Buck-boost anti-swing control system based on multi-working condition adaptive compensation

Abstract

The application discloses a lifting and pressure anti-swing control system based on multi-working condition adaptive compensation, and relates to the technical field of industrial automation control. The system comprises a multi-dimensional working condition recognition, a super capacitor intelligent energy storage, a hybrid topology lifting and pressure conversion, a load collaborative adaptive compensation, a predictive double protection, a man-machine interaction and a communication unit, and respectively realizes working condition recognition output three-dimensional instruction, dynamic charging and discharging and redundancy switching, wide range voltage rapid regulation, compensation strategy matching and optimization, all-around protection and self-checking, state presentation and parameter setting. Each unit cooperates to form a closed-loop control system through a CAN FD bus. The application cooperates the multi-dimensional working condition recognition, the super capacitor intelligent energy storage, the hybrid topology lifting and pressure conversion, the load collaborative adaptive compensation and the predictive double protection unit, adapts to power grid fluctuation and various loads, reduces equipment downtime, adapts to complex industrial environment, prolongs equipment service period and reduces related maintenance investment.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology, specifically to a buck-boost anti-voltage fluctuation control system based on multi-condition adaptive compensation. Background Technology

[0002] In industries requiring continuous production, such as chemical, thermal power, and petrochemical manufacturing, power grids often experience short-term voltage drops, fluctuations, or even outages due to lightning strikes, the start-up and shutdown of large equipment, or line faults. This phenomenon is known as "voltage flicker." Such grid anomalies directly cause AC contactors to release, frequency converters to shut down, and PLC control systems to malfunction on production lines. This not only disrupts normal production rhythms but can also lead to raw material losses, equipment damage, and even safety hazards, causing unnecessary losses for enterprises. With the increasing level of industrial automation, the number of contactors, frequency converters, soft starters, and other equipment on production lines is growing. These devices have higher requirements for grid voltage stability, necessitating an anti-voltage flicker solution that can cope with complex grid fluctuations and adapt to different loads to maintain continuous production.

[0003] Existing anti-voltage fluctuation devices have several limitations in practical applications. In terms of operating condition identification, most can only simply detect voltage drops, failing to distinguish different types of grid fluctuations or accurately identify the characteristics of different loads such as motors, pumps, and fans, resulting in a lack of targeted compensation solutions. Energy storage components often use ordinary capacitors or lithium batteries, which have slow charging speeds, significant performance degradation at low temperatures, and short lifespans, making it difficult to meet the requirements for rapid response and long-term stable operation. The buck-boost topology is relatively simple, with a narrow voltage regulation range, insufficient switching response, and poor voltage and current regulation. Furthermore, the lack of effective predictive protection mechanisms makes them poorly adaptable to complex industrial environments such as high altitudes and wide temperature ranges, and their compatible voltage levels are limited, failing to meet the diverse equipment usage needs in industrial scenarios. This results in unsatisfactory anti-voltage fluctuation performance and makes it difficult to prevent unplanned equipment shutdowns. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a buck-boost anti-power fluctuation control system based on multi-condition adaptive compensation. This system integrates core units such as multi-dimensional condition identification, supercapacitor intelligent energy storage, hybrid topology buck-boost conversion, load-coordinated adaptive compensation, and predictive dual protection. Through a CAN FD high-speed data bus, the modules work collaboratively to form a complete closed-loop control system. The multi-dimensional condition identification unit accurately captures grid fluctuations and load characteristics, outputting three-dimensional commands; the supercapacitor unit dynamically adjusts its charging and discharging state to adapt to different environments and needs; the hybrid topology structure enables wide-range voltage rapid adjustment; the compensation unit adapts to various load operations through strategy matching and algorithm optimization; and the protection unit comprehensively monitors equipment status to avoid fault risks. The entire system does not rely on fixed numerical settings and can flexibly adapt to complex scenarios in industries such as chemical and thermal power, reducing equipment downtime caused by grid fluctuations and ensuring continuous and stable industrial production.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a buck-boost anti-voltage fluctuation control system based on multi-condition adaptive compensation, the system comprising:

[0006] Multi-dimensional operating condition identification unit: integrates voltage sensor, current sensor and load characteristic acquisition subunit, collects grid incoming voltage, load current and load harmonic characteristic signals in real time, classifies multiple types of grid fluctuation and load operating conditions through multi-dimensional identification algorithm, and outputs three-dimensional operating condition command including grid fluctuation type, current demand characteristics and load type.

[0007] Supercapacitor intelligent energy storage unit: It adopts a modular supercapacitor group, and is equipped with a balanced charging subunit, a temperature monitoring subunit and an adaptive charging and discharging control subunit. It dynamically adjusts the charging and discharging rate according to three-dimensional operating condition commands, and sets up a series-parallel redundant switching subunit to realize the redundant input and output of energy storage modules.

[0008] Hybrid topology buck-boost converter unit: It adopts a hybrid topology of mechanical switches and IGBT electronic switches, integrating rectifier sub-units, precharge sub-units, BOOST boost sub-units and BUCK buck sub-units, and completes wide-range voltage regulation and millisecond-level switching through coordinated switching action;

[0009] Load Coordination Adaptive Compensation Control Unit: Built-in working condition-load-compensation strategy mapping library, dynamically matches compensation mode according to three-dimensional working condition instructions, and sets up a strategy self-learning sub-unit to optimize compensation parameters through gradient descent algorithm to achieve load coordination adaptation;

[0010] Predictive dual protection unit: includes hardware protection subunit, software protection subunit and predictive protection subunit, monitors the trend of equipment operating parameters and outputs signals, and has an adaptive self-test subunit to adjust the self-test cycle according to preset conditions;

[0011] Human-computer interaction and communication unit: configuration display subunit, parameter tuning subunit and communication subunit, to present the system operation status and data in real time, and support parameter setting and historical event query;

[0012] Each unit works collaboratively via the CAN FD high-speed data bus to form a closed-loop control system of identification, energy storage, compensation, protection, and interaction.

[0013] Furthermore, the multi-dimensional recognition algorithm includes the following mathematical decision model:

[0014] Power grid fluctuation type determination: This is due to a low voltage sag. For high voltage fluctuations, U=0 represents a short-term voltage interruption; among them, This is the real-time voltage of the incoming power grid line. This is the rated voltage of the power grid;

[0015] Current demand characteristics determination: For high voltage and low current, It is a small voltage and a large current; among them, This represents the real-time load current.

[0016] Load type determination: based on the total harmonic distortion of the load. Identification, ;in, The total harmonic distortion of the load is . The amplitude of the fundamental voltage. The voltage amplitude of the nth harmonic (n≥2, where n is the harmonic order); For motor-type loads, For pump-type loads, For wind turbine type loads.

[0017] Furthermore, the single-module capacity of the supercapacitor intelligent energy storage unit is ≥600F, the initial charging time is ≤2.5min, and the no-load power consumption in standby mode is ≤3W; when the ambient temperature T is -20℃ to 0℃, the charging current compensation coefficient is [missing information]. ;in, This is the charging current compensation coefficient. The value ranges from 0.3 to 0.5, where T is the real-time ambient temperature; the energy storage efficiency meets the requirements. Where η is the energy storage efficiency in an environment of -20℃ to 0℃. Energy storage efficiency at 25℃; single-module capacitor voltage decay rate. When the series-parallel redundant switching subunit operates, ΔU% represents the voltage decay rate of a single module capacitor. This is the initial voltage of the single-module capacitor. The real-time voltage of a single module capacitor; the unit's lifespan is ≥60,000 hours (at 25°C).

[0018] Furthermore, the input voltage range of the hybrid topology buck-boost converter unit is AC90-528V, and the output voltage range is DC300-900V; the voltage regulation accuracy in constant voltage mode is [not specified]. ,in, For constant voltage mode voltage regulation accuracy, This represents the actual output voltage of the unit. Voltage setpoint; current regulation accuracy in constant current mode. ,in, To ensure the accuracy of current regulation in constant current mode, This represents the actual output current of the unit. Set the current value; the switching response time is ≤8ms, and the pre-charge sub-unit uses an intelligent current limiting algorithm to control the inrush current. ,in, To initiate the inrush current, The rated current of the unit; the BOOST boost subunit realizes the conversion from DC220V to DC300-900V, and the BUCK buck subunit realizes the conversion from DC900V to DC300-500V, with both boost and buck efficiencies ≥97%.

[0019] Furthermore, the compensation modes of the load-coordinated adaptive compensation control unit include boost compensation, buck compensation, constant voltage compensation, and constant current compensation; and a torque maintenance coefficient is configured for motor-type loads. ≥0.95, of which, This is the torque maintenance coefficient; a flow stability coefficient is configured for pump-type loads. ≥0.9, of which, For flow stability coefficient; configure airflow fluctuation for fan-type loads. Where ΔQ% is the percentage of airflow fluctuation; the strategy self-learning sub-unit optimizes the compensation parameters through gradient descent algorithm every 1000 hours of cumulative operation, with the objective function being: The parameter update formula is: ,in, Let be the objective function. The actual operating parameters (torque / flow rate / air volume) of the load sampled for the i-th time. The rated operating parameters of the load are (torque / flow rate / air volume), and N is the number of samplings. The parameters to be optimized are (torque maintenance coefficient, speed stability threshold, etc.). Let the parameter value be the value of the k-th iteration. For the parameter value in the (k+1)th iteration, The learning rate (ranging from 0.01 to 0.1). For the objective function in The gradient at that point.

[0020] Furthermore, the hardware protection subunit includes IGBT module fault protection, DC bus overvoltage protection, and overcurrent protection; the software protection subunit includes over-temperature protection, voltage surge protection, and insulation monitoring; and the predictive protection subunit monitors IGBT temperature trends. Supercapacitor voltage decay rate and load current distortion rate A warning signal will be output when any of the following conditions are met:

[0021] ,in, The temperature change rate of the IGBT module. The real-time temperature of the IGBT module is given by t, where t is time.

[0022] ,in, This refers to the voltage decay rate of the supercapacitor. This refers to the real-time voltage of the supercapacitor.

[0023] ,in, This represents the change in load current distortion rate. The real-time distortion rate of the load current. The current distortion rate during normal load operation;

[0024] The protection setting range is: DC overvoltage protection 500-1000V, DC overcurrent protection 0-1200A, and overtemperature protection 40-75℃.

[0025] Furthermore, the triggering condition for the adaptive self-test subunit is: daily cumulative runtime. Trigger daily self-check, among which, Daily cumulative runtime; Annual cumulative runtime Triggering the annual self-check, among which, The annual cumulative running time; a temporary self-test is triggered when the number of power outages n ≥ 3 times within a single 24-hour period, where n is the cumulative number of power outages within 24 hours; the self-test process includes DC boost detection, main circuit device status detection, energy storage unit capacity detection, and protection function verification.

[0026] Furthermore, the transmission rate of the CAN FD high-speed data bus is ≥2Mbps; after the operating condition identification unit outputs a command, the system response time is... ,in, This represents the total system response time. Time spent on identifying operating conditions The time required for energy storage unit scheduling To compensate for the time consumed by the step-up and step-down operation; the pre-charge electronic unit includes a 20Ω / 100W current-limiting resistor and an IGBT switch, the mechanical switch has a rated current of ≥80A, and the IGBT electronic switch is selected as the FF400R12KT4 module.

[0027] Furthermore, the operating environment parameters of the system are: temperature -20℃ to +55℃, relative humidity 10% to 95%, altitude ≤2000m; it is compatible with AC110V, AC220V, and AC380V contactors, frequency converters, and soft starters, and supports customized AC690V voltage levels; when the overload capacity meets 1.2 times the rated power, the interval between two fluctuations is ≥10s for long-term operation; when the overload capacity meets 1.5 times the rated power, the interval between two fluctuations is ≥1min for long-term operation.

[0028] Compared with existing technologies, this buck-boost anti-voltage fluctuation control system based on multi-condition adaptive compensation has the following advantages:

[0029] I. This invention integrates voltage sensors, current sensors, and load characteristic acquisition subunits through a multi-dimensional operating condition identification unit. It collects real-time signals of grid incoming voltage, load current, and load harmonic characteristics. Combined with a multi-dimensional identification algorithm, it classifies various types of grid fluctuations and load conditions, outputting a three-dimensional operating condition command containing grid fluctuation type, current demand characteristics, and load type. This provides accurate reference for load-coordinated adaptive compensation control. The system has a built-in operating condition-load-compensation strategy mapping library, which can dynamically match compensation modes based on the three-dimensional operating condition command. Furthermore, the strategy self-learning subunit continuously optimizes compensation parameters, enabling the system to flexibly adapt to grid voltage changes and the operating characteristics of different loads such as motors, pumps, and fans. This reduces equipment downtime caused by grid fluctuations and load switching, ensuring continuous industrial production and maintaining a stable production process.

[0030] II. This invention utilizes a modular design of a supercapacitor intelligent energy storage unit, combined with an equalization charging subunit, a temperature monitoring subunit, and a series-parallel redundant switching subunit. It integrates a hybrid topology buck-boost converter unit to coordinate the operation of mechanical switches and IGBT electronic switches, incorporating a wide-range voltage regulation and stable energy conversion mechanism. Simultaneously, leveraging the parameter trend monitoring and adaptive self-testing functions of the predictive dual protection unit, the system can adapt to industrial equipment with various voltage levels such as AC110V, AC220V, and AC380V, and is suitable for industrial environments with different temperatures and altitudes, extending the overall service life of the equipment and reducing maintenance-related costs.

[0031] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0033] Figure 1 This is a block diagram of a buck-boost anti-voltage fluctuation control system based on multi-condition adaptive compensation.

[0034] Figure 2 Workflow diagram for the multi-dimensional working condition identification unit;

[0035] Figure 3 Flowchart of the load collaborative adaptive compensation control unit. Detailed Implementation

[0036] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0037] Example

[0038] This embodiment discloses the complete implementation process of a buck-boost anti-power fluctuation control system based on multi-condition adaptive compensation. This system is based on multi-dimensional condition identification and organically combines supercapacitor intelligent energy storage, hybrid topology buck-boost conversion, load-coordinated adaptive compensation, and predictive dual protection. It utilizes the CAN FD high-speed data bus to achieve coordinated operation of each unit, forming a closed-loop control system. Its core lies in accurately identifying grid and load conditions, dynamically adjusting energy storage, compensation, and protection strategies to adapt to the complex needs of different industrial scenarios, ensuring continuous and stable operation of equipment during grid fluctuations, and providing a reliable anti-power fluctuation solution for continuous production industries such as chemical, thermal power, and petrochemical industries.

[0039] like Figure 1 As shown in the figure, the buck-boost anti-voltage fluctuation control system based on multi-condition adaptive compensation provided in this embodiment of the invention mainly includes six core units, and the specific execution process of the system is as follows:

[0040] 1. System initialization and parameter configuration

[0041] After the system is powered on, a full initialization process is immediately initiated, and each functional unit simultaneously enters the self-test and parameter loading state, laying the foundation for subsequent operation.

[0042] The human-machine interaction and communication unit is activated first, the display sub-unit lights up and displays the self-test progress of each unit in a loop, the parameter setting sub-unit reads the preset basic parameters from the built-in storage module, including the grid rated voltage, the reference harmonic distortion rate of various loads, the initial charging parameters of the energy storage unit, the action threshold of the protection unit, etc. At the same time, the communication sub-unit initiates the communication protocol handshake process to establish a connection with the background monitoring system to ensure smooth data transmission.

[0043] After the supercapacitor intelligent energy storage unit is started, the equalization charging subunit first performs a consistency test on the modular supercapacitor group to determine whether the initial voltage of each capacitor module is consistent. If a deviation exists, the equalization charging program is initiated, adjusting the charging current of each module to bring the voltage of all modules to a unified reference value. The temperature monitoring subunit collects ambient temperature data in real time and transmits it to the adaptive charge and discharge control subunit, providing an initial basis for the dynamic adjustment of the charging current.

[0044] The pre-charge sub-unit of the hybrid topology buck-boost converter unit begins operation, and its internal intelligent current-limiting algorithm is activated. By controlling the conduction level of the IGBT switches, the input current is gradually increased to avoid damage to the rectifier sub-unit, IGBT electronic switches, and mechanical switches caused by the inrush current at startup. The rectifier sub-unit completes the initialization of its internal circuitry, preparing to convert the AC input voltage to DC voltage. At the same time, the BOOST boost sub-unit and BUCK buck sub-unit are in standby mode, waiting for the operating condition command to trigger the switching.

[0045] The hardware protection subunit of the predictive dual protection unit detects the connection status of each power device, the software protection subunit loads the preset protection threshold, the predictive protection subunit initializes the parameter monitoring module, and the adaptive self-test subunit completes the parameter configuration of the self-test trigger conditions, ensuring that various protection functions can be started at any time.

[0046] 2. Operation mechanism and data processing of the multi-dimensional working condition identification unit

[0047] The multi-dimensional operating condition identification unit is the core of the system's perception. Through multi-sensor data acquisition and multi-algorithm fusion, it achieves comprehensive and accurate identification of power grid and load operating conditions. Its operation encompasses four key stages: signal acquisition, preprocessing, algorithm calculation, and command generation. Its workflow is as follows: Figure 2 As shown.

[0048] During the signal acquisition phase, the voltage sensor acquires the AC voltage signal from the incoming power grid in real time, with the sampling frequency synchronized with the power grid frequency to ensure the capture of instantaneous voltage changes; the current sensor acquires the real-time current signal from the load side, covering the entire operating period of the load; the load characteristic acquisition subunit acquires the harmonic signals generated during load operation through a dedicated harmonic detection circuit, recording the amplitude and phase information of each harmonic. All signals acquired by the three types of sensors are transmitted in analog form to the signal preprocessing module within the unit.

[0049] In the signal preprocessing stage, the analog signal is first filtered by using low-pass filtering to remove high-frequency interference signals. Then, the analog signal is converted into a digital signal by analog-to-digital conversion. Subsequently, the signal is amplified and normalized to unify the signals of different ranges into a standard format that is easy for the algorithm to calculate, thus avoiding recognition errors caused by differences in signal amplitude.

[0050] In the algorithm calculation stage, the preprocessed digital signal is input into the multi-dimensional recognition algorithm. This algorithm contains three independent but interrelated mathematical judgment models, which respectively complete the identification of power grid fluctuation type, current demand characteristics and load type.

[0051] The core of the power grid fluctuation type determination model is to judge the power grid operating status by comparing the relationship between real-time voltage and rated voltage. The real-time voltage of the power grid incoming line is... The rated voltage of the power grid is When the algorithm calculates When the voltage drops below the normal operating range, it indicates a low-voltage sag condition, requiring the system to initiate boost compensation and energy storage discharge. When the calculation results meet the requirements... When the grid voltage is higher than the normal range, it is determined to be a high voltage fluctuation condition, and the system needs to start voltage reduction compensation; when U=0, the grid completely interrupts power supply, which is determined to be a short-term voltage interruption condition, and the system needs to rely on the energy storage unit to maintain the load power supply.

[0052] The current demand characteristic determination model judges the intensity of the load's current demand by calculating the ratio of the real-time load current I to the real-time grid incoming voltage U. When When the current required by the load is relatively small under the current voltage, it is determined to be a high-voltage, low-current operating condition, and the energy storage unit can adopt a low-rate discharge mode; when At this time, the load requires a large current under the current voltage, which is determined to be a low voltage and high current operating condition. The energy storage unit needs to increase the discharge rate to ensure that the load current requirement is met.

[0053] The load type determination model calculates the total harmonic distortion rate of the load. Achieve classification and recognition. The calculation formula is ,in, The total harmonic distortion of the load is . The amplitude of the fundamental voltage. This represents the amplitude of the nth harmonic voltage. The core logic of this formula is to reflect the electrical characteristics of the load through the proportion of harmonic components. Different types of loads have different harmonic generation mechanisms. Motor-type loads operate smoothly and have fewer harmonic components, therefore... Pump loads generate certain harmonics due to changes in fluid resistance. The harmonic content is between 15% and 30%; wind turbine loads are affected by airflow, resulting in significant operational fluctuations and the highest harmonic content. .

[0054] During the instruction generation phase, the multi-dimensional operating condition identification unit integrates the judgment results of the above three models to form a three-dimensional operating condition instruction that includes grid fluctuation type, current demand characteristics, and load type. This instruction not only clarifies the current operating condition status but also implicitly contains the operational requirements for subsequent energy storage and compensation units. It is then synchronously transmitted to the supercapacitor intelligent energy storage unit, the hybrid topology buck-boost converter unit, and the load collaborative adaptive compensation control unit via the CAN FD high-speed data bus, providing precise basis for the actions of each unit.

[0055] 3. Dynamic operation and state regulation of supercapacitor intelligent energy storage units

[0056] As the energy core of the system, the supercapacitor intelligent energy storage unit dynamically adjusts its operating status according to three-dimensional operating condition commands. Through modular design, temperature compensation, redundancy switching and other technologies, it ensures the stability and reliability of energy storage and supply. Its operation process revolves around three core links: charging, discharging, and status monitoring and regulation.

[0057] The charging process is closely related to the grid conditions and the energy storage unit status. When the three-dimensional operating condition command indicates that the grid is in a normal state and the load current demand is low, the adaptive charge-discharge control subunit determines that the system is in the energy storage phase and initiates the charging program. The equalization charging subunit monitors the voltage of each capacitor module in real time and adjusts the charging current of each module through the shunt resistor to ensure that all modules charge synchronously, avoiding damage from overcharging due to excessively fast charging or insufficient energy storage due to excessively slow charging. During the charging process, the temperature monitoring subunit continuously collects the ambient temperature T. When the ambient temperature is in the low temperature range of -20℃ to 0℃, the electrochemical characteristics of the capacitor will change, and the energy storage efficiency will decrease. At this time, it is necessary to use the charging current compensation coefficient. Adjust the charging current. The calculation formula is: This formula uses a linear relationship to dynamically adjust the compensation coefficient based on temperature, allowing the charging current to increase appropriately as the temperature decreases. This compensates for energy storage degradation at low temperatures and ensures that the energy storage efficiency meets the requirements. Where η is the energy storage efficiency in a low-temperature environment. This refers to the energy storage efficiency at room temperature.

[0058] The discharge process is initiated by a three-dimensional operating condition command. When the power grid experiences fluctuations such as low voltage drops or short-term interruptions, the adaptive charge-discharge control subunit immediately switches to discharge mode, adjusting the discharge rate according to the current demand characteristics in the command: under high voltage and low current conditions, a low discharge rate is used to extend the power supply time; under low voltage and high current conditions, the discharge rate is increased to meet the instantaneous current demand of the load. During the discharge process, the equalization charging subunit continues to operate to ensure that the discharge rate of each module is consistent, avoiding voltage fluctuations caused by uneven energy output between modules, which could affect load operation.

[0059] The state monitoring and regulation process is integrated throughout the entire charging and discharging process. Series-parallel redundant switching subunits monitor the voltage changes of each capacitor module in real time using formulas. Calculate the voltage decay rate of a single module capacitor, where, This is the initial voltage of the module. To monitor voltage in real time, when ΔU% ≥ 10%, it indicates that the module's performance has significantly degraded. Continued use would affect the energy storage and supply efficiency of the entire unit. In this case, the series-parallel redundancy switching subunit immediately activates, disconnecting the circuit connection of the degraded module and simultaneously connecting the backup redundant module to the circuit. This ensures that the total capacity and output stability of the energy storage unit are not affected, extending the overall lifespan of the unit. Furthermore, the unit also monitors the no-load power consumption in standby mode in real time, ensuring it remains at a low level through optimized circuit design.

[0060] 4. Wide-range adjustment and fast response of hybrid topology buck-boost converter unit

[0061] As the core of the system's voltage regulation, the hybrid topology buck-boost converter unit integrates the advantages of mechanical switches and IGBT electronic switches. Through the collaborative work of multiple sub-units, it achieves wide-range voltage regulation and millisecond-level switching, meeting the voltage requirements of the load under different operating conditions. Its operation process covers three key links: voltage conversion, topology switching, and precision regulation.

[0062] The voltage conversion stage is initiated by the rectifier subunit. Regardless of the range of the input AC voltage, the rectifier subunit first converts it into DC voltage. This process is achieved through a controllable rectifier bridge. The conduction angle of the thyristors in the rectifier bridge is dynamically adjusted according to the amplitude of the input voltage to ensure the stability of the output DC voltage and provide a stable DC bus voltage for subsequent step-up and step-down processing.

[0063] The topology switching stage is the core innovation of this unit. Mechanical switches and IGBT electronic switches work in tandem according to three-dimensional operating condition commands to achieve switching between different topologies. Mechanical switches offer advantages such as low conduction loss and high current carrying capacity, making them suitable for circuit connections during steady-state operation. IGBT electronic switches feature fast switching speed and high control precision, making them suitable for dynamic adjustment and rapid switching scenarios. When a boost command is required, the BOOST boost subunit starts, the mechanical switch closes to turn on the main circuit, and the IGBT electronic switch frequently switches on and off according to voltage regulation requirements, achieving voltage boost through inductor energy storage and release. When a buck command is required, the BUCK buck subunit starts, again using the synergy of mechanical switches and IGBT electronic switches, utilizing the filtering effect of inductors and capacitors to reduce voltage. When the grid voltage is normal and the load demand is stable, only the mechanical switch remains closed, and the IGBT electronic switch is in standby mode, reducing energy loss. The pre-charge subunit operates continuously during topology switching, controlling the inrush current through an intelligent current limiting algorithm to avoid sudden current surges impacting the switching devices and load, ensuring a smooth switching process.

[0064] The precision adjustment is achieved through dynamic switching between constant pressure and constant current modes, selecting the appropriate mode based on load requirements and changes in operating conditions. In constant pressure mode, the adjustment is achieved using a formula... Calculate voltage regulation accuracy and compare with actual output voltage in real time. With set voltage If the deviation exceeds the allowable range, the output voltage is corrected by adjusting the on / off duty cycle of the IGBT electronic switch to ensure... Maintain within the set threshold; in constant current mode, use the formula The unit calculates current regulation accuracy and uses the same feedback regulation logic to ensure output current stability. Simultaneously, the unit optimizes the control algorithm to shorten switching response time, ensuring rapid voltage output adjustment in response to grid fluctuations or load changes, meeting dynamic load demands and improving energy conversion efficiency during the buck-boost process.

[0065] 5. Precise matching and parameter optimization of the load collaborative adaptive compensation control unit

[0066] The load-coordinated adaptive compensation control unit, as the core of the system, achieves adaptive compensation for different loads through precise matching and continuous optimization of operating conditions, loads, and compensation strategies, ensuring load operational stability. Its operation encompasses three stages: strategy matching, parameter optimization, and compensation execution. Its control flow is as follows: Figure 3 As shown.

[0067] The core of the strategy matching process is the built-in operating condition-load-compensation strategy mapping library. This library pre-stores the optimal compensation modes corresponding to different combinations of grid fluctuation types, current demand characteristics, and load types. Compensation modes include boost compensation, buck compensation, constant voltage compensation, and constant current compensation. Upon receiving a three-dimensional operating condition command, the unit retrieves the mapping library using key information from the command and quickly matches the appropriate compensation mode: boost compensation is prioritized for low-voltage grid drops; buck compensation is matched for high-voltage fluctuations; motor loads, which require high torque stability, are paired with constant voltage compensation and torque maintenance strategies; pump loads, which need to ensure stable flow, employ constant current compensation and flow regulation strategies; and fan loads, which are sensitive to airflow fluctuations, are combined with a constant voltage and constant current composite compensation strategy.

[0068] The parameter optimization phase is handled by the strategy self-learning subunit. Its core logic involves continuously optimizing compensation parameters using a gradient descent algorithm to reduce the deviation between the actual operating parameters and the rated parameters of the load. The subunit collects the load's operating parameters (torque, flow rate, air volume, etc.) in real time, and initiates the parameter optimization process after a certain cumulative operating time. First, the objective function is constructed. ,in, Let be the objective function. Let x be the actual operating parameters of the load during the i-th sampling, x be the rated operating parameters of the load, and N be the number of samplings. This function quantifies the total deviation between the actual parameters and the rated parameters using a sum of squares; the smaller the deviation, the better the compensation effect. Then, the parameter update formula is applied. Iterative optimization of the parameter to be optimized θ, where, Let be the parameter value for the k-th iteration. For the updated parameter values, The learning rate (used to control the speed of parameter updates and avoid iterative oscillations). For the objective function in The gradient at the given point. Through multiple iterations, the objective function J is minimized, meaning the compensation parameters achieve optimal adaptation to the current operating conditions and load.

[0069] The compensation execution stage transforms the matched compensation mode and optimized parameters into specific control signals, which are transmitted to the hybrid topology buck-boost converter and the supercapacitor intelligent energy storage unit via the CAN FD bus. This controls the buck-boost unit to adjust the output voltage and current, and controls the energy storage unit to adjust the charging and discharging rate, forming a closed-loop compensation logic of "command-matching-optimization-execution". This ensures that the load maintains a stable operating state when the grid fluctuates or its own characteristics change.

[0070] 6. Comprehensive protection and status self-checking of the predictive dual protection unit

[0071] As the core of the system's security, the predictive dual protection unit provides triple protection through hardware protection, software protection, and predictive protection, combined with adaptive self-testing function, to comprehensively ensure the safe operation of the system and the load. Its operation process covers three stages: real-time monitoring, protection actions, and self-testing verification.

[0072] The real-time monitoring is accomplished collaboratively by three types of protection subunits, covering key parameters and statuses of system operation. The hardware protection subunit uses dedicated detection circuits to monitor the conduction status of the IGBT modules and the voltage and current of the DC bus in real time. Once a short circuit, open circuit, or other fault is detected in the IGBT module, or the DC bus voltage or current exceeds a preset threshold, a hardware protection signal is immediately generated. The software protection subunit analyzes system operating data in real time using software algorithms, monitoring parameters such as ambient temperature, voltage change rate, and insulation resistance. When the temperature exceeds the allowable range, the voltage fluctuation is too large, or the insulation resistance decreases, a software protection command is triggered. The predictive protection subunit focuses on parameter trend changes and calculates the IGBT temperature change rate. Supercapacitor voltage decay rate and load current distortion rate Anticipate potential faults: The temperature rise reflects the heating trend of the IGBT module; excessively rapid temperature rise may indicate overload or heat dissipation failure. ,in, The temperature change rate of the IGBT module. The real-time temperature of the IGBT module is given by t, where t is time. This reflects the rate of performance degradation of the supercapacitor; accelerated degradation indicates potential damage to the module. ,in, This refers to the voltage decay rate of the supercapacitor. This refers to the real-time voltage of the supercapacitor. This reflects the load's operating status; sudden changes indicate potential load failure or increased grid interference. This represents the change in load current distortion rate. The real-time distortion rate of the load current. This represents the current distortion rate when the load is operating normally.

[0073] The protection mechanism executes different levels of protection measures based on monitoring results. Upon triggering a hardware protection signal, the main circuit power is immediately cut off to prevent the fault from escalating. After a software protection command is issued, system operating parameters are first adjusted, such as reducing output power or adjusting the charging / discharging rate. If the system cannot return to normal after parameter adjustments, a shutdown protection is initiated. When the predictive protection subunit detects abnormal parameter trends, it outputs a warning signal to the human-machine interface unit, alerting staff to investigate potential problems and simultaneously adjusting protection thresholds and operating parameters to reduce the probability of fault occurrence.

[0074] The adaptive self-test subunit triggers different types of self-tests based on system operating conditions to ensure that the protection functions remain effective. (Daily cumulative runtime) Trigger daily self-check, among which, This refers to the cumulative daily runtime; the self-test starts after the cumulative daily runtime reaches the set value, focusing on checking the continuity of the hardware protection circuit and the accuracy of the software protection threshold; the annual cumulative runtime... Triggering the annual self-check, among which, The self-test is performed after the annual cumulative running time reaches the target, comprehensively testing the energy storage unit capacity, buck-boost unit adjustment accuracy, and protection unit response speed. A temporary self-test is triggered when the number of power dips (n) within a single 24-hour period reaches ≥3, where n is the cumulative number of power dips within 24 hours. The temporary self-test is initiated after the number of power dips within 24 hours reaches a set value, specifically testing the emergency discharge capability of the energy storage unit and the rapid response capability of the compensation unit. The self-test process includes DC boost detection, main circuit component status detection, energy storage unit capacity detection, and protection function verification. Self-test results are fed back to the human-machine interface unit in real time; if a problem is detected, an alarm is triggered and fault information is recorded.

[0075] 7. Unit collaboration mechanism and data interaction logic

[0076] Each unit forms a closely coordinated closed-loop control system through the CAN FD high-speed data bus, with high data transmission rate and low latency, ensuring real-time interaction between commands and feedback signals. Its collaborative logic runs through the entire system operation process.

[0077] The data transmission content covers three core signal categories: command signals, status signals, and feedback signals. Command signals mainly include three-dimensional operating condition commands output by the multi-dimensional operating condition identification unit, compensation control commands output by the load collaborative adaptive compensation control unit, and parameter adjustment commands issued by the human-machine interaction unit; status signals include the operating status and key parameters of each unit; feedback signals include the result feedback after each unit executes the commands and the load operating status feedback.

[0078] The collaborative timing logic strictly follows the process of "perception-decision-execution-feedback": the multi-dimensional operating condition identification unit first completes the operating condition perception and outputs three-dimensional instructions; after receiving the instructions, the supercapacitor intelligent energy storage unit, the hybrid topology buck-boost converter unit, and the load collaborative adaptive compensation control unit formulate energy storage, buck-boost, and compensation decisions respectively; each unit executes relevant actions synchronously; the status signals and load feedback signals during the execution process are transmitted back to the predictive dual protection unit and the human-machine interaction unit in real time. The protection unit adjusts the protection strategy according to the feedback signals, and the human-machine interaction unit updates the operating status display.

[0079] For example, when a low voltage drop occurs in the power grid and the load is a motor, the collaborative process is as follows: the multi-dimensional operating condition identification unit identifies and outputs a three-dimensional command of "low voltage drop + low voltage and high current + motor load"; the load collaborative adaptive compensation control unit matches the boost compensation and constant voltage strategies, optimizes the torque maintenance parameters, and outputs control commands; the hybrid topology boost-boost converter switches to BOOST boost mode and adjusts the output voltage according to the command; the supercapacitor intelligent energy storage unit increases the discharge rate and replenishes energy; the protection unit monitors the IGBT temperature and bus voltage in real time to ensure the safety of the boost process; each unit feeds back the operating status to the human-machine interaction unit, displaying information such as "boost compensation in progress" and "energy storage discharge normal", forming a complete collaborative closed loop.

[0080] In summary, this embodiment details the complete operation process of a buck-boost anti-power fluctuation control system based on multi-condition adaptive compensation. The multi-dimensional condition identification unit achieves comprehensive perception of the power grid and load, providing a reliable basis for subsequent control; the supercapacitor intelligent energy storage unit ensures the stability and sustainability of energy supply through temperature compensation and redundant switching; the hybrid topology buck-boost converter unit integrates the advantages of two types of switches to achieve wide-range voltage regulation and rapid response; the load-coordinated adaptive compensation control unit achieves precise compensation through strategy matching and algorithm optimization; the predictive dual protection unit constructs a triple protection system to ensure safe system operation; and all units achieve real-time coordination through the CAN FD bus, forming a closed-loop control. The entire system does not rely on specific numerical settings and adapts to different industrial scenarios through a generalized design, effectively solving the problems of traditional anti-power fluctuation devices such as single condition identification, poor compensation targeting, and weak environmental adaptability.

[0081] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A step-up / step-down voltage fluctuation control system based on multi-condition adaptive compensation, characterized in that, The system includes: Multi-dimensional operating condition identification unit: integrates voltage sensor, current sensor and load characteristic acquisition subunit, collects grid incoming voltage, load current and load harmonic characteristic signals in real time, classifies multiple types of grid fluctuation and load operating conditions through multi-dimensional identification algorithm, and outputs three-dimensional operating condition command including grid fluctuation type, current demand characteristics and load type. Supercapacitor intelligent energy storage unit: It adopts a modular supercapacitor group, and is equipped with a balanced charging subunit, a temperature monitoring subunit and an adaptive charging and discharging control subunit. It dynamically adjusts the charging and discharging rate according to three-dimensional operating condition commands, and sets up a series-parallel redundant switching subunit to realize the redundant input and output of energy storage modules. Hybrid topology buck-boost converter unit: It adopts a hybrid topology of mechanical switches and IGBT electronic switches, integrating rectifier sub-units, precharge sub-units, BOOST boost sub-units and BUCK buck sub-units, and completes wide-range voltage regulation and millisecond-level switching through coordinated switching action; Load Coordination Adaptive Compensation Control Unit: Built-in working condition-load-compensation strategy mapping library, dynamically matches compensation mode according to three-dimensional working condition instructions, and sets up a strategy self-learning sub-unit to optimize compensation parameters through gradient descent algorithm to achieve load coordination adaptation; Predictive dual protection unit: includes hardware protection subunit, software protection subunit and predictive protection subunit, monitors the trend of equipment operating parameters and outputs signals, and has an adaptive self-test subunit to adjust the self-test cycle according to preset conditions; Human-computer interaction and communication unit: configuration display subunit, parameter tuning subunit and communication subunit, to present the system operation status and data in real time, and support parameter setting and historical event query.

2. The buck-boost anti-voltage fluctuation control system based on multi-condition adaptive compensation according to claim 1, characterized in that, The multi-dimensional recognition algorithm includes the following mathematical judgment model: Power grid fluctuation type determination: This is due to a low voltage sag. For high voltage fluctuations, U=0 represents a short-term voltage interruption; among them, This is the real-time voltage of the incoming power grid line. This is the rated voltage of the power grid; Current demand characteristics determination: For high voltage and low current, It is a small voltage and a large current; among them, This represents the real-time load current. Load type determination: based on the total harmonic distortion of the load. Identification, ;in, The total harmonic distortion of the load is . The amplitude of the fundamental voltage. The voltage amplitude of the nth harmonic; For motor-type loads, For pump-type loads, For wind turbine type loads.

3. The buck-boost anti-voltage fluctuation control system based on multi-condition adaptive compensation according to claim 1, characterized in that, The single-module capacity of the supercapacitor intelligent energy storage unit is ≥600F, the initial charging time is ≤2.5min, and the no-load power consumption in standby mode is ≤3W; when the ambient temperature T is -20℃ to 0℃, the charging current compensation coefficient is [missing information]. ;in, This is the charging current compensation coefficient. The value ranges from 0.3 to 0.5, where T is the real-time ambient temperature; the energy storage efficiency meets the requirements. Where η is the energy storage efficiency in an environment of -20℃ to 0℃. Energy storage efficiency at 25℃; single-module capacitor voltage decay rate. When the series-parallel redundant switching subunit operates, ΔU% represents the voltage decay rate of a single module capacitor. This is the initial voltage of the single-module capacitor. This represents the real-time voltage of a single-module capacitor.

4. The buck-boost anti-voltage fluctuation control system based on multi-condition adaptive compensation according to claim 1, characterized in that, The hybrid topology buck-boost converter unit has an input voltage range of AC90-528V and an output voltage range of DC300-900V; voltage regulation accuracy in constant voltage mode. ,in, For constant voltage mode voltage regulation accuracy, This represents the actual output voltage of the unit. Voltage setpoint; current regulation accuracy in constant current mode. ,in, To ensure the accuracy of current regulation in constant current mode, This represents the actual output current of the unit. Set the current value; the switching response time is ≤8ms, and the pre-charge sub-unit uses an intelligent current limiting algorithm to control the inrush current. ,in, To initiate the inrush current, The rated current of the unit; the BOOST boost subunit realizes the conversion from DC220V to DC300-900V, and the BUCK buck subunit realizes the conversion from DC900V to DC300-500V, with both boost and buck efficiencies ≥97%.

5. The buck-boost anti-voltage fluctuation control system based on multi-condition adaptive compensation according to claim 1, characterized in that, The load-coordinated adaptive compensation control unit includes compensation modes such as boost compensation, buck compensation, constant voltage compensation, and constant current compensation; a torque maintenance coefficient is configured for motor-type loads. ≥0.95, of which, This is the torque maintenance coefficient; a flow stability coefficient is configured for pump-type loads. ≥0.9, of which, For flow stability coefficient; configure airflow fluctuation for fan-type loads. Where ΔQ% is the percentage of airflow fluctuation; the strategy self-learning sub-unit optimizes the compensation parameters through gradient descent algorithm every 1000 hours of cumulative operation, with the objective function being: The parameter update formula is: ,in, Let be the objective function. The actual operating parameters (torque / flow rate / air volume) of the load sampled for the i-th time. Here are the rated operating parameters for the load, and N is the number of samples. For parameters to be optimized, Let be the parameter value for the k-th iteration. For the parameter value in the (k+1)th iteration, For learning rate, For the objective function in The gradient at that point.

6. The buck-boost anti-voltage fluctuation control system based on multi-condition adaptive compensation according to claim 1, characterized in that, The hardware protection subunit includes IGBT module fault protection, DC bus overvoltage protection, and overcurrent protection; the software protection subunit includes over-temperature protection, voltage surge protection, and insulation monitoring; the predictive protection subunit monitors IGBT temperature trends. Supercapacitor voltage decay rate and load current distortion rate A warning signal will be output when any of the following conditions are met: ,in, The temperature change rate of the IGBT module. The real-time temperature of the IGBT module is given by t, where t is time. ,in, This refers to the voltage decay rate of the supercapacitor. This refers to the real-time voltage of the supercapacitor. ,in, This represents the change in load current distortion rate. The real-time distortion rate of the load current. The current distortion rate during normal load operation; The protection setting range is: DC overvoltage protection 500-1000V, DC overcurrent protection 0-1200A, and overtemperature protection 40-75℃.

7. The buck-boost anti-voltage fluctuation control system based on multi-condition adaptive compensation according to claim 1, characterized in that, The trigger condition for the adaptive self-test subunit is: daily cumulative runtime. Trigger daily self-check, among which, Daily cumulative runtime; Annual cumulative runtime Triggering the annual self-check, among which, The annual cumulative running time; a temporary self-test is triggered when the number of power outages n ≥ 3 times within a single 24-hour period, where n is the cumulative number of power outages within 24 hours; the self-test process includes DC boost detection, main circuit device status detection, energy storage unit capacity detection, and protection function verification.

8. The buck-boost anti-voltage fluctuation control system based on multi-condition adaptive compensation according to claim 1, characterized in that, The transmission rate of the CAN FD high-speed data bus is ≥2Mbps; After the operating condition identification unit outputs a command, the system response time is... ,in, This represents the total system response time. Time spent on identifying operating conditions The time required for energy storage unit scheduling To compensate for the time consumed by the buck-boost operation; the precharge sub-unit includes a 20Ω / 100W current-limiting resistor and an IGBT switch, with the mechanical switch having a rated current ≥80A.

9. The buck-boost anti-voltage fluctuation control system based on multi-condition adaptive compensation according to claim 1, characterized in that, The system's operating environment parameters are: temperature -20℃ to +55℃, relative humidity 10% to 95%, altitude ≤2000m; it is compatible with AC110V, AC220V, and AC380V contactors, frequency converters, and soft starters, and supports customized AC690V voltage levels; when the overload capacity meets 1.2 times the rated power, the interval between two fluctuations is ≥10s for long-term operation; when the overload capacity meets 1.5 times the rated power, the interval between two fluctuations is ≥1min for long-term operation.

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