Temperature Control Method and Device for Transformer Assembly Based on Adaptive Heat Dissipation

Abstract

This invention relates to the field of transformer control technology, and more particularly to a method and apparatus for temperature control of a complete transformer substation based on adaptive heat dissipation. The method includes: acquiring historical operating data of the complete transformer substation based on the temperature regulation environment; selecting multiple initial temperature monitoring points; obtaining the status of multiple monitoring points from multiple heat dissipation areas; and combining the status of multiple monitoring points to obtain multiple temperature detection points, thereby achieving adaptive heat dissipation of the complete transformer substation and obtaining a heat-dissipating transformer. This invention achieves accurate and stable heat dissipation control of the complete transformer substation through temperature response characteristic analysis, temperature detection point screening, internal temperature distribution reconstruction, and heat dissipation coupling compensation.

Description

Technical Field

[0001] This invention relates to the field of transformer control technology, and in particular to a method and device for temperature control and regulation of a complete set of transformer equipment based on adaptive heat dissipation. Background Technology

[0002] With the continuous expansion of power distribution network construction, transformer sets, as crucial equipment in power distribution systems, typically require long-term operation under varying loads, ambient temperatures, and ventilation conditions. During operation, the windings, core, terminals, and associated electrical components of transformer sets continuously generate heat. If this heat cannot be dissipated promptly, it can easily lead to increased internal temperatures, localized heat accumulation, and insulation aging, thereby affecting the operational safety and service life of the transformer set. Therefore, effective temperature monitoring and heat dissipation regulation of transformer sets are of paramount importance.

[0003] In existing technologies, temperature sensors are typically installed at certain locations on the transformer substation equipment to collect temperature values ​​at those points. These collected values ​​are then compared to preset temperature thresholds. When the temperature exceeds the preset threshold, fans, ventilation devices, or other heat dissipation units are activated or their heat dissipation intensity is increased to regulate the temperature of the transformer substation equipment. This method can reduce equipment temperature to some extent and respond to significant overheating situations.

[0004] However, existing temperature control methods typically rely on manual experience to set temperature detection points, making it difficult to ensure that these points accurately characterize temperature changes in different areas within the transformer substation. Furthermore, temperature sensors primarily collect localized temperature data, failing to reflect the overall temperature distribution of windings, cores, terminals, heat sink connections, and localized hot spots. In addition, the effective ranges of multiple heat dissipation units often overlap; adjusting one unit may simultaneously affect multiple heat-generating areas, and the temperature of a single heat-generating area may be influenced by multiple heat dissipation units. Directly controlling heat dissipation units based solely on single-point temperature deviations can easily lead to insufficient heat dissipation, excessive heat dissipation, or temperature overshoot. Therefore, it is urgent to achieve accurate and stable heat dissipation control of the transformer substation by analyzing temperature response characteristics, selecting temperature detection points, reconstructing internal temperature distribution, and implementing heat dissipation coupling compensation. Summary of the Invention

[0005] This invention provides a temperature control method for transformer station equipment based on adaptive heat dissipation and a computer-readable storage medium. Its main purpose is to achieve accurate and stable heat dissipation control of transformer station equipment through temperature response characteristic analysis, temperature detection point screening, internal temperature distribution reconstruction and heat dissipation coupling compensation.

[0006] To achieve the above objectives, the present invention provides a temperature control and regulation method for a complete set of transformer equipment based on adaptive heat dissipation, comprising: Receive temperature regulation command, confirm temperature regulation environment based on temperature regulation command, wherein temperature regulation environment includes transformer set equipment and multiple heat dissipation units; Based on the temperature regulation environment, historical operating data of the transformer set equipment is obtained, and multiple initial temperature monitoring points of the transformer set equipment are obtained based on the historical operating data. Obtain structural information and heat dissipation structure information of multiple heat dissipation units, and construct multiple heat dissipation areas based on the structural information and heat dissipation structure information; The status of multiple monitoring points is obtained based on multiple initial temperature monitoring points in multiple heat dissipation areas; Multiple temperature detection points are obtained based on the status of multiple monitoring points; Based on multiple temperature detection points and multiple heat dissipation units, adaptive heat dissipation is performed on the complete set of transformer equipment to obtain a heat dissipation transformer.

[0007] Optionally, the acquisition of multiple initial temperature monitoring points for the transformer assembly based on historical operating data includes: The equipment temperature field sequence is constructed based on historical operating data, which includes the equipment temperature field at multiple different times. Multiple device temperature sequences at multiple spatial locations are extracted from the device temperature field sequence. The spatial locations correspond one-to-one with the device temperature sequences. Each device temperature sequence includes multiple device temperatures, and each device temperature comes from a different device temperature field. Extract a device temperature sequence from multiple device temperature sequences sequentially to obtain a target temperature sequence, and then perform the following operations on the target temperature sequence: Perform a hybrid decomposition process on the target temperature sequence to obtain the temperature change sequence; Obtain the temperature response feature set from the temperature change sequence; By summarizing the temperature response feature sets, multiple temperature response feature sets are obtained; Multiple initial temperature monitoring points are obtained based on multiple temperature response feature sets.

[0008] Optionally, the step of performing a hybrid decomposition process on the target temperature sequence to obtain a temperature change sequence includes: Perform a multivariate decomposition operation on the target temperature sequence to obtain multiple decomposed temperature sequences, wherein the target temperature sequence includes multiple target temperatures and the decomposed temperature sequences include multiple decomposed temperatures; Extract a decomposition temperature sequence from multiple decomposition temperature sequences sequentially to obtain the target decomposition temperature sequence, and then perform the following operations on the target decomposition temperature sequence: Multiple target decomposition temperatures are extracted from the target decomposition temperature sequence, and multiple target temperatures are extracted from the target temperature sequence; Multiple temperature weights are calculated based on the target temperature sequence, where the temperature weights, target decomposition temperatures, and target temperatures correspond one-to-one. Based on multiple temperature weights, multiple target decomposition temperatures, and multiple target temperatures, the sequence correlation coefficient of the target decomposition temperature sequence is calculated using the formula for calculating the sequence correlation coefficient. By summing the sequence correlation coefficients, we can obtain the sequence correlation coefficients corresponding to multiple decomposition temperature sequences. A temperature change sequence was constructed based on multiple sequence correlation coefficients and multiple decomposed temperature sequences.

[0009] Optionally, the formula for calculating the sequence correlation coefficient is as follows: ; in, Represents the correlation coefficient between sequences. Represents the first of multiple target decomposition temperatures An index of the target decomposition temperature. This represents the total number of decomposition temperatures for multiple targets. Represents the weight of multiple temperature values. Temperature weights corresponding to the target decomposition temperature. Represents the first of multiple target decomposition temperatures The target decomposition temperature, Indicates the first of multiple target temperatures The target temperature corresponding to the target decomposition temperature.

[0010] Optionally, constructing a temperature change sequence based on multiple sequence correlation coefficients and multiple decomposed temperature sequences includes: Calculate the average of the correlation coefficients of multiple sequences to obtain the average coefficient, and compare the average coefficient with the correlation coefficients of multiple sequences to obtain multiple comparison results; Based on multiple comparison results, multiple comparative correlation coefficients with sequence correlation coefficients greater than the average coefficient are extracted from multiple sequence correlation coefficients, and decomposition temperature sequences corresponding to multiple comparative correlation coefficients are extracted from multiple decomposition temperature sequences to obtain multiple correlation temperature sequences. Sequence reconstruction is performed on multiple related temperature sequences to obtain a temperature change sequence.

[0011] Optionally, obtaining multiple initial temperature monitoring points based on multiple temperature response feature sets includes: Normalization is performed on multiple temperature response feature sets to obtain multiple normalized temperature response feature sets; Multiple temperature response feature vectors are constructed based on multiple normalized temperature response feature sets; Multiple spatial locations of the transformer set equipment are obtained, and density clustering is performed on multiple spatial locations based on multiple temperature response feature vectors to obtain multiple initial temperature monitoring points.

[0012] Optionally, the adaptive cooling of the transformer assembly based on multiple temperature detection points and multiple heat dissipation units to obtain a heat dissipation transformer includes: Multiple transformer temperature sets of a complete set of transformer equipment are obtained based on multiple temperature detection points; Internal temperature reconstruction is performed on multiple transformer temperature sets to obtain the temperature distribution of multiple complete sets of equipment. Multiple actual temperature values ​​were obtained based on the temperature distribution of multiple sets of equipment, and multiple target temperature values ​​were also obtained. Calculate multiple temperature deviation values ​​based on multiple actual temperature values ​​and multiple target temperature values; Multiple initial heat dissipation adjustment sets are generated based on multiple temperature deviation values ​​for multiple heat dissipation units; Based on historical operating data, obtain multiple adjustment value changes and multiple temperature change values ​​corresponding to multiple heat dissipation units; Based on multiple adjustment value changes and multiple temperature value changes, calculate multiple temperature influence coefficients; A heat dissipation coupling matrix is ​​constructed based on multiple temperature influence coefficients; Inverse decoupling compensation is performed based on the heat dissipation coupling matrix and multiple initial heat dissipation adjustment sets to obtain multiple compensated heat dissipation adjustment sets. Based on multiple sets of compensation heat dissipation adjustment quantities and multiple heat dissipation units, adaptive heat dissipation is performed on the complete set of transformer equipment to obtain a heat dissipation transformer.

[0013] Optionally, the inverse decoupling compensation based on the heat dissipation coupling matrix and multiple initial heat dissipation adjustment sets is performed to obtain multiple compensated heat dissipation adjustment sets, including: If the heat dissipation coupling matrix is ​​a pre-constructed square matrix, then obtain the determinant value of the heat dissipation coupling matrix; If the determinant value is not zero, calculate the inverse matrix of the heat dissipation coupling matrix and determine the inverse matrix as the inverse decoupling matrix; If the determinant is zero, or the heat dissipation coupling matrix is ​​not a square matrix, then calculate the generalized inverse matrix of the heat dissipation coupling matrix and determine the generalized inverse matrix as the inverse decoupling matrix; For each of the multiple initial heat dissipation adjustment values ​​in the multiple initial adjustment value sets, perform vector permutation to obtain multiple initial heat dissipation adjustment vectors; Matrix operations are performed based on the inverse decoupling matrix and multiple initial heat dissipation adjustment vectors to obtain multiple decoupled heat dissipation adjustment vectors; Multiple sets of compensation heat dissipation adjustment values ​​are obtained based on multiple decoupled heat dissipation adjustment vectors.

[0014] Optionally, obtaining multiple sets of compensated heat dissipation adjustment amounts based on multiple decoupled heat dissipation adjustment vectors includes: Perform vector splitting on multiple decoupled heat dissipation adjustment vectors to obtain multiple sets of decoupled heat dissipation adjustment amounts corresponding to the multiple heat dissipation units; Obtain multiple temperature deviation values ​​and multiple rates of temperature change; Multiple predicted temperature peaks are calculated based on multiple temperature deviation values ​​and multiple temperature rise rates. Calculate the differences between multiple predicted temperature peaks and multiple target temperature values ​​to obtain multiple predicted overshoot values; Compensation and correction are performed on multiple sets of decoupled heat dissipation adjustment parameters based on multiple predicted overshoot parameters, resulting in multiple sets of compensated heat dissipation adjustment parameters.

[0015] To achieve the above objectives, the present invention also provides a temperature control and regulation device for a transformer station assembly based on adaptive heat dissipation, comprising: The instruction receiving module is used to receive temperature regulation instructions and confirm the temperature regulation environment based on the temperature regulation instructions. The temperature regulation environment includes the transformer set equipment and multiple heat dissipation units. The monitoring point selection module is used to acquire historical operating data of the transformer set equipment based on the temperature regulation environment, and to acquire multiple initial temperature monitoring points of the transformer set equipment based on the historical operating data. The monitoring point verification module is used to acquire the structural information and heat dissipation structure information of multiple heat dissipation units, construct multiple heat dissipation areas based on the structural information and heat dissipation structure information, acquire the status of multiple monitoring points of multiple initial temperature monitoring points based on the multiple heat dissipation areas, and acquire multiple temperature detection points based on the status of multiple monitoring points. An adaptive heat dissipation module is used to perform adaptive heat dissipation on the transformer set equipment based on multiple temperature detection points and multiple heat dissipation units to obtain a heat-dissipating transformer.

[0016] To address the above problems, the present invention also provides an electronic device, the electronic device comprising: Memory, storing at least one instruction; The processor executes the instructions stored in the memory to implement the temperature control and regulation method for the transformer set equipment based on adaptive heat dissipation described above.

[0017] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the above-described temperature control adjustment method for a transformer assembly based on adaptive heat dissipation.

[0018] To address the problems described in the background art, this invention receives a temperature regulation command, confirms the temperature regulation environment including the transformer station assembly and multiple heat dissipation units based on the command, and acquires historical operating data of the transformer station assembly based on this environment. Further, it constructs an equipment temperature field sequence based on the historical operating data, extracts equipment temperature sequences corresponding to multiple spatial locations from this sequence, and performs a mixed decomposition process on the temperature sequences to obtain a temperature change sequence that characterizes the temperature variation patterns at spatial locations. Then, it obtains a temperature response feature set from the temperature change sequence. Thus, based on the heating, cooling, and fluctuation patterns of different spatial locations during historical operation, spatial locations with similar temperature change patterns can be categorized, providing a data foundation for determining initial temperature monitoring points, rather than relying solely on manual experience to set up temperature measurement points. This invention performs normalization processing on multiple temperature response feature sets and constructs multiple temperature response feature vectors. Based on these feature vectors, it performs density clustering on multiple spatial locations to obtain multiple initial temperature monitoring points. This method allows spatial locations with similar temperature rise, fall, rate of temperature change, or temperature drop after heat dissipation to be grouped into the same category. From each category, a location representing that type of temperature change pattern is selected as the initial temperature monitoring point, enabling subsequent adaptive cooling to coordinate the processing of locations with similar temperature rise or fall. By acquiring structural and heat dissipation structure information from multiple heat dissipation units, multiple heat dissipation regions are constructed based on this information. The status of multiple monitoring points for multiple initial temperature monitoring points is then obtained based on these heat dissipation regions, and finally, multiple temperature detection points are obtained based on these statuses. Therefore, by considering the installation location, airflow direction, exhaust direction, and effective range of the heat dissipation units, it is possible to determine whether the initial temperature monitoring point is suitable for installing a temperature sensor, whether it is located within an effective heat dissipation area, and whether it is subject to direct cold airflow or structural obstruction, thereby improving the matching degree between the temperature detection point and the actual heat dissipation area. Multiple transformer temperature sets are acquired based on multiple temperature detection points, and internal temperature reconstruction is performed on these sets to obtain the temperature distribution of multiple sets of equipment. Then, based on the temperature distribution of these sets, multiple actual temperature values ​​corresponding to multiple objects to be cooled are obtained, along with multiple target temperature values, and multiple temperature deviation values ​​are calculated. Therefore, even if temperature sensors are not directly installed in winding hot spots, localized high-temperature areas in the core, oil outlet locations, heat sink connections, or internal heat accumulation points, the corresponding actual temperature values ​​can still be obtained based on the reconstructed internal temperature distribution. This expands the basis for heat dissipation control from single-point temperature to the actual temperature state of the objects to be cooled. Multiple initial heat dissipation adjustment sets corresponding to multiple heat dissipation units are generated based on multiple temperature deviation values. Based on historical operating data, multiple adjustment change values ​​corresponding to multiple heat dissipation units and multiple temperature change values ​​corresponding to multiple objects to be cooled are obtained, and multiple temperature influence coefficients are calculated. Finally, a heat dissipation coupling matrix is ​​constructed based on these multiple temperature influence coefficients.The heat dissipation coupling matrix characterizes the cross-heat dissipation influence between multiple heat dissipation units and multiple objects to be cooled, avoiding localized insufficient or excessive heat dissipation caused by directly controlling heat dissipation units based solely on a single temperature deviation. Finally, this invention performs inverse decoupling compensation based on the heat dissipation coupling matrix and multiple initial heat dissipation adjustment sets to obtain multiple decoupled heat dissipation adjustment quantities. These quantities are then compensated and corrected by combining the temperature rise rate, predicted peak temperature, and predicted overshoot, resulting in multiple compensated heat dissipation adjustment quantity sets. Adaptive heat dissipation is then performed on the transformer station equipment based on these sets and multiple heat dissipation units. Through these methods, this invention achieves accurate and stable heat dissipation control of the transformer station equipment by analyzing temperature response characteristics, selecting temperature detection points, reconstructing internal temperature distribution, and performing heat dissipation coupling compensation. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a method for temperature control adjustment of a transformer set based on adaptive heat dissipation, according to an embodiment of the present invention. Figure 2 This is a functional block diagram of a temperature control and regulation device for a transformer station assembly based on adaptive heat dissipation, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of an electronic device for implementing the temperature control and regulation method for a transformer set based on adaptive heat dissipation, according to an embodiment of the present invention.

[0020] Explanation of reference numerals in the attached figures: 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0021] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0023] This application provides a method for temperature control of a transformer station assembly based on adaptive heat dissipation. The executing entity of this method includes, but is not limited to, at least one electronic device configured to execute the method provided in this application, such as a server or a terminal. In other words, the method can be executed by software or hardware installed on a terminal device or server device, and the software may be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0024] Reference Figure 1 The diagram shown is a flowchart illustrating a temperature control adjustment method for a transformer station assembly based on adaptive heat dissipation, according to an embodiment of the present invention. In this embodiment, the temperature control adjustment method for the transformer station assembly based on adaptive heat dissipation includes: S1. Receive temperature adjustment command, and confirm the temperature adjustment environment based on the temperature adjustment command. The temperature adjustment environment includes the transformer set equipment and multiple heat dissipation units.

[0025] Understandably, the temperature adjustment command is a pre-set command manually used during the operation of the transformer station equipment to monitor the temperature of the transformer station equipment and perform adaptive heat dissipation based on the monitored temperature. For example, if the temperature of the transformer station equipment is pre-set to be collected every 1 minute and adaptive heat dissipation is performed based on the collected temperature, then the temperature adjustment command would be: collect the temperature of the transformer station equipment every 1 minute and perform adaptive heat dissipation based on the collected temperature.

[0026] It should be noted that the temperature regulation environment refers to the complete set of transformer substation equipment and the collection of multiple heat dissipation units. The complete set of transformer substation equipment refers to the transformer and its supporting equipment, including but not limited to distribution transformers, surge arresters, fuses, and distribution boxes. The heat dissipation unit refers to a component installed on the complete set of transformer substation equipment for supplying cooling airflow into the interior of the equipment or exhausting hot airflow from the interior to the exterior. Examples include exhaust fans installed on the top of the equipment and sidewall fans installed on both sides.

[0027] S2. Obtain historical operating data of the transformer set equipment based on the temperature regulation environment, and obtain multiple initial temperature monitoring points of the transformer set equipment based on the historical operating data.

[0028] It is clear that the historical operating data refers to the data during the historical operation of the transformer set equipment, including but not limited to the equipment temperature field of the transformer set equipment during historical operation, the initial heat dissipation adjustment set, the structural information and heat dissipation structure information, etc.

[0029] Furthermore, the acquisition of multiple initial temperature monitoring points for the transformer assembly based on historical operating data includes: The equipment temperature field sequence is constructed based on historical operating data, which includes the equipment temperature field at multiple different times. Multiple device temperature sequences at multiple spatial locations are extracted from the device temperature field sequence. The spatial locations correspond one-to-one with the device temperature sequences. Each device temperature sequence includes multiple device temperatures, and each device temperature comes from a different device temperature field. Extract a device temperature sequence from multiple device temperature sequences sequentially to obtain a target temperature sequence, and then perform the following operations on the target temperature sequence: Perform a hybrid decomposition process on the target temperature sequence to obtain the temperature change sequence; Obtain the temperature response feature set from the temperature change sequence; By summarizing the temperature response feature sets, multiple temperature response feature sets are obtained; Multiple initial temperature monitoring points are obtained based on multiple temperature response feature sets.

[0030] It should be noted that the construction of the equipment temperature field sequence based on historical operating data refers to first extracting the equipment temperature field at all different sampling times during multiple operations of the same transformer assembly from historical operating data, resulting in multiple equipment temperature fields. These temperature fields at different sampling times are then arranged in chronological order to obtain the equipment temperature field sequence. The equipment temperature field refers to the temperature distribution map of the transformer assembly at a given sampling time, and the sampling time refers to the time point when multiple temperature monitoring points were collected.

[0031] Specifically, the step of extracting multiple equipment temperature sequences from multiple spatial locations from the equipment temperature field sequence refers to extracting the temperature value (temperature value) corresponding to each spatial location from each equipment temperature field in the equipment temperature field sequence for each spatial location on the transformer platform equipment. Then, from the multiple equipment temperature fields corresponding to multiple sampling times, multiple temperature values ​​of a spatial location at multiple sampling times can be extracted. The multiple temperature values ​​at multiple sampling times are arranged in chronological order according to the time of the multiple sampling times to obtain the equipment temperature sequence.

[0032] It should be understood that the spatial location referred to here is a point represented by a three-dimensional coordinate in the three-dimensional model constructed from the transformer platform equipment. The three-dimensional model of the transformer platform equipment can be constructed using existing technologies such as three-dimensional laser scanning, which will not be elaborated upon here. It should be noted that the three-dimensional model consists of multiple points, each corresponding to a three-dimensional coordinate.

[0033] Furthermore, the step of performing a hybrid decomposition process on the target temperature sequence to obtain a temperature change sequence includes: Perform a multivariate decomposition operation on the target temperature sequence to obtain multiple decomposed temperature sequences, wherein the target temperature sequence includes multiple target temperatures and the decomposed temperature sequences include multiple decomposed temperatures; Extract a decomposition temperature sequence from multiple decomposition temperature sequences sequentially to obtain the target decomposition temperature sequence, and then perform the following operations on the target decomposition temperature sequence: Multiple target decomposition temperatures are extracted from the target decomposition temperature sequence, and multiple target temperatures are extracted from the target temperature sequence; Multiple temperature weights are calculated based on the target temperature sequence, where the temperature weights, target decomposition temperatures, and target temperatures correspond one-to-one. Based on multiple temperature weights, multiple target decomposition temperatures, and multiple target temperatures, the sequence correlation coefficient of the target decomposition temperature sequence is calculated using the formula for calculating the sequence correlation coefficient. By summing the sequence correlation coefficients, we can obtain the sequence correlation coefficients corresponding to multiple decomposition temperature sequences. A temperature change sequence was constructed based on multiple sequence correlation coefficients and multiple decomposed temperature sequences.

[0034] It should be noted that the target temperature sequence typically contains components of slow temperature rise caused by load changes, periodic fluctuations caused by the start and stop of the heat dissipation unit, sudden temperature rises caused by local heat accumulation, and noise components caused by sensor jitter or external interference. Directly extracting temperature response features based on the target temperature sequence can easily allow noise components or transient interference to affect the accuracy of the temperature response features. Therefore, this invention performs a multi-variation decomposition operation on the target temperature sequence, decomposing it into decomposed temperature sequences representing multiple different temperature changes.

[0035] It is clear that performing a multivariate decomposition operation on the target temperature sequence refers to using existing techniques such as empirical mode decomposition to decompose the target temperature sequence and obtain multiple decomposed temperature sequences.

[0036] It is understood that the target decomposition temperature refers to the temperature value extracted from the target decomposition temperature sequence, and the target temperature refers to the target temperature extracted from the target temperature sequence.

[0037] Specifically, the step involves calculating multiple temperature weights based on the target temperature sequence, performing a sliding window extraction operation on the target temperature sequence with two temperature values ​​as sliding windows and one temperature value as the step size, resulting in multiple target temperature windows. The difference between the two temperature values ​​in each temperature window is calculated, resulting in multiple temperature difference values. The absolute value of each temperature difference is then calculated, resulting in multiple absolute temperature difference values. These absolute temperature difference values ​​are then normalized to obtain multiple normalized temperature differences. Finally, the normalized temperature differences are arranged according to the extraction order of the multiple target temperature windows to obtain a normalized temperature difference sequence. Furthermore, a sliding window extraction operation is performed on multiple normalized temperature difference sequences, using two normalized temperature differences as sliding windows and one normalized temperature difference as the step size, to obtain multiple normalized windows. The average value of the two normalized temperature differences in each normalized window is calculated, resulting in multiple normalized average values. The first and last normalized temperature differences are extracted from the normalized temperature difference sequences. The multiple normalized average values ​​are sorted according to the extraction order of the multiple normalized windows to obtain a normalized weight sequence. The first and last normalized temperature differences extracted from the normalized temperature difference sequences are used as the first and last normalized weights of the normalized weight sequence, resulting in a temperature weight sequence, which includes multiple temperature weights. The normalization of the absolute values ​​of multiple temperature differences is implemented using existing normalization techniques such as Max-Min normalization, which will not be elaborated further in this invention.

[0038] Importantly, after obtaining multiple decomposition temperature sequences, this invention calculates the sequence correlation coefficient between each decomposition temperature sequence and the target temperature sequence. The sequence correlation coefficient characterizes the degree of consistency between the decomposition temperature sequence and the target temperature sequence in terms of temperature change trends. If the sequence correlation coefficient of a certain decomposition temperature sequence is large, it indicates that the decomposition temperature sequence can well characterize the heating, cooling, or fluctuation changes in the target temperature sequence; if the sequence correlation coefficient of a certain decomposition temperature sequence is small, it indicates that the decomposition temperature sequence may mainly correspond to noise, local disturbances, or invalid change components.

[0039] Understandably, multiple temperature weights are introduced when calculating the sequence correlation coefficient to correlate the calculated result with the temperature changes in the target temperature sequence, thereby ensuring that the extracted correlated temperature sequence follows the same trend as the original target temperature sequence. These temperature weights are calculated based on the magnitude of temperature changes between adjacent samples in the target temperature sequence. The absolute value of the temperature difference characterizes the magnitude of the temperature change between two adjacent sampling times. A larger absolute value indicates a more significant temperature change within the corresponding time interval, potentially corresponding to increased load, localized heat accumulation, start-up or shutdown of heat dissipation units, or changes in heat dissipation response. Conversely, a smaller absolute value indicates a more gradual temperature change within the corresponding time interval.

[0040] Furthermore, normalizing multiple absolute temperature differences is performed to map the temperature variation amplitudes across different numerical ranges to a uniform scale, preventing the overall fluctuation range of the target temperature sequence from being too large or too small, which could affect subsequent weight calculations. A larger normalized temperature difference indicates that the corresponding time interval belongs to a range with relatively significant temperature changes within the entire target temperature sequence; a smaller normalized temperature difference indicates that the corresponding time interval belongs to a range with relatively gentle temperature changes within the entire target temperature sequence.

[0041] Specifically, performing sliding window extraction and calculating the normalized average value on the normalized temperature difference sequence is to determine whether there are significant temperature changes across multiple time intervals. If the normalized average value is large, it indicates that the temperature changes are significant over several consecutive time intervals near the corresponding sampling time. For example, there may be significant temperature rises and falls or temperature response changes after adjustments to the heat dissipation unit. Therefore, when calculating the sequence correlation coefficient between the target decomposition temperature sequence and the target temperature sequence, the temperature weight corresponding to that sampling time is set to be large, so that the target decomposition temperature and target temperature at that sampling time occupy a higher proportion in the sequence correlation coefficient calculation. If the normalized average value corresponding to a certain sampling time is small, it indicates that there are no significant temperature changes or only small temperature changes over several time intervals near that sampling time. Therefore, when calculating the sequence correlation coefficient, the temperature weight corresponding to that sampling time is set to be small, so that the target decomposition temperature and target temperature at that sampling time occupy a lower proportion in the sequence correlation coefficient calculation. Thus, this invention uses the normalized average value as the temperature weight, so that the calculation result of the sequence correlation coefficient is more affected by sampling times with "significant temperature changes" and less affected by sampling times with "small or no significant temperature changes." By using the above methods, we can screen out the decomposed temperature sequences that are consistent with the changes in the target temperature sequence during significant heating, significant cooling, or heat dissipation response, providing a data foundation for subsequent temperature response feature set extraction, initial temperature monitoring point selection, and adaptive heat dissipation adjustment.

[0042] Furthermore, the formula for calculating the sequence correlation coefficient is as follows: ; in, Represents the correlation coefficient between sequences. Represents the first of multiple target decomposition temperatures An index of the target decomposition temperature. This represents the total number of decomposition temperatures for multiple targets. Represents the weight of multiple temperature values. Temperature weights corresponding to the target decomposition temperature. Represents the first of multiple target decomposition temperatures The target decomposition temperature, Indicates the first of multiple target temperatures The target temperature corresponding to the target decomposition temperature.

[0043] It should be noted that the formula for calculating the sequence correlation coefficient is the same as that for the weighted Pearson correlation coefficient. The weighted Pearson correlation coefficient is an existing correlation calculation method that introduces weighting terms into the Pearson correlation coefficient, and is used to calculate the degree of consistency between the changes in the target decomposition temperature sequence and the target temperature sequence.

[0044] Specifically, the calculation process first calculates the weighted average of the target decomposition temperature sequence and the target temperature sequence based on multiple temperature weights. Then, it calculates the deviation of the target decomposition temperature from the weighted average of the target decomposition temperature sequence and the deviation of the target temperature from the weighted average of the target temperature sequence at each sampling time. Finally, it calculates the correlation between the target decomposition temperature sequence and the target temperature sequence based on the product of the two deviations at the same sampling time. The temperature weights are used to adjust the proportion of different sampling times in the sequence correlation coefficient calculation. If the temperature weight corresponding to a certain sampling time is large, the target decomposition temperature and the target temperature at that sampling time have a greater impact on the sequence correlation coefficient calculation result; if the temperature weight corresponding to a certain sampling time is small, the target decomposition temperature and the target temperature at that sampling time have a smaller impact on the sequence correlation coefficient calculation result. Since the temperature weights are determined based on whether there are large temperature changes in the target temperature sequence, the sequence correlation coefficient can better reflect the consistency between the target decomposition temperature sequence and the target temperature sequence at times of large temperature changes.

[0045] Understandably, the larger the sequence correlation coefficient, the higher the consistency between the target decomposition temperature sequence and the target temperature sequence in terms of temperature change, and the stronger the characterization effect of the target decomposition temperature sequence on the temperature change of the target temperature sequence; when the sequence correlation coefficient is small, the lower the consistency between the target decomposition temperature sequence and the target temperature sequence in terms of temperature change, and the weaker the characterization effect of the target decomposition temperature sequence on the temperature change of the target temperature sequence.

[0046] It should be explained that by introducing temperature weights, this invention avoids the problem of ordinary Pearson correlation coefficients treating sampling times with relatively stable temperatures and those with large temperature changes equally. This allows the sequence correlation coefficient to focus more on determining whether the target decomposed temperature sequence can follow the larger temperature changes in the target temperature sequence. Therefore, it can more accurately screen out decomposed temperature sequences that can characterize the main temperature change patterns, providing a basis for subsequent temperature change sequence reconstruction and temperature response feature set extraction.

[0047] Furthermore, the construction of the temperature change sequence based on multiple sequence correlation coefficients and multiple decomposed temperature sequences includes: Calculate the average of the correlation coefficients of multiple sequences to obtain the average coefficient, and compare the average coefficient with the correlation coefficients of multiple sequences to obtain multiple comparison results; Based on multiple comparison results, multiple comparative correlation coefficients with sequence correlation coefficients greater than the average coefficient are extracted from multiple sequence correlation coefficients, and decomposition temperature sequences corresponding to multiple comparative correlation coefficients are extracted from multiple decomposition temperature sequences to obtain multiple correlation temperature sequences. Sequence reconstruction is performed on multiple related temperature sequences to obtain a temperature change sequence.

[0048] It is understood that the average coefficient refers to the average of multiple sequence correlation coefficients. The comparison result refers to the magnitude of the average coefficient compared to a single sequence correlation coefficient.

[0049] Specifically, the step of extracting multiple comparative correlation coefficients from multiple sequence correlation coefficients based on multiple comparison results means extracting the sequence correlation coefficients with a comparison result of a sequence correlation coefficient greater than the average coefficient from the multiple sequence correlation coefficients as multiple comparative correlation coefficients.

[0050] It is understood that the sequence correlation coefficient is calculated from the target decomposition temperature sequence and the target temperature sequence. Therefore, the sequence correlation coefficient corresponds one-to-one with the decomposition temperature sequence. Thus, multiple decomposition temperature sequences corresponding to the comparison correlation coefficients can be extracted from multiple decomposition temperature sequences as multiple correlation temperature sequences.

[0051] It should be understood that the relevant temperature sequence includes multiple relevant temperatures, all of which are obtained from the same target temperature sequence through multiple transformation decompositions. Each relevant temperature sequence includes the same number of relevant temperatures, and each relevant temperature corresponds to the same sampling time and the target temperature at the same sampling time in the target temperature sequence. The multiple transformation decomposition involves extracting a decomposed temperature sequence from the target temperature sequence, then subtracting the decomposed temperatures at the same sampling time from each target temperature in the target temperature sequence to obtain a temperature difference sequence. This temperature difference sequence is then used as the target temperature sequence, and the process returns to the step of extracting a decomposed temperature sequence from the target temperature sequence. Decomposing the target temperature sequence into multiple decomposed temperature sequences, which are then summed, yields the original target temperature sequence's decomposed temperature sequence.

[0052] Importantly, the multiple decomposed temperature sequences derived from the target temperature sequence are multiple trend terms (i.e., multiple intrinsic mode components and residual terms in empirical mode decomposition). According to the principle of empirical mode decomposition, by adding multiple trend terms, the target temperature sequence can be reconstructed. Therefore, it can be seen that the sequence reconstruction of multiple related temperature sequences refers to adding multiple temperature values ​​corresponding to the same sampling time in multiple related temperature sequences to obtain multiple sampled temperature values. The multiple sampled temperature values ​​are then arranged in chronological order according to the time of the multiple sampling times to obtain the temperature change sequence.

[0053] Understandably, this invention constructs a temperature change sequence based on multiple sequence correlation coefficients and multiple decomposed temperature sequences. Specifically, it determines effective decomposed temperature sequences from multiple decomposed temperature sequences based on multiple sequence correlation coefficients, and then reconstructs the temperature change sequence based on these effective decomposed temperature sequences. This temperature change sequence retains the main temperature response components of the target temperature sequence while reducing noise or irrelevant perturbations, thus providing a more accurate data foundation for subsequent acquisition of temperature response feature sets.

[0054] It should be understood that obtaining the temperature response feature set from the temperature change sequence refers to extracting temperature features from the temperature change sequence that can reflect the heating, cooling, and fluctuation of that spatial location, thus obtaining the temperature response feature set. Specifically, one or more of the following can be extracted from the temperature change sequence: maximum temperature value, average temperature value, minimum temperature value, rate of temperature rise, rate of temperature fall, temperature fluctuation amplitude, and temperature drop amplitude after heat dissipation, as the temperature response feature set. This invention does not limit this to any particular feature set.

[0055] Furthermore, the step of obtaining multiple initial temperature monitoring points based on multiple temperature response feature sets includes: Normalization is performed on multiple temperature response feature sets to obtain multiple normalized temperature response feature sets; Multiple temperature response feature vectors are constructed based on multiple normalized temperature response feature sets; Multiple spatial locations of the transformer set equipment are obtained, and density clustering is performed on multiple spatial locations based on multiple temperature response feature vectors to obtain multiple initial temperature monitoring points.

[0056] Understandably, the temperature response feature set is used to characterize the temperature changes of a spatial location during its historical operation, such as the magnitude of temperature increase, the magnitude of temperature decrease, the rate of temperature change, the magnitude of temperature fluctuation, and the magnitude of temperature decrease after heat dissipation. One temperature response feature set corresponds to one spatial location.

[0057] Specifically, the normalization process performed on multiple temperature response feature sets is to adjust different types of temperature response features to a uniform numerical range, preventing features with larger values ​​from having an excessive weight in subsequent clustering processes. This normalization process can be implemented using existing techniques such as Max-Min normalization.

[0058] In detail, constructing multiple temperature response feature vectors based on multiple normalized temperature response feature sets refers to arranging multiple normalized temperature response features corresponding to the same spatial location into a vector according to a preset feature order. For example, the normalized temperature response feature set consists of a, b, and c, and the preset feature order is arbitrarily set. The feature order can be set as the rate of temperature change, the magnitude of temperature increase, and the magnitude of temperature decrease. Then, the temperature response feature vector is... Where a represents the rate of temperature change, b represents the magnitude of temperature increase, and c represents the magnitude of temperature decrease.

[0059] It should be understood that the density clustering of multiple spatial locations based on multiple temperature response feature vectors refers to grouping spatial locations with similar temperature change patterns into the same category based on the similarity between the temperature response feature vectors corresponding to the multiple spatial locations. Specifically, if multiple spatial locations have similar temperature rise amplitudes, temperature drop amplitudes, rates of temperature rise, or decrease amplitudes after heat dissipation during historical operation, it indicates that the temperature change patterns of these spatial locations are similar and they can be clustered into the same category; if the temperature response features of multiple spatial locations differ significantly, they are classified into different categories. This density clustering can be implemented using existing density clustering techniques such as DBSCAN.

[0060] It is clear that obtaining multiple initial temperature monitoring points refers to selecting multiple representative locations from spatial locations with similar temperature change patterns within each cluster after density clustering is completed, as initial temperature monitoring points. These representative locations can be positions near the cluster center or positions within the cluster that facilitate the placement of temperature sensors.

[0061] Specifically, the method for selecting multiple representative locations as initial temperature monitoring points from spatial locations with similar temperature change patterns is as follows: calculate the Euclidean distance between each spatial location with similar temperature change patterns and the cluster center, and select the top 3 or top 5 spatial locations with the shortest distance from the cluster center as multiple representative locations. More specifically, the selection can be determined based on the number of sensors to be set. For example, if one sensor is planned, but it may be inconvenient to set up a sensor at these locations, then the number of locations needs to be greater than the number of sensors. For example, three representative locations can be set to facilitate subsequent screening of temperature detection points based on the status of the monitoring points.

[0062] By employing the above method, this invention can classify spatial locations with similar temperature change patterns into the same category, and select multiple initial temperature monitoring points from each category. This ensures that each initial temperature monitoring point better represents the temperature rise and fall changes of the same category of spatial locations. Consequently, during subsequent adaptive heat dissipation, coordinated heat dissipation adjustment can be performed on locations experiencing similar temperature rises or falls based on the temperature change patterns of each category of spatial locations.

[0063] S3. Obtain structural information and heat dissipation structure information of multiple heat dissipation units, construct multiple heat dissipation areas based on the structural information and heat dissipation structure information, obtain the status of multiple monitoring points of multiple initial temperature monitoring points based on the multiple heat dissipation areas, and obtain multiple temperature detection points based on the status of multiple monitoring points.

[0064] Understandably, the structural information refers to the installation location, dimensions, and coverage area of ​​multiple heat dissipation units, while the heat dissipation structure information refers to the airflow direction, airflow range, heat dissipation power, and airflow force of the multiple heat dissipation units. Constructing multiple heat dissipation zones based on the structural and heat dissipation structure information means dividing the transformer platform assembly into multiple heat dissipation zones, each corresponding to the effective range of a heat dissipation unit. The effective range refers to the area within which the heat dissipation unit dissipates heat. For example, if a heat dissipation unit can ventilate and dissipate heat in a fixed 10×10 square area, then the effective range is the fixed 10×10 square area. The fixed location refers to the center of the effective range on the transformer platform assembly corresponding to a heat dissipation unit.

[0065] Importantly, the monitoring point status is used to characterize whether each initial temperature monitoring point is suitable for installing a temperature sensor, including whether it is installable or not. The monitoring point status is comprehensively judged by factors such as whether the initial temperature monitoring point is located in a heat dissipation area, whether it is close to the main heat-generating component, whether it is in a position where the heat dissipation airflow blows directly, whether it is blocked by the equipment structure, whether it is convenient to install a temperature sensor, and whether it is easily affected by abnormal interference from local cold air or local heat sources. For example, regarding the conditions such as whether it is located in a heat dissipation area, whether it is close to the main heat-generating component, whether it is in a position where the heat dissipation airflow blows directly, whether it is blocked by the equipment structure, whether it is convenient to install a temperature sensor, and whether it is easily affected by abnormal interference from local cold air or local heat sources, the condition suitable for installing a temperature sensor (such as not easily affected by abnormal interference from local cold air or local heat sources or convenient to install a temperature sensor) can be set as installable. Only when multiple conditions are installable will the monitoring point status be comprehensively judged as installable. Conversely, if one of the conditions is not installable, then the monitoring point status will be judged as not installable. For example, although a temperature sensor can be installed at an initial temperature monitoring point, if it is located directly in front of the cooling fan's outlet, the temperature collected at this point is easily affected by direct cold airflow and cannot accurately represent the temperature of the corresponding heat dissipation area. Therefore, this initial temperature monitoring point can be deemed unsuitable as a candidate temperature detection point, and its monitoring point status is "uninstallable." Conversely, if an initial temperature monitoring point is close to the winding hotspot area and located in the middle of the heat dissipation area, it is less susceptible to short-term impacts from localized cold air. In this case, the monitoring point status of this initial temperature monitoring point can be determined as a candidate temperature detection point. Therefore, obtaining multiple temperature detection points based on the status of multiple monitoring points means eliminating monitoring points from multiple initial temperature monitoring points that are unsuitable in location, easily affected by localized interference, cannot be installed, or cannot represent the temperature changes of the heat dissipation area, retaining only those monitoring points that meet the status requirements. This avoids subsequent temperature detection points being selected solely based on manual experience, which could lead to monitoring points containing temperature data but failing to effectively represent the actual temperature of the heat dissipation area.

[0066] It should be noted that if the status of multiple monitoring points selected for multiple representative locations with similar temperature change patterns is not suitable for installation, then an expert scoring method can be used to score whether multiple representative locations are suitable for installing temperature sensors based on the status of multiple monitoring points, resulting in multiple installable scores. The representative location with the highest installable score is then extracted as the temperature detection point.

[0067] S4. Based on multiple temperature detection points and multiple heat dissipation units, adaptive heat dissipation is performed on the complete set of transformer equipment to obtain a heat dissipation transformer.

[0068] Furthermore, the adaptive cooling of the transformer assembly based on multiple temperature detection points and multiple heat dissipation units to obtain a heat dissipation transformer includes: Multiple transformer temperature sets of a complete set of transformer equipment are obtained based on multiple temperature detection points; Internal temperature reconstruction is performed on multiple transformer temperature sets to obtain the temperature distribution of multiple complete sets of equipment. Multiple actual temperature values ​​were obtained based on the temperature distribution of multiple sets of equipment, and multiple target temperature values ​​were also obtained. Calculate multiple temperature deviation values ​​based on multiple actual temperature values ​​and multiple target temperature values; Multiple initial heat dissipation adjustment sets are generated based on multiple temperature deviation values ​​for multiple heat dissipation units; Based on historical operating data, obtain multiple adjustment value changes and multiple temperature change values ​​corresponding to multiple heat dissipation units; Based on multiple adjustment value changes and multiple temperature value changes, calculate multiple temperature influence coefficients; A heat dissipation coupling matrix is ​​constructed based on multiple temperature influence coefficients; Inverse decoupling compensation is performed based on the heat dissipation coupling matrix and multiple initial heat dissipation adjustment sets to obtain multiple compensated heat dissipation adjustment sets. Based on multiple sets of compensation heat dissipation adjustment quantities and multiple heat dissipation units, adaptive heat dissipation is performed on the complete set of transformer equipment to obtain a heat dissipation transformer.

[0069] It should be noted that the process of obtaining multiple transformer temperature sets based on multiple temperature detection points involves collecting temperatures at multiple different times for each temperature detection point, resulting in multiple transformer temperature sets. Each transformer temperature set corresponds to the temperatures at multiple different times for a single temperature detection point. The temperature distribution of the complete set of equipment corresponds to the temperature field of the complete set of transformer equipment at a given time. Multiple transformer temperatures corresponding to the same time are extracted from multiple transformer temperature sets, resulting in multiple simultaneous temperatures. Based on these simultaneous temperatures, existing spatial interpolation techniques such as Kriging interpolation are used to calculate the temperatures at multiple spatial locations where no temperature detection points are set. These simultaneous temperatures are then combined with the simultaneous temperatures collected from multiple temperature detection points to construct the temperature field of the complete set of transformer equipment corresponding to a given sampling time, thus obtaining the temperature distribution of the complete set of equipment.

[0070] It should be explained that temperature values ​​collected from multiple temperature detection points can only represent the local temperature at the location of the detection point, and cannot fully represent the continuous temperature changes between the windings, core, tank, air ducts, and heat dissipation areas. Therefore, this invention performs internal temperature reconstruction on multiple transformer temperature sets to obtain the temperature distribution of multiple complete sets of equipment. This allows subsequent adaptive heat dissipation to determine heat dissipation requirements not only based on single-point temperature values, but also by combining the internal temperature distribution of the transformer complete sets of equipment, thereby improving the accuracy of heat dissipation regulation.

[0071] It should be understood that the object to be cooled refers to the equipment component that needs to participate in heat dissipation control, including but not limited to hot spots in the windings, localized high-temperature areas in the iron core, oil outlet locations, heat sink connections, and heat accumulation points inside the cabinet. The actual temperature value refers to the temperature value at the corresponding location of the object to be cooled, extracted based on the temperature distribution of the complete set of equipment. Specifically, when the object to be cooled is a sampling point, the temperature value of that sampling point in the temperature distribution of the complete set of equipment can be used as the actual temperature value; when the object to be cooled is a component or area, the highest temperature value among multiple spatial locations corresponding to that component or area can be used as the actual temperature value.

[0072] Furthermore, the target temperature value refers to the upper limit of the allowable temperature of the object to be cooled during operation, which is determined based on the equipment's technical parameters, material temperature resistance rating, or safe operation requirements, and can be obtained from the equipment's technical parameters. The temperature deviation value refers to the difference between the actual temperature value and the target temperature value; when the temperature deviation value is greater than zero, it indicates that the actual temperature value of the object to be cooled is higher than the target temperature value, and there is a need for cooling; when the temperature deviation value is less than or equal to zero, it indicates that the actual temperature value of the object to be cooled does not exceed the target temperature value, and heat dissipation is not required.

[0073] It is clear that the process of generating multiple initial heat dissipation adjustment sets corresponding to multiple heat dissipation units based on multiple temperature deviation values ​​involves using multiple temperature deviation values ​​as feedback control inputs and employing existing control technologies such as proportional control, proportional-integral control, or PID control to calculate multiple initial heat dissipation adjustment sets corresponding to multiple heat dissipation units. The initial heat dissipation adjustment sets include multiple initial heat dissipation adjustment values.

[0074] Specifically, the initial heat dissipation adjustment amount can be a fan speed adjustment amount, an air supply volume adjustment amount, an exhaust volume adjustment amount, a fan frequency adjustment amount, or a heat dissipation power adjustment amount, etc. For example, when the heat dissipation unit is a variable frequency fan, the control quantity output by the PID control can be converted into a fan frequency adjustment amount or a PWM duty cycle adjustment amount, thereby changing the fan speed; when the heat dissipation unit is an air supply unit or an exhaust unit, the control quantity can be converted into an air supply volume adjustment amount or an exhaust volume adjustment amount.

[0075] It should also be noted that the initial heat dissipation adjustment set is only a preliminary control quantity generated based on the temperature deviation value, and does not take into account the heat dissipation coupling relationship between multiple heat dissipation units, nor the overshoot risk in the temperature adjustment process. Therefore, the present invention further performs inverse decoupling compensation on multiple initial heat dissipation adjustment sets based on the heat dissipation coupling matrix, and performs compensation correction on the decoupled heat dissipation adjustment sets based on the predicted overshoot, thereby obtaining multiple compensated heat dissipation adjustment sets.

[0076] It is understood that the aforementioned adjustment change value refers to the change in adjustment of the heat dissipation unit during its historical operation, such as changes in fan speed, fan frequency, air supply volume, exhaust volume, or heat dissipation power. The aforementioned temperature change value refers to the temperature change of the object to be cooled within the corresponding historical period. By obtaining multiple adjustment change values ​​and multiple temperature change values, the correspondence between the changes in the adjustment of the heat dissipation unit and the temperature changes of the object to be cooled can be obtained.

[0077] Specifically, the temperature influence coefficient is used to characterize the degree of influence of a change in the adjustment amount of a heat dissipation unit on the temperature change of an object to be cooled. If the temperature change of an object to be cooled is significant after a change in the adjustment amount of a heat dissipation unit, then the temperature influence coefficient of that heat dissipation unit on the object to be cooled is large; if the temperature change is small, then the corresponding temperature influence coefficient is small. The temperature influence coefficient can be calculated using existing techniques such as least squares method, recursive least squares method, step response curve method, or linear regression.

[0078] In detail, for a heat dissipation unit and an object to be cooled, the changes in the adjustment amount of the heat dissipation unit over multiple historical operating processes can be used as the horizontal axis, and the temperature change of the object to be cooled within the corresponding time window can be used as the vertical axis. Existing techniques such as least squares, recursive least squares, step response curve method, or linear regression are used for fitting to obtain a temperature response curve characterizing the correspondence between the changes in the adjustment amount of the heat dissipation unit and the changes in the temperature of the object to be cooled. Furthermore, the slope of the temperature response curve at the current historical sampling time corresponding to the current moment is used as the temperature influence coefficient. If a change in the adjustment amount of a heat dissipation unit results in a significant temperature change in the corresponding object to be cooled, the corresponding temperature influence coefficient is large; if the temperature change is small, the corresponding temperature influence coefficient is small.

[0079] Furthermore, the heat dissipation coupling matrix refers to a matrix formed by arranging multiple temperature influence coefficients according to the correspondence between heat dissipation units and the object to be cooled. Each element in the heat dissipation coupling matrix corresponds to a temperature influence coefficient of a heat dissipation unit on an object to be cooled, used to characterize the strength of the heat dissipation influence of that heat dissipation unit on the object to be cooled. The heat dissipation coupling matrix is ​​shown below: ; in, Represents the heat dissipation coupling matrix. This represents the temperature influence coefficient of the first heat dissipation unit on the first object to be cooled. This represents the temperature influence coefficient of the first heat dissipation unit on the second object to be cooled. This indicates that the first heat dissipation unit affects the second... Temperature influence coefficient of the object to be cooled This represents the temperature influence coefficient of the second heat dissipation unit on the first object to be cooled. This represents the temperature influence coefficient of the second heat dissipation unit on the second object to be cooled. This indicates that the second heat dissipation unit affects the first... Temperature influence coefficient of the object to be cooled Indicates the first The temperature influence coefficient of each heat dissipation unit on the first object to be cooled. Indicates the first The temperature influence coefficient of each heat dissipation unit on the second object to be cooled. Indicates the first The heat dissipation unit for the first Temperature influence coefficient of the object to be cooled.

[0080] It should be understood that adjusting one heat dissipation unit may simultaneously affect multiple objects to be cooled, and the temperature of one object to be cooled may also be affected by multiple heat dissipation units simultaneously. Therefore, by constructing a heat dissipation coupling matrix, the cross-heat dissipation influence relationship between multiple heat dissipation units and multiple objects to be cooled can be represented, providing a data foundation for subsequent inverse decoupling compensation based on the heat dissipation coupling matrix.

[0081] Furthermore, the inverse decoupling compensation is performed based on the heat dissipation coupling matrix and multiple initial heat dissipation adjustment sets to obtain multiple compensated heat dissipation adjustment sets, including: If the heat dissipation coupling matrix is ​​a pre-constructed square matrix, then obtain the determinant value of the heat dissipation coupling matrix; If the determinant value is not zero, calculate the inverse matrix of the heat dissipation coupling matrix and determine the inverse matrix as the inverse decoupling matrix; If the determinant is zero, or the heat dissipation coupling matrix is ​​not a square matrix, then calculate the generalized inverse matrix of the heat dissipation coupling matrix and determine the generalized inverse matrix as the inverse decoupling matrix; For each of the multiple initial heat dissipation adjustment values ​​in the multiple initial adjustment value sets, perform vector permutation to obtain multiple initial heat dissipation adjustment vectors; Matrix operations are performed based on the inverse decoupling matrix and multiple initial heat dissipation adjustment vectors to obtain multiple decoupled heat dissipation adjustment vectors; Multiple sets of compensation heat dissipation adjustment values ​​are obtained based on multiple decoupled heat dissipation adjustment vectors.

[0082] Understandably, the heat dissipation coupling matrix is ​​used to characterize the cross-heat dissipation influence relationship between multiple heat dissipation units and multiple objects to be cooled. Therefore, if the initial heat dissipation adjustment is directly output to multiple heat dissipation units, it is possible that one object to be cooled will be over-cooled while another object will be under-cooled. The inverse decoupling compensation refers to constructing an inverse decoupling matrix based on the heat dissipation coupling matrix, and using the inverse decoupling matrix to perform matrix correction on multiple initial heat dissipation adjustment sets, so that the adjustment amounts output by multiple heat dissipation units can offset or weaken the cross-influence characterized by the heat dissipation coupling matrix. The inverse decoupling compensation can be understood as the application of the existing control technology of inverse decoupling in a scenario of collaborative heat dissipation by multiple heat dissipation units.

[0083] Specifically, if the heat dissipation coupling matrix is ​​a pre-constructed square matrix, it means that the number of rows and columns of the heat dissipation coupling matrix are the same, that is, the number of objects to be cooled and the number of heat dissipation units are the same in matrix relation. In this case, the determinant value of the heat dissipation coupling matrix can be obtained to determine whether the heat dissipation coupling matrix can be used to calculate a common inverse matrix. The calculation of the determinant and the calculation of the inverse matrix belong to the existing linear algebra technique of matrix inversion.

[0084] In detail, if the determinant value is not zero, it indicates that the heat dissipation coupling matrix is ​​invertible. The inverse of this matrix can be directly calculated and used as the inverse decoupling matrix. In other words, when the heat dissipation coupling matrix can be normally inverted, its inverse is used as a decoupling compensation relationship, enabling subsequent matrix operations to weaken the coupling effect between multiple heat dissipation units.

[0085] Importantly, if the determinant is zero, or the heat dissipation coupling matrix is ​​not a square matrix, it indicates that the heat dissipation coupling matrix cannot be directly calculated into a common inverse matrix. For example, since the objects to be cooled are determined based on the temperature distribution of the complete set of equipment, and the heat dissipation units are determined based on the actual heat dissipation structure of the equipment, their numbers are not necessarily the same. For example, when multiple hot spots are acted upon by a small number of heat dissipation units, the number of objects to be cooled may be greater than the number of heat dissipation units; when a region to be cooled is acted upon by multiple heat dissipation units separately, the number of heat dissipation units may also be greater than the number of objects to be cooled. Therefore, when the number of heat dissipation units is not equal to the number of objects to be cooled, the heat dissipation coupling matrix is ​​not a square matrix. Furthermore, even if the heat dissipation coupling matrix is ​​a square matrix, if the determinant is zero, the matrix is ​​a singular matrix. In this case, the generalized inverse matrix of the heat dissipation coupling matrix can be calculated, and the generalized inverse matrix can be determined as the inverse decoupling matrix. The generalized inverse matrix is ​​preferably the Moore-Penrose pseudo-inverse, which can be obtained by singular value decomposition. Both the Moore-Penrose pseudo-inverse and singular value decomposition are existing matrix calculation techniques, and will not be elaborated upon here.

[0086] Importantly, the initial heat dissipation adjustment set includes multiple initial heat dissipation adjustment values, each corresponding to a heat dissipation unit. Each initial heat dissipation adjustment value corresponds to a heat dissipation parameter, which is a value of that parameter. These parameters include, but are not limited to, fan speed, air supply volume, exhaust volume, fan frequency, or heat dissipation power. It should be noted that the multiple heat dissipation units have identical structures; for example, they are all fans, or all cooling plates, or combinations of fans and cooling plates. All initial heat dissipation adjustment sets correspond to the same sampling time.

[0087] Understandably, the phrase "performing vector arrangements of multiple initial heat dissipation adjustment quantities in each of multiple initial adjustment quantity sets to obtain multiple initial heat dissipation adjustment vectors" refers to arranging multiple initial heat dissipation adjustment quantities of different heat dissipation units with the same heat dissipation parameters into a vector according to the numbering order of the heat dissipation units. For example, arranging multiple initial heat dissipation adjustment quantities into initial heat dissipation adjustment vectors according to the order of the first heat dissipation unit, the second heat dissipation unit, and so on up to the nth heat dissipation unit. These initial heat dissipation adjustment vectors are used to uniformly represent the cooling requirements of multiple objects to be cooled as inputs for matrix operations. If there are n heat dissipation units, then the initial heat dissipation adjustment vector can be expressed as: ; in, This represents the initial heat dissipation adjustment vector. This indicates the initial heat dissipation adjustment amount for the first heat dissipation unit. Indicates the first The initial heat dissipation adjustment of each heat dissipation unit. These are all different values ​​of the same heat dissipation parameter.

[0088] It is clear that the statement about obtaining multiple decoupled heat dissipation adjustment vectors by performing matrix operations based on the inverse decoupling matrix and multiple initial heat dissipation adjustment vectors refers to obtaining them by matrix multiplication of the inverse decoupling matrix and each initial heat dissipation adjustment vector. This matrix multiplication belongs to existing linear algebra operations, which will not be elaborated upon here. The decoupled heat dissipation adjustment vector is a combination of adjustment amounts of the heat dissipation parameters of the heat dissipation unit after correction. That is, the initial heat dissipation adjustment vector represents the adjustment amount directly obtained based on the temperature deviation, while the decoupled heat dissipation adjustment vector represents the adjustment amount obtained after correction considering the mutual influence between heat dissipation units.

[0089] Understandably, inverse decoupling compensation primarily addresses the coupling interference problem between multiple heat dissipation units. By correcting the initial heat dissipation adjustment vector using the inverse matrix of the heat dissipation coupling matrix or the Moore-Penrose pseudo-inverse, the adjustment amount of multiple heat dissipation units is no longer determined solely by their individual temperature deviations, but rather by their simultaneous impact on other objects to be cooled, thereby improving the independence and stability of temperature regulation for multiple objects to be cooled.

[0090] Furthermore, the step of obtaining multiple sets of compensated heat dissipation adjustment amounts based on multiple decoupled heat dissipation adjustment vectors includes: Perform vector splitting on multiple decoupled heat dissipation adjustment vectors to obtain multiple sets of decoupled heat dissipation adjustment amounts corresponding to the multiple heat dissipation units; Obtain multiple temperature deviation values ​​and multiple rates of temperature change; Multiple predicted temperature peaks are calculated based on multiple temperature deviation values ​​and multiple temperature rise rates. Calculate the differences between multiple predicted temperature peaks and multiple target temperature values ​​to obtain multiple predicted overshoot values; Compensation and correction are performed on multiple sets of decoupled heat dissipation adjustment parameters based on multiple predicted overshoot parameters, resulting in multiple sets of compensated heat dissipation adjustment parameters.

[0091] It is understood that the decoupling heat dissipation adjustment vector is a vector obtained through inverse decoupling matrix operation, which includes the decoupling heat dissipation adjustment amount corresponding to multiple heat dissipation units.

[0092] Specifically, the vector splitting refers to extracting multiple vector elements from the decoupling heat dissipation adjustment vector according to the arrangement order of multiple heat dissipation units, and assigning them to each heat dissipation unit. For example, if the first vector element in the decoupling heat dissipation adjustment vector corresponds to the first heat dissipation unit, then this vector element is used as the decoupling heat dissipation adjustment amount for the first heat dissipation unit; if the second vector element corresponds to the second heat dissipation unit, then this vector element is used as the decoupling heat dissipation adjustment amount for the second heat dissipation unit.

[0093] It should be understood that the set of decoupled heat dissipation adjustment quantities is a set of adjustment quantities that has taken into account the heat dissipation coupling relationship, but it still belongs to the adjustment result under the current control cycle and has not yet taken into account the risk of overshoot caused by the continued rise in temperature in the subsequent short period of time.

[0094] Importantly, the temperature deviation value refers to the difference between the actual temperature of the object to be cooled and the target temperature value, used to characterize whether the object exceeds the upper limit of the allowable temperature. The temperature rise rate refers to the temperature increase of the object to be cooled per unit time, used to characterize the temperature change trend of the object to be cooled, where unit time is the time interval between two sampling moments. For example, if the current temperature has not yet significantly exceeded the target temperature, but the temperature rise rate is large, it indicates that the object to be cooled may continue to heat up. In detail, the actual temperature values ​​of the most recent sampling moments can be extracted from the historical actual temperature values ​​of the object to be cooled, the temperature difference between adjacent sampling moments can be calculated, and multiple unit time temperature change values ​​can be calculated based on the temperature difference and the time interval between adjacent sampling moments; then, the average of the multiple unit time temperature change values ​​is taken to obtain the temperature rise rate.

[0095] Specifically, if it is necessary to reduce the impact of temperature sampling noise on the rate of temperature change, the temperature sequence can be smoothed first by using moving average, median filtering or Gaussian filtering, and then the rate of temperature change can be calculated.

[0096] It is important to understand that the predicted peak temperature refers to the highest temperature that the object to be cooled may reach in the short term, based on the current temperature deviation and the rate of temperature change. Transformer sets have thermal inertia; even if the heat dissipation unit has begun to enhance heat dissipation, the temperature of the windings, core, or hot spots may continue to rise in a short period. Therefore, looking only at the current temperature deviation may underestimate the risk of subsequent overheating. Furthermore, a linear extrapolation method can be used to calculate the predicted peak temperature. This method uses the current actual temperature as the starting point for prediction, the rate of temperature change as the temperature trend in the short term, and combines this with a preset prediction time window to estimate the temperature that the object to be cooled may reach at the end of the prediction time window, which is then taken as the predicted peak temperature. The preset prediction time window can be determined by obtaining historical data, based on the response time required for the heat dissipation unit to generate a significant cooling effect after receiving the adjustment. If the current actual temperature is high and the rate of temperature change is large, the predicted peak temperature will be large, indicating that the object to be cooled is at risk of continuing to overheat. If the current actual temperature is low or the rate of temperature change is less than or equal to zero, the predicted peak temperature will be small, indicating that the object to be cooled is at low risk of continuing to heat up.

[0097] It should be understood that the predicted overshoot refers to the difference between the predicted peak temperature and the target temperature value, used to characterize the degree to which the object to be cooled may subsequently exceed the target temperature value. If the predicted overshoot is greater than zero, it indicates that the object to be cooled has an overshoot risk; if the predicted overshoot is less than or equal to zero, it indicates that the object to be cooled does not have a significant overshoot risk within the predicted time range.

[0098] In detail, the compensation and correction of multiple decoupled heat dissipation adjustment sets based on multiple predicted overshoots refers to a secondary adjustment of the decoupled heat dissipation adjustment amounts based on the predicted overshoots. If the predicted overshoot is large, the decoupled heat dissipation adjustment amount of the heat dissipation unit that has a heat dissipation function on the corresponding object to be cooled is increased; if the predicted overshoot is small, the decoupled heat dissipation adjustment amount of the corresponding heat dissipation unit is maintained or slightly adjusted. The compensation and correction can be implemented using existing control techniques such as proportional control, feedforward compensation, and amplitude limiting.

[0099] To address the problems described in the background art, this invention receives a temperature regulation command, confirms the temperature regulation environment including the transformer station assembly and multiple heat dissipation units based on the command, and acquires historical operating data of the transformer station assembly based on this environment. Further, it constructs an equipment temperature field sequence based on the historical operating data, extracts equipment temperature sequences corresponding to multiple spatial locations from this sequence, and performs a mixed decomposition process on the temperature sequences to obtain a temperature change sequence that characterizes the temperature variation patterns at spatial locations. Then, it obtains a temperature response feature set from the temperature change sequence. Thus, based on the heating, cooling, and fluctuation patterns of different spatial locations during historical operation, spatial locations with similar temperature change patterns can be categorized, providing a data foundation for determining initial temperature monitoring points, rather than relying solely on manual experience to set up temperature measurement points. This invention performs normalization processing on multiple temperature response feature sets and constructs multiple temperature response feature vectors. Based on these feature vectors, it performs density clustering on multiple spatial locations to obtain multiple initial temperature monitoring points. This method allows spatial locations with similar temperature rise, fall, rate of temperature change, or temperature drop after heat dissipation to be grouped into the same category. From each category, a location representing that type of temperature change pattern is selected as the initial temperature monitoring point, enabling subsequent adaptive cooling to coordinate the processing of locations with similar temperature rise or fall. By acquiring structural and heat dissipation structure information from multiple heat dissipation units, multiple heat dissipation regions are constructed based on this information. The status of multiple monitoring points for multiple initial temperature monitoring points is then obtained based on these heat dissipation regions, and finally, multiple temperature detection points are obtained based on these statuses. Therefore, by considering the installation location, airflow direction, exhaust direction, and effective range of the heat dissipation units, it is possible to determine whether the initial temperature monitoring point is suitable for installing a temperature sensor, whether it is located within an effective heat dissipation area, and whether it is subject to direct cold airflow or structural obstruction, thereby improving the matching degree between the temperature detection point and the actual heat dissipation area. Multiple transformer temperature sets are acquired based on multiple temperature detection points, and internal temperature reconstruction is performed on these sets to obtain the temperature distribution of multiple sets of equipment. Then, based on the temperature distribution of these sets, multiple actual temperature values ​​corresponding to multiple objects to be cooled are obtained, along with multiple target temperature values, and multiple temperature deviation values ​​are calculated. Therefore, even if temperature sensors are not directly installed in winding hot spots, localized high-temperature areas in the core, oil outlet locations, heat sink connections, or internal heat accumulation points, the corresponding actual temperature values ​​can still be obtained based on the reconstructed internal temperature distribution. This expands the basis for heat dissipation control from single-point temperature to the actual temperature state of the objects to be cooled. Multiple initial heat dissipation adjustment sets corresponding to multiple heat dissipation units are generated based on multiple temperature deviation values. Based on historical operating data, multiple adjustment change values ​​corresponding to multiple heat dissipation units and multiple temperature change values ​​corresponding to multiple objects to be cooled are obtained, and multiple temperature influence coefficients are calculated. Finally, a heat dissipation coupling matrix is ​​constructed based on these multiple temperature influence coefficients.The heat dissipation coupling matrix characterizes the cross-heat dissipation influence between multiple heat dissipation units and multiple objects to be cooled, avoiding localized insufficient or excessive heat dissipation caused by directly controlling heat dissipation units based solely on a single temperature deviation. Finally, this invention performs inverse decoupling compensation based on the heat dissipation coupling matrix and multiple initial heat dissipation adjustment sets to obtain multiple decoupled heat dissipation adjustment quantities. These quantities are then compensated and corrected by combining the temperature rise rate, predicted peak temperature, and predicted overshoot, resulting in multiple compensated heat dissipation adjustment quantity sets. Adaptive heat dissipation is then performed on the transformer station equipment based on these sets and multiple heat dissipation units. Through these methods, this invention achieves accurate and stable heat dissipation control of the transformer station equipment by analyzing temperature response characteristics, selecting temperature detection points, reconstructing internal temperature distribution, and performing heat dissipation coupling compensation.

[0100] like Figure 2 The diagram shown is a functional block diagram of a temperature control and regulation device for a transformer set based on adaptive heat dissipation, provided in an embodiment of the present invention.

[0101] The temperature control and regulation device 100 for a transformer station assembly based on adaptive heat dissipation described in this invention can be installed in an electronic device. Depending on the functions implemented, the temperature control and regulation device 100 for a transformer station assembly based on adaptive heat dissipation may include an instruction receiving module 101, a monitoring point selection module 102, a monitoring point verification module 103, and an adaptive heat dissipation module 104. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0102] The instruction receiving module 101 is used to receive temperature adjustment instructions and confirm the temperature adjustment environment based on the temperature adjustment instructions. The temperature adjustment environment includes a complete set of transformer equipment and multiple heat dissipation units. The monitoring point selection module 102 is used to obtain historical operating data of the transformer set equipment based on the temperature regulation environment, and to obtain multiple initial temperature monitoring points of the transformer set equipment based on the historical operating data. The monitoring point verification module 103 is used to acquire structural information and heat dissipation structure information of multiple heat dissipation units, construct multiple heat dissipation areas based on the structural information and heat dissipation structure information, acquire the status of multiple monitoring points of multiple initial temperature monitoring points based on the multiple heat dissipation areas, and acquire multiple temperature detection points based on the status of multiple monitoring points. The adaptive heat dissipation module 104 is used to perform adaptive heat dissipation on the transformer set equipment based on multiple temperature detection points and multiple heat dissipation units to obtain a heat dissipation transformer.

[0103] In detail, the modules in the temperature control and regulation device 100 for a transformer station based on adaptive heat dissipation described in this embodiment of the invention employ the same methods as described above during use. Figure 1 The temperature control method for transformer set equipment based on adaptive heat dissipation described in the article is the same as the technical means and can produce the same technical effect, so it will not be repeated here.

[0104] like Figure 3 The diagram shown is a structural schematic of an electronic device for implementing a temperature control adjustment method for a transformer set based on adaptive heat dissipation, according to an embodiment of the present invention.

[0105] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a temperature control adjustment method program for a transformer station complete set of equipment based on adaptive heat dissipation.

[0106] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a temperature control adjustment method program for a transformer station complete set of equipment based on adaptive heat dissipation, but also to temporarily store data that has been output or will be output.

[0107] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a temperature control adjustment method program for a transformer station based on adaptive heat dissipation) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0108] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0109] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0110] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management system, thereby enabling functions such as charging management, discharging management, and power consumption management through the power management system. The power supply may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0111] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0112] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0113] The program for temperature control adjustment of a transformer station complete set of equipment based on adaptive heat dissipation, stored in the memory 11 of the electronic device 1, is a combination of multiple instructions. When run in the processor 10, it can achieve the following: Receive temperature regulation command, confirm temperature regulation environment based on temperature regulation command, wherein temperature regulation environment includes transformer set equipment and multiple heat dissipation units; Based on the temperature regulation environment, historical operating data of the transformer set equipment is obtained, and multiple initial temperature monitoring points of the transformer set equipment are obtained based on the historical operating data. Obtain structural information and heat dissipation structure information of multiple heat dissipation units, and construct multiple heat dissipation areas based on the structural information and heat dissipation structure information; The status of multiple monitoring points is obtained based on multiple initial temperature monitoring points in multiple heat dissipation areas; Multiple temperature detection points are obtained based on the status of multiple monitoring points; Based on multiple temperature detection points and multiple heat dissipation units, adaptive heat dissipation is performed on the complete set of transformer equipment to obtain a heat dissipation transformer.

[0114] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0115] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0116] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following: Receive temperature regulation command, confirm temperature regulation environment based on temperature regulation command, wherein temperature regulation environment includes transformer set equipment and multiple heat dissipation units; Based on the temperature regulation environment, historical operating data of the transformer set equipment is obtained, and multiple initial temperature monitoring points of the transformer set equipment are obtained based on the historical operating data. Obtain structural information and heat dissipation structure information of multiple heat dissipation units, and construct multiple heat dissipation areas based on the structural information and heat dissipation structure information; The status of multiple monitoring points is obtained based on multiple initial temperature monitoring points in multiple heat dissipation areas; Multiple temperature detection points are obtained based on the status of multiple monitoring points; Based on multiple temperature detection points and multiple heat dissipation units, adaptive heat dissipation is performed on the complete set of transformer equipment to obtain a heat dissipation transformer.

[0117] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0118] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0119] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0120] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions 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 solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for temperature control and regulation of a complete set of transformer equipment based on adaptive heat dissipation, characterized in that, The method includes: Receive temperature regulation command, confirm temperature regulation environment based on temperature regulation command, wherein temperature regulation environment includes transformer set equipment and multiple heat dissipation units; Historical operating data of the transformer set equipment is obtained based on the temperature regulation environment. The historical operating data includes the equipment temperature field sequence. Multiple initial temperature monitoring points of the transformer set equipment are obtained based on the historical operating data. Obtain structural information and heat dissipation structure information of multiple heat dissipation units, and construct multiple heat dissipation areas based on the structural information and heat dissipation structure information; The status of multiple monitoring points is obtained based on multiple initial temperature monitoring points in multiple heat dissipation areas; The status of the monitoring point is determined by a comprehensive assessment of factors such as whether the initial temperature monitoring point is located within the heat dissipation area, whether it is close to the main heat-generating components, whether it is in a position where the heat dissipation airflow blows directly, whether it is blocked by the equipment structure, whether it is easy to install a temperature sensor, and whether it is easily affected by abnormal interference from local cold air or local heat sources. Multiple temperature detection points are obtained based on the status of multiple monitoring points; Based on multiple temperature detection points and multiple heat dissipation units, adaptive heat dissipation is performed on the complete set of transformer equipment to obtain a heat dissipation transformer; The method of performing adaptive heat dissipation on the transformer assembly based on multiple temperature detection points and multiple heat dissipation units to obtain a heat dissipation transformer includes: Multiple transformer temperature sets of a complete set of transformer equipment are obtained based on multiple temperature detection points; Internal temperature reconstruction is performed on multiple transformer temperature sets to obtain the temperature distribution of multiple complete sets of equipment. Multiple actual temperature values ​​were obtained based on the temperature distribution of multiple sets of equipment, and multiple target temperature values ​​were also obtained. Calculate multiple temperature deviation values ​​based on multiple actual temperature values ​​and multiple target temperature values; Multiple initial heat dissipation adjustment sets are generated based on multiple temperature deviation values ​​for multiple heat dissipation units; Based on historical operating data, obtain multiple adjustment value changes and multiple temperature change values ​​corresponding to multiple heat dissipation units; Based on multiple adjustment value changes and multiple temperature value changes, calculate multiple temperature influence coefficients; A heat dissipation coupling matrix is ​​constructed based on multiple temperature influence coefficients; Inverse decoupling compensation is performed based on the heat dissipation coupling matrix and multiple initial heat dissipation adjustment sets to obtain multiple compensated heat dissipation adjustment sets. Based on multiple sets of compensation heat dissipation adjustment quantities and multiple heat dissipation units, adaptive heat dissipation is performed on the complete set of transformer equipment to obtain a heat dissipation transformer; The inverse decoupling compensation based on the heat dissipation coupling matrix and multiple initial heat dissipation adjustment sets yields multiple compensated heat dissipation adjustment sets, including: If the heat dissipation coupling matrix is ​​a pre-constructed square matrix, then obtain the determinant value of the heat dissipation coupling matrix; If the determinant value is not zero, calculate the inverse matrix of the heat dissipation coupling matrix and determine the inverse matrix as the inverse decoupling matrix; If the determinant is zero, or the heat dissipation coupling matrix is ​​not a square matrix, then calculate the generalized inverse matrix of the heat dissipation coupling matrix and determine the generalized inverse matrix as the inverse decoupling matrix; For each of the multiple initial heat dissipation adjustment values ​​in the multiple initial adjustment value sets, perform vector permutation to obtain multiple initial heat dissipation adjustment vectors; Matrix operations are performed based on the inverse decoupling matrix and multiple initial heat dissipation adjustment vectors to obtain multiple decoupled heat dissipation adjustment vectors; Multiple sets of compensation heat dissipation adjustment values ​​are obtained based on multiple decoupled heat dissipation adjustment vectors; The step of obtaining multiple sets of compensated heat dissipation adjustment amounts based on multiple decoupled heat dissipation adjustment vectors includes: Perform vector splitting on multiple decoupled heat dissipation adjustment vectors to obtain multiple sets of decoupled heat dissipation adjustment amounts corresponding to the multiple heat dissipation units; Obtain multiple temperature deviation values ​​and multiple rates of temperature change; Multiple predicted temperature peaks are calculated based on multiple temperature deviation values ​​and multiple temperature rise rates. Calculate the differences between multiple predicted temperature peaks and multiple target temperature values ​​to obtain multiple predicted overshoot values; Compensation corrections are performed on multiple sets of decoupled heat dissipation adjustment parameters based on multiple predicted overshoot parameters, resulting in multiple sets of compensated heat dissipation adjustment parameters.

2. The temperature control and regulation method for a complete set of transformer equipment based on adaptive heat dissipation as described in claim 1, characterized in that, The method of acquiring multiple initial temperature monitoring points for the transformer set based on historical operating data includes: The equipment temperature field sequence is constructed based on historical operating data, which includes the equipment temperature field at multiple different times. Multiple device temperature sequences at multiple spatial locations are extracted from the device temperature field sequence. The spatial locations correspond one-to-one with the device temperature sequences. Each device temperature sequence includes multiple device temperatures, and each device temperature comes from a different device temperature field. Extract a device temperature sequence from multiple device temperature sequences sequentially to obtain a target temperature sequence, and then perform the following operations on the target temperature sequence: Perform a hybrid decomposition process on the target temperature sequence to obtain the temperature change sequence; Obtain the temperature response feature set from the temperature change sequence; By summarizing the temperature response feature sets, multiple temperature response feature sets are obtained; Multiple initial temperature monitoring points are obtained based on multiple temperature response feature sets.

3. The temperature control and regulation method for a complete set of transformer equipment based on adaptive heat dissipation as described in claim 2, characterized in that, The process of performing hybrid decomposition on the target temperature sequence to obtain the temperature change sequence includes: Perform a multivariate decomposition operation on the target temperature sequence to obtain multiple decomposed temperature sequences, wherein the target temperature sequence includes multiple target temperatures and the decomposed temperature sequences include multiple decomposed temperatures; Extract a decomposition temperature sequence from multiple decomposition temperature sequences sequentially to obtain the target decomposition temperature sequence, and then perform the following operations on the target decomposition temperature sequence: Multiple target decomposition temperatures are extracted from the target decomposition temperature sequence, and multiple target temperatures are extracted from the target temperature sequence; Multiple temperature weights are calculated based on the target temperature sequence, where the temperature weights, target decomposition temperatures, and target temperatures correspond one-to-one. Based on multiple temperature weights, multiple target decomposition temperatures, and multiple target temperatures, the sequence correlation coefficient of the target decomposition temperature sequence is calculated using the formula for calculating the sequence correlation coefficient. By summing the sequence correlation coefficients, we can obtain the sequence correlation coefficients corresponding to multiple decomposition temperature sequences. A temperature change sequence was constructed based on multiple sequence correlation coefficients and multiple decomposed temperature sequences.

4. The temperature control and regulation method for a complete set of transformer equipment based on adaptive heat dissipation as described in claim 3, characterized in that, The formula for calculating the sequence correlation coefficient is as follows: ； in, Represents the correlation coefficient between sequences. Represents the first of multiple target decomposition temperatures An index of the target decomposition temperature. This represents the total number of decomposition temperatures for multiple targets. Represents the weight of multiple temperature values. Temperature weights corresponding to the target decomposition temperature. Represents the first of multiple target decomposition temperatures The target decomposition temperature, Indicates the first of multiple target temperatures The target temperature corresponding to the target decomposition temperature.

5. The temperature control and regulation method for a complete set of transformer equipment based on adaptive heat dissipation as described in claim 4, characterized in that, The construction of the temperature change sequence based on multiple sequence correlation coefficients and multiple decomposed temperature sequences includes: Calculate the average of the correlation coefficients of multiple sequences to obtain the average coefficient, and compare the average coefficient with the correlation coefficients of multiple sequences to obtain multiple comparison results; Based on multiple comparison results, multiple comparative correlation coefficients with sequence correlation coefficients greater than the average coefficient are extracted from multiple sequence correlation coefficients, and decomposition temperature sequences corresponding to multiple comparative correlation coefficients are extracted from multiple decomposition temperature sequences to obtain multiple correlation temperature sequences. Sequence reconstruction is performed on multiple related temperature sequences to obtain a temperature change sequence.

6. The temperature control and regulation method for a complete set of transformer equipment based on adaptive heat dissipation as described in claim 5, characterized in that, The process of obtaining multiple initial temperature monitoring points based on multiple temperature response feature sets includes: Normalization is performed on multiple temperature response feature sets to obtain multiple normalized temperature response feature sets; Multiple temperature response feature vectors are constructed based on multiple normalized temperature response feature sets; Multiple spatial locations of the transformer set equipment are obtained, and density clustering is performed on multiple spatial locations based on multiple temperature response feature vectors to obtain multiple initial temperature monitoring points.

7. A temperature control and regulation device for a transformer station assembly based on adaptive heat dissipation, characterized in that, The device includes: The instruction receiving module is used to receive temperature regulation instructions and confirm the temperature regulation environment based on the temperature regulation instructions. The temperature regulation environment includes the transformer set equipment and multiple heat dissipation units. The monitoring point selection module is used to acquire historical operating data of the transformer set equipment based on the temperature regulation environment. The historical operating data includes the equipment temperature field sequence, and multiple initial temperature monitoring points of the transformer set equipment are acquired based on the historical operating data. The monitoring point verification module is used to acquire the structural information and heat dissipation structure information of multiple heat dissipation units, construct multiple heat dissipation areas based on the structural information and heat dissipation structure information, acquire the status of multiple monitoring points of multiple initial temperature monitoring points based on the multiple heat dissipation areas, and acquire multiple temperature detection points based on the status of multiple monitoring points. The status of the monitoring point is determined by a comprehensive assessment of factors such as whether the initial temperature monitoring point is located within the heat dissipation area, whether it is close to the main heat-generating components, whether it is in a position where the heat dissipation airflow blows directly, whether it is blocked by the equipment structure, whether it is easy to install a temperature sensor, and whether it is easily affected by abnormal interference from local cold air or local heat sources. An adaptive heat dissipation module is used to perform adaptive heat dissipation on the transformer set equipment based on multiple temperature detection points and multiple heat dissipation units to obtain a heat-dissipating transformer. The method of performing adaptive heat dissipation on the transformer assembly based on multiple temperature detection points and multiple heat dissipation units to obtain a heat dissipation transformer includes: Multiple transformer temperature sets of a complete set of transformer equipment are obtained based on multiple temperature detection points; Internal temperature reconstruction is performed on multiple transformer temperature sets to obtain the temperature distribution of multiple complete sets of equipment. Multiple actual temperature values ​​were obtained based on the temperature distribution of multiple sets of equipment, and multiple target temperature values ​​were also obtained. Calculate multiple temperature deviation values ​​based on multiple actual temperature values ​​and multiple target temperature values; Multiple initial heat dissipation adjustment sets are generated based on multiple temperature deviation values ​​for multiple heat dissipation units; Based on historical operating data, obtain multiple adjustment value changes and multiple temperature change values ​​corresponding to multiple heat dissipation units; Based on multiple adjustment value changes and multiple temperature value changes, calculate multiple temperature influence coefficients; A heat dissipation coupling matrix is ​​constructed based on multiple temperature influence coefficients; Inverse decoupling compensation is performed based on the heat dissipation coupling matrix and multiple initial heat dissipation adjustment sets to obtain multiple compensated heat dissipation adjustment sets. Based on multiple sets of compensation heat dissipation adjustment quantities and multiple heat dissipation units, adaptive heat dissipation is performed on the complete set of transformer equipment to obtain a heat dissipation transformer; The inverse decoupling compensation based on the heat dissipation coupling matrix and multiple initial heat dissipation adjustment sets yields multiple compensated heat dissipation adjustment sets, including: If the heat dissipation coupling matrix is ​​a pre-constructed square matrix, then obtain the determinant value of the heat dissipation coupling matrix; If the determinant value is not zero, calculate the inverse matrix of the heat dissipation coupling matrix and determine the inverse matrix as the inverse decoupling matrix; If the determinant is zero, or the heat dissipation coupling matrix is ​​not a square matrix, then calculate the generalized inverse matrix of the heat dissipation coupling matrix and determine the generalized inverse matrix as the inverse decoupling matrix; For each of the multiple initial heat dissipation adjustment values ​​in the multiple initial adjustment value sets, perform vector permutation to obtain multiple initial heat dissipation adjustment vectors; Matrix operations are performed based on the inverse decoupling matrix and multiple initial heat dissipation adjustment vectors to obtain multiple decoupled heat dissipation adjustment vectors; Multiple sets of compensation heat dissipation adjustment values ​​are obtained based on multiple decoupled heat dissipation adjustment vectors; The step of obtaining multiple sets of compensated heat dissipation adjustment amounts based on multiple decoupled heat dissipation adjustment vectors includes: Perform vector splitting on multiple decoupled heat dissipation adjustment vectors to obtain multiple sets of decoupled heat dissipation adjustment amounts corresponding to the multiple heat dissipation units; Obtain multiple temperature deviation values ​​and multiple rates of temperature change; Multiple predicted temperature peaks are calculated based on multiple temperature deviation values ​​and multiple temperature rise rates. Calculate the differences between multiple predicted temperature peaks and multiple target temperature values ​​to obtain multiple predicted overshoot values; Compensation corrections are performed on multiple sets of decoupled heat dissipation adjustment parameters based on multiple predicted overshoot parameters, resulting in multiple sets of compensated heat dissipation adjustment parameters.

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