An energy-saving control system based on DC bus system

Abstract

The present invention belongs to the technical field of equipment energy-saving control, and specifically discloses an energy-saving control system based on a DC bus system, which includes: a DC bus architecture module, an equipment status acquisition module, an equipment status analysis module, and an energy flow control module. The present invention collects DC bus voltage and multi-dimensional operating data of equipment motors in real time, and uses a Kalman filter to build a motor power state space model to accurately predict the power mutation probability in a preset time period in the future. At the same time, it combines the total power demand generated by production scheduling and dynamically adjusts the energy distribution strategy according to the braking type and power mutation probability. It effectively overcomes the defect of the fixed threshold feedforward compensation in the existing technology that cannot track the dynamic power difference, thereby avoiding bus energy congestion and realizing the on-demand distribution of braking energy. At the same time, it can ensure that the braking energy can be recycled and utilized in a timely and correct manner, reducing energy waste caused by misjudgment.

Description

Technical Field

[0001] The present invention belongs to the technical field of equipment energy-saving control, and in particular relates to an energy-saving control system based on a DC bus system. Background Art

[0002] For numerous fans, pumps, motors, and other equipment in workshops and production lines, traditional methods for handling feedback energy during shutdown typically rely on the combination of dynamic braking and holding brakes in frequency converters, or direct inertial stopping. These methods often dissipate this inertial energy as heat, resulting in significant energy waste and highlighting the necessity and importance of energy-saving control.

[0003] Existing technology, such as the Chinese invention patent application with application number 202111347872.1, discloses an energy recovery control system and method using a DC bus. This system monitors energy status through a voltage / current detection module and uses polar coordinate / synchronous coordinate transformation and PWM control to achieve energy transfer. This eliminates the energy waste caused by traditional resistor energy consumption and converts braking heat into reusable electrical energy, achieving energy recovery during braking and electrical energy release during startup, thereby achieving energy conservation and emission reduction.

[0004] Existing technologies, such as the energy management and control device for a multi-motor energy storage system operating on a common DC bus, disclosed in Chinese invention patent application number 202011270303.7, implement a dynamic power feedforward compensation strategy by collecting real-time data from the bus, capacitors, and motors. This device addresses the energy distribution challenges of multi-motor systems, achieves self-consumption of energy between loads, and achieves coordinated energy distribution and smooth operation of the motors in both braking and motoring states, improving system energy utilization.

[0005] Existing technologies address the energy waste caused by dissipating braking energy as heat during shutdown in traditional variable-frequency equipment by employing a common DC bus architecture and supercapacitor energy storage. However, existing control strategies are unable to adapt in real time to random variations in braking power across multiple machines. For example, the first technology lacks a transient overvoltage suppression algorithm, resulting in delayed active rectification response and energy waste. The second technology's fixed-threshold feedforward compensation struggles to track dynamic power differentials, leading to bus energy congestion. Summary of the Invention

[0006] In view of this, in order to solve the above problems, an energy-saving control system based on a DC bus system is proposed.

[0007] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides an energy-saving control system based on a DC bus system, which includes: a DC bus architecture module, which constructs a common DC bus network, connects variable frequency drive equipment, active rectifiers and energy storage equipment.

[0008] The device status acquisition module collects the voltage of the DC bus and the speed, current, temperature and power of the motors of each connected device in real time, sets the braking judgment threshold and determines whether the motor is in the braking state.

[0009] The equipment status analysis module counts the total braking energy and determines the braking type. At the same time, it uses the Kalman filter to establish a motor power state space model, and calculates the power mutation probability of the electric state equipment in the future preset time period through the said model.

[0010] The energy flow control module generates a total power demand based on the production schedule. If the difference between the total braking energy and the total power demand is greater than a preset threshold, the allocation strategy is dynamically adjusted based on the braking type and the probability of power mutation, and energy is allocated based on the strategy.

[0011] Compared with the existing technology, the beneficial effects of the present invention are as follows: (1) The present invention collects DC bus voltage and multi-dimensional operating data of equipment motors in real time, and uses the Kalman filter to build a motor power state space model to accurately predict the power mutation probability in the future preset time period. At the same time, it combines the total power demand generated by production scheduling and dynamically adjusts the energy distribution strategy based on the braking type and power mutation probability. It can effectively overcome the defect of the fixed threshold feedforward compensation in the existing technology that cannot track the dynamic power difference, thereby avoiding bus energy congestion, realizing on-demand distribution of braking energy, and improving energy utilization.

[0012] (2) The present invention sets the braking judgment threshold by combining the DC bus voltage and the speed, current, temperature and power of the motors of each device. Compared with the fuzzy braking identification method in the prior art, this threshold setting method can quickly and accurately identify different braking scenarios, providing a reliable prerequisite for subsequent energy recovery and distribution strategies, thereby ensuring that the braking energy can be recovered and utilized in a timely and correct manner, and reducing energy waste caused by misjudgment.

[0013] (3) The present invention dynamically adjusts the energy distribution strategy by combining the formulation type and the power mutation probability, and can adapt to the random mutation of the braking power of multiple machines in real time and reasonably distribute energy to the feedback bus or energy storage equipment. It can then flexibly coordinate the energy flow between various devices according to the characteristics of different working conditions and production needs, thereby ensuring the self-consumption of energy between loads and realizing efficient energy coordination between motor braking and electric states, significantly improving the adaptability in complex industrial environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 This is a schematic diagram of the system module connection of the present invention.

[0016] Figure 2 This is a schematic diagram of the DC bus architecture.

[0017] Figure 3 It is a schematic diagram of the overall implementation process of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] See also Figure 1 and Figure 3 As shown, the present invention provides an energy-saving control system based on a DC bus system, which includes: a DC bus architecture module, a device status acquisition module, a device status analysis module and an energy flow control module.

[0020] In the above, the device status acquisition module is connected to the DC bus architecture module and the device status analysis module respectively, and the device status analysis module is connected to the energy flow control module.

[0021] See also Figure 2 As shown, the DC bus architecture module constructs a common DC bus network to connect the variable frequency drive equipment, active rectifier and energy storage equipment.

[0022] It should be noted that the OPCUA protocol can be used to connect to the factory MES system to obtain production scheduling information such as equipment start and stop plans and load switching cycles.

[0023] It can be understood that the grid side rectifies the power supply output DC through the active rectifier unit and boosts it to a DC bus voltage suitable for the on-site drive motor. The DC bus connects the inverter of the on-site drive motor and some distribution cabinet switches to form a common DC bus network.

[0024] It's important to note that the DC bus, serving as a common energy channel connecting multiple motors, energy storage devices, and the power grid, offers bidirectional energy transmission capabilities. During motoring, the grid or energy storage device supplies power to the motor via the bus, which then converts the electrical energy into mechanical energy. During braking, the motor operates as a generator, converting the mechanical energy into DC power via a frequency converter (VFD), such as a four-quadrant VFD, which then feeds the DC power back to the bus.

[0025] The device status acquisition module collects the voltage of the DC bus and the speed, current, temperature and power of the motors of each corresponding device in real time, sets a braking determination threshold and determines whether the motor is in a braking state.

[0026] Specifically, the setting of the braking determination threshold includes: S1, importing the rotational inertia and torque coefficient of the equipment motor, and calculating the real-time load torque estimation value in combination with the real-time speed and current.

[0027] S2. Calculate the difference between the estimated values ​​at adjacent time points, take the maximum difference between the motors of each device as the target load torque difference, and then take the maximum value among all devices as the analysis load torque difference.

[0028] S3. Calculate the standard deviation of the voltage collected in real time on the DC bus to obtain the voltage fluctuation of the DC bus.

[0029] S4. Take the maximum temperature value of each device motor as the analysis motor temperature.

[0030] S5. traverse and analyze the load torque difference, voltage fluctuation and motor temperature. If none of the three exceed the corresponding set thresholds, the initial braking determination threshold is used as the final braking determination threshold.

[0031] S6. If any one of the items exceeds the threshold, a braking threshold compensation factor is set, and the initial threshold is corrected based on the compensation factor to serve as the final braking determination threshold.

[0032] Understandably, the correction refers to subtracting the product of the braking compensation factor and the preset single braking determination threshold adjustment value from the initial braking determination threshold. The braking determination threshold is composed of a speed change rate threshold and a current reversal threshold.

[0033] It should be added that the preset single braking determination threshold adjustment value is usually determined based on the characteristics of the factory workshop equipment, historical operating data and process requirements.

[0034] Taking a group of CNC machine tools operating in a workshop as an example, an analysis of historical fault records revealed that when the braking determination threshold was set to its initial value, the frequency of false triggering of the brakes due to sudden changes in cutting loads was as high as eight times per month, affecting production continuity. To address this issue, during normal machine operation, different machining conditions were simulated, such as heavy loads for roughing and light loads for finishing. Data such as spindle speed, servo motor current, and brake resistor temperature were collected to analyze the fluctuation range of the brake signal. By gradually fine-tuning the braking determination threshold and recording the equipment operating status, it was found that lowering the threshold by 15% reduced the false triggering frequency to two times per month, and no equipment damage due to brake response delays occurred. Therefore, 15% was determined as the adjustment value for the single braking determination threshold.

[0035] In addition, the equipment design parameters can also be combined, for example, the theoretical safe braking threshold range can be calculated based on the maximum braking torque of the servo motor and the inertia of the transmission system, and used as the preset single braking judgment threshold adjustment value.

[0036] It should also be noted that the maximum load torque difference can effectively capture sudden changes in load torque and promptly identify potential braking trends. The DC bus voltage fluctuation quantifies voltage stability by calculating the standard deviation, preventing voltage anomalies from interfering with braking decisions. The motor temperature is analyzed and the highest temperature is selected to reflect the most demanding state of the system's thermal load. Considering the impact of temperature on motor performance, when all three are within the normal threshold range, the initial braking decision threshold is sufficient to ensure the accuracy of the judgment result. If any parameter exceeds the limit, the initial threshold is dynamically corrected using the braking threshold compensation factor. For example, when the load torque changes significantly, the threshold is lowered to detect braking in advance, and when the voltage fluctuates violently, the threshold is adjusted to avoid misjudgment.

[0037] The embodiment of the present invention sets the braking judgment threshold by combining the DC bus voltage and the speed, current, temperature and power of the motors of each device. Compared with the fuzzy braking identification method in the prior art, this threshold setting method can quickly and accurately identify different braking scenarios, providing reliable prerequisites for subsequent energy recovery and distribution strategies, thereby ensuring that the braking energy can be recovered and utilized in a timely and correct manner, reducing energy waste caused by misjudgment.

[0038] Furthermore, it should be added that the specific calculation formula for the load torque estimation value is: ,in, represents the estimated value of the load torque, Indicates the set moment of inertia of the device motor, represents the change in the motor angular velocity, which is obtained by performing differential calculation on the rotational speed. , Indicates the speed, Indicates the sampling time interval, which is the reciprocal of the sensor sampling frequency. is the electromagnetic torque, and the value of the electromagnetic torque is the product of the torque coefficient and the current.

[0039] Furthermore, the specific setting of the braking threshold compensation factor is as follows: S61, calculate the difference between the maximum temperature of each device motor and the reference temperature. If all differences are less than or equal to 0, assign the temperature compensation factor to 0; otherwise, select the device motor with a difference greater than 0 as the target motor.

[0040] S62. Normalize the target motor temperature difference to obtain a temperature deviation, calculate its standard deviation, and if the standard deviation is less than or equal to a set threshold, take the maximum temperature deviation as the analysis temperature deviation; otherwise, take the average analysis temperature deviation as the analysis temperature deviation.

[0041] S63. Calculate the ratio of the number of target motors to the total number of motors in the equipment as a temperature correction coefficient.

[0042] S64. Compare the voltage statistics of the DC bus at adjacent times to obtain the voltage change rate, and take the maximum change rate as the bus voltage change rate.

[0043] S65: Obtain an estimated power value based on a preset mapping relationship between the bus voltage change rate and the power demand. .

[0044] S66. Process the target load torque difference of each device motor in the same manner as the temperature difference to obtain an analyzed load torque deviation and a load torque correction coefficient.

[0045] S67, the analysis temperature deviation, the temperature correction coefficient, the analysis load torque deviation and the load torque correction coefficient are recorded as 、 、 and ,comprehensive 、 、 、 and Set the speed change rate threshold compensation factor separately and current reverse threshold compensation factor ,Will and The integration is performed as a braking threshold compensation factor.

[0046] in, and The specific formula is as follows: , is a natural constant.

[0047] , Indicates the rated power of the DC bus.

[0048] Understandably, the motor temperature will affect its electromagnetic properties such as winding resistance and flux linkage, and an abnormal temperature increase may change the motor's braking response characteristics. When the temperature is less than or equal to 0, it means that the motor temperature has not exceeded the reference and has little impact on the braking threshold, so the temperature compensation factor is assigned to 0. If there are target motors with a temperature difference greater than 0, it indicates that these motors have special operating conditions and require in-depth analysis. Normalizing the target motor temperature difference and calculating the standard deviation is to quantify the degree of dispersion of the temperature deviation. A small standard deviation indicates that the temperature deviation is concentrated, and taking the maximum temperature deviation can highlight the worst temperature impact. A large standard deviation means that the deviation is dispersed, and taking the average can be considered in a balanced manner. Statistically calculating the proportion of the target motor number as a temperature correction coefficient can reflect the proportional weight of such special motors in the system, so that the impact of temperature on the braking threshold matches the actual operating condition distribution.

[0049] Bus voltage changes directly reflect the state of system power flow. For example, energy regeneration during braking causes the voltage to rise, while during motoring, the voltage drops. The maximum rate of change captures the most dramatic power fluctuations. Based on a preset mapping relationship, the voltage rate of change derives an estimated power value, converting voltage fluctuations into a representation of power demand. This bridges the gap between braking energy and system requirements, allowing braking threshold compensation to incorporate power-level operating condition changes.

[0050] Sudden changes in load torque, such as operating mode switching and load drop / stuck, are one of the core factors triggering braking. The logic of their impact on the braking threshold is similar to that of temperature deviation, and the degree and distribution of the deviation need to be quantified.

[0051] Understandably, the parameters such as the analyzed temperature deviation, temperature correction coefficient, analyzed load torque deviation, load torque correction coefficient, and estimated power are substituted into the formula to calculate the speed change rate threshold compensation factor and the current reverse threshold compensation factor. The exponential function and hyperbolic tangent function are introduced into the formula to utilize their nonlinear characteristics to reasonably limit the variation range of the compensation factor, so that the compensation amount can not only fit the changes in working conditions, but also avoid excessive correction of the threshold due to extreme values ​​of the parameters. By integrating the two compensation factors, the braking threshold can be adjusted synergistically from the two key dimensions of speed and current, and fully adapt to the changes in the braking characteristics of the motor caused by changes in working conditions such as temperature, load, and voltage.

[0052] It should be noted that the temperature difference, voltage rate of change, and load torque difference parameters are selected to correspond to the motor's inherent thermal characteristics, system power flow, and load mechanical characteristics, respectively, covering key factors affecting braking determination from different dimensions. Motor temperature affects electromagnetic parameters, thereby changing the relationship between braking current and speed. The voltage rate of change reflects the system's energy balance, and the load torque difference reflects sudden changes in mechanical load. The combination of these three can comprehensively capture the changing braking trigger conditions under complex operating conditions, resolving the problem of multiple interference factors that cannot be accounted for by a single parameter.

[0053] In another specific embodiment, the specific judgment process for determining whether the motor is in a braking state is: B1, extracting the set speed change rate threshold and the set current reversal threshold from the set braking determination threshold, and calculating the speed change rate of each device motor corresponding to adjacent time.

[0054] B2. When the speed change rate of a certain device motor at a certain adjacent time is less than or equal to the set speed change rate threshold, and the current of a certain device motor at a certain time is less than the opposite of the set current reverse threshold, it is marked as a preliminary determination that the motor enters the braking state.

[0055] B3. Perform time consistency check in several consecutive time windows on the signal initially determined to be in the braking state.

[0056] B4. If the braking determination threshold conditions are continuously met in several time windows, the device motor is judged to be in a braking state and a braking state mark is performed. Otherwise, it is judged to be an interference signal or a false trigger and an electric state mark is performed.

[0057] In one specific embodiment, when a conveyor belt is stuck, the motor speed approaches zero, but the current is less than zero due to a stalled reverse direction. Comparing only the current threshold will indicate braking, but the actual fault is a stall, rendering energy recovery meaningless. Another example is when a hoist is lowering a load, the motor reverses, the speed is less than zero, and the motor is in a motoring state. Comparing only the current threshold will indicate braking, but the motor is actually in a motoring state. Energy recovery can cause abnormal bus voltage. Therefore, a combined judgment based on both conditions is required.

[0058] Understandably, the current direction is usually defined as positive in the motoring state, where the motor draws power from the power supply, and negative in the braking state, where the motor feeds power to the power supply. The current reversal threshold itself is set to a negative value, such as Ampere. When the actual current is less than the set current reverse threshold, it means that the reverse current exceeds the threshold and the motor is in the braking feeding state.

[0059] The equipment state analysis module counts the total braking energy and determines the braking type, and uses the Kalman filter to establish a motor power state space model, and calculates the power mutation probability of the electric state equipment in the future preset time period through the model.

[0060] Specifically, the specific statistical process of the total braking energy is: C1. Recording the starting time point and the ending time point of each device motor in the braking state.

[0061] C2. Based on the starting time point and the ending time point, form a braking time period and evenly divide it into several sampling periods. Record the interval length of each sampling period. .

[0062] C3. For the motor of the equipment in the braking state Calculate the braking energy in the sampling period , , Indicates the DC bus is the first The voltage at a time point, Indicates the The brake motor is in the sampling period The absolute value of the braking current at a time point, Indicates the motor number of the device in the braking state. , Indicates the time point number, .

[0063] C4. Traverse the braking energy micro-element of each device motor in the braking state, and accumulate the total braking energy micro-element of the system in a single sampling period.

[0064] C5. From the start time point to the end time point of the braking state, accumulate the braking energy elements of all sampling periods to obtain the total braking energy.

[0065] Specifically, the specific confirmation process of the braking type is as follows: D1. Recording the equipment motor in the braking state as a braking motor.

[0066] D2. If the number of braking motors is 1, mark the braking type as single braking. Otherwise, calculate the intersection of the braking time periods corresponding to each braking motor.

[0067] D3. If the intersection is empty, mark the brake type as multiple independent brakes.

[0068] D4. If the intersection is not empty, calculate the correlation coefficient of the corresponding energy elements between the brake motors based on the Pearson correlation coefficient formula.

[0069] D5. When any of the following conditions is met, the braking type is marked as multiple coordinated braking, otherwise the braking type is marked as multiple independent braking. The conditions are as follows: the voltage fluctuation of the DC bus is greater than the corresponding set threshold.

[0070] The correlation coefficients of the corresponding energy elements between all brake motors are greater than the corresponding set correlation coefficient threshold.

[0071] Understandably, the correlation coefficient measures the linear correlation between two variables. A correlation coefficient greater than the set correlation coefficient threshold indicates that the change trends of the energy elements are highly consistent, and the fluctuation amplitudes are similar.

[0072] Understandably, the core of cooperative braking is the dynamic energy exchange between multiple motors through the DC bus or mechanical coupling. For example, the braking energy of one motor can be directly utilized by another motor, or energy coupling can be achieved through bus voltage fluctuations. 15 In this case, the energy elements of all motors must exhibit highly synchronized changes, otherwise an effective synergistic effect cannot be achieved. Therefore, the correlation coefficients of the corresponding energy elements of all braking motors must be set to be greater than the corresponding set correlation coefficient threshold.

[0073] It should be added that by setting the number of braking motors, the intersection of braking time, the energy correlation and the progressive judgment of voltage fluctuation, we first exclude the time-sharing braking scenario, and then accurately identify the collaborative braking from the two levels of energy synchronization and bus voltage coupling, thereby ensuring the accuracy of the braking type judgment, thereby avoiding the misjudgment of independent braking as collaborative, and reducing the erroneous execution of the energy distribution strategy.

[0074] It's also important to note that in actual operating conditions, simultaneous braking of multiple motors does not necessarily equate to coordinated braking. Misjudgment can lead to ineffective energy allocation strategies. For example, forcing independent braking energies to be coordinated increases bus losses. By eliminating asynchronous braking through time intersection, quantifying energy synchronization using correlation coefficients, and verifying energy coupling using voltage fluctuations, we can accurately distinguish between different braking types, providing a reliable basis for subsequent energy recovery, storage, and reuse strategies. This allows the system to operate efficiently under complex operating conditions and maximize energy savings.

[0075] In a specific embodiment, the specific calculation example process of calculating the power mutation probability of the electric state device in the future preset period by the model is as follows: 1) Define the state matrix: directly measure the real-time power of the motor of the electric state device through the power sensor, which is recorded as , Indicates the time number, , calculate the real-time power change rate based on the ratio of the power difference and time difference between two adjacent sampling moments ,Will and Composition state matrix , .

[0076] 2) Define the equation of state : Construct the state transfer matrix , , Set the inverse of the sampling frequency for the motor of the electric state device. The state equation is: , Indicates the The process noise at each moment, The mean is 0 and the covariance is The normal distribution of is process noise, which can be identified through historical data, that is, the historical data statistical power fluctuation variance can be constructed. For example, a typical setting can be used. , is the standard deviation of power, is the standard deviation of the power change rate.

[0077] 3) Define the observation equation : Construct the observation matrix , , the observation equation is: , For the The observation noise at each moment, The variance matrix is ​​zero and is The normal distribution of is the observation noise, where the value of the observation noise is determined by the sensor accuracy.

[0078] 4) Define power mutation: If the preset time period in the future Internal power meets satisfy: or , using the predictive power of the Kalman filter to predict N steps forward, , Indicates the absolute power threshold, Represents the relative threshold, The motor power measurement value at the current moment, Indicates the round-up symbol.

[0079] The state equation satisfies: , Indicates based on Time and previous data The predicted estimated value of the state at time t, Indicates based on Time and previous observations The predicted estimated value of the state at the moment is obtained through the observation matrix Bundle The estimated value of the state at the moment Mapping to predicted power value .

[0080] 6) Forward propagation of the predicted covariance matrix: , Indicates the step, , represents the transpose symbol, It reflects the propagation of the initial error after N steps of state transfer. If there is an error at the beginning, the transfer matrix will make the error amplify, reduce or deform. This part quantifies this propagation. Reflect the cumulative contribution of process noise to the error in N-step prediction and extract the power prediction variance , .

[0081] 7) Assumptions Obey Centered on For the normal distribution of discrete degree, calculate the absolute mutation probability and relative mutation probability The formulas are: , , represents the standard normal cumulative distribution function.

[0082] The energy flow control module generates a total power demand based on the production schedule. If the difference between the total braking energy and the total power demand is greater than a preset threshold, the allocation strategy is dynamically adjusted based on the braking type and the power mutation probability, and energy is allocated based on the strategy.

[0083] Specifically, the specific process of generating the total power demand includes: E1, obtaining the production line equipment sequence, the time window of the production task and the process route allocation from the production schedule.

[0084] E2. Establish a unified timeline covering the time range of all production tasks. The timeline resolution matches the power curve sampling period and marks the task start and end event points.

[0085] E3. For each time point on the timeline, perform production task association, process type identification, and process stage judgment, and then obtain the production task, execution process type, and process stage of the corresponding time point.

[0086] E4. Matching the corresponding power curve function based on the pre-set power curve function of each device at different process stages and the process stage at the current time point;

[0087] E5. Calculate the instantaneous power of each device's motor based on the matched power curve function.

[0088] E6. Traverse all device motors and accumulate the instantaneous power of the devices to obtain the total power demand.

[0089] Understandably, production task association can determine the current production task of the equipment through task time window matching, and mark it as idle when there is no task. Process type identification can identify the type of process currently being performed by the equipment, such as metal cutting and surface treatment, based on the process route allocation.

[0090] The specific implementation of process phase determination is as follows: Startup Phase: The first 5% of the time period after the task begins. If the task lasts 100 seconds, the first 5 seconds are the startup phase. Shutdown Phase: The last 5% of the time period before the task ends. If the task lasts 100 seconds, the last 5 seconds are the shutdown phase. Steady-State Phase: The middle 90% of the time period of the task. Idle State: The period when no tasks are assigned, that is, the power output is 0 or the basic standby power.

[0091] In a specific embodiment, the power curve function in the startup phase adopts an exponential rise function, and the specific formula is: , Indicates the instantaneous power of the equipment during startup. Indicates the peak power of the equipment when it starts, that is, the maximum power that the equipment can reach at the moment of starting. It is obtained by measuring the starting curve with a power sensor. For example, the peak power of a stamping machine when it starts is 200kW. Indicates the timestamp of the startup phase, which is counted from the start of the task in seconds. If the startup phase lasts for 5 seconds, , It represents the starting time constant, which mainly reflects the characteristic parameter of the equipment starting speed. It is obtained by fitting the exponential rising section of the starting curve, such as the motor starting Indicates that the power has risen to 63.2% The time is 2s.

[0092] The power curve function in the steady-state stage adopts the wave function, and the specific formula is: , represents the instantaneous power of the device in the steady-state stage, Indicates the steady-state basic power, specifically the average power when the process is running stably. It can be obtained by statistically averaging historical steady-state data. For example, the steady-state basic power of a welding machine is 80kW. Indicates the power fluctuation amplitude, which is the power change amplitude caused by process periodic fluctuations and is obtained by FFT analysis of the power spectrum of the process cycle. For example, in coating equipment, due to the reciprocating motion of the spray gun, A=50W, is the angular frequency of fluctuation, which is related to the process cycle. For example, if the welding cycle is 20s, then , represents pi, Indicates the timestamp of the steady-state phase. Assuming the total duration of the task is 100 seconds, if the steady-state lasts for 90 seconds from the end of the startup phase, then .

[0093] The power curve function during the shutdown phase adopts an exponential decay function , Indicates the instantaneous power of the equipment during shutdown. Indicates the timestamp of the downtime phase. Assuming the total duration of the task is 100s, the time starts from the downtime phase. If the downtime lasts for 5s, then , is the shutdown time constant, which reflects the characteristic parameter of the equipment shutdown speed and is obtained by fitting the exponential decay section of the shutdown curve. For example, due to the large inertia of CNC machine tools, Indicates that the power is reduced to 36.8% The time is 3 seconds.

[0094] In another specific embodiment, the dynamic adjustment allocation strategy includes: F1, determining the braking energy reference allocation ratio of the current feedback bus based on the braking type and power mutation probability, recorded as .

[0095] F2. Calculate the standard deviation of the power corresponding to the motor of the electric state equipment collected in real time to obtain the power fluctuation, and set the priority weight of the feedback bus based on the current time point. ,Will As the priority weight value of energy storage, it is recorded as .

[0096] F3, if , calculate the dynamic adjustment allocation ratio of the feedback bus , , It is the set maximum feedback bus braking energy distribution ratio.

[0097] F4, if , calculate the dynamic adjustment allocation ratio of the feedback bus , , It is the set minimum feedback bus braking energy distribution ratio.

[0098] F5. Subtract the dynamic allocation ratio of the feedback bus from 1 to obtain the dynamic allocation ratio of energy storage.

[0099] Understandably, The value can be determined by testing the equipment hardware capacity, such as the maximum energy feedback power that the bus can accept, and the critical value can be determined by overload testing. The allocation ratio is dynamically increased along with the bus priority. Taking the minimum value ensures that the hardware limit is not exceeded. Moreover, the value is calibrated through extreme working condition testing, simulating the bus pressure of full energy feedback under high weight.

[0100] The specific value can be determined by analyzing the minimum energy flow demand of the system, such as the energy feedback required for bus self-maintenance and basic power supply interaction of equipment through low-load working condition testing, that is, simulating the energy distribution of only maintaining the basic operation of the bus, and multiplying it by The allocation is reduced as the bus priority decreases, and the maximum value is taken to ensure that it does not fall below the basic demand. The value is verified by long-term low-power operation, which ensures that the bus is stable under low allocation.

[0101] The embodiment of the present invention dynamically adjusts the energy distribution strategy by combining the formulation type and power mutation probability, and can adapt to the random mutations of the braking power of multiple machines in real time to reasonably distribute energy to the feedback bus or energy storage equipment. It can then flexibly coordinate the energy flow between various devices according to the characteristics of different working conditions and production needs, thereby ensuring the self-consumption of energy between loads and realizing efficient energy coordination under motor braking and electric states, significantly improving the adaptability in complex industrial environments.

[0102] Furthermore, the specific confirmation process of the reference distribution ratio is as follows: F11, matching the braking type with a preset braking type-initial distribution ratio mapping relationship to obtain an initial mapping ratio.

[0103] F12. If the braking type is multi-unit coordinated braking, when the power mutation probability is greater than or equal to the preset warning value, the initial mapping ratio is multiplied by the first correction coefficient to obtain the baseline mapping ratio; otherwise, the initial mapping ratio is multiplied by the second correction coefficient to obtain the baseline mapping ratio, and the second correction coefficient is greater than the first correction coefficient.

[0104] F13: If the braking type is not multi-unit coordinated braking, the initial mapping ratio will be used as the benchmark allocation ratio.

[0105] Understandably, due to the small scale of energy recovery and independent operation of the equipment, a single brake unit often prioritizes meeting its own energy storage needs, and the remaining energy is then fed back to the bus. When multiple units brake individually, the devices lack coordination, which can easily lead to energy saturation of some devices, resulting in energy waste, and large bus power fluctuations, making it difficult to systematically plan energy distribution. Coordinated braking of multiple units relies on information interaction and strategy linkage between devices to dynamically adjust the energy distribution ratio based on the energy storage status, power fluctuations, and system requirements of each device. For example, when energy storage is insufficient, priority is given to feeding back to the bus to power the power equipment, while when energy storage is sufficient, emphasis is placed on storage, thereby optimizing energy flow, reducing bus pressure, and improving overall utilization. Therefore, single-unit braking, multiple individual brakes, and multiple coordinated brakes have a significant impact on the setting plan of the feedback bus and energy storage, and require targeted compensation settings based on the braking type.

[0106] It should be added that in the multi-unit coordinated braking scenario, the benchmark ratio when the probability of power mutation is high is set lower than the benchmark ratio when the probability of power mutation is low. This is mainly due to the comprehensive consideration of system stability, energy storage component protection and energy utilization efficiency balance.

[0107] When the power mutation probability exceeds the set power mutation probability warning value, it indicates significant uncertainty in the equipment's operating status, potentially leading to rapid power fluctuations within a short period of time. Small corrections can prevent excessive energy from being fed back to the busbar, causing voltage surges and threatening system safety. This also prevents damage to a device's energy storage components due to overload caused by excessive energy intake.

[0108] When the probability of power surges is low, the system operates relatively smoothly, feeding more energy back to the bus. This not only fully utilizes the immediate energy needs of electric equipment, reduces energy conversion losses in the energy storage link, but also improves overall energy efficiency while maintaining a stable bus voltage. This differentiated setting dynamically balances energy distribution, ensuring safe and efficient system operation at varying risk levels.

[0109] Furthermore, the setting of the priority weight is specifically as follows: F21, the power fluctuation is normalized and divided by the rated power of the electric state device motor to obtain the normalized power fluctuation factor .

[0110] F22. Determine the electricity price period category to which the current time point belongs, where the electricity price period category includes a peak electricity period, a valley electricity period, or a normal electricity period.

[0111] F23. Obtain the corresponding time weight coefficient according to the electricity price period category .

[0112] F24, obtain the initial weight value of the feedback bus that matches the current braking type , if the power fluctuation factor Exceeds the corresponding preset threshold , , It is the maximum allowable reduction ratio of the feedback bus.

[0113] F25, if , , The maximum permissible increase ratio of the feedback bus is set. and They are the weights corresponding to the set power fluctuation and time respectively.

[0114] In a specific embodiment, the initial weight distribution ratio of the feedback bus and the energy storage can be obtained through system simulation or experimental calibration according to the technical characteristics of different braking behaviors, such as energy size, safety requirements, etc.

[0115] Understandably, from the perspective of operational stability, the power fluctuation weight can reflect the dynamic changes in power during the operation of the electric equipment. When the power fluctuation factor is high, it means that the power jitter of the equipment is severe. At this time, increasing the proportion of the fluctuation weight can reduce the energy distribution ratio of the feedback bus, reduce the risk of a sudden rise or fall in the bus voltage due to power mutation, and avoid exceeding the carrying capacity of the bus to damage the equipment or cause a safety accident. From the perspective of energy utilization and cost control, the time weight is combined with the peak-valley electricity price mechanism to increase the weight of the feedback bus during the peak power period, which can give priority to feeding back the braking energy to the bus for use by other equipment, reducing the cost of purchasing electricity from the power grid. Reduce the feedback weight during the valley power period and store more energy for use when the electricity price is higher, so as to achieve the off-peak utilization of energy and ultimately achieve the purpose of reducing the overall electricity cost of the factory. The present invention takes operational stability as the primary guarantee goal, so when the power fluctuation factor exceeds the corresponding preset threshold, the priority weight value is directly lowered.

[0116] Understandably, the power fluctuation factor is obtained by normalizing the power fluctuation and dividing it by the rated power of the electric motor of the electric state equipment in order to eliminate the impact of the difference in the rated power of different equipment on the power fluctuation assessment, so that the fluctuation factor can measure the degree of power mutation on a unified scale.

[0117] The time weight coefficient is an economic lever that uses time-of-use electricity prices to guide energy flow. That is, it is set to a high value during peak power periods to encourage braking energy to be fed back to the bus. It is set to a low value during off-peak power periods to reduce the bus feedback weight. The specific value can be determined by analyzing the cost and benefit of historical operating data, combining the company's electricity cost tolerance and energy saving goals, and through simulation, such as comparing different The optimal coefficient is determined based on the electricity expenditure and energy utilization rate under the current conditions. For example, the optimal coefficient is 0.7 during peak hours, 0.3 during valley hours, and 0.5 during flat hours.

[0118] It is also understandable that the maximum permissible reduction ratio is based on safety redundancy design, through fault simulation, such as simulation Different when exceeding the limit The system stability under the current is balanced between ensuring safety and minimizing energy loss. The maximum permissible increase ratio can be based on the busbar redundancy capacity, such as testing the busbar voltage fluctuation under different increase ratios to ensure that it does not exceed the safety range. At the same time and The value of can be determined through multi-objective optimization, such as taking the highest energy utilization rate and the lowest electricity bill as the goals, using genetic algorithms for iterative calculation, and combining it with actual working conditions such as the sensitivity of the equipment to power stability to reasonably distribute the impact of power fluctuations and time electricity prices.

[0119] The embodiment of the present invention collects DC bus voltage and multi-dimensional operating data of the equipment motor in real time, and uses a Kalman filter to construct a motor power state-space model to accurately predict the probability of power mutations in a preset future time period. It also combines the total power demand generated by production scheduling and dynamically adjusts the energy allocation strategy based on the braking type and power mutation probability. This effectively overcomes the drawback of the existing technology of fixed-threshold feedforward compensation, which cannot track dynamic power differences. This prevents bus energy congestion, enables on-demand allocation of braking energy, and improves energy utilization.

[0120] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. An energy-saving control system based on a DC bus system, characterized in that: The system includes: DC bus architecture module, building a common DC bus network to connect variable frequency drive equipment, active rectifiers and energy storage equipment; The device status acquisition module collects the voltage of the DC bus and the speed, current, temperature and power of the motors of each connected device in real time, sets the braking judgment threshold and determines whether the motor is in the braking state; The equipment state analysis module calculates the total braking energy and determines the braking type. It also uses a Kalman filter to establish a motor power state space model, which is used to calculate the probability of power mutations in electric equipment during a preset period in the future. An energy flow control module generates a total power demand based on the production schedule. If the difference between the total braking energy and the total power demand exceeds a preset threshold, the module dynamically adjusts the allocation strategy based on the braking type and the probability of power mutation, and distributes energy based on the strategy. The setting of the braking determination threshold specifically includes: Import the device motor's moment of inertia and torque coefficient, and calculate the real-time load torque estimate based on the real-time speed and current; Calculate the difference between the estimated values ​​at adjacent time points, take the maximum difference of each device motor as the target load torque difference, and then take the maximum value of the target load torque differences corresponding to all device motors as the analysis load torque difference; The standard deviation of the DC bus voltage collected in real time is calculated to obtain the voltage fluctuation of the DC bus. Take the maximum temperature value of each device's motor as the analysis motor temperature; Traverse and analyze the load torque difference, voltage fluctuation and motor temperature. If none of the three exceed the corresponding set thresholds, the initial braking determination threshold is used as the final braking determination threshold. If any one of the items exceeds the threshold, a braking threshold compensation factor is set, and the initial threshold is corrected based on the compensation factor to serve as the final braking determination threshold.

2. The energy-saving control system based on a DC bus system according to claim 1, characterized in that: The specific setting of the braking threshold compensation factor is as follows: Calculate the difference between the maximum temperature of each device's motor and the reference temperature. If all differences are less than or equal to 0, assign the temperature compensation factor to 0. Otherwise, select the device motor with a difference greater than 0 as the target motor. Normalize the target motor temperature difference to obtain the temperature deviation, calculate its standard deviation, and if the standard deviation is less than or equal to the set threshold, take the maximum temperature deviation as the analysis temperature deviation; otherwise, take the average analysis temperature deviation as the analysis temperature deviation; Calculate the ratio of the number of target motors to the total number of motors in the equipment as the temperature correction coefficient; Compare the voltage statistics of the DC bus at adjacent times to obtain the voltage change rate, and take the maximum change rate as the bus voltage change rate; The estimated power value is obtained based on the preset mapping relationship between the bus voltage change rate and the power demand ; The target load torque difference of each device motor is processed in the same way as the temperature difference to obtain the load torque deviation and load torque correction coefficient; The comprehensive temperature deviation, temperature correction coefficient, analytical load torque deviation and load torque correction coefficient are recorded as 、 、 and ,comprehensive 、 、 、 and Set the speed change rate threshold compensation factor separately and current reverse threshold compensation factor , and and After integrated analysis, it is used as the braking threshold compensation factor; in, and The specific formula is as follows: , is a natural constant; , Indicates the rated power of the DC bus.

3. The energy-saving control system based on a DC bus system according to claim 1, characterized in that: The specific judgment process of judging whether the motor is in a braking state is as follows: Extract the speed change rate threshold and the current reversal threshold from the set braking determination threshold, and calculate the speed change rate of each device motor corresponding to adjacent time; When the speed change rate of a certain device motor at a certain adjacent time is less than or equal to the set speed change rate threshold, and the current of a certain device motor at a certain time is less than the inverse of the set current reverse threshold, it is marked that the motor enters the preliminary judgment of the braking state; Perform time consistency checks on the signal initially determined to be in braking state in several consecutive time windows; If the braking determination threshold conditions are continuously met in several time windows, the device motor is judged to be in a braking state and the braking state is marked. Otherwise, it is judged to be an interference signal or a false trigger and the electric state is marked.

4. The energy-saving control system based on a DC bus system according to claim 1, characterized in that: The specific statistical process of the total braking energy is as follows: Record the start time and end time of each device motor in the braking state; A braking time period is formed based on the starting time point and the ending time point, and is evenly divided into a number of sampling periods, and the interval length of a single sampling period is recorded; Calculate the braking energy of the motor in the braking state during the sampling period ; , Indicates the DC bus is the first The voltage at a time point, Indicates the The brake motor is in the sampling period The absolute value of the braking current at a time point, Indicates the motor number of the device in the braking state. , Indicates the time point number, ; Traverse the braking energy micro-element of each device motor in the braking state and accumulate the total braking energy micro-element of the system in a single sampling period; From the start time point to the end time point of the braking state, the braking energy elements of all sampling periods are accumulated to obtain the total braking energy.

5. The energy-saving control system based on a DC bus system according to claim 4, characterized in that: The specific confirmation process of the braking type is as follows: The equipment motor in braking state is recorded as brake motor; If the number of braking motors is 1, the braking type is marked as single braking. Otherwise, the intersection of the braking time periods corresponding to each braking motor is calculated. If the intersection is empty, mark the brake type as multiple independent brakes; If the intersection is not empty, the correlation coefficient of the corresponding energy elements between the brake motors is calculated based on the Pearson correlation coefficient formula; If any of the following conditions is met, the braking type is marked as multiple coordinated braking; otherwise, the braking type is marked as multiple independent braking. The conditions are as follows: The voltage fluctuation of the DC bus is greater than the corresponding set threshold; The correlation coefficients of the corresponding energy elements between all brake motors are greater than the corresponding set correlation coefficient threshold.

6. The energy-saving control system based on a DC bus system according to claim 1, characterized in that: The specific process of generating the total power requirement includes: Obtain production line equipment sequence, production task time window and process route allocation from production schedule; Establish a unified timeline covering the time range of all production tasks. The timeline resolution matches the power curve sampling period and marks the task start and end event points. For each time point on the timeline, production task association, process type identification, and process stage judgment are performed to obtain the production task, execution process type, and process stage of the corresponding time point; Based on the pre-set power curve function of each device at different process stages and the process stage at the current time point, the corresponding power curve function is matched; The instantaneous power of each device's motor is calculated based on the matching power curve function; Traverse all device motors and accumulate the instantaneous power of the devices to obtain the total power demand.

7. The energy-saving control system based on a DC bus system according to claim 1, characterized in that: The dynamically adjusting allocation strategy includes: Determine the braking energy reference distribution ratio of the current feedback bus based on the braking type and power mutation probability ; Calculate the standard deviation of the power corresponding to the motor of the electric state equipment collected in real time to obtain the power fluctuation, and set the priority weight of the feedback bus based on the current time point ,Will As the priority weight value of energy storage, it is recorded as ; like , calculate the dynamic adjustment allocation ratio of the feedback bus , , The maximum feedback bus braking energy distribution ratio is set; like , calculate the dynamic adjustment allocation ratio of the feedback bus , , The lowest feedback bus braking energy distribution ratio is set; The dynamic allocation ratio of the energy storage is obtained by subtracting the dynamic allocation ratio of the feedback bus from 1.

8. The energy-saving control system based on a DC bus system according to claim 7, characterized in that: The specific confirmation process of the benchmark allocation ratio is as follows: Matching the braking type with a preset braking type-initial distribution ratio mapping relationship to obtain an initial mapping ratio; If the braking type is multi-unit coordinated braking, when the power mutation probability is greater than or equal to the preset warning value, the initial mapping ratio is multiplied by the first correction coefficient to obtain a reference mapping ratio; otherwise, the initial mapping ratio is multiplied by the second correction coefficient to obtain a reference mapping ratio, and the second correction coefficient is greater than the first correction coefficient; If the braking type is not multi-unit coordinated braking, the initial mapping ratio will be matched as the benchmark allocation ratio.

9. The energy-saving control system based on a DC bus system according to claim 7, characterized in that: The priority weights are set as follows: The power fluctuation is normalized and divided by the rated power of the electric state device motor to obtain the normalized power fluctuation factor ; Determine the electricity price period category to which the current time point belongs, wherein the electricity price period category includes a peak electricity period, a valley electricity period, or a normal electricity period; Get the corresponding time weight coefficient according to the electricity price period category ; Get the initial weight value of the feedback bus that matches the current braking type , if the power fluctuation factor Exceeds the corresponding preset threshold , , The maximum permissible reduction ratio of the feedback bus is set; like , , The maximum permissible increase ratio of the feedback bus is set. and They are the weights corresponding to the set power fluctuation and time respectively.

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