Intelligent power distribution load balancing method and system for composite material production workshop

Abstract

The invention belongs to the technical field of power distribution control, and particularly relates to an intelligent power distribution load balancing method and system for a composite material production workshop, and the method comprises the steps: synchronously collecting the active power data, process state data and system constraint parameters of composite material production equipment in a power monitoring period, calculating a heat demand urgency degree representing a rigid demand degree of the equipment for electric energy input; constructing a system pressure index representing the current distribution network regulation margin; obtaining a reduction suitability degree; constructing an equipment adjustment queue with clear priority; obtaining a target reduction amount; executing a step-by-step load regulation and control operation; and carrying out real-time thermodynamic response monitoring on all adjusted equipment. The problem that in the prior art, accurate load adjustment cannot be conducted according to the equipment thermal inertia and the process urgency degree is effectively solved.

Description

Technical Field

[0001] This invention relates to the field of power distribution control technology. More specifically, this invention relates to an intelligent power distribution load balancing method and system for a composite material production workshop. Background Technology

[0002] In the aerospace and high-performance industrial manufacturing fields, the molding quality of composite material components, such as carbon fiber wing skin and honeycomb sandwich structures, directly depends on the precise control of the curing process. High-power electric heating equipment such as autoclaves and curing ovens, as core loads on the production line, exhibit significant periodicity and high energy consumption characteristics in their operation. The power requirements of these devices vary greatly during the heating, holding, and cooling stages, and they have high requirements for power supply continuity. Especially during the critical chemical cross-linking reaction stage, any unplanned power outage or drastic power fluctuations will lead to disordered internal temperature fields in the materials, resulting in uneven stress distribution, delamination, or excessive porosity, and even scrapping the entire batch of expensive raw materials. Therefore, ensuring the process stability of multiple concurrently operating devices under limited power capacity is of paramount importance in workshop power distribution management.

[0003] Existing technologies for handling power load balancing in production workshops typically employ total capacity control or priority-based load shedding strategies based on static rules. This means that when the total power load approaches the transformer's rated capacity or a safety threshold, the control system forcibly cuts off power to some equipment based on a pre-set list of fixed equipment or a simple equipment importance ranking. This control logic relies heavily on instantaneous electrical parameters, treating all loads as indiscriminate electrical components, focusing on protecting the transformer from overload burnout, without considering the underlying physical and thermodynamic characteristics of the loads. However, this approach suffers from staticity and a singular physical dimension; it cannot perceive the current thermal inertia of the equipment or the urgency of the process. For example, a cold furnace that has just started heating will experience a rapid temperature drop after power is cut off due to its small heat storage capacity, leading to process interruption; while a large autoclave already in the heat preservation stage, due to its enormous thermal inertia, may have a relatively small impact on the internal temperature field with a short power outage. Existing power distribution balancing methods lack comprehensive analysis of the process thermal state and real-time electrical load, which often leads to misjudgments when adjusting during peak load periods. This not only results in low energy utilization efficiency but also brings significant product quality risks. Summary of the Invention

[0004] To address the technical problem that the existing technology cannot accurately adjust the load based on the thermal inertia of the equipment and the urgency of the process, the present invention provides solutions in the following aspects.

[0005] In a first aspect, the present invention provides an intelligent power distribution load balancing method for a composite material production workshop, comprising: During the power monitoring cycle, active power data, process status data, and system constraint parameters of composite material production equipment are collected synchronously. The mean and standard deviation of active power data of all equipment are statistically analyzed in real time to obtain the power mean and power dispersion. Based on the differences between active power data, current temperature difference data of equipment, and the power mean and temperature difference mean, the heat demand urgency, which characterizes the rigidity of the equipment's demand for power input, is calculated. Based on the dispersion characteristics of the heat demand urgency and active power data distribution of all equipment, a system pressure index characterizing the current adjustment margin of the distribution network is constructed. Using the system pressure index as a suppression factor, the ratio of active power data of equipment to heat demand urgency is corrected to obtain the reduction suitability. The reduction suitability values ​​of all equipment are obtained, and a priority-defined equipment adjustment queue is constructed based on the value. The surplus / deficit status of the distribution network is defined based on the total active power of all equipment to obtain the target reduction amount. Based on the target reduction amount and the equipment adjustment queue, step-by-step load control operations are performed. Real-time thermodynamic response monitoring is conducted on all regulated equipment, and power restoration and removal are performed on regulated equipment with process runaway risk.

[0006] This invention breaks down information barriers by synchronously collecting multi-dimensional operational data. It then calculates key indicators such as heat demand urgency, system pressure index, and reduction suitability to accurately distinguish between equipment needs and system status, clearly defining the adjustment targets, priorities, and reduction amounts. Employing a closed-loop mechanism of step-by-step control and real-time status monitoring avoids the problems of blindly shutting down equipment or improper adjustment, reducing the interference of energy supply fluctuations on production and lowering quality risks. Simultaneously, it avoids over- or under-adjustment, balancing system safety and production efficiency, making power distribution regulation more targeted and scientific, and providing strong support for the stable operation of the production process.

[0007] Preferably, active power data, process status data, and system constraint parameters of the composite material production equipment are collected synchronously during the power monitoring cycle, including: By installing intelligent power meters in each equipment circuit, the active power data of all operating equipment is collected in real time, and the mean and standard deviation of the active power data of all equipment are calculated in real time and recorded as power mean and power dispersion. At the same time, the PLC control system synchronously reads the current temperature difference, process priority mark and current temperature drop rate of each equipment to obtain process status data, and calculates the mean of the current temperature difference of all equipment and records it as temperature difference mean. The rated capacity of the transformer, safety margin threshold and maximum allowable temperature drop rate of the process are retrieved from the power distribution management database to obtain system constraint parameters.

[0008] Preferably, the urgency of heat demand satisfies the following expression: ; In the formula, This indicates the urgency of heat demand; Mark process priority; This represents the current temperature difference of the equipment. This is the average temperature difference. This is active power data; This is the average power value; Power dispersion; It is the natural logarithm function; It is the hyperbolic tangent function; and It is the first and second smallest positive number.

[0009] This invention calculates the energy input requirements of equipment by comprehensively considering multiple influencing factors, effectively distinguishing the differences in energy requirements of different equipment at different stages. It avoids the misconception of judging the importance of equipment based on a single indicator, allowing energy resources to be more rationally allocated to equipment with urgent needs. This reduces the adverse effects on production caused by improper energy supply, helps ensure the stability of the production process, and reduces the risk of quality problems caused by energy interruption or insufficiency.

[0010] Preferably, the system pressure index satisfies the following expression: ; In the formula, Indicates the system stress index; This indicates the urgency of heat demand; This is active power data; Power dispersion; This is the average power value; It is an exponential function with the natural constant as its base; and It is the third smallest positive number and the fourth smallest positive number.

[0011] This invention comprehensively reflects the overall operating status of the power distribution system, focusing not only on the current total load but also on the balance of load distribution and the rigidity of equipment demand. It helps the system determine the scope and direction of adjustments from a macroscopic perspective, avoiding situations where only individual equipment is focused on while ignoring the overall system risk.

[0012] Preferably, the shaving suitability satisfies the following expression: ; In the formula, This indicates a reduction in suitability. This is active power data; This indicates the urgency of heat demand; Indicates the system stress index; It is the fifth smallest positive number, ensuring that the denominator is not 0; It is the natural logarithm function.

[0013] This invention selects the most suitable equipment for regulation by combining the energy consumption and demand of the devices with reference to the overall system pressure status. It balances the effect of load reduction with the impact on production, ensuring that the reduction action effectively lowers the system load while minimizing interference with the production process. This avoids accidentally cutting critical equipment or reducing ineffective equipment, making the regulation action more cost-effective and improving the scientific rigor and practicality of the power distribution balancing process.

[0014] Preferably, the equipment adjustment queue includes: Each device's severance score is bound to its corresponding device hardware ID to form a key-value pair dataset. The key-value pair dataset is then sorted in descending order of severance score using a quicksort algorithm. After sorting, the device at the head of the queue is adjusted first, while the device at the tail of the queue is adjusted last, thus generating a device adjustment queue.

[0015] This invention clearly defines the adjustment priorities of equipment, forming an orderly execution sequence and avoiding chaos and blind selection during the adjustment process. When the system needs to reduce load, it can quickly identify the optimal adjustment target, reducing the time cost of decision-making and operation, and improving adjustment efficiency. Especially in emergency situations where the load is close to the safety threshold, it can avoid decision delays caused by repeatedly screening equipment, while reducing unnecessary switching operations, lowering equipment wear and tear, and reducing the communication burden on the control system.

[0016] Preferably, obtaining the target reduction amount includes: The active power of all devices is acquired in real time and summed, and the result is recorded as the real-time load. The rated capacity is subtracted from the real-time load, and the result is recorded as the real-time load margin. If the real-time load margin is greater than or equal to the safety margin threshold, it is determined that the device does not need to be adjusted. If the real-time load margin is less than the safety margin threshold, it is determined that the device needs to be adjusted, and the difference between the safety margin threshold and the real-time load margin is calculated and recorded as the target reduction amount.

[0017] Preferably, performing a step-by-step load control operation includes: Starting from the head of the adjustment queue, the system sequentially reads the equipment information of the devices to be adjusted; sends control commands to the currently read devices to reduce power or pause heating, and confirms the command execution feedback; initializes a cumulative power reduction counter to zero, and adds the active power of the device to the cumulative power reduction counter; after each adjustment operation, it immediately checks whether the value of the cumulative power reduction counter is greater than or equal to the target reduction amount; once the condition is met, it immediately stops the adjustment operation on the subsequent devices in the queue and ends the current round of control process.

[0018] Preferably, real-time thermodynamic response monitoring is performed on all regulated devices, including: The system compares the current temperature drop rate of each regulated device with the maximum allowable temperature drop rate preset in the system constraint parameters. Once the current temperature drop rate of the regulated device exceeds the maximum allowable temperature drop rate, the system immediately determines that the device is in a state of process runaway risk and controls the system to forcibly send a command to restore full power supply to the device. At the same time, the device is removed from the regulation queue to prevent it from being accidentally disconnected again in the current cycle. Subsequently, the system automatically triggers a new round of intelligent power distribution balancing to find a new load balance point.

[0019] Secondly, the present invention provides an intelligent power distribution load balancing system for a composite material production workshop, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned intelligent power distribution load balancing method for a composite material production workshop is implemented.

[0020] By adopting the above technical solution, a computer program for the intelligent power distribution load balancing method in a composite material production workshop is generated and stored in a memory for loading and execution by a processor. Terminal equipment is then manufactured based on the memory and processor for convenient use.

[0021] The beneficial effects of this invention are as follows: Through scientific data analysis and calculation, this invention achieves rational allocation of energy resources and stable load control, reducing unnecessary production interruptions and equipment wear and tear, and ensuring the continuity and stability of the production process. Its adjustment logic and execution method can adapt to complex production scenarios, maximizing energy utilization value under limited energy capacity conditions while maintaining production quality. This method provides a feasible implementation path for energy allocation and load balancing in similar production scenarios, helping the production process to proceed safely and efficiently, promoting a steady improvement in production efficiency and quality, and possessing significant practical application value. Attached Figure Description

[0022] Figure 1 The flowchart of an intelligent power distribution load balancing method for a composite material production workshop according to the present invention is illustrated in the schematic diagram. Figure 2 This diagram schematically illustrates a comparison of the suitability of equipment reduction in this invention. Figure 3 The schematic diagram illustrates the intelligent load balancing closed-loop control flowchart of this invention. Detailed Implementation

[0023] This invention discloses an intelligent power distribution load balancing method for composite material production workshops, referring to... Figure 1 This includes steps S1-S4: S1: During the power monitoring cycle, the active power data, process status data and system constraint parameters of the composite material production equipment are collected synchronously. The mean and standard deviation of the active power data of all equipment are calculated in real time to obtain the power mean and power dispersion.

[0024] It's important to note that in the complex operating conditions of composite material production workshops, single-dimensional monitoring data often suffers from significant judgment errors. Electrical engineers focus on current and power fluctuations to prevent transformer overload tripping, while process engineers focus on temperature profiles and curing times to ensure material performance meets standards. This disconnect in focus leads to blind spots in power distribution control; simply monitoring power fails to distinguish whether it's used for critical heating reactions or insignificant auxiliary heating; focusing solely on temperature fails to perceive the real-time impact of the equipment on the power grid. This is especially true in scenarios where multiple autoclaves operate simultaneously, as different devices may be at entirely different process stages—one heating, another holding—and their tolerance to power outages varies drastically. Therefore, to achieve precise peak shaving and valley filling during peak load periods, a global view encompassing electrical properties, thermodynamic states, and safety boundaries must first be established. By synchronously collecting operational electrical sequences, process status data, and system constraint parameters, and calculating global statistics, the system can construct a global view of workshop energy consumption, providing a solid data foundation for subsequently distinguishing between rigid and flexible loads.

[0025] Specifically, during the power monitoring cycle, active power data, process status data, and system constraint parameters of the composite material production equipment are collected synchronously. The mean and standard deviation of the active power data of all equipment are calculated in real time to obtain the power mean and power dispersion, including: By installing intelligent power meters in each equipment circuit, the active power data of all operating equipment is collected in real time, and the mean and standard deviation of the active power data of all equipment are calculated in real time and recorded as power mean and power dispersion. At the same time, the PLC control system synchronously reads the current temperature difference, process priority mark and current temperature drop rate of each equipment to obtain process status data, and calculates the mean of the current temperature difference of all equipment and records it as temperature difference mean. The rated capacity of the transformer, safety margin threshold and maximum allowable temperature drop rate of the process are retrieved from the power distribution management database to obtain system constraint parameters.

[0026] Thus, active power data, process status data, system constraint parameters, as well as power mean, power dispersion, and temperature difference mean were obtained.

[0027] S2: Based on the active power data, the current temperature difference data of the equipment, and the differences between the average power and the average temperature difference, calculate the heat demand urgency, which characterizes the rigidity of the equipment's demand for electrical energy input; based on the discrete characteristics of the heat demand urgency and active power data distribution of all equipment, construct a system pressure index characterizing the current adjustment margin of the power distribution network; use the system pressure index as a suppression factor to correct the ratio of the active power data of the equipment to the heat demand urgency, thereby obtaining the appropriateness of reduction.

[0028] It's important to note that in heat treatment processes, the electrical energy demand of equipment is not linearly constant but dynamically changes with the temperature difference and current power status. Traditional control logic often assumes that high power or high temperature equates to importance, which is a significant misconception in composite material curing. A high-temperature device in the heat preservation phase, which only needs to compensate for heat loss, may have a much lower urgency in its actual electrical energy demand than a medium-temperature device undergoing rapid heating with a large temperature difference. Furthermore, the process priorities of different equipment, such as those producing aerospace components versus civilian components, determine their tolerance for power outages. Without a unified evaluation metric to measure these multi-dimensional demands, the system will fall into decision-making confusion when facing load limits. For example, when faced with two pieces of equipment, one with a large temperature difference but low power, and the other with a small temperature difference but high power, from a process quality assurance perspective, it is essential to construct a metric that integrates the temperature difference gap representing the intensity of demand, the real-time power representing the working state, and the process priority representing value weight. This invention constructs a heat demand urgency level, which uses a mathematical model to transform the physical heat demand into a control level weight, ensuring that limited power resources are prioritized for critical equipment that is both short of heat and is heating at full capacity and has high product value, thereby minimizing process quality risks.

[0029] Specifically, based on the active power data, the current temperature difference data of the equipment, and the differences between the average power and the average temperature difference, the urgency of heat demand, which characterizes the rigidity of the equipment's demand for electrical energy input, is calculated, including: The urgency of heat demand satisfies the following expression: ; In the formula, This indicates the urgency of heat demand; Mark process priority; This represents the current temperature difference of the equipment. This is the average temperature difference. This is active power data; This is the average power value; Power dispersion; It is the natural logarithm function; It is the hyperbolic tangent function; and It is the first and second smallest positive number.

[0030] In the formula, This ratio represents the intensity of the equipment's heating demand relative to the average level of the entire workshop. The larger the ratio, the farther the equipment is from the target process temperature, and the more urgent the heating demand. The temperature difference factor is smoothed to prevent large temperature differences from causing excessively high values. It is a standardized processing of equipment power, reflecting its relative energy consumption position in the current load group; Map the relative position of power to During the high-power heating phase, i.e. When the function value is positive, This amplifies the effect, indicating that the equipment is operating at full capacity to meet process requirements, at which point the risk of a power outage is high; The value takes into account the importance of the process, the temperature difference requirement, and the current working status. The larger the value, the more rigid the equipment's demand for electrical energy.

[0031] For example, if the curing oven priority , Average temperature difference in the workshop , , , ,but ; ; Calculation results , , All values ​​are rounded to three decimal places.

[0032] Thus, the urgency of thermal demand, which characterizes the rigidity of the equipment's demand for electrical energy input, was obtained.

[0033] It is important to note that the safety of a power distribution network depends not only on the instantaneous total power value but also on the structural characteristics of the load distribution. In actual production, there are two typical dangerous scenarios: one is that all equipment operates at medium power, and the total load slowly approaches the threshold; the other is that a few high-power devices suddenly run at full speed, leading to uneven load distribution and a surge in dispersion. The latter risk is often more insidious and destructive because it can easily trigger local voltage drops or harmonic interference, thereby affecting the power quality of the entire workshop. Simple total power monitoring cannot distinguish between these two states. In this case, focusing only on the urgency of individual heat demand may overlook the macro-level risks at the system level. Therefore, this invention introduces a system stress index to describe the overall stress level of the system. This index not only reflects the current total load level but also combines power dispersion and urgency distribution to dynamically assess the system's adjustment margin. That is, when the dispersion is large and the urgency is high, it means that the system is in an unstable high-pressure state, and any small load fluctuation may trigger a chain reaction. By calculating the system stress index, the control system can determine from a macro perspective whether the current adjustment strategy should be aggressive or conservative, ensuring that load distribution is carried out without overwhelming the power grid.

[0034] Preferably, based on the discrete characteristics of the heat demand urgency and active power data distribution of all equipment, a system pressure index characterizing the current adjustment margin of the power distribution network is constructed, including: The system pressure index satisfies the following expression: ; In the formula, Indicates the system stress index; This indicates the urgency of heat demand; This is active power data; Power dispersion; This is the average power value; It is an exponential function with the natural constant as its base; and It is the third smallest positive number and the fourth smallest positive number.

[0035] In the formula, The sum of squares highlights the weight of the impact of high-urgency equipment groups on the system, reflecting the total rigid demand for electricity from current production tasks; the denominator... The ratio of the two represents the current actual total load, indicating the density of process urgency under unit load; The coefficient of variation (CV) characterizes the unevenness of load distribution within a workshop. A larger CV indicates a higher risk of grid load fluctuations when some equipment is heating at full power while others are on standby. This imbalance is used to nonlinearly amplify the base pressure. The larger the value, the more likely the current distribution network is to be carrying high-priority process tasks, with uneven load distribution, the system under high voltage, and limited room for adjustment.

[0036] For example, if , , , Then the coefficient of variation term is ; ; Calculation results , Retain to three decimal places.

[0037] Thus, a system pressure index representing the current adjustment capacity of the power distribution network was obtained.

[0038] It's important to note that the core of intelligent power distribution balancing lies in selecting precise load reduction targets. Ideally, these targets should possess two characteristics: first, high power output, significantly reducing the overall load after reduction; and second, low urgency, minimizing the impact on product quality after reduction. However, in reality, a contradiction often exists: the most powerful equipment may be the most urgent. Focusing solely on power output could lead to the scrapping of critical equipment; focusing solely on urgency might result in cutting off a bunch of smaller devices without reducing the overall load. Furthermore, the overall system stress level should also influence the decision-making logic: when system stress is high, such as when the grid is on the verge of collapse, it's necessary to avoid the transient impacts, such as voltage backlash, caused by cutting off extremely high-power equipment. Therefore, this invention requires constructing a load reduction suitability model that nonlinearly combines the individual characteristics of the equipment with the macroscopic state of the system. In this way, the system can intelligently select those devices that, under the current system stress, offer the greatest benefit and the lowest cost upon removal, achieving a shift from passive power cut-off to proactive optimization.

[0039] Preferably, the ratio of the equipment's active power data to the urgency of heat demand is corrected using the system pressure index as a suppression factor, thereby obtaining the appropriateness of the reduction, including: The suitability for reduction satisfies the following expression: ; In the formula, This indicates a reduction in suitability. This is active power data; This indicates the urgency of heat demand; Indicates the system stress index; It is the fifth smallest positive number, ensuring that the denominator is not 0; It is the natural logarithm function.

[0040] In the formula, It is the basic suitability, which represents the power consumed by the equipment under a unit of urgency. It is the opposite of the power gain under a unit of urgency. The larger the value, the more power the equipment consumes, but its process urgency is not high. For example, high-power equipment in a non-critical heating stage is more suitable to be cut. As a suppressor, combining total system stress with individual power, when higher and When the value is large, the logarithmic term increases, the denominator increases, thus appropriately lowering the value. The value is set to prevent excessive transient impact caused by directly cutting off high-power equipment when the system is vulnerable; The physical meaning of the final value is: under the premise of ensuring system safety, prioritize the selection of equipment with high power but low process requirements for adjustment.

[0041] For example, device A, , , Equipment B, , For device A: Basic items , ,final For B: The inhibition terms are the same, and ultimately... .visible The system will prioritize selecting equipment B, which has lower adjustment process requirements. , , All values ​​are rounded to three decimal places.

[0042] It should be noted that, Figure 2 This chart compares the suitability of equipment reduction. It shows the corresponding results of heat demand urgency and reduction suitability for two pieces of equipment with the same power, when F=0.330. Equipment A has a heat demand urgency of 20, corresponding to a reduction suitability of approximately 1.188; Equipment B has a heat demand urgency of 5, corresponding to a reduction suitability of approximately 4.419. When the two pieces of equipment have the same power, the equipment with lower heat demand urgency has a higher reduction suitability. This demonstrates that this calculation method can combine the equipment's power consumption with process requirements, allowing the more suitable equipment to obtain a higher suitability value, thereby distinguishing the adjustment priority of different equipment.

[0043] This allows us to identify the appropriate level of energy consumption and low demand for equipment reduction.

[0044] S3: Obtain the load reduction suitability values ​​of all devices and construct a priority-based device adjustment queue based on the value values; define the surplus / deficit status of the distribution network based on the total active power of all devices and obtain the target load reduction amount; and perform step-by-step load control operations based on the target load reduction amount and the device adjustment queue.

[0045] It's important to note that in dynamic energy scheduling at the workshop level, load shedding suitability merely assigns a static, adjustable potential label to each piece of equipment. However, to truly execute smooth load shedding, these discrete values ​​must be transformed into an ordered sequence of actions. If the system randomly selects equipment with high suitability for disconnection, it might mistakenly disconnect multiple small-to-medium power devices while missing a high-power device. This not only increases the frequency of switching operations but also increases the communication burden on the control system. More importantly, in emergency situations where the grid load is about to exceed the safety threshold, the control logic doesn't have time to repeatedly traverse the equipment list to find the next best sacrifice. Therefore, generating a pre-sorted adjustment queue is like triage in an emergency room. It ensures that when the system issues a load shedding command, it can instantly identify equipment that, under the current system pressure, will release capacity to the maximum extent while having the least impact on the process after disconnection. This sorting step is the crucial bridge connecting theoretical calculations and physical execution; it guarantees the determinism and efficiency of the adjustment process and avoids decision-making delays in emergency situations.

[0046] Specifically, obtain the reduction suitability values ​​for all devices, and construct a priority-based device adjustment queue based on the value magnitude, including: Each device's severance score is bound to its corresponding device hardware ID to form a key-value pair dataset. The key-value pair dataset is then sorted in descending order of severance score using a quicksort algorithm. After sorting, the device at the head of the queue is adjusted first, while the device at the tail of the queue is adjusted last, thus generating a device adjustment queue.

[0047] At this point, a clearly prioritized equipment adjustment queue has been obtained.

[0048] It should be noted that load balancing does not mean constant intervention. Frequent starts and stops not only wear down equipment contactors but also interfere with normal temperature control logic. Therefore, this invention establishes a reasonable triggering mechanism, namely a safety boundary. This boundary is determined by the transformer's physical capacity and a preset safety margin. During production, the load fluctuates dynamically. Sometimes, although it may momentarily exceed the rated value, the transformer has short-term overload capacity and does not necessarily need to be immediately disconnected. However, to ensure absolute safety, we need to calculate the real-time load margin. This is similar to calculating the braking distance when driving a car; sufficient buffer space must be reserved. Only when the real-time margin is compressed below the safety warning line, i.e., the safety margin threshold, will the system be activated to enter the regulation mode. By accurately calculating the target reduction amount, i.e., the minimum power reduction required to bring the system back to the safe zone, excessive unnecessary equipment can be disconnected for safety, thus finding the optimal balance between ensuring power supply security and maintaining production efficiency.

[0049] Preferably, the surplus or deficit status of the distribution network is determined based on the total active power of all devices to obtain the target reduction amount, including: The active power of all devices is acquired in real time and summed, and the result is recorded as the real-time load. The rated capacity is subtracted from the real-time load, and the result is recorded as the real-time load margin. If the real-time load margin is greater than or equal to the safety margin threshold, it is determined that the device does not need to be adjusted. If the real-time load margin is less than the safety margin threshold, it is determined that the device needs to be adjusted, and the difference between the safety margin threshold and the real-time load margin is calculated and recorded as the target reduction amount.

[0050] At this point, the target reduction amount for the equipment that needs adjustment has been obtained.

[0051] It's important to note that load shedding is not a simple one-click power cut, but a precise, step-by-step approximation process. After determining the target load reduction, the system needs to draw load from the regulation queue to fill the gap. If the system simultaneously disconnects multiple devices in the queue without control, it can easily cause overshoot, leading to an excessive instantaneous drop in grid load, resulting in voltage spikes or reactive power oscillations, which can be fatal to precision electronic equipment. Conversely, if the disconnection speed is too slow, the load reduction may not be completed before the transformer overheats. Therefore, a sequential execution and real-time accumulation strategy is essential. This strategy involves the system starting from the head of the queue, evaluating and disconnecting devices one by one, calculating the cumulative load reduction after each device is disconnected. This mechanism ensures that the system disconnects only the necessary number of devices, maximizing the preservation of the normal operation of other equipment in the workshop. It represents the final execution action at the physical level, implementing optimization strategies and balancing grid safety and production efficiency.

[0052] Preferably, based on the target load reduction amount and the equipment adjustment queue, a step-by-step load control operation is performed, including: Starting from the head of the adjustment queue, the system sequentially reads the equipment information of the devices to be adjusted; sends control commands to the currently read devices to reduce power or pause heating, and confirms the command execution feedback; initializes a cumulative power reduction counter to zero, and adds the active power of the device to the cumulative power reduction counter; after each adjustment operation, it immediately checks whether the value of the cumulative power reduction counter is greater than or equal to the target reduction amount; once the condition is met, it immediately stops the adjustment operation on the subsequent devices in the queue and ends the current round of control process.

[0053] At this point, a step-by-step load control operation has been performed.

[0054] S4: Perform real-time thermodynamic response monitoring on all regulated equipment, and restore power to and remove regulated equipment that is at risk of process runaway.

[0055] It should be noted that the insulation performance of equipment may decline due to aging, and ambient temperature may change abruptly. This can cause the temperature of some equipment that has been disconnected from power to drop much faster than expected. If a piece of equipment is deemed suitable for disconnection, but its temperature drops precipitously after disconnection, this will directly disrupt the crystallization kinetics of the material, leading to product scrap. This discrepancy between model predictions and physical reality must be corrected. Therefore, this invention constructs a closed-loop feedback mechanism, acting as a safety net for the system. It assigns the highest priority to process quality relative to load balance. That is, when the actual temperature drop rate is detected to reach the red line, the system must acknowledge that the adjustment strategy has failed for that equipment and immediately restore power. Although this will increase the grid load again, it is a necessary cost compared to the scrapping of expensive composite materials. Subsequently, the system will trigger a new round of calculations to find other alternative reduction targets, thereby maintaining balance through dynamic correction.

[0056] Preferably, real-time thermodynamic response monitoring is performed on all regulated equipment, and power restoration and removal are carried out on regulated equipment at risk of process runaway, including: The system compares the current temperature drop rate of each regulated device with the maximum allowable temperature drop rate preset in the system constraint parameters. Once the current temperature drop rate of the regulated device exceeds the maximum allowable temperature drop rate, the system immediately determines that the device is in a state of process runaway risk and controls the system to forcibly send a command to restore full power supply to the device. At the same time, the device is removed from the regulation queue to prevent it from being accidentally disconnected again in the current cycle. Subsequently, the system automatically triggers a new round of intelligent power distribution balancing to find a new load balance point.

[0057] It should be noted that, Figure 3This is a flowchart of an intelligent load balancing closed-loop control system. The flowchart illustrates the changes in total power distribution load during real-time adjustments and the process of ensuring process safety. The transformer's rated capacity is 1000kW. Starting from the initial load at time point T0, the system performs operations such as equipment disconnection, monitoring feedback, power restoration, and supplementary equipment disconnection until load balance is achieved at time point T7. Specifically, after equipment C is disconnected at time point T2, a sudden drop in its temperature is detected, and its power supply is immediately restored. Subsequent adjustments continue to compensate for the target load reduction. This process demonstrates that the closed-loop control process can simultaneously promote load balancing and respond promptly to process safety anomalies, achieving coordination between load adjustment and process assurance.

[0058] This completes the method for intelligent power distribution load balancing in composite material production workshops.

[0059] This invention also discloses an intelligent power distribution load balancing system for a composite material production workshop, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement an intelligent power distribution load balancing method for a composite material production workshop according to the present invention.

[0060] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0061] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A method for intelligent power distribution load balancing in a composite material production workshop, characterized in that, include: During the power monitoring cycle, active power data, process status data and system constraint parameters of composite material production equipment are collected synchronously. The mean and standard deviation of active power data of all equipment are statistically analyzed in real time to obtain the power mean and power dispersion. Based on the active power data, the current temperature difference data of the equipment, and the differences between the average power and the average temperature difference, the heat demand urgency, which characterizes the rigidity of the equipment's demand for electrical energy input, is calculated. Based on the discrete characteristics of the heat demand urgency and active power data distribution of all equipment, a system pressure index characterizing the current adjustment margin of the power distribution network is constructed. Using the system pressure index as a suppression factor, the ratio of the active power data of the equipment to the heat demand urgency is corrected to obtain the appropriateness of the reduction. Obtain the reduction suitability values ​​of all devices and construct a priority-based device adjustment queue based on the value values; determine the surplus or deficit status of the distribution network based on the total active power of all devices and obtain the target reduction amount; Based on the target reduction amount and the equipment adjustment queue, perform step-by-step load control operations; Real-time thermodynamic response monitoring is performed on all regulated equipment, and power restoration and removal are carried out on regulated equipment at risk of process runaway.

2. The intelligent power distribution load balancing method for a composite material production workshop according to claim 1, characterized in that, The synchronous acquisition of active power data, process status data, and system constraint parameters of the composite material production equipment during the power monitoring cycle includes: By installing intelligent power meters in each equipment circuit, the active power data of all operating equipment is collected in real time, and the mean and standard deviation of the active power data of all equipment are calculated in real time and recorded as power mean and power dispersion. At the same time, the PLC control system synchronously reads the current temperature difference, process priority mark and current temperature drop rate of each equipment to obtain process status data, and calculates the mean of the current temperature difference of all equipment and records it as temperature difference mean. The rated capacity of the transformer, safety margin threshold and maximum allowable temperature drop rate of the process are retrieved from the power distribution management database to obtain system constraint parameters.

3. The intelligent power distribution load balancing method for a composite material production workshop according to claim 1, characterized in that, The urgency of heat demand satisfies the following expression: ； In the formula, This indicates the urgency of heat demand; Mark process priority; This represents the current temperature difference of the equipment. This is the average temperature difference. This is active power data; This is the average power value; Power dispersion; It is the natural logarithm function; It is the hyperbolic tangent function; and It is the first and second smallest positive number.

4. The intelligent power distribution load balancing method for a composite material production workshop according to claim 1, characterized in that, The system pressure index satisfies the following expression: ； In the formula, Indicates the system stress index; This indicates the urgency of heat demand; This is active power data; Power dispersion; This is the average power value; It is an exponential function with the natural constant as its base; and It is the third smallest positive number and the fourth smallest positive number.

5. The intelligent power distribution load balancing method for a composite material production workshop according to claim 1, characterized in that, The suitability of the reduction satisfies the following expression: ； In the formula, This indicates a reduction in suitability. This is active power data; This indicates the urgency of heat demand; Indicates the system stress index; It is the fifth smallest positive number, ensuring that the denominator is not 0; It is the natural logarithm function.

6. The intelligent power distribution load balancing method for a composite material production workshop according to claim 1, characterized in that, The device adjustment queue includes: The reduction suitability of each device is bound to its corresponding device hardware ID to form a key-value pair dataset. The key-value pair dataset is sorted in descending order of reduction suitability using a quicksort algorithm. After sorting, the device at the head of the queue is adjusted first, while the device at the tail of the queue is adjusted last, thus generating a device adjustment queue.

7. The intelligent power distribution load balancing method for a composite material production workshop according to claim 1, characterized in that, The process of obtaining the target reduction amount includes: The active power of all devices is acquired in real time and summed, and the result is recorded as the real-time load. The rated capacity is subtracted from the real-time load, and the result is recorded as the real-time load margin. If the real-time load margin is greater than or equal to the safety margin threshold, it is determined that the device does not need to be adjusted. If the real-time load margin is less than the safety margin threshold, it is determined that the device needs to be adjusted, and the difference between the safety margin threshold and the real-time load margin is calculated and recorded as the target reduction amount.

8. The intelligent power distribution load balancing method for a composite material production workshop according to claim 1, characterized in that, The step-by-step load control operation includes: Starting from the head of the adjustment queue, the system sequentially reads the equipment information of the devices to be adjusted; sends control commands to the currently read devices to reduce power or pause heating, and confirms the command execution feedback; initializes a cumulative power reduction counter to zero, and adds the active power of the device to the cumulative power reduction counter; after each adjustment operation, it immediately checks whether the value of the cumulative power reduction counter is greater than or equal to the target reduction amount; once the condition is met, it immediately stops the adjustment operation on the subsequent devices in the queue and ends the current round of control process.

9. The intelligent power distribution load balancing method for a composite material production workshop according to claim 1, characterized in that, The real-time thermodynamic response monitoring of all regulated devices includes: The system compares the current temperature drop rate of each regulated device with the maximum allowable temperature drop rate preset in the system constraint parameters. Once the current temperature drop rate of the regulated device exceeds the maximum allowable temperature drop rate, the system immediately determines that the device is in a state of process runaway risk and controls the system to forcibly send a command to restore full power supply to the device. At the same time, the device is removed from the regulation queue to prevent it from being accidentally disconnected again in the current cycle. Subsequently, the system automatically triggers a new round of intelligent power distribution balancing to find a new load balance point.

10. An intelligent power distribution load balancing system for a composite material production workshop, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement a smart power distribution load balancing method for a composite material production workshop according to any one of claims 1-9.

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