 further includes: The winding temperature, core temperature, load rate, and ambient temperature are converted into relative state values ​​and then weighted and fused to generate a thermal state substrate.

[0007] Preferably, the step of converting the winding temperature, core temperature, load rate, and ambient temperature into relative state values ​​and then weighting and fusing them specifically involves: The winding temperature, core temperature and ambient temperature are linearly normalized according to their respective design allowable ranges or reference ranges to obtain the corresponding relative state values. The load factor is directly obtained from the ratio of the current operating current to the rated current. The weights of the weighted fusion are preset according to the degree of contribution of each factor to the thermal state, with the winding temperature having the highest weight.

[0008] Preferably, the first-order difference is the difference between the thermal state baseline at the current moment and the thermal state baseline at the previous moment, which is used to represent the rate of change of the thermal state. The second-order difference is the current thermal state basis minus twice the thermal state basis of the previous moment plus the thermal state basis of the moment before that, used to represent the acceleration of the rate of change. The trend consistency term is determined based on whether the thermal state base shows an upward trend in two consecutive sampling intervals. When it rises continuously, the first value is taken; otherwise, the second value is taken. The first value is 1, and the second value is 0.

[0009] Preferably, the linear adjustment term is calculated and generated based on the linear adjustment coefficient, the thermal risk assessment results, and the benchmark risk value; The secondary enhancement term is only included in the calculation when the thermal risk assessment result exceeds the enhanced heat dissipation threshold. It is calculated and generated based on the nonlinear enhancement coefficient, the thermal risk assessment result, and the enhanced heat dissipation threshold. The target heat dissipation intensity coefficient is generated based on the linear adjustment term and the quadratic enhancement term, and the amplitude is constrained.

[0010] Preferably, the mapping relationship between the intensity range where the target heat dissipation intensity coefficient is located and the heat dissipation control decision is determined during the equipment commissioning stage based on the fan performance curve, heat dissipation efficiency test results and typical operating data, and stored in the control unit and called by a lookup table. When the target heat dissipation intensity coefficient falls exactly on the boundary of two adjacent intensity intervals, the control unit generates a heat dissipation control decision according to the higher heat dissipation level correspondence. For equipment without a flow guiding component or without an auxiliary cooling device, the corresponding fields in the heat dissipation control decision remain in their current position or stop, without affecting the generation of control decisions for other configured heat dissipation components.

[0011] Preferably, the target heat dissipation intensity coefficient in the heat dissipation control decision is gradually approximated using a first-order inertial recursive method to generate the execution intensity coefficient for the current sampling period, specifically as follows: The execution intensity coefficient for the current sampling period is calculated by combining the execution intensity coefficient of the previous sampling period, the difference between the execution intensity coefficient of the previous period based on the execution adjustment coefficient and the target execution intensity coefficient. The execution adjustment coefficient is determined during the equipment commissioning phase based on the fan speed-up time, the flow guiding mechanism action time, and the allowable start-stop interval of the auxiliary cooling device.

[0012] Preferably, S4 further includes: When performing heat dissipation control operations, the fan speed, the position of the air guide mechanism, and the start and stop of the auxiliary cooling should be completed in accordance with the rhythm of the equipment's operation. When the auxiliary cooling device involves starting or stopping, the control unit will only issue a start / stop command after the target execution intensity coefficient enters the corresponding high intensity range and the preset minimum start / stop interval is met. If the minimum start / stop interval is not met, the previous auxiliary cooling state will be maintained and the gradual adjustment of the fan and guide components will continue.

[0013] Preferably, S4 further includes: The device execution status is generated by the heat dissipation control unit based on the executed device status register, inverter operation signal, airflow guide mechanism position signal and auxiliary cooling device operation signal, which indicates that the heat dissipation control operation has been implemented on the specific device. If no feedback is received from the inverter operation, the flow guide mechanism positioning, or the auxiliary cooling device operation within the preset execution confirmation time, the equipment execution status in the current cycle will remain the previous confirmed execution status.

[0014] A second aspect of the present invention provides an intelligent heat dissipation system for dry-type transformers, the system comprising: The thermal state substrate generation unit is used to collect the winding temperature, core temperature, load rate and ambient temperature of the target dry-type transformer in order to construct the thermal state substrate. The thermal risk assessment unit is used to cache the thermal state base in a continuous sampling period, calculate the first-order difference, the second-order difference and the trend consistency term based on the thermal state base at the current time and the previous two sampling periods, and calculate and generate the thermal risk assessment result in combination with the current thermal state base. The heat dissipation decision generation unit is used to compare the thermal risk assessment results with the baseline risk value and the enhanced heat dissipation threshold, calculate the target heat dissipation intensity coefficient by combining a linear adjustment term and a quadratic enhancement term, and map the target heat dissipation intensity coefficient into a heat dissipation control decision based on the intensity range in which the target heat dissipation intensity coefficient is located; wherein, the heat dissipation control decision includes the target heat dissipation intensity coefficient, the corresponding target fan speed, the target position of the air guiding component, and the target state of the auxiliary cooling device; The progressive execution control unit is used to gradually approximate the target heat dissipation intensity coefficient in the heat dissipation control decision through a first-order inertial recursion method, generate the execution intensity coefficient of the current sampling period, and adjust the target fan speed, the target position of the air guide component, and the target state of the auxiliary cooling device of the fan successively according to the interval corresponding to the execution intensity coefficient of the current sampling period, so as to complete the heat dissipation control operation.

[0015] The beneficial technical effects of the present invention are at least as follows: This invention first generates a thermal state baseline based on winding temperature, core temperature, load rate, and ambient temperature, allowing field operating quantities from different sources to reflect the current hot spot state, overall heat accumulation state, load heating effect, and air intake heat dissipation conditions under the same evaluation scale. Then, it constructs a thermal risk assessment result using the thermal state baseline within a continuous sampling period, incorporating the current thermal state, rate of increase, acceleration trend, and continuous temperature rise characteristics into the judgment, enabling the system to identify the formation process of thermal risks before the temperature reaches the traditional alarm threshold. Furthermore, it generates a target heat dissipation intensity coefficient based on the thermal risk assessment result, and through a control relationship with high-risk enhancement characteristics, it unifies the gradual adjustment of the low-risk stage and the rapid enhancement of the high-risk stage in the same control process, mapping it to a heat dissipation control decision composed of fan speed, target position of the guide component, and target state of the auxiliary cooling device. Finally, it introduces a progressive execution process when the field equipment operates, allowing fan speed increase, guide component adjustment, and auxiliary cooling start / stop to be completed step by step according to the equipment's response capability, and automatically sent by the heat dissipation control unit to the corresponding actuators and written into the operating status record. Therefore, this invention does not simply add new sensors or superimpose more control actions, but rather forms a complete technical chain based on existing dry-type transformer temperature monitoring and heat dissipation execution, from unified expression of thermal state, early identification of thermal risks, generation of heat dissipation intensity grading to smooth execution of equipment actions. It can better solve the problems of traditional threshold control intervention being too late, insufficient precision of heat dissipation actions, frequent switching of actuators, and insufficient adaptability to different field conditions. Attached Figure Description

[0016] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0017] Figure 1 This is a flowchart of an intelligent heat dissipation method for a dry-type transformer according to the present invention. Detailed Implementation

[0018] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0019] like Figure 1 As shown in the figure, an embodiment of the present invention provides an intelligent heat dissipation method for a dry-type transformer, the method comprising: S1. Collect the winding temperature, core temperature, load rate, and ambient temperature of the target dry-type transformer to construct a thermal state substrate.

[0020] Specifically, S1 is the field operation process for intelligent heat dissipation control of dry-type transformers. It collects four types of core data: winding temperature, core temperature, load rate, and ambient temperature, and organizes them into a thermal state base that can be directly used for subsequent thermal risk assessment. Winding temperature Temperature data is collected by a platinum resistance temperature sensor located near the hot spots of the winding. This sensor is typically connected to a dry-type transformer temperature controller, which reads the data according to a set sampling period. (Core temperature) Temperature data is collected by temperature sensors attached to the surface of the iron core or near the yoke of the iron core, reflecting the heat accumulation state of the iron core and adjacent structures; load rate. The transformer operating current is collected by the current transformer, and then calculated by the monitoring unit according to the ratio of the current current to the rated current; ambient temperature. Temperature sensors located at air inlets, internal ventilation channels of the cabinet, or transformer installation spaces collect data to reflect the air temperature entering the heat dissipation channel. These collected values ​​are aggregated by the dry-type transformer temperature controller, integrated online monitoring device, or power distribution monitoring unit and transmitted to the heat dissipation control unit. The heat dissipation control unit matches the four types of data at the same sampling time, ensuring that the same set of data corresponds to the same operating state. If there is only a preset allowable deviation between the timestamp of a certain type of collected value and the current sampling time, the valid reading closest to the current sampling time within that allowable deviation range is used for matching. If there is no valid reading for a certain type of data in the current sampling period, the valid value corresponding to the previous sampling period is used and a missing value is marked. If the system has not yet generated a previous valid value, the current sampling period only performs buffering without generating new original thermal state data. For example, in one sampling period, the temperature controller reads the winding temperature and core temperature, the current transformer synchronously provides the operating current, and the ambient temperature sensor reads the air inlet temperature. The control unit merges the four collected results into a set of original thermal state data according to the timestamp for subsequent relative state transitions.

[0021] Understandably, because the numerical ranges and engineering implications of winding temperature, core temperature, ambient temperature, and load rate differ, the heat dissipation control unit first performs a relative state transformation on temperature data. This transformation originates from the linear normalization approach commonly used in engineering condition evaluation, which involves placing the collected temperature values ​​into the corresponding allowable operating range of the equipment for relative expression, thus representing the current temperature's position within the allowable range. Winding temperature Converted according to the design allowable range of winding temperature core temperature Converted according to the design allowable range of iron core temperature Ambient temperature Converted according to the design reference range of the installation environment Load rate The value has already been calculated using the current ratio and is directly used as the subsequent fusion input. All the above relative state transitions are completed according to the method of "the offset of the current value relative to the lower reference limit divided by the width of the corresponding reference interval". When the current value is higher than the upper reference limit, the relative state value is allowed to be greater than... When the current value is below the reference lower limit, the relative state value is allowed to be less than [a certain value]. If the upper and lower reference limits for a certain type of temperature are the same, then the relative state values ​​for that type are determined according to... The process is handled, and an equivalent reference flag is written to it in the current sampling period. For example, when the winding temperature, although not yet reaching the alarm value, is close to its long-term operating reference upper limit, It will be at a high level; when the load rate increases simultaneously, This will further demonstrate the driving effect of subsequent temperature rise; when the ambient temperature rises, This will reflect a deterioration in intake air cooling conditions. Through this process, the four types of data are transformed into the same evaluation scale, and subsequent fusion can simultaneously reflect the current hotspot status, overall heat accumulation status, load-driven effect, and external heat dissipation conditions.

[0022] Furthermore, in obtaining , , and Subsequently, the heat dissipation control unit generates a thermal state substrate. This calculation originates from the weighted linear combination method in multi-index state evaluation, and incorporates the heat dissipation mechanism of dry-type transformers for weight allocation. Winding hotspots in dry-type transformers typically have a more direct impact on insulation life and overheating risk; therefore, the winding temperature item is assigned a higher weight. Core temperature changes relatively gradually but reflects the heat accumulation within the equipment itself; therefore, it is included in the fusion as an overall heat accumulation item. Load rate directly affects subsequent heat generation intensity; therefore, it is included in the fusion as an item that reflects thermal risk in advance. Ambient temperature affects air heat exchange conditions; therefore, it is included in the fusion as a heat dissipation boundary condition. The specific calculation method is as follows: ; in, The thermal state of the substrate is calculated by the heat dissipation control unit. This represents the relative state value of the winding temperature, derived from the winding temperature. Obtained by conversion according to the allowable range of winding design; This represents the relative state value of the core temperature, derived from the core temperature. Obtained by conversion according to the allowable range of the iron core design; The load factor is calculated by the monitoring unit after the operating current is collected by the current transformer. This is the relative state value of ambient temperature, derived from ambient temperature. The weights are derived from the installation environment design reference range. Weights can be determined during the equipment commissioning phase based on historical operating samples. For example, typical operating conditions such as rapid load increase, high-temperature summer operation, and low-speed fan operation can be selected to observe the contribution of winding temperature changes and core temperature changes to subsequent thermal risks. It is more sensitive to increases in winding hot spots and load rates.

[0023] For example, the relative state result of a dry-type transformer at a sampling time is: the relative state value of winding temperature. for Relative state value of iron core temperature for Load rate for Ambient temperature relative to state value for Substituting the thermal control unit into the above formula yields... Under this operating condition, the winding temperature may not yet have reached the traditional alarm threshold, but due to the high load rate and the relatively high winding temperature, This indicates that the equipment is currently under high thermal pressure.

[0024] S2. Cache the thermal state base during the continuous sampling period. Based on the thermal state base of the current time and the previous two sampling periods, calculate the first-order difference, the second-order difference and the trend consistency term respectively. Combined with the current thermal state base, calculate and generate the thermal risk assessment result.

[0025] Specifically, this step directly uses the thermal state substrate output from step one. As input, and multiple sampling periods within consecutive sampling periods are cached sequentially within the control unit. This process constructs a discrete-time series reflecting the thermal state changes of a dry-type transformer. This approach originates from the difference method in time series analysis, which characterizes the evolution trend of the system state by comparing data changes at adjacent moments. In the operating scenario of a dry-type transformer, the windings and insulation structure exhibit significant thermal inertia, with temperature changes showing a process of "slow accumulation—accelerated rise." Relying solely on the thermal state at a single moment is insufficient to reflect potential risks. Therefore, this step introduces time difference and trend discrimination to enable thermal risk assessment to reflect the coupling relationship between the current thermal state and future development trends. Before the control unit has accumulated three consecutive valid sampling periods, this step only performs buffering and maintains the previously executed heat dissipation state, without initiating thermal risk enhancement calculations based on the difference trend.

[0026] In practice, after each sampling cycle, the control unit will convert the data obtained in step one... Write to the circular buffer, and always keep the data from the three most recent sampling periods, i.e. , and ,in This represents the current thermal state of the substrate. This represents the thermal state of the substrate in the previous sampling period. This represents the thermal state baseline of the previous sampling period. All three variables are derived from the calculation results in step one, and the sampling periods are consistent. Based on these three values, a first-order difference term is first constructed. This difference is derived from the first derivative approximation of the discrete-time series, used to represent the rate of change of the current thermal state; a second-order difference term is further constructed. This term, derived from the discrete form of the second derivative, indicates whether the rate of change is accelerating, reflecting whether the heating process has entered an accelerated phase. To ensure that the thermal risk assessment reflects the typical characteristic of "continuous heating" in dry-type transformers, a trend consistency term is introduced based on the above difference. This term quantifies whether the thermal state shows an upward trend in both consecutive sampling intervals. and When, the value of this item is... Otherwise, the value is This allows for the differentiation between two operating conditions: continuous temperature rise and short-term fluctuations.

[0027] Furthermore, based on this, a thermal risk assessment result is constructed. This construction process originates from a linear prediction model and a weighted combination method of difference sequences. A trend consistency correction term is added to the original difference model, enabling the model to be improved for the thermal inertia scenario of dry-type transformers. Specifically, it is expressed as follows: ; in, This indicates the results of the thermal risk assessment; , , All are thermal state bases output from step one and cached by the control unit; It is a first-order difference term; It is a second-order difference term; This is a first-order difference weighting coefficient used to adjust the impact of the rate of change of thermal state on the risk outcome; These are second-order difference weighting coefficients, used to adjust the impact of the acceleration of change; The weighting coefficient for the trend consistency term; This is the trend consistency term, and its value is determined by the aforementioned continuous temperature rise judgment rule. Because... The relative state transition has been completed in step one, and each difference term and Having the same scale, the above linear combinations maintain computational consistency.

[0028] Understandably, in actual calculations, the above parameters can be calibrated using typical operating data. For example, the thermal state baseline for three consecutive sampling periods within a certain operating segment is as follows: , , Then the first difference is divided into The second difference is divided into Since the three sampling points show a continuous upward relationship, the trend consistency term... If calibrated based on historical operating data... , , Then, substituting into the calculation, we get This result is higher than the current thermal baseline, indicating that although the current temperature is still within acceptable limits, the thermal risk has significantly increased due to the continuous upward trend. Further considering another set of data, when... , , When the first-order difference is negative, the second-order difference also reflects the slowing trend, and the trend consistency term... After substitution Approaching or below the current level This indicates that the system is in a thermally stable or declining phase.

[0029] S3. The thermal risk assessment results are compared with the baseline risk value and the enhanced heat dissipation threshold. The target heat dissipation intensity coefficient is calculated by combining the linear adjustment term and the quadratic enhancement term. The target heat dissipation intensity coefficient is mapped into a heat dissipation control decision based on the intensity range in which it is located. The heat dissipation control decision includes the target heat dissipation intensity coefficient, the corresponding target fan speed, the target position of the air guiding component, and the target state of the auxiliary cooling device.

[0030] Specifically, this step uses the thermal risk assessment results obtained in step two. The input is used to convert the data into control decisions that can directly drive the heat dissipation actuator of the dry-type transformer. Step two has already unified the current level and development trend of the thermal state base through time difference and trend term as follows: Therefore, in this step, we directly use As the sole criterion for heat dissipation control, this ensures that heat dissipation decisions are aligned with prior thermal risk assessments within the same evaluation scale. In engineering implementation, the heat dissipation system of a dry-type transformer typically consists of a fan, a flow-guiding structure, and auxiliary cooling units. Its adjustment capability exhibits graded characteristics, thus requiring the continuous variation of heat... This is converted into heat dissipation intensity with a clear physical meaning, and further mapped into specific control actions.

[0031] Furthermore, in the control unit, the target heat dissipation intensity coefficient is first constructed. This construction process originates from the proportional control concept in automatic control, where the control quantity is proportional to the deviation. The thermal risk assessment results are then applied. Compared with the benchmark risk value By comparison, the basic deviation term is obtained. This term represents the degree of deviation of the current thermal risk from the stable operating level. Based on this, a secondary enhancement term is introduced to describe the rapid increase in heat dissipation demand of the dry-type transformer in the high thermal risk range. This term originates from the secondary amplification method commonly used in nonlinear control, which significantly enhances the impact of larger deviations on the control quantity through squaring. Combining the above two parts and considering the executable intensity range of the heat dissipation equipment, the calculation expression for the target heat dissipation intensity coefficient is obtained as follows: ; in, The target heat dissipation intensity coefficient; The thermal risk assessment results are output from step two; The baseline risk value is determined by the equipment under long-term stable operating conditions. The average value is determined; To enhance the heat dissipation threshold, it was determined from historical high-temperature operating condition samples; This is a linear adjustment coefficient used to control the heat dissipation intensity within the low-risk range. The magnitude of the increase; This is a nonlinear enhancement coefficient used to control the rate of increase in heat dissipation intensity within the high-risk range; This is a step function; when the variable inside the parentheses is greater than zero, it takes the value of... Otherwise, the value is The construction process of this expression reflects the derivation from linear proportional control to piecewise nonlinear control: when Not exceeding At that time, the quadratic enhancement term is not included in the calculation. It is mainly determined by the linear adjustment term; when Exceed At that time, the quadratic enhancement term begins to participate in the calculation, causing the heat dissipation intensity to increase more rapidly with increasing thermal risk; the outer layer value constraint makes... Maintain within the intensity range identifiable by the heat dissipation actuator. Because As a dimensionless risk indicator, all terms in the above formula are on the same calculation scale, and the calculation results are... The target heat dissipation intensity coefficient.

[0032] Furthermore, after obtaining The control unit then translates this into a heat dissipation control decision. This conversion process is based on a segmented control strategy commonly used in industrial control, and is implemented in conjunction with the actual configuration of the dry-type transformer heat dissipation device. The control unit pre-establishes a correspondence between intensity ranges and equipment actions, for example, dividing the system into several ranges based on fan speed and auxiliary cooling capacity: when... When in the lower range, Record the target status of the low-end fan; when When entering the middle section, Record the target status of the mid-range fan and the target air supply position on the winding side of the air guide component; when When it rises further, Record the target status of the high-end fan; when When entering the high-intensity zone, Record the target status of the high-end fan, the target position of the enhanced air supply on the winding side, and the target status of the auxiliary cooling device startup. This mapping relationship is determined during the equipment commissioning phase based on the fan performance curve, heat dissipation efficiency test results, and typical operating data, and is stored in the control unit, retrieved via a lookup table; when When the intensity falls exactly on the boundary between two adjacent intensity ranges, the control unit generates a value according to the higher heat dissipation level. For equipment that is not equipped with airflow guiding components or auxiliary cooling devices, The corresponding fields in the control unit retain their current position or stop state, without affecting the control decision generation for other configured heat dissipation components. Write This serves as the target execution intensity coefficient for subsequent execution phases.

[0033] The method's operation is explained with specific calculation steps. For example, under a certain operating condition, step two yields... If determined based on debugging data , , , Then the linear term is ,because The step function takes the value of The quadratic term is The sum of the two is: The control unit queries the mapping table based on this result and generates... Records are kept for medium-intensity heat dissipation targets, including the target status of the fan at medium speed and the target location of airflow on the winding side of the air guide component. Under another operating condition... When, the linear term is The quadratic term is ,get The control unit generates Recorded as high-intensity heat dissipation targets, including the high-speed target state of the fan and the target state of the auxiliary cooling device being activated. Through the above calculation process, as... The increase, Based on the basic linear regulation, the thermal risk assessment accelerates growth in the high-risk range, enabling the heat dissipation control to respond promptly to high-risk conditions. Through this method, the thermal risk assessment results... Converted into target heat dissipation intensity coefficient And further form heat dissipation control decisions ,in, It consists of the target execution intensity coefficient, the target fan speed, the target position of the air guide component, and the target state of the auxiliary cooling device.

[0034] S4. The target heat dissipation intensity coefficient in the heat dissipation control decision is gradually approximated by a first-order inertial recursion method to generate the execution intensity coefficient of the current sampling period. The target fan speed, the target position of the air guide component, and the target state of the auxiliary cooling device are adjusted successively according to the interval corresponding to the execution intensity coefficient of the current sampling period to complete the heat dissipation control operation.

[0035] Specifically, this step uses the heat dissipation control decision generated in step three. As input, the thermal control unit will... This translates into the actual operation of the fan, airflow guide components, and auxiliary cooling devices. This includes the target execution intensity coefficient, the target fan speed, the target position of the airflow guiding components, and the target status of the auxiliary cooling device. The heat dissipation control unit receives... Then, the target execution strength coefficient is analyzed as... The system analyzes the target speed of the fan and sends corresponding operating commands to the frequency converter or fan control module; it analyzes the target position of the air guiding component and sends angle adjustment commands to the electric actuator; and it analyzes the target status of the auxiliary cooling device and completes start-stop control through relays, contactors, or the cooling device control interface. Thus, the heat dissipation control decision formed in step three is transformed into the actual heat dissipation actions of the field equipment, ensuring that the intelligent heat dissipation process is integrated into the physical heat dissipation system of the dry-type transformer.

[0036] Furthermore, in practical implementation, considering that the fan, airflow guiding components, and auxiliary cooling devices all have mechanical response processes, the heat dissipation control unit adopts a progressive execution method. The corresponding target action. This method originates from the discrete representation of a first-order inertial element in the field of automatic control. Its basic idea is to make the execution state gradually approach the target state. This application applies this idea to the heat dissipation execution stage of a dry-type transformer, converting the target action obtained in step three into a continuously transitioning execution intensity, so that the fan speed, the position of the guide mechanism, and the start and stop of the auxiliary cooling are completed according to the rhythm of the equipment's allowed actions. When the auxiliary cooling device involves start and stop, the control unit only... Start-stop commands are only issued after the system enters the corresponding high-intensity zone and meets the preset minimum start-stop interval. If the minimum start-stop interval is not met, the previous auxiliary cooling state is maintained and the gradual adjustment of the fan and airflow components continues. The specific calculation method is as follows: ; in, The execution intensity coefficient for the current sampling period is calculated by the heat dissipation control unit; The intensity coefficients that were executed in the previous sampling cycle are read from the status register of the heat dissipation control unit; The target intensity coefficient is determined by the heat dissipation control decision output from step three. Read from; To implement the adjustment coefficient, it is determined during the equipment commissioning phase based on the fan speed-up time, the flow guiding mechanism's action time, and the allowable start-stop interval of the auxiliary cooling device. This formula is derived from the discrete recursive form of a first-order inertial element, ensuring that the executed action approximates the target intensity according to the set gradient. Because... , and All are relative intensity coefficients, and the calculation process is completed within the same scale.

[0037] For example, the heat dissipation control decision generated in step three Corresponding to "the target state of the fan in medium speed, the target position of the air supply on the winding side of the guide component, and the target state of the auxiliary cooling device being stopped", and The target execution strength coefficient recorded in the middle is If the intensity coefficient was applied in the previous sampling period Execute adjustment coefficient Then the current sampling period is calculated as follows The control unit, according to The specified interval sends a corresponding frequency command to the frequency converter, causing the fan to gradually increase from low to medium speed. Simultaneously, it sends a winding-side air supply position command to the flow guide actuator. The calculation continues in the next sampling cycle. Continue to Get closer, until you reach... The corresponding execution status. If the output of subsequent step three... Corresponding to high-intensity heat dissipation, As the speed increases, the control unit gradually raises the fan to a higher setting, and... Once the high-intensity range is reached, the auxiliary cooling device is activated to enhance heat dissipation according to the level of risk.

[0038] This step establishes the device's execution status. It consists of the actual operating speed of the fan, the actual position of the guide component, and the actual operating status of the auxiliary cooling device. The status is generated by the heat dissipation control unit based on the executed equipment status register, inverter operation signal, airflow guide mechanism positioning signal, and auxiliary cooling device operation signal. This status indicates that the heat dissipation control operation has been implemented on the specific equipment. If no inverter operation feedback, airflow guide mechanism positioning feedback, or auxiliary cooling device operation feedback is received within the preset execution confirmation time, then the current cycle's status is... Maintain the status as the previously confirmed execution state and synchronously write execution exception records for use by the monitoring and operation and maintenance systems.

[0039] This invention also provides an intelligent heat dissipation system for dry-type transformers, the system comprising: The thermal state substrate generation unit is used to collect the winding temperature, core temperature, load rate and ambient temperature of the target dry-type transformer in order to construct the thermal state substrate. The thermal risk assessment unit is used to cache the thermal state base in a continuous sampling period, calculate the first-order difference, the second-order difference and the trend consistency term based on the thermal state base at the current time and the previous two sampling periods, and calculate and generate the thermal risk assessment result in combination with the current thermal state base. The heat dissipation decision generation unit is used to compare the thermal risk assessment results with the baseline risk value and the enhanced heat dissipation threshold, calculate the target heat dissipation intensity coefficient by combining a linear adjustment term and a quadratic enhancement term, and map the target heat dissipation intensity coefficient into a heat dissipation control decision based on the intensity range in which the target heat dissipation intensity coefficient is located; wherein, the heat dissipation control decision includes the target heat dissipation intensity coefficient, the corresponding target fan speed, the target position of the air guiding component, and the target state of the auxiliary cooling device; The progressive execution control unit is used to gradually approximate the target heat dissipation intensity coefficient in the heat dissipation control decision through a first-order inertial recursion method, generate the execution intensity coefficient of the current sampling period, and adjust the target fan speed, the target position of the air guide component, and the target state of the auxiliary cooling device of the fan successively according to the interval corresponding to the execution intensity coefficient of the current sampling period, so as to complete the heat dissipation control operation.

[0040] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0041] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or units may be electrical, mechanical, or other forms.

[0042] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0043] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for intelligent heat dissipation of dry-type transformers, characterized in that, The method includes: S1. Collect the winding temperature, core temperature, load rate and ambient temperature of the target dry-type transformer to construct a thermal state substrate; S2. Cache the thermal state base during the continuous sampling period, and calculate the first-order difference, second-order difference and trend consistency term based on the thermal state base of the current time and the previous two sampling periods, and calculate and generate thermal risk assessment results in combination with the current thermal state base. S3. Compare the thermal risk assessment results with the benchmark risk value and the enhanced heat dissipation threshold, calculate the target heat dissipation intensity coefficient by combining the linear adjustment term and the quadratic enhancement term, and map the target heat dissipation intensity coefficient into a heat dissipation control decision based on the intensity range in which the target heat dissipation intensity coefficient is located; wherein, the heat dissipation control decision includes the target heat dissipation intensity coefficient, the corresponding target fan speed, the target position of the air guiding component, and the target state of the auxiliary cooling device; S4. The target heat dissipation intensity coefficient in the heat dissipation control decision is gradually approximated by a first-order inertial recursion method to generate the execution intensity coefficient of the current sampling period. The target fan speed, the target position of the air guide component, and the target state of the auxiliary cooling device are adjusted successively according to the interval corresponding to the execution intensity coefficient of the current sampling period to complete the heat dissipation control operation.

2. The intelligent heat dissipation method for a dry-type transformer according to claim 1, characterized in that, S1 further includes: The winding temperature, core temperature, load rate, and ambient temperature are converted into relative state values ​​and then weighted and fused to generate a thermal state substrate.

3. The intelligent heat dissipation method for a dry-type transformer according to claim 2, characterized in that, The process of converting the winding temperature, core temperature, load rate, and ambient temperature into relative state values ​​and then weighting and fusing them is as follows: The winding temperature, core temperature and ambient temperature are linearly normalized according to their respective design allowable ranges or reference ranges to obtain the corresponding relative state values. The load factor is directly obtained from the ratio of the current operating current to the rated current. The weights of the weighted fusion are preset according to the degree of contribution of each factor to the thermal state, with the winding temperature having the highest weight.

4. The intelligent heat dissipation method for a dry-type transformer according to claim 1, characterized in that, The first-order difference is the difference between the thermal state basis at the current moment and the thermal state basis at the previous moment, which is used to represent the rate of change of the thermal state. The second-order difference is the current thermal state basis minus twice the thermal state basis of the previous moment plus the thermal state basis of the moment before that, used to represent the acceleration of the rate of change. The trend consistency term is determined based on whether the thermal state base shows an upward trend in two consecutive sampling intervals. When it rises continuously, the first value is taken; otherwise, the second value is taken. The first value is 1, and the second value is 0.

5. The intelligent heat dissipation method for a dry-type transformer according to claim 1, characterized in that, The linear adjustment term is calculated and generated based on the linear adjustment coefficient, thermal risk assessment results, and benchmark risk value. The secondary enhancement term is only included in the calculation when the thermal risk assessment result exceeds the enhanced heat dissipation threshold. It is calculated and generated based on the nonlinear enhancement coefficient, the thermal risk assessment result, and the enhanced heat dissipation threshold. The target heat dissipation intensity coefficient is generated based on the linear adjustment term and the quadratic enhancement term, and the amplitude is constrained.

6. The intelligent heat dissipation method for a dry-type transformer according to claim 1, characterized in that, The mapping relationship between the intensity range where the target heat dissipation intensity coefficient is located and the heat dissipation control decision is determined during the equipment commissioning stage based on the fan performance curve, heat dissipation efficiency test results and typical operating data, and stored in the control unit, and called by a lookup table. When the target heat dissipation intensity coefficient falls exactly on the boundary of two adjacent intensity intervals, the control unit generates a heat dissipation control decision according to the higher heat dissipation level correspondence. For equipment without a flow guiding component or without an auxiliary cooling device, the corresponding fields in the heat dissipation control decision remain in their current position or stop, without affecting the generation of control decisions for other configured heat dissipation components.

7. The intelligent heat dissipation method for a dry-type transformer according to claim 1, characterized in that, The step of approximating the target heat dissipation intensity coefficient in the heat dissipation control decision through a first-order inertial recursion to generate the execution intensity coefficient for the current sampling period is as follows: The execution intensity coefficient for the current sampling period is calculated by combining the execution intensity coefficient of the previous sampling period, the difference between the execution intensity coefficient of the previous period based on the execution adjustment coefficient and the target execution intensity coefficient. The execution adjustment coefficient is determined during the equipment commissioning phase based on the fan speed-up time, the flow guiding mechanism action time, and the allowable start-stop interval of the auxiliary cooling device.

8. The intelligent heat dissipation method for a dry-type transformer according to claim 1, characterized in that, The S4 further includes: When performing heat dissipation control operations, the fan speed, the position of the air guide mechanism, and the start and stop of the auxiliary cooling should be completed in accordance with the rhythm of the equipment's operation. When the auxiliary cooling device involves starting or stopping, the control unit will only issue a start / stop command after the target execution intensity coefficient enters the corresponding high intensity range and the preset minimum start / stop interval is met. If the minimum start / stop interval is not met, the previous auxiliary cooling state will be maintained and the gradual adjustment of the fan and guide components will continue.

9. The intelligent heat dissipation method for a dry-type transformer according to claim 1, characterized in that, The S4 further includes: The device execution status is generated by the heat dissipation control unit based on the executed device status register, inverter operation signal, airflow guide mechanism position signal and auxiliary cooling device operation signal, which indicates that the heat dissipation control operation has been implemented on the specific device. If no feedback is received from the inverter operation, the flow guide mechanism positioning, or the auxiliary cooling device operation within the preset execution confirmation time, the equipment execution status in the current cycle will remain the previous confirmed execution status.

10. A smart heat dissipation system for dry-type transformers, characterized in that, include: The thermal state substrate generation unit is used to collect the winding temperature, core temperature, load rate and ambient temperature of the target dry-type transformer in order to construct the thermal state substrate. The thermal risk assessment unit is used to cache the thermal state base in a continuous sampling period, calculate the first-order difference, the second-order difference and the trend consistency term based on the thermal state base at the current time and the previous two sampling periods, and calculate and generate the thermal risk assessment result in combination with the current thermal state base. The heat dissipation decision generation unit is used to compare the thermal risk assessment results with the baseline risk value and the enhanced heat dissipation threshold, calculate the target heat dissipation intensity coefficient by combining a linear adjustment term and a quadratic enhancement term, and map the target heat dissipation intensity coefficient into a heat dissipation control decision based on the intensity range in which the target heat dissipation intensity coefficient is located; wherein, the heat dissipation control decision includes the target heat dissipation intensity coefficient, the corresponding target fan speed, the target position of the air guiding component, and the target state of the auxiliary cooling device; The progressive execution control unit is used to gradually approximate the target heat dissipation intensity coefficient in the heat dissipation control decision through a first-order inertial recursion method, generate the execution intensity coefficient of the current sampling period, and adjust the target fan speed, the target position of the air guide component, and the target state of the auxiliary cooling device of the fan successively according to the interval corresponding to the execution intensity coefficient of the current sampling period, so as to complete the heat dissipation control operation.

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