Transformer predictive cooling system and method based on pcm thermal buffering and current correction

Abstract

This application proposes a transformer predictive cooling system and method based on PCM thermal buffering and current correction, belonging to the field of transformer thermal management and control technology. The method includes: taking the transformer's top oil temperature, bottom oil temperature, winding hot spot temperature, ambient temperature, global effective residual thermal buffering capacity, zoned effective residual thermal buffering capacity, equivalent winding current ratio, three-phase current imbalance, current ratio rise rate, and feedback state as a joint state; inputting this joint state into a thermal state prediction model in the prediction time domain to obtain the fan frequency and oil pump frequency for each control cycle; generating a feedback state based on the actual operating conditions of the variable frequency fan and variable frequency oil pump, and feeding it back into the thermal state prediction model. This application deeply couples the PCM residual thermal buffering capacity with model predictive control, enabling control to no longer rely solely on oil temperature or hot spot temperature thresholds, and to enhance cooling in advance before the thermal buffering capacity is exhausted.

Description

Technical Field

[0001] This application belongs to the field of transformer thermal management and control technology, specifically relating to a transformer predictive cooling system and method based on PCM thermal buffering and current correction. Background Technology

[0002] During operation, oil-immersed power transformers continuously generate heat due to winding copper losses, core losses, and losses at lead connection points. The temperature of the transformer winding hot spots is a crucial factor affecting insulation aging, service life, and safety margin. Existing transformer cooling systems often rely on preset thresholds for starting and stopping fans or oil pumps, such as top-layer oil temperature, winding hot spot temperature, or load current, or adjust the cooling intensity using variable frequency fans or oil pumps. While these methods can improve cooling efficiency to some extent, their control primarily reflects the current temperature or load condition, failing to accurately reflect the transformer's internal thermal buffer capacity to absorb short-term thermal shocks. This approach suffers from at least the following problems:

[0003] (1) Response lag. Control based on fixed thresholds usually starts cooling only after the temperature rise has occurred, making it difficult to make timely use of the internal and external thermal buffer resources of the transformer, which can easily lead to higher hot spot temperature peaks under conditions of rapid load fluctuation.

[0004] (2) Inadequate control. Traditional solutions often use fixed gears or simple linkage strategies, which cannot comprehensively consider load change trends, ambient temperature changes, and the degree of heat accumulation in different areas inside the transformer, making it difficult to reflect the risk of overheating in local areas in a timely manner.

[0005] (3) High energy consumption. Under light load, low ambient temperature at night, or short-term fluctuation conditions, the cooling system may still operate for a long time according to a conservative strategy, resulting in increased power consumption of the fan and oil pump.

[0006] Phase change materials (PCMs) can absorb or release a large amount of latent heat during phase change, serving as a passive thermal buffer medium in transformer thermal management. Existing transformer cooling solutions using PCMs primarily utilize them as auxiliary heat dissipation, heat storage, or heat exchange components. They mainly mitigate oil temperature rise through heat exchange between the PCM and transformer oil, or leverage the latent heat of the PCM to enhance the transformer's short-term thermal buffering capacity. While these solutions can utilize the passive heat absorption function of PCMs, they typically focus on the arrangement of PCM materials, heat exchange structures, or heat dissipation paths, without further quantitatively sensing the PCM phase change process. They also fail to convert the solid-state, phase change, and liquid-state changes of PCM into liquid phase fraction, residual thermal buffering capacity, and effective residual thermal buffering capacity that can be used for active cooling control. Therefore, existing PCM cooling solutions struggle to accurately determine whether the PCM still has the capacity to absorb heat, and they also find it difficult to adjust the operation of fans and oil pumps in advance based on the PCM's residual latent heat state.

[0007] On the other hand, the heat generated by transformer windings is closely related to the winding current, and copper losses are usually approximately related to the square of the current. When the load current rises rapidly, the three-phase current is unbalanced, or the current in a certain phase is consistently high, the heat load on the local windings and oil flow areas will increase rapidly. If the winding current is only used as a general monitoring quantity input to the cooling controller, without using the equivalent winding current ratio, three-phase current imbalance, and current ratio rise rate to correct the PCM's thermal buffering capacity, it will be difficult to reflect the risk that the thermal buffering capacity will be rapidly consumed in the short term. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this application provides a transformer predictive cooling system and method based on PCM thermal buffering and current correction. This solves the problems in existing technologies, such as PCMs being primarily used as passive heat storage components, unquantifiable thermal buffering capacity, lack of coupling between winding current thermal load and PCM state, lag in cooling control response, and coarse-grained fan and oil pump control.

[0009] Firstly, a transformer predictive cooling system based on PCM thermal buffering and current correction is provided, including:

[0010] The PCM module array includes multiple PCM modules of different types. Each PCM module is arranged in layers along the height of the transformer tank to respond to the thermal state of different heights and different phase zones of the transformer tank.

[0011] The data acquisition module is used to acquire the temperature of the PCM module array, the top oil temperature of the transformer, the bottom oil temperature of the transformer, the winding hot spot temperature of the transformer, the ambient temperature, the effective value of the three-phase current of the transformer, the solidus temperature of the PCM module array, the liquidus temperature of the PCM module array, the temperature change rate of the PCM module array, the duration of the phase change platform, the mass of each PCM module, and the latent heat of phase change.

[0012] The PCM state determination and residual heat buffer capacity calculation unit is connected to the data acquisition module. It is used to determine the phase state of the PCM module array and calculate the liquid phase fraction based on the temperature of the PCM module array, the solidus temperature of the PCM module array, the liquidus temperature of the PCM module array, the temperature change rate of the PCM module array, and the duration of the phase change platform. Based on the liquid phase fraction, the mass of each PCM module, and the latent heat of phase change, it calculates the global residual heat buffer capacity and the zoned residual heat buffer capacity of the transformer.

[0013] The current ratio heat load correction calculation unit is connected to the data acquisition module and the PCM state determination and residual heat buffer capacity calculation unit, respectively. It is used to obtain the equivalent winding current ratio, three-phase current imbalance and current ratio rise rate based on the effective value of the three-phase current of the transformer. Based on the equivalent winding current ratio, three-phase current imbalance and current ratio rise rate, it establishes the current heat load correction coefficient. Based on the current heat load correction coefficient, it corrects the global residual heat buffer capacity and the zoned residual heat buffer capacity of the transformer, respectively, to obtain the global effective residual heat buffer capacity and the zoned effective residual heat buffer capacity of the transformer.

[0014] The model predictive controller is connected to both the current ratio and heat load correction calculation unit and the data acquisition module. It is used to collect data on the transformer's top oil temperature, bottom oil temperature, winding hot spot temperature, ambient temperature, global effective residual heat buffer capacity, zoned effective residual heat buffer capacity, equivalent winding current ratio, three-phase current imbalance, current ratio rise rate, and feedback status. As a joint state, the joint state is input into the thermal state prediction model in the prediction time domain to obtain the fan frequency and oil pump frequency in each control cycle.

[0015] The execution unit, connected to the model predictive controller, is used to control the variable frequency fan according to the fan frequency and the variable frequency oil pump according to the oil pump frequency.

[0016] The feedback acquisition unit, connected to the execution unit and the model prediction controller, is used to generate feedback status based on the actual operating status of the execution unit. , will provide feedback status Send to the model prediction controller.

[0017] The data acquisition module includes: a temperature acquisition unit, a winding current acquisition unit, and a signal conditioning unit;

[0018] The temperature acquisition unit is connected to the PCM module array and is used to acquire the temperature of the PCM module array, the top oil temperature of the transformer, the bottom oil temperature of the transformer, the winding hot spot temperature of the transformer, and the ambient temperature.

[0019] The winding current acquisition unit is used to acquire the winding current of the transformer's A phase, B phase, and C phase.

[0020] The signal conditioning unit, connected to the winding current acquisition unit, is used to calculate and output the effective value of the three-phase current of the transformer based on the winding current.

[0021] The PCM module parameter acquisition unit is connected to the PCM module array and is used to acquire the solidus temperature, liquidus temperature, temperature change rate, phase change plateau duration, mass of each PCM module, and latent heat of phase change of the PCM module array.

[0022] Secondly, a transformer predictive cooling method based on PCM thermal buffering and current correction is provided, including:

[0023] Step S1: Use a PCM module array to obtain the thermal state of the transformer tank at different heights and in different phase zones;

[0024] Step S2: Collect the temperature of the PCM module array, the top oil temperature of the transformer, the bottom oil temperature of the transformer, the winding hot spot temperature of the transformer, and the ambient temperature.

[0025] Step S3: Collect the winding currents of phase A, phase B, and phase C of the transformer, and calculate and output the effective values ​​of the three-phase currents based on the winding currents.

[0026] Step S4: Based on the temperature of the PCM module array, the solidus temperature of the PCM module array, the liquidus temperature of the PCM module array, the temperature change rate of the PCM module array, and the duration of the phase change platform, determine the phase state of the PCM module array and calculate the liquid phase fraction. Based on the liquid phase fraction, the mass of each PCM module, and the latent heat of phase change, calculate the global residual heat buffer capacity of the transformer and the zoned residual heat buffer capacity of the transformer.

[0027] Step S5: Based on the effective value of the three-phase current of the transformer, obtain the equivalent winding current ratio, the three-phase current imbalance, and the current ratio rise rate. Establish the current heat load correction coefficient based on the equivalent winding current ratio, the three-phase current imbalance, and the current ratio rise rate. Based on the current heat load correction coefficient, correct the global residual heat buffer capacity and the regional residual heat buffer capacity of the transformer respectively, and obtain the global effective residual heat buffer capacity and the regional effective residual heat buffer capacity of the transformer.

[0028] Step S6: Collect the transformer's top oil temperature, bottom oil temperature, winding hot spot temperature, ambient temperature, overall effective residual heat buffer capacity, zoned effective residual heat buffer capacity, equivalent winding current ratio, three-phase current imbalance, current ratio rise rate, and feedback status. As a joint state, the joint state is input into the thermal state prediction model in the prediction time domain to obtain the fan frequency and oil pump frequency in each control cycle.

[0029] Step S7: Control the variable frequency fan according to the fan frequency, and control the variable frequency oil pump according to the oil pump frequency;

[0030] Step S8: Generate feedback status based on the actual operating conditions of the variable frequency fan and variable frequency oil pump. Return to step S6.

[0031] The process of determining the phase state of the PCM module array and calculating the liquid phase fraction based on the PCM module array's temperature, solidus temperature, liquidus temperature, temperature change rate, and phase transition plateau duration includes:

[0032] when When the i-th PCM module is in solid state, it is determined that the i-th PCM module is in solid state.

[0033] when At that time, it is determined that the i-th PCM module is in a liquid state;

[0034] when When the i-th PCM module is in a phase transition state, it is determined that the i-th PCM module is in a phase transition state. Let be the temperature at the k-th sampling time of the i-th PCM module. Let be the solidus temperature corresponding to the i-th PCM module. Let be the liquidus temperature of the i-th PCM module at the k-th sampling time. Let be the temperature change rate of the i-th PCM module at the k-th sampling time. The threshold for the rate of temperature change;

[0035] when hour, ;

[0036] when hour, ;

[0037] when At that time, the initial estimated value of the liquid phase fraction was:

[0038] ;

[0039] in, This is the initial estimate of the liquid phase fraction of the i-th PCM module within the phase transition temperature region;

[0040] The initial estimate of the liquid phase fraction is corrected based on the temperature change rate and the duration of the phase change plateau, resulting in the final liquid phase fraction of the PCM module array. The corrected expression is as follows:

[0041] ;

[0042] in, The continuous duration during which the i-th PCM module is within the phase transition temperature zone. The first correction factor is... This is the second correction factor. This means limiting the calculation result to between 0 and 1. Let be the liquid phase fraction at the k-th sampling time of the i-th PCM module. This refers to the total continuous duration during which the PCM module remains within the phase change temperature range.

[0043] The global residual heat buffer capacity of the transformer is calculated as follows:

[0044] ;

[0045] ;

[0046] in, This represents the global residual heat buffer capacity of the transformer at the k-th sampling time. For the quality of the i-th PCM module, Let be the latent heat of phase change of the i-th PCM module. Let be the liquid phase fraction at the k-th sampling time of the i-th PCM module. Let n be the remaining hot buffer capacity of the i-th PCM module at the k-th sampling time, and n be the total number of PCM modules.

[0047] The residual heat buffer capacity of the transformer is calculated as follows:

[0048] ;

[0049] ;

[0050] ;

[0051] in, This represents the remaining thermal buffer capacity of phase A of the transformer at the k-th sampling time. This represents the remaining thermal buffer capacity of phase B in the transformer at the k-th sampling time. This represents the remaining thermal buffer capacity of the C-phase partition at the k-th sampling time of the transformer. This refers to the set of PCM modules corresponding to the partition of transformer A. The set of PCM modules corresponding to the partition of transformer B. The set of PCM modules corresponding to the partition of transformer C.

[0052] The global effective residual heat buffer capacity of the transformer is expressed as follows:

[0053] ;

[0054] ;

[0055] in, To determine the global effective residual heat buffer capacity of the transformer at k sampling times, This represents the global residual heat buffer capacity of the transformer at the k-th sampling time. This is the current heat load correction factor at the k-th sampling time. The first corrected weighting coefficient, This is the second corrected weighting coefficient. This is the third corrected weighting coefficient. Let be the equivalent winding current ratio at the k-th sampling time. Let be the three-phase current imbalance at the k-th sampling time. The current ratio rise rate at the k-th sampling time;

[0056] The effective residual heat buffer capacity of the transformer partition is expressed as follows:

[0057] ;

[0058] in, This represents the effective remaining thermal buffer capacity of the transformer partition at the k-th sampling time. This represents the remaining heat buffer capacity of the transformer at the k-th sampling time. is the correction coefficient for the thermal load of the corresponding partition current at the k-th sampling time.

[0059] The thermal state prediction model in the prediction time domain includes: a discrete prediction model and an objective function. Fan frequency commands and oil pump frequency commands are used as control variables. The transformer's top oil temperature, bottom oil temperature, winding hot spot temperature, global effective residual heat buffer capacity, and zoned effective residual heat buffer capacity are used as state variables. The equivalent winding current ratio, three-phase current imbalance, current ratio rise rate, and ambient temperature are used to construct the discrete prediction model. The objective function is constructed using the increments of the control variables from the thermal state prediction model. The output of the discrete prediction model is used as the input to the objective function. The weights of the objective function are dynamically adjusted using the equivalent winding current ratio, three-phase current imbalance, and current ratio rise rate. The objective function is then solved to obtain the fan frequency and oil pump frequency in each control cycle.

[0060] The objective function is expressed as follows:

[0061] ;

[0062] in, Let be the objective function. To predict the length of the time domain, The output of the discrete prediction model is used to predict the hotspot temperature at step k+j. For the target hotspot temperature, Energy consumption for cooling fans and oil pumps, For the first Step control quantity increment, This is a reference value for effective residual heat buffering capacity. As the first dynamic weight, As the second dynamic weight, As the third dynamic weight, The fourth dynamic weight is given, where k is the k-th sampling time and j is the prediction step number. This represents the effective residual heat buffer capacity of the transformer in step k+j.

[0063] The weights of the objective function, which are dynamically adjusted using the equivalent winding current ratio, three-phase current imbalance, and current ratio rise rate, are calculated as follows:

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] in, As the first dynamic weight, As the second dynamic weight, As the third dynamic weight, As the fourth dynamic weight, As the first basic weight, As the second basic weight, As the third basic weight, As the fourth basic weight, The first weighted adjustment coefficient, This is the second weighting adjustment coefficient. This is the third weighting adjustment coefficient. Fourth weighting adjustment coefficient, This is the fifth weighting adjustment coefficient. This is the sixth weighting adjustment coefficient. This is the seventh weighting adjustment coefficient. This is the eighth weighting adjustment coefficient. This is the ninth weighting adjustment coefficient. This is the tenth weighting adjustment coefficient. This represents the ratio of effective remaining heat buffer capacity at sampling time k. This represents the effective remaining heat buffer capacity of the transformer at the k-th sampling time. For rated residual heat buffer capacity, Let be the equivalent winding current ratio at the k-th sampling time. Let be the three-phase current imbalance at the k-th sampling time. Let be the rate of increase of the current ratio at the k-th sampling time.

[0070] The feedback status is generated based on the actual operating conditions of the variable frequency fan and variable frequency oil pump. ,include:

[0071] When the frequency, speed, or motor current of the variable frequency fan exceeds the first set threshold, while the frequency, speed, or motor current of the variable frequency oil pump does not exceed the second set threshold, a feedback state is generated. When the measured frequency, speed, or motor current of the variable frequency oil pump exceeds the second set threshold, while the frequency, speed, or motor current of the variable frequency fan does not exceed the first set threshold, a feedback state is generated. When the frequency, speed, or motor current of the variable frequency fan exceeds the first set threshold, and the frequency, speed, or motor current of the variable frequency oil pump both exceed the second set threshold, a feedback state is generated. A feedback state is generated when the frequency, speed, or motor current of the variable frequency fan does not exceed the first set threshold, and the frequency, speed, or motor current of the variable frequency oil pump does not exceed the second set threshold. ;

[0072] When feedback status When, proceed to step S2 to enter the next control cycle; when , or When the time comes, the feedback state E(k) is sent to the model prediction controller, and the process proceeds to step S6.

[0073] Beneficial technical effects:

[0074] (1) This invention extends PCM from a simple passive heat storage material to a thermal state sensing unit that can be sensed, quantified, and participate in control decisions. The potential of PCM to continue to absorb heat is characterized by liquid phase fraction and residual heat buffering capacity.

[0075] (2) This invention deeply couples the residual heat buffer capacity of PCM with model predictive control, so that the fan and oil pump control no longer depends solely on oil temperature or hot spot temperature threshold, but can enhance cooling in advance before the heat buffer capacity is exhausted.

[0076] (3) The present invention introduces the winding current ratio to correct the remaining heat buffer capacity, which can reflect the influence of the current square heat effect, the three-phase unbalanced heat effect and the rapid rise of current on the heat buffer capacity consumption rate, thereby improving the accuracy of the next step of fan and oil pump working status prediction.

[0077] (4) By using the layered and partitioned PCM layout and the calculation of the remaining heat buffer capacity of the partitions, this invention can identify the insufficient heat buffer areas of phase A, phase B, phase C, as well as the upper, middle, and lower regions of the tank, thus avoiding the masking of local thermal risks by a single global temperature index.

[0078] (5) The present invention enables the control strategy to switch smoothly between economic operation mode, coordinated control mode, safety priority mode and forced cooling mode by dynamic weight adjustment and dynamic temperature constraint tightening, thereby reducing the peak temperature of hot spots and reducing the frequent start-stop of fans and oil pumps.

[0079] (6) The present invention controls the fan speed, oil pump speed, inverter output frequency and motor current through the execution unit. It can automatically enter the safety priority mode when the execution unit is abnormal, thereby improving the reliability of system operation. Attached Figure Description

[0080] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0081] Figure 1This is a structural block diagram of a transformer predictive cooling system based on PCM thermal buffering and current correction according to an embodiment of the present invention.

[0082] Figure 2 This is a schematic diagram of the layered and partitioned arrangement of the PCM module in the transformer tank according to an embodiment of the present invention;

[0083] Figure 3 A flowchart of the model predictive control based on PCM thermal buffering and current correction in an embodiment of the present invention;

[0084] Among them, 1-phase A, 2-phase B, 3-phase C, 4-low temperature PCM module, 5-medium temperature PCM module, and 6-high temperature PCM module. Detailed Implementation

[0085] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0086] Example 1

[0087] This embodiment provides a transformer predictive cooling system based on PCM thermal buffering and current correction, such as... Figure 1 ,include:

[0088] The PCM module array includes multiple PCM modules of different types. Each PCM module is arranged in layers along the height of the transformer tank to respond to the thermal state of different heights and different phase zones of the transformer tank.

[0089] The data acquisition module is used to acquire the temperature of the PCM module array, the top oil temperature of the transformer, the bottom oil temperature of the transformer, the winding hot spot temperature of the transformer, the ambient temperature, the effective value of the three-phase current of the transformer, the solidus temperature of the PCM module array, the liquidus temperature of the PCM module array, the temperature change rate of the PCM module array, the duration of the phase change platform, the mass of each PCM module, and the latent heat of phase change.

[0090] The data acquisition module includes a temperature acquisition unit, a winding current acquisition unit, and a signal conditioning unit.

[0091] In this embodiment, the PCM module array has multiple PCM modules, which are arranged in different transformer sections, such as... Figure 2As shown, three PCM modules with different phase transition temperatures are selected to form a gradient thermal barrier: low-temperature PCM module 4, medium-temperature PCM module 5, and high-temperature PCM module 6. The temperature range of low-temperature PCM module 4 is 55℃~65℃, the temperature range of medium-temperature PCM module 5 is 70℃~80℃, and the temperature range of high-temperature PCM module 6 is 85℃~95℃. These temperatures can be adjusted according to the transformer parameters. The external casing of the PCM modules is fitted with a metal seal compatible with transformer oil. An insulating coating or insulating support structure is provided on the outer surface of the metal seal casing to ensure the electrical safety and material compatibility of the PCM modules during long-term operation in transformer oil.

[0092] Subsequently, based on the different PCM modules, the PCMs are layered, sealed, and fixed to the inner wall of the transformer tank. Low-temperature PCM module 4 is preferentially arranged in the lower area of ​​the tank, medium-temperature PCM module 5 is arranged in the middle area of ​​the tank, and high-temperature PCM module 6 is arranged in the area near the winding hot spot and the upper part of the tank; at the same time, in the horizontal direction, the PCMs are arranged in zones according to the corresponding areas of transformer phase A1, transformer phase B2, and transformer phase C3.

[0093] The temperature acquisition unit is connected to the PCM module array and is used to acquire the temperature of the PCM module array, the top oil temperature of the transformer, the bottom oil temperature of the transformer, the winding hot spot temperature of the transformer, and the ambient temperature.

[0094] In this embodiment, the temperature acquisition unit is used to collect the temperature near each PCM module. It can accurately monitor the temperature of each PCM module, and simultaneously collect the top oil temperature of the transformer, the bottom oil temperature of the transformer, the hot spot temperature of the transformer windings, and the ambient temperature, with a collection cycle of 5 seconds.

[0095] The winding current acquisition unit is used to acquire the winding current of transformer phase A1, transformer phase B2 and transformer phase C3;

[0096] The signal conditioning unit, connected to the winding current acquisition unit, is used to calculate and output the effective value of the three-phase current of the transformer based on the winding current.

[0097] The PCM module parameter acquisition unit is connected to the PCM module array and is used to acquire the solidus temperature, liquidus temperature, temperature change rate, phase change plateau duration, mass of each PCM module, and latent heat of phase change of the PCM module array.

[0098] In this embodiment, the winding current acquisition unit acquires the winding currents of phase A1, phase B2, and phase C3 of the transformer. The sampling period can be consistent with the temperature sampling period or set independently. The winding current acquisition unit is not located inside the windings, but preferably at the output terminals of phases A1, B2, and C3 on the high-voltage and low-voltage sides of the transformer. The winding current acquisition unit may include bushing-type current transformers, through-type current transformers, open-type current transformers, Rogowski coils, Hall effect current sensors, or low-power current transformers. After signal conditioning, isolation acquisition, and RMS calculation, the acquired phase output currents are equivalent to the corresponding phase winding currents and input to the current-to-heat-load correction calculation unit and the model prediction controller. The signal conditioning unit is used to filter, isolate, perform proportional conversion, analog-to-digital conversion, and RMS calculation on the signal output from the current sensor to obtain the RMS values ​​of the currents of phases A1, B2, and C3 of the transformer.

[0099] In this embodiment, it can be understood that the solidus temperature and liquidus temperature of the PCM module array are factory parameters of the PCM material and are necessary parameters for defining the phase transition temperature range. The temperature change rate is calculated from the temperature of the PCM module array at adjacent sampling times.

[0100] The PCM state determination and residual heat buffer capacity calculation unit is connected to the data acquisition module. It is used to determine the phase state of the PCM module array and calculate the liquid phase fraction based on the temperature of the PCM module array, the solidus temperature of the PCM module array, the liquidus temperature of the PCM module array, the temperature change rate of the PCM module array, and the duration of the phase change platform. Based on the liquid phase fraction, the mass of each PCM module, and the latent heat of phase change, it calculates the global residual heat buffer capacity and the zoned residual heat buffer capacity of the transformer.

[0101] In this context, latent heat of phase change refers to the heat absorbed or released per unit mass of PCM module when it transitions from one phase to another; in this embodiment, it primarily refers to the absorbed heat. The liquid phase fraction indicates the proportion of latent heat already consumed. Latent heat of phase change is a material property, as can be found in the PCM material handbook.

[0102] The current ratio heat load correction calculation unit is connected to the data acquisition module and the PCM state determination and residual heat buffer capacity calculation unit, respectively. It is used to obtain the equivalent winding current ratio, three-phase current imbalance and current ratio rise rate based on the effective value of the three-phase current of the transformer. Based on the equivalent winding current ratio, three-phase current imbalance and current ratio rise rate, it establishes the current heat load correction coefficient. Based on the current heat load correction coefficient, it corrects the global residual heat buffer capacity and the zoned residual heat buffer capacity of the transformer, respectively, to obtain the global effective residual heat buffer capacity and the zoned effective residual heat buffer capacity of the transformer.

[0103] The model predictive controller is connected to the current ratio heat load correction calculation unit and the temperature acquisition unit, respectively. It is used to take the transformer top oil temperature, transformer bottom oil temperature, transformer winding hot spot temperature, ambient temperature, global effective residual heat buffer capacity, zone effective residual heat buffer capacity, equivalent winding current ratio, three-phase current imbalance, current ratio rise rate and feedback status as joint states, and input the joint states into the thermal state prediction model in the prediction time domain to obtain the fan frequency and oil pump frequency in each control cycle.

[0104] The execution unit, connected to the model predictive controller, is used to control the variable frequency fan according to the fan frequency and the variable frequency oil pump according to the oil pump frequency.

[0105] The feedback acquisition unit, connected to the execution unit and the model prediction controller, is used to generate feedback status based on the actual operating status of the execution unit. .

[0106] In this embodiment, the feedback acquisition unit is used to acquire the operating data of the variable frequency fan and the variable frequency oil pump. The operating data includes: speed, frequency, and motor current. Based on whether the operating data of the variable frequency fan exceeds a first preset threshold and whether the operating data of the variable frequency oil pump exceeds a second preset threshold, a feedback state is generated corresponding to the preset thresholds, and the feedback state is sent to the model prediction controller. The first preset threshold is a set of thresholds used to determine abnormal variable frequency fan response, including at least a fan frequency deviation threshold, a fan speed deviation threshold, and a fan motor current threshold; the second preset threshold is a set of thresholds used to determine abnormal variable frequency oil pump response, including at least an oil pump frequency deviation threshold, an oil pump speed deviation threshold, and an oil pump motor current threshold.

[0107] Specifically, when the frequency, speed, or motor current of the variable frequency fan exceeds a first set threshold, while the frequency, speed, or motor current of the variable frequency oil pump does not exceed a second set threshold, the feedback acquisition unit generates a feedback status. When the frequency, speed, or motor current of the variable frequency oil pump exceeds the second set threshold, while the frequency, speed, or motor current of the variable frequency fan does not exceed the first set threshold, the feedback acquisition unit generates a feedback status. When the frequency, speed, or motor current of the variable frequency fan exceeds the first set threshold, and the frequency, speed, or motor current of the variable frequency oil pump exceeds the second set threshold, the feedback acquisition unit generates a feedback status. When the frequency, speed, or motor current of the variable frequency fan does not exceed the first set threshold, and the frequency, speed, or motor current of the variable frequency oil pump does not exceed the second set threshold, the feedback acquisition unit generates a feedback status. .

[0108] The feedback status is a state quantity generated by the feedback acquisition unit based on the actual operating conditions of the execution unit. It is used to characterize whether the variable frequency fan and variable frequency oil pump respond normally according to the frequency command output by the model predictive controller. The feedback status is generated by the feedback acquisition unit collecting the speed, frequency, and current of the variable frequency fan and variable frequency oil pump and comparing them with the target frequency output by the model predictive controller.

[0109] Example 2

[0110] This embodiment provides a transformer predictive cooling method based on PCM thermal buffering and current correction, such as... Figure 3 As shown, it includes:

[0111] Step S1: Use a PCM module array to obtain the thermal state of the transformer tank at different heights and in different phase zones;

[0112] Step S2: Collect the temperature of the PCM module array, the top oil temperature of the transformer, the bottom oil temperature of the transformer, the winding hot spot temperature of the transformer, and the ambient temperature.

[0113] Step S3: Collect the winding currents of phase A, phase B, and phase C of the transformer, and calculate and output the effective values ​​of the three-phase currents based on the winding currents.

[0114] In this embodiment, the data acquisition methods in steps S1 to S3 are implemented through the data acquisition module in embodiment 1, and will not be described again in this embodiment.

[0115] Step S4: Based on the temperature of the PCM module array, the solidus temperature of the PCM module array, the liquidus temperature of the PCM module array, the temperature change rate of the PCM module array, and the duration of the phase change platform, determine the phase state of the PCM module array and calculate the liquid phase fraction. Based on the liquid phase fraction, the mass of each PCM module, and the latent heat of phase change, calculate the global residual heat buffer capacity of the transformer and the zoned residual heat buffer capacity of the transformer.

[0116] In this embodiment, the PCM state determination and remaining heat buffer capacity calculation unit first reads the temperature of each PCM module, then calculates the temperature change rate, phase state and liquid phase fraction, and finally calculates the remaining heat buffer capacity of a single module, the global system and the partition.

[0117] (1) Calculation of the rate of temperature change:

[0118] The temperature of the i-th PCM module at the k-th sampling time is The rate of temperature change at this time is denoted as:

[0119] ;

[0120] in, The sampling interval is... Let be the temperature change rate of the i-th PCM module at the k-th sampling time.

[0121] (2) Phase determination rules:

[0122] Let the solidus temperature corresponding to the i-th PCM module be . The liquidus temperature of the i-th PCM module at the k-th sampling time is The threshold for the rate of temperature change is The total continuous duration of the PCM module within the phase transition temperature range is .but:

[0123] when When the i-th PCM module is in solid state, it is determined that the i-th PCM module is in solid state.

[0124] when At that time, it is determined that the i-th PCM module is in a liquid state;

[0125] when When the i-th PCM module is in a phase transition state, it can be determined that the i-th PCM module is in a phase transition state, and this can be combined with... The stability of the phase change platform is determined by the duration of the phase change.

[0126] (3) Calculation of liquid phase fraction:

[0127] Liquid phase fraction The following formula can be used to estimate:

[0128] when hour, ;

[0129] when hour, ;

[0130] when At this point, the initial estimate of the liquid phase fraction is:

[0131] ;

[0132] in, This is the initial estimate of the liquid phase fraction for the i-th PCM module within the phase transition temperature region. Let be the temperature at the k-th sampling time of the i-th PCM module. Let be the solidus temperature corresponding to the i-th PCM module. Let be the liquidus temperature of the i-th PCM module at the k-th sampling time.

[0133] To improve the stability of phase determination, the liquid phase fraction can be corrected using the rate of temperature change and the duration of the phase transition plateau.

[0134] ;

[0135] in, The continuous duration during which the i-th PCM module is within the phase transition temperature zone. The first correction factor is... This is the second correction factor. This means limiting the calculation result to between 0 and 1. Let be the liquid phase fraction at the k-th sampling time of the i-th PCM module. This refers to the total continuous duration during which the PCM module remains within the phase change temperature range.

[0136] (4) Calculation of remaining heat buffer capacity:

[0137] The remaining hot buffer capacity of the i-th PCM module is:

[0138] ;

[0139] in, For the quality of the i-th PCM module, Let be the latent heat of phase change of the i-th PCM module. Let be the liquid phase fraction at the k-th sampling time of the i-th PCM module. This represents the remaining hot buffer capacity of the i-th PCM module at the k-th sampling time.

[0140] Global Remaining Hot Buffer Capacity Calculate according to the following formula:

[0141] ;

[0142] in, This represents the global residual heat buffer capacity of the transformer at the k-th sampling time. The larger the value, the more latent heat the system can continue to absorb. The smaller the value, the closer the PCM hot buffer capacity is to being exhausted, where n is the total number of PCM modules.

[0143] The corresponding regions for phases A, B, and C can be calculated separately:

[0144] ;

[0145] ;

[0146] ;

[0147] in, This represents the remaining thermal buffer capacity of phase A of the transformer at the k-th sampling time. This represents the remaining thermal buffer capacity of phase B in the transformer at the k-th sampling time. This represents the remaining thermal buffer capacity of the C-phase partition at the k-th sampling time of the transformer. This refers to the set of PCM modules corresponding to the partition of transformer A. The set of PCM modules corresponding to the partition of transformer B. The set of PCM modules corresponding to the partition of transformer C.

[0148] Further calculation of the partitioned hot buffer imbalance index:

[0149] ;

[0150] in, This is an indicator of uneven heat buffering in different zones. This indicates taking the maximum value. This indicates taking the minimum value. When When the value is large, it indicates that the residual heat buffering capacity of a certain phase region is significantly lower than that of other phase regions. In this case, the system can prioritize increasing the oil flow or overall cooling intensity of the corresponding region.

[0151] Step S5: Based on the effective value of the three-phase current of the transformer, obtain the equivalent winding current ratio, the three-phase current imbalance, and the current ratio rise rate. Establish the current heat load correction coefficient based on the equivalent winding current ratio, the three-phase current imbalance, and the current ratio rise rate. Based on the current heat load correction coefficient, correct the global residual heat buffer capacity and the regional residual heat buffer capacity of the transformer respectively, and obtain the global effective residual heat buffer capacity and the regional effective residual heat buffer capacity of the transformer.

[0152] In this embodiment, based on the thermal load correction of the winding current ratio, the current ratio thermal load correction calculation unit receives the three-phase winding current and the rated current, calculates the equivalent winding current ratio, the three-phase current imbalance and the current ratio rise rate, and thereby generates the current thermal load correction coefficient.

[0153] The winding current acquisition unit acquires the three-phase winding current in real time, including: the effective value of the A-phase winding current. RMS value of phase B winding current C-phase winding current Let the rated current be... , The equivalent winding current ratio at the k-th sampling time is calculated as follows:

[0154] ;

[0155] In this embodiment, since the copper loss of the transformer winding is approximately related to the square of the current, It can characterize the impact of the current load current on the thermal load.

[0156] The average three-phase current is:

[0157] ;

[0158] in, The average current of the three phases. Let be the three-phase current imbalance at the k-th sampling time, used to characterize the maximum relative degree of deviation of the three-phase current from the average value. When An increase indicates that a higher heat load may exist in certain local phase regions. The calculation formula is as follows:

[0159] ;

[0160] Current ratio rise rate:

[0161] ;

[0162] in, Let the current ratio rise rate be at the k-th sampling time. This represents the equivalent winding current ratio at the previous sampling time. The sampling interval is denoted as . This means that only the rising portion of the current ratio is taken, and no heat load correction is added when the load decreases.

[0163] The correction factor for current heat load is:

[0164] ;

[0165] in, This is the current heat load correction factor at the k-th sampling time. The first corrected weighting coefficient, This is the second corrected weighting coefficient. The third correction weighting coefficient can be obtained through offline thermal simulation, runtime data identification, or empirical tuning. The larger the value, the higher the current load and the faster the thermal buffer capacity is consumed. The overall effective remaining thermal buffer capacity of the transformer is:

[0166] ;

[0167] in, To determine the global effective residual heat buffer capacity of the transformer at k sampling times, This represents the global residual heat buffer capacity of the transformer at the k-th sampling time. is the current heat load correction factor at the k-th sampling time.

[0168] The effective remaining hot buffer capacity of the partition is:

[0169] ;

[0170] Where p represents phase A, phase B, phase C, or a zone in the height direction. This represents the effective remaining thermal buffer capacity of the transformer partition at the k-th sampling time. This represents the remaining heat buffer capacity of the transformer at the k-th sampling time. This is the correction factor for the zone current heat load at the k-th sampling time. For each phase zone, It can be calculated based on the current ratio of the corresponding phase winding. For example, the current ratio of phase A can be taken as... .

[0171] Using the above method, the system not only knows the current remaining latent heat capacity of the PCM, but also judges the risk of this capacity being consumed in the short term based on the winding current status. Therefore, even if the hot spot temperature has not yet reached the traditional start-up threshold, as long as the current ratio rises rapidly and the effective remaining heat buffering capacity decreases, the system can increase the operating frequency of the fan and oil pump in advance.

[0172] Step S6: Take the transformer top oil temperature, transformer bottom oil temperature, transformer winding hot spot temperature, ambient temperature, transformer global effective residual heat buffer capacity, transformer zone effective residual heat buffer capacity, equivalent winding current ratio, three-phase current imbalance, current ratio rise rate, and feedback state as a joint state, input the joint state into the thermal state prediction model in the prediction time domain, and obtain the fan frequency and oil pump frequency in each control cycle.

[0173] In this embodiment, the thermal state prediction model in the prediction time domain adopts a deep coupling strategy:

[0174] (1) Thermodynamic model:

[0175] The state variables of the model predictive controller can be represented as:

[0176] ;

[0177] in, Let K be the state variable of the model predictive controller at the k-th sampling time. This refers to the top oil temperature of the transformer. This refers to the hot spot temperature of the transformer windings. This refers to the bottom oil temperature of the transformer. For global effective remaining heat buffer capacity, The effective remaining heat buffer capacity of the area corresponding to A. The effective remaining heat buffer capacity of the area corresponding to B. This represents the effective remaining heat buffer capacity of the region corresponding to C.

[0178] The control quantity is:

[0179] ;

[0180] in, For the model predictive controller's control inputs, including fan frequency commands. and oil pump frequency command .

[0181] The disturbance or external input is:

[0182] ;

[0183] in, This is a disturbance or external input. Let be the equivalent winding current ratio at the k-th sampling time. Let be the three-phase current imbalance at the k-th sampling time. Let the current ratio rise rate be at the k-th sampling time. Let be the ambient temperature at the k-th sampling time.

[0184] The discrete prediction model (i.e., the thermal state prediction model in the prediction time domain) can be expressed as:

[0185] ;

[0186] ;

[0187] in, It can be established using transformer thermal circuit models and data-driven models. This includes predicting hotspot temperatures, top oil temperature, and effective residual heat buffer capacity. Let G be the state variable at the (k+1)th sampling time of the model predictive controller, and let G be the prediction function.

[0188] In this embodiment, the predicted winding hot spot temperature, predicted top oil temperature, and predicted effective residual heat buffer capacity obtained by the discrete prediction model are mainly used to determine the changing trends of these parameters in the next few cycles. If abnormal trends occur, the frequencies of the fan and oil pump can be adjusted in advance to ensure the stability of the transformer's internal temperature in the recent cycles. Finally, the predicted hot spot temperature and the actually collected parameters (i.e., the transformer's zoned effective residual heat buffer capacity, equivalent winding current ratio, three-phase current imbalance, and current ratio rise rate as inputs to the objective function) are simultaneously fed into the objective function to calculate the appropriate fan frequency and oil pump frequency.

[0189] (2) Objective function:

[0190] Within each control cycle, the model predictive controller solves for the following objective function:

[0191] ;

[0192] in, Let be the objective function. To predict the length of the time domain, To predict the hotspot temperature in step k+j, For the target hotspot temperature, The cooling energy consumption of the fan and oil pump at step k+j is... For the first Step control quantity increment, This is a reference value for effective residual heat buffering capacity. As the first dynamic weight, As the second dynamic weight, As the third dynamic weight, The fourth dynamic weight is given, where k is the k-th sampling time and j is the prediction step number. The effective remaining heat buffer capacity of the transformer in step k+j is the partitioned heat buffer capacity. Let be the equivalent winding current ratio at the k-th sampling time. Let be the three-phase current imbalance at the k-th sampling time. Let be the rate of increase of the current ratio at the k-th sampling time.

[0193] Cooling energy consumption can be expressed as:

[0194] ;

[0195] in, The cooling energy consumption of the fan and oil pump at the k-th data acquisition time is [data missing]. The first coefficient related to the characteristics of the fan and oil pump. This is a second coefficient related to the characteristics of the fan and oil pump.

[0196] (3) Weighting adjustment rules:

[0197] The effective remaining heat buffer capacity ratio is:

[0198] ;

[0199] in, This is the rated residual heat buffer capacity. When... When the value is large, it indicates that the PCM still has a strong thermal buffering capacity; when When the value is low, it indicates that the PCM's ability to absorb heat is insufficient, requiring the active cooling system to intervene in advance.

[0200] This embodiment introduces an equivalent current ratio. Three-phase current imbalance and current ratio rise rate To achieve deep coupling between the PCM residual heat buffer capacity assessment and the winding current thermal load assessment, the objective function weights are dynamically adjusted using the following formula.

[0201] ;

[0202] ;

[0203] ;

[0204] ;

[0205] in, As the first basic weight, As the second basic weight, As the third basic weight, As the fourth basic weight, The first weighted adjustment coefficient, This is the second weighting adjustment coefficient. This is the third weighting adjustment coefficient. Fourth weighting adjustment coefficient, This is the fifth weighting adjustment coefficient. This is the sixth weighting adjustment coefficient. This is the seventh weighting adjustment coefficient. This is the eighth weighting adjustment coefficient. This is the ninth weighting adjustment coefficient. This is the tenth weighting adjustment coefficient. This represents the ratio of effective remaining heat buffer capacity at sampling time k. This represents the effective remaining heat buffer capacity of the transformer at the k-th sampling time. This is the rated residual heat buffer capacity. When... Reduce or , , As the temperature rises, the weight of temperature control and thermal buffering capacity increases, while the weight of cooling energy consumption decreases relatively, thus prompting the controller to increase cooling intensity in advance.

[0206] Hotspot temperature upper limit constraint The calculation is as follows:

[0207] ;

[0208] in, This represents the upper limit of the dynamic winding hotspot temperature in the k-th control cycle. Based on the basic hotspot temperature limit, The first constraint tightening coefficient, This is the second constraint tightening coefficient. This is the third constraint tightening coefficient. This is the fourth constraint tightening coefficient. The first current ratio threshold, This is the threshold for three-phase current imbalance. This constraint causes the controller to tighten the hot spot temperature safety boundary in advance when the effective residual heat buffer capacity of the PCM decreases or the current heat load increases.

[0209] The following thresholds are for illustrative purposes only, taking the first remaining hot buffer capacity threshold: Second remaining heat buffer capacity threshold: Take the first current ratio threshold: Second current ratio threshold: Third current ratio threshold: Take the three-phase current imbalance threshold: Take the current ratio rise rate threshold: The aforementioned thresholds and weights can be adjusted based on transformer capacity, voltage level, insulation heat resistance level, cooling method, latent heat parameters of PCM materials, oil flow structure, and operating procedures.

[0210] Economic operation mode:

[0211] When the following conditions are met:

[0212] ;

[0213] ;

[0214] The above expression indicates that the PCM still has sufficient residual heat buffering capacity, the winding current load is low, the three-phase current is relatively balanced, and the load current does not show a rapid upward trend. At this time, the model predictive controller adopts an energy-saving priority weight, for example, taking... And take the upper limit constraint of hot spot temperature as In this mode, the controller prioritizes reducing the energy consumption of the fan and oil pump, allowing the PCM to continue its short-term thermal buffering function. The next operating state of the fan and oil pump is to remain stopped or run at low speed. In this mode, when the effective remaining thermal buffering capacity ratio of the PCM is not less than 0.6 and the equivalent winding current ratio is less than 0.8, the system considers the current thermal risk to be low and does not require an immediate and significant increase in cooling intensity.

[0215] Coordinated control mode:

[0216] When any of the following conditions are met:

[0217] And satisfy ;

[0218] or ;or ;

[0219] At this point, the system enters the coordinated control mode, indicating that the PCM's remaining thermal buffer capacity is beginning to decrease, or the winding current is approaching the rated operating range, or the load current is showing a rapid upward trend.

[0220] At this point, the model predictive controller adopts a weighted approach that balances temperature safety and energy consumption, for example, taking... And take the upper limit constraint of hot spot temperature as In this mode, the controller increases cooling intensity in advance, and the next operating state of the fan and oil pump is preferentially adjusted from stopping or low-speed operation to low-speed or medium-speed operation. In this mode, when the PCM effective residual heat buffer capacity ratio is between 0.3 and 0.6, or the equivalent winding current ratio is between 0.8 and 1.0, the system no longer simply pursues energy saving, but begins to intervene in cooling in advance to prevent the effective residual heat buffer capacity from continuing to decline rapidly.

[0221] Security Priority Mode:

[0222] When any of the following conditions are met: ;or ;or ;or ;or ;

[0223] At this point, the system enters a safety-priority mode, indicating that the effective residual heat buffer capacity of the PCM is below the safety threshold, or the winding current has reached or exceeded the rated value, or the three-phase current imbalance exceeds the set threshold, posing a risk of accelerated hotspot temperature rise or localized overheating. In this mode, the model predictive controller adopts a weighted approach that balances temperature safety and energy consumption, for example, by taking... , , , And further tighten the upper limit constraint on hotspot temperature to In this mode, the controller prioritizes suppressing hotspot temperature rise and maintaining effective residual heat buffering capacity. The fan and oil pump will then be adjusted to medium or high speed operation. In this mode, when the PCM effective residual heat buffering capacity ratio is less than 0.3, or the equivalent winding current ratio reaches 1.0 or higher, the system considers the thermal risk to have significantly increased. At this point, energy saving is no longer the primary goal; instead, ensuring the safety of the winding hotspot temperature is prioritized.

[0224] Forced cooling mode:

[0225] When any of the following conditions are met:

[0226] ;or ;or ;

[0227] Alternatively, if the feedback unit determines that the execution unit has an abnormal response, the system will enter a forced cooling mode.

[0228] At this point, the model predictive controller uses forced safety weights, for example, taking... The fan and oil pump frequencies are directly increased to the preset safe frequency or the maximum allowable frequency, and the next operating state output of the fan and oil pump is a safe forced operation state. In this mode, when the equivalent winding current ratio reaches 1.2 or above, or the winding hot spot temperature has reached 90℃ and the effective residual heat buffer capacity ratio of the PCM is less than 0.3, the system directly enters a forced cooling state to prevent the hot spot temperature from continuing to rise.

[0229] The combined judgment principle of PCM residual heat buffering capacity and winding current ratio:

[0230] In the above judgment, if the judgment result of the effective remaining heat buffer capacity is inconsistent with the judgment result of the winding current ratio, then the higher-level control mode shall be selected according to the principle of safety priority.

[0231] For example, when At this time, although the PCM has sufficient residual heat buffer capacity, the system still enters the safety priority mode because the winding current has exceeded the rated current. For example, when... At this time, although the load current is low, the remaining thermal buffer capacity of the PCM is insufficient, and the system also enters the safety priority mode.

[0232] This enables the joint judgment of PCM status quantification, residual heat buffer capacity, and winding current heat load, allowing the control system to not only determine cooling requirements based on the current temperature and PCM status, but also predict the next operating status of the fan and oil pump in advance based on the impact of winding current on future heat load.

[0233] The constraints of model predictive control include:

[0234] ;

[0235] ;

[0236] ;

[0237] ;

[0238] ;

[0239] ;

[0240] Where j is the step number in the prediction time domain. Minimum allowed fan frequency, For the maximum allowed frequency of the fan, The minimum allowable frequency for the oil pump, For the maximum allowable frequency of the oil pump, For the fan frequency at step k+j, Let the pump frequency be at step k+j. This represents the change in fan frequency at step k+j. This represents the change in pump frequency at step k+j. This represents the maximum frequency variation allowed in a single fan control cycle. This represents the maximum allowable frequency variation in a single control cycle of the oil pump. To predict the winding hot spot temperature in step k+j, This represents the upper limit of the dynamic winding hotspot temperature in the k-th control cycle. To predict the top oil temperature in step k+j, This is the upper limit of the top oil temperature. Among them, according to , , and The constraints are dynamically adjusted and are not simply a superposition of fixed empirical thresholds, but are jointly determined by the physical operating boundaries of the actuators, the thermal safety boundaries of the transformer, and the effective residual heat buffer capacity of the PCM. Specifically, the upper and lower limits of the fan and oil pump frequencies are determined by the allowable operating range of the variable frequency fan, variable frequency oil pump, and their inverters; the control increment constraints are determined by the mechanical inertia of the fan and oil pump, the speed regulation capability of the inverter, and the requirement to avoid frequent start-stop cycles; the top oil temperature constraint is determined by the overall thermal safety requirements of the transformer's oil-paper insulation system; and the hot spot temperature upper limit constraint is dynamically generated based on the ratio of effective residual heat buffer capacity, winding current ratio, three-phase current imbalance, and current ratio rise rate. When the effective residual heat buffer capacity is high and the winding current ratio is low, the hot spot temperature upper limit constraint is relatively relaxed to reduce cooling energy consumption; when the effective residual heat buffer capacity decreases or the winding current ratio increases, the hot spot temperature upper limit constraint is gradually tightened to prompt the model predictive controller to increase the fan and oil pump frequencies in advance, thereby enhancing active cooling before the PCM's heat buffer capacity is exhausted or the hot spot temperature rises rapidly. Thus, the constraints enable dynamic coupling between PCM state quantization, winding current thermal load prediction, and active cooling control.

[0241] With the above settings, the model predictive controller incorporates the PCM's remaining heat buffer capacity, current-to-heat load status, and future temperature trends into the optimization process, thereby achieving coordinated control of the passive heat buffer and active cooling systems.

[0242] Step S7: Control the variable frequency fan according to the fan frequency, and control the variable frequency oil pump according to the oil pump frequency;

[0243] Step S8: Generate feedback status based on the actual operating conditions of the variable frequency fan and variable frequency oil pump. Return to step S6.

[0244] In this embodiment, the feedback acquisition unit generates a feedback state based on a first preset threshold and a second preset threshold. and will Feedback is sent to the model prediction controller. When When, enter the next control cycle; when , or When the model predictive controller enters either the safety priority mode or the forced cooling mode based on the anomaly type, it simultaneously alarms and records data, and then proceeds to step S6.

[0245] Furthermore, display the feedback status. The specific generation process is as follows: the feedback acquisition unit can first construct fan anomaly criteria respectively. Oil pump malfunction criteria The feedback state is then obtained by combining the two. The calculation formula is as follows:

[0246] ;

[0247] ;

[0248] ;

[0249] ;

[0250] in, As a criterion for fan malfunction, This serves as a criterion for determining oil pump malfunction. The target fan frequency for the k-th control cycle. The target frequency of the oil pump in the k-th control cycle; For fan feedback frequency, This refers to the oil pump feedback frequency. Target fan speed The target speed of the oil pump; For fan speed feedback, These are the oil pump feedback speeds; This is the fan motor current. This refers to the current of the oil pump motor. This is the fan frequency deviation threshold. This is the oil pump frequency deviation threshold. This is the threshold for fan speed deviation. This is the threshold value for oil pump speed deviation. This is the fan motor current threshold. This is the threshold current for the oil pump motor.

[0251] Among the above variables, and The model predictive controller performs rolling optimization of the output in the kth control cycle; and Obtained from feedback from the strain gauge frequency converter; and The target frequency can be calculated based on the frequency and speed calibration relationship of the fan and oil pump, or it can be given by the speed control command of the frequency converter. and The speed is obtained from a speed sensor or internal speed feedback from the frequency converter. and It is obtained from the inverter output current feedback or the motor circuit current acquisition device; , , , , and The control accuracy of the frequency converter, the rated parameters of the fan and oil pump, the accuracy of the sensors, the results of on-site commissioning, and the operating procedures are preset and stored in the feedback acquisition unit or the model predictive controller.

[0252] In this embodiment, the model prediction controller predicts the next operating state of the fan and oil pump. Upon receiving the calculation results, it makes a joint judgment based on economic operation mode, coordinated control mode, safety priority mode, and forced cooling mode. When the judgment result of effective remaining heat buffer capacity is inconsistent with the judgment result of current heat load, the system selects a higher-level control mode according to the safety priority principle.

[0253] The fan frequency for the next control cycle is obtained by the model predictive controller through rolling solution. and oil pump frequency The feedback acquisition unit collects and executes feedback, judges deviations, and further generates feedback status. : This indicates that the execution unit is functioning normally. This indicates a fan malfunction. This indicates an oil pump malfunction. This indicates that both the fan and oil pump are malfunctioning. This feedback status... As the joint state input for the next control cycle, the model predictive controller can adjust the subsequent control strategy based on the actual response of the actuators. The system divides the next operating state of the fan and oil pump into the following categories based on the frequency threshold:

[0254] Stopped state: Frequency equal to 0 or lower than the start frequency;

[0255] Low-speed operation: The frequency is between the startup frequency and the first frequency threshold;

[0256] Medium-speed operation: The frequency is between the first frequency threshold and the second frequency threshold;

[0257] High-speed operation: The frequency is between the second frequency threshold and the maximum allowable frequency;

[0258] Forced safe operation: When the execution unit malfunctions, the hot spot temperature approaches the upper limit, or the effective remaining heat buffer capacity is lower than the safety threshold, the fan and oil pump will operate at the preset safe frequency or the maximum allowable frequency.

[0259] Execution feedback and security priority mode:

[0260] The actuator includes a variable frequency fan, a variable frequency oil pump, and a corresponding variable frequency drive. The feedback quantity must include at least the fan speed. Oil pump speed Inverter output frequency and motor current The fan feedback includes the measured fan frequency. Fan speed and fan motor current The oil pump feedback includes the measured frequency of the oil pump. Oil pump speed and oil pump motor current .

[0261] When the difference between the target frequency output by the controller and the measured frequency exceeds the threshold for multiple consecutive control cycles, or when the measured motor current increases abnormally but the speed fails to reach the target speed, the execution unit is deemed to have a response abnormality. At this time, the system performs the following actions:

[0262] (1) Switch the fan and oil pump control commands to the preset safe frequency or the maximum permissible frequency;

[0263] (2) Increase the weight of temperature control and the weight of residual heat buffering capacity in the model predictive control objective function;

[0264] (3) Tighten the upper limit constraint on hotspot temperature;

[0265] (4) Record the time of the anomaly, target frequency, measured frequency, motor current, hot spot temperature, top oil temperature, winding current ratio, effective residual heat buffer capacity, and feedback status. ;

[0266] (5) Output alarm information.

[0267] The aforementioned execution feedback safety processing enables the present invention not only to predict cooling demand, but also to perform closed-loop verification of the actuator response, avoiding control strategy failure due to fan, oil pump or frequency converter malfunction.

[0268] Model predictive control process steps: First, collect the transformer operating status, including top oil temperature, bottom oil temperature, winding hot spot temperature, ambient temperature, A-phase winding current, B-phase winding current, C-phase winding current, and feedback status generated in the previous control cycle. Then, the PCM liquid phase fraction is calculated based on the collected data. The global residual heat buffer capacity of the transformer at the k-th sampling time. Equivalent winding current ratio Three-phase current imbalance Current ratio rise rate and current heat load correction factor Furthermore, the global effective residual heat buffer capacity of the transformer at k sampling times is obtained. and the ratio of effective residual heat buffer capacity .

[0269] Model predictive controller based on , , , and feedback status The system determines the control mode. When the low-heat-risk condition is met, it enters the economic operation mode, and the fan and oil pump output stop or low-speed operation commands. When the effective residual heat buffer capacity decreases, the winding current approaches the rated value, or the current ratio rises rapidly, it enters the coordinated control mode, and outputs low-speed or medium-speed operation commands. When the effective residual heat buffer capacity is insufficient, the winding current reaches the rated value or above, or the three-phase current imbalance exceeds the limit, it enters the safety priority mode, and outputs medium-speed or high-speed operation commands. When the equivalent winding current ratio reaches the forced cooling threshold, the winding hot spot temperature reaches the set temperature and the effective residual heat buffer capacity is insufficient, or the execution feedback status indicates that the execution unit is abnormal, it enters the forced cooling mode and outputs a safe forced operation command.

[0270] After determining the control mode, the model predictive controller performs a safety-priority joint decision. If the effective remaining thermal buffer capacity determination result differs from the equivalent winding current ratio, three-phase current imbalance, or current ratio rise rate determination results, a higher-level control mode is selected. Subsequently, the model predictive controller performs rolling optimization, dynamically adjusting the objective function weights based on the current thermal state and control mode. , , , And the upper limit of the dynamic winding hot spot temperature in the kth control cycle. It also outputs the fan frequency command for the next control cycle. and oil pump frequency command .

[0271] The execution unit controls the operation of the variable frequency fan and variable frequency oil pump according to the aforementioned frequency commands. The feedback acquisition unit collects the measured frequency, speed, and motor current of the variable frequency fan and variable frequency oil pump, and determines whether they exceed the corresponding set thresholds, thereby generating a feedback status. .in, This indicates that the execution unit is functioning normally. This indicates a fan malfunction. This indicates an oil pump malfunction. This indicates that both the fan and oil pump are malfunctioning. At this time, the system enters the next control cycle and, based on the new... , , , Reassess the economic operating model or coordination and control model; when , or When the system is in operation, it will input the feedback status into the model predictive controller, switch to safety priority mode or forced cooling mode, and output alarm information and record operating data.

[0272] This embodiment proposes a transformer predictive cooling method based on thermal buffering and current correction. The core of this method is as follows: First, the phase change state of the PCM is transformed from a simple material state into a calculable liquid phase fraction and residual thermal buffering capacity. Second, the winding current ratio, three-phase current imbalance, and current ratio rise rate are transformed into current heat load correction coefficients to effectively correct the residual thermal buffering capacity of the PCM. Third, the effective residual thermal buffering capacity is used as the state input of the model predictive controller, the basis for dynamic weight adjustment, and the basis for tightening hot spot temperature constraints, thereby achieving coordinated predictive control between passive thermal buffering and active cooling.

[0273] This embodiment integrates PCM thermal buffer margin, current heat load consumption risk, and future temperature trends into the same rolling optimization process, which can enhance cooling in advance before the PCM thermal buffer capacity is exhausted or during the rapid current rise phase.

[0274] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A transformer predictive cooling system based on PCM thermal buffering and current correction, characterized in that, include: The PCM module array includes multiple PCM modules of different types, arranged in layers along the height of the transformer tank to respond to the thermal state of different tank heights and phase zones. The data acquisition module is used to acquire the temperature of the PCM module array, the top oil temperature of the transformer, the bottom oil temperature of the transformer, the winding hot spot temperature of the transformer, the ambient temperature, the effective value of the three-phase current of the transformer, the solidus temperature of the PCM module array, the liquidus temperature of the PCM module array, the temperature change rate of the PCM module array, the phase change plateau duration, the mass of each PCM module, and the latent heat of phase change. The PCM state determination and residual heat buffer capacity calculation unit is connected to the data acquisition module. It is used to determine the phase state of the PCM module array and calculate the liquid phase fraction based on the temperature of the PCM module array, the solidus temperature of the PCM module array, the liquidus temperature of the PCM module array, the temperature change rate of the PCM module array, and the duration of the phase change platform. Based on the liquid phase fraction, the mass of each PCM module, and the latent heat of phase change, it calculates the global residual heat buffer capacity and the zoned residual heat buffer capacity of the transformer. The current ratio heat load correction calculation unit is connected to the data acquisition module and the PCM state determination and residual heat buffer capacity calculation unit, respectively. It is used to obtain the equivalent winding current ratio, three-phase current imbalance and current ratio rise rate based on the effective value of the three-phase current of the transformer. Based on the equivalent winding current ratio, three-phase current imbalance and current ratio rise rate, it establishes the current heat load correction coefficient. Based on the current heat load correction coefficient, it corrects the global residual heat buffer capacity and the zoned residual heat buffer capacity of the transformer, respectively, to obtain the global effective residual heat buffer capacity and the zoned effective residual heat buffer capacity of the transformer. The model predictive controller is connected to both the current ratio and heat load correction calculation unit and the data acquisition module. It is used to collect data on the transformer's top oil temperature, bottom oil temperature, winding hot spot temperature, ambient temperature, global effective residual heat buffer capacity, zoned effective residual heat buffer capacity, equivalent winding current ratio, three-phase current imbalance, current ratio rise rate, and feedback status. As a joint state, the joint state is input into the thermal state prediction model in the prediction time domain to obtain the fan frequency and oil pump frequency in each control cycle. The execution unit, connected to the model predictive controller, is used to control the variable frequency fan according to the fan frequency and the variable frequency oil pump according to the oil pump frequency. The feedback acquisition unit, connected to the execution unit and the model prediction controller, is used to generate feedback status based on the actual operating status of the execution unit. , will provide feedback status Send to the model prediction controller.

2. The transformer predictive cooling system based on PCM thermal buffering and current correction according to claim 1, characterized in that, The data acquisition module includes: a temperature acquisition unit, a winding current acquisition unit, and a signal conditioning unit; The temperature acquisition unit is connected to the PCM module array and is used to acquire the temperature of the PCM module array, the top oil temperature of the transformer, the bottom oil temperature of the transformer, the winding hot spot temperature of the transformer, and the ambient temperature. The winding current acquisition unit is used to acquire the winding current of the transformer's A phase, B phase, and C phase. The signal conditioning unit, connected to the winding current acquisition unit, is used to calculate and output the effective value of the three-phase current of the transformer based on the winding current. The PCM module parameter acquisition unit is connected to the PCM module array and is used to acquire the solidus temperature, liquidus temperature, temperature change rate, phase change plateau duration, mass of each PCM module, and latent heat of phase change of the PCM module array.

3. A transformer predictive cooling method based on PCM thermal buffering and current correction, implemented using the transformer predictive cooling system based on PCM thermal buffering and current correction as described in claim 1 or 2, characterized in that, include: Step S1: Use a PCM module array to obtain the thermal state of the transformer tank at different heights and in different phase zones; Step S2: Collect the temperature of the PCM module array, the top oil temperature of the transformer, the bottom oil temperature of the transformer, the winding hot spot temperature of the transformer, and the ambient temperature. Step S3: Collect the winding currents of phase A, phase B, and phase C of the transformer, and calculate and output the effective values ​​of the three-phase currents based on the winding currents. Step S4: Based on the temperature of the PCM module array, the solidus temperature of the PCM module array, the liquidus temperature of the PCM module array, the temperature change rate of the PCM module array, and the duration of the phase change platform, determine the phase state of the PCM module array and calculate the liquid phase fraction. Based on the liquid phase fraction, the mass of each PCM module, and the latent heat of phase change, calculate the global residual heat buffer capacity of the transformer and the zoned residual heat buffer capacity of the transformer. Step S5: Based on the effective value of the three-phase current of the transformer, obtain the equivalent winding current ratio, the three-phase current imbalance, and the current ratio rise rate. Establish the current heat load correction coefficient based on the equivalent winding current ratio, the three-phase current imbalance, and the current ratio rise rate. Based on the current heat load correction coefficient, correct the global residual heat buffer capacity and the regional residual heat buffer capacity of the transformer respectively, and obtain the global effective residual heat buffer capacity and the regional effective residual heat buffer capacity of the transformer. Step S6: Collect the transformer's top oil temperature, bottom oil temperature, winding hot spot temperature, ambient temperature, overall effective residual heat buffer capacity, zoned effective residual heat buffer capacity, equivalent winding current ratio, three-phase current imbalance, current ratio rise rate, and feedback status. As a joint state, the joint state is input into the thermal state prediction model in the prediction time domain to obtain the fan frequency and oil pump frequency in each control cycle. Step S7: Control the variable frequency fan according to the fan frequency, and control the variable frequency oil pump according to the oil pump frequency; Step S8: Generate feedback status based on the actual operating conditions of the variable frequency fan and variable frequency oil pump. Return to step S6.

4. The transformer predictive cooling method based on PCM thermal buffering and current correction according to claim 3, characterized in that, The process of determining the phase state of the PCM module array and calculating the liquid phase fraction based on the PCM module array's temperature, solidus temperature, liquidus temperature, temperature change rate, and phase transition plateau duration includes: when When the i-th PCM module is in solid state, it is determined that the i-th PCM module is in solid state. when At that time, it is determined that the i-th PCM module is in a liquid state; when When the i-th PCM module is in a phase transition state, it is determined that the i-th PCM module is in a phase transition state. Let be the temperature at the k-th sampling time of the i-th PCM module. Let be the solidus temperature corresponding to the i-th PCM module. Let be the liquidus temperature of the i-th PCM module at the k-th sampling time. Let be the temperature change rate of the i-th PCM module at the k-th sampling time. The threshold for the rate of temperature change; when hour, ; when hour, ; when At that time, the initial estimated value of the liquid phase fraction was: ； in, This is the initial estimate of the liquid phase fraction of the i-th PCM module within the phase transition temperature region; The initial estimate of the liquid phase fraction is corrected based on the temperature change rate and the duration of the phase change plateau, resulting in the final liquid phase fraction of the PCM module array. The corrected expression is as follows: ； in, The duration for which the i-th PCM module is continuously within the phase transition temperature zone. This is the first correction factor. This is the second correction factor. This means limiting the calculation result to between 0 and 1. Let be the liquid phase fraction at the k-th sampling time of the i-th PCM module. This refers to the total continuous duration during which the PCM module remains within the phase change temperature range.

5. The transformer predictive cooling method based on PCM thermal buffering and current correction according to claim 3, characterized in that, The global residual heat buffer capacity of the transformer is calculated as follows: ； ； in, This represents the global residual heat buffer capacity of the transformer at the k-th sampling time. For the quality of the i-th PCM module, Let be the latent heat of phase change of the i-th PCM module. Let be the liquid phase fraction at the k-th sampling time of the i-th PCM module. Let n be the remaining hot buffer capacity of the i-th PCM module at the k-th sampling time, and n be the total number of PCM modules. The residual heat buffer capacity of the transformer is calculated as follows: ； ； ； in, This represents the remaining thermal buffer capacity of phase A of the transformer at the k-th sampling time. This represents the remaining thermal buffer capacity of phase B in the transformer at the k-th sampling time. This represents the remaining thermal buffer capacity of the C-phase partition at the k-th sampling time of the transformer. This refers to the set of PCM modules corresponding to the partition of transformer A. The set of PCM modules corresponding to the partition of transformer B. The set of PCM modules corresponding to the partition of transformer C.

6. The transformer predictive cooling method based on PCM thermal buffering and current correction according to claim 3, characterized in that, The global effective residual heat buffer capacity of the transformer is expressed as follows: ； ； in, To determine the global effective residual heat buffer capacity of the transformer at k sampling times, This represents the global residual heat buffer capacity of the transformer at the k-th sampling time. This is the current heat load correction factor at the k-th sampling time. The first corrected weighting coefficient, This is the second corrected weighting coefficient. This is the third corrected weighting coefficient. Let be the equivalent winding current ratio at the k-th sampling time. Let be the three-phase current imbalance at the k-th sampling time. The current ratio rise rate at the k-th sampling time; The effective residual heat buffer capacity of the transformer partition is expressed as follows: ； in, This represents the effective remaining thermal buffer capacity of the transformer partition at the k-th sampling time. This represents the remaining heat buffer capacity of the transformer at the k-th sampling time. is the correction coefficient for the thermal load of the corresponding partition current at the k-th sampling time.

7. The transformer predictive cooling method based on PCM thermal buffering and current correction according to claim 3, characterized in that, The thermal state prediction model in the prediction time domain includes: a discrete prediction model and an objective function. Fan frequency commands and oil pump frequency commands are used as control variables. The transformer's top oil temperature, bottom oil temperature, winding hot spot temperature, global effective residual heat buffer capacity, and zoned effective residual heat buffer capacity are used as state variables. The equivalent winding current ratio, three-phase current imbalance, current ratio rise rate, and ambient temperature are used to construct the discrete prediction model. The objective function is constructed using the increments of the control variables from the thermal state prediction model. The output of the discrete prediction model is used as the input to the objective function. The weights of the objective function are dynamically adjusted using the equivalent winding current ratio, three-phase current imbalance, and current ratio rise rate. The objective function is then solved to obtain the fan frequency and oil pump frequency in each control cycle.

8. The transformer predictive cooling method based on PCM thermal buffering and current correction according to claim 7, characterized in that, The objective function is expressed as follows: ； in, Let be the objective function. To predict the length of the time domain, The output of the discrete prediction model is used to predict the hotspot temperature at step k+j. For the target hotspot temperature, Energy consumption for cooling fans and oil pumps, For the first Step control quantity increment, This is a reference value for effective residual heat buffering capacity. As the first dynamic weight, As the second dynamic weight, As the third dynamic weight, The fourth dynamic weight is given, where k is the k-th sampling time and j is the prediction step number. This represents the effective residual heat buffer capacity of the transformer in step k+j.

9. The transformer predictive cooling method based on PCM thermal buffering and current correction according to claim 7, characterized in that, The weights of the objective function, which are dynamically adjusted using the equivalent winding current ratio, three-phase current imbalance, and current ratio rise rate, are calculated as follows: ； ； ； ； ； in, As the first dynamic weight, As the second dynamic weight, As the third dynamic weight, As the fourth dynamic weight, As the first basic weight, As the second basic weight, As the third basic weight, As the fourth basic weight, The first weighted adjustment coefficient, This is the second weighting adjustment coefficient. This is the third weighting adjustment coefficient. Fourth weighting adjustment coefficient, This is the fifth weighting adjustment coefficient. This is the sixth weighting adjustment coefficient. This is the seventh weighting adjustment coefficient. This is the eighth weighting adjustment coefficient. This is the ninth weighting adjustment coefficient. This is the tenth weighting adjustment coefficient. This represents the ratio of effective remaining heat buffer capacity at sampling time k. This represents the effective remaining heat buffer capacity of the transformer at the k-th sampling time. For rated residual heat buffer capacity, Let be the equivalent winding current ratio at the k-th sampling time. Let be the three-phase current imbalance at the k-th sampling time. Let be the rate of increase of the current ratio at the k-th sampling time.

10. The transformer predictive cooling method based on PCM thermal buffering and current correction according to claim 3, characterized in that, The feedback status is generated based on the actual operating conditions of the variable frequency fan and variable frequency oil pump. ,include: When the frequency, speed, or motor current of the variable frequency fan exceeds the first set threshold, while the frequency, speed, or motor current of the variable frequency oil pump does not exceed the second set threshold, a feedback state is generated. When the measured frequency, speed, or motor current of the variable frequency oil pump exceeds the second set threshold, while the frequency, speed, or motor current of the variable frequency fan does not exceed the first set threshold, a feedback state is generated. A feedback state is generated when the frequency, speed, or motor current of the variable frequency fan exceeds the first set threshold, and the frequency, speed, or motor current of the variable frequency oil pump both exceed the second set threshold. A feedback state is generated when the frequency, speed, or motor current of the variable frequency fan does not exceed the first set threshold, and the frequency, speed, or motor current of the variable frequency oil pump does not exceed the second set threshold. ; When feedback status When, proceed to step S2 to enter the next control cycle; when , or When the time comes, the feedback state E(k) is sent to the model prediction controller, and the process proceeds to step S6.

Images