A prefabricated cabin type energy storage system power supply control method

Abstract

The application provides a prefabricated cabin type energy storage system power supply control method, comprising the following steps: obtaining a turn-off signal trigger of a secondary output channel to form an initial voltage fluctuation data set; analyzing the influence degree of turn-off energy dissipation on the main output channel according to the initial voltage fluctuation data set, and determining specific parameters of a fluctuation interference source; obtaining real-time data of a turn-off frequency range and a response delay after turn-off of the secondary output channel, and judging an adaptive frequency adjustment interval; dynamically calibrating the voltage according to the judged frequency adjustment interval to obtain a preliminarily stable voltage output curve; and obtaining system response data after turn-off of the secondary output channel through a final voltage stabilization adjustment scheme, and determining voltage stabilization guarantee parameters under long-term operation.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a power supply control method for a prefabricated cabin-type energy storage system. Background Technology

[0002] In the field of modern energy management and security, the stable operation of prefabricated modular energy storage systems is crucial for ensuring power supply to critical equipment. In the low-voltage auxiliary power distribution system of a prefabricated modular energy storage system, the uninterruptible power supply (UPS) system is a core component, primarily providing normal and backup power for critical loads such as battery management systems and fire protection systems. Especially in high-reliability scenarios such as fire alarms and fire-fighting linkages, the seamless switching and voltage stability of the UPS system are directly related to the safety of life and property. UPS systems typically employ a dual-channel architecture, with the primary output channel handling core power supply and the secondary output channel providing auxiliary or emergency support, to ensure the continuity and reliability of power supply to critical loads. When the mains power is interrupted, the UPS immediately switches the DC power from the backup battery to the load via an inverter, continuing to supply 220V AC power, allowing the load to maintain normal operation and protecting the load's hardware and software from damage. Prefabricated modular energy storage systems, as an integrated solution, have attracted much attention due to their flexibility and efficiency; however, the dynamic response of their UPS systems during load switching still requires further in-depth research. Currently, solutions for UPS load switching mostly focus on hardware-level redundancy design or simple control logic adjustments. However, these methods often overlook the complexity of the interactions between different output channels during dynamic switching of the UPS system, especially when multiple channels are working together, failing to fully consider the potential interference of instantaneous changes on the overall system stability. This limitation makes the UPS system prone to brief instability when dealing with sudden load changes, thus affecting the normal operation of critical equipment such as battery management systems and fire protection systems. Specifically, the core challenge in this field lies in the instantaneous impact of the secondary output channel shutdown during UPS load switching. When the secondary output channel reaches the preset power supply time and prepares to shut down, it causes a brief fluctuation in the voltage of the primary output channel. This fluctuation not only stems from the power distribution imbalance between channels but also leads to a rapid switching of the operating state of the power devices inside the UPS. For example, when the voltage drops, the IGBT module will instantly switch from the saturation conduction state to the linear amplification region, causing a sharp increase in power consumption and thermal stress concentration. This chain reaction, from voltage fluctuations to changes in device state, makes it difficult for UPS systems to maintain stable output characteristics during dynamic adjustments. This is particularly problematic in scenarios with extremely high requirements for power continuity, easily leading to hidden dangers. For example, fire alarm controllers may experience false alarms or missed alarms during voltage fluctuations, directly threatening the timeliness of personnel evacuation and fire response. Therefore, effectively mitigating the impact of voltage fluctuations and device state switching by dynamically adjusting the voltage stabilization control strategy of the main output channel at the moment the secondary output channel of the UPS system is shut down has become a key issue in ensuring the operational reliability of prefabricated energy storage systems and the accuracy of critical equipment response. Summary of the Invention

[0003] This invention provides a power supply control method for a prefabricated cabin-type energy storage system, mainly including:

[0004] The shutdown signal of the secondary output channel is obtained to trigger the initial voltage fluctuation dataset.

[0005] Based on the initial voltage fluctuation dataset, analyze the impact of shutdown energy dissipation on the main output channel and determine the specific parameters of the source of fluctuation interference;

[0006] Obtain real-time data on the shutdown frequency range and response delay after shutdown of the secondary output channel to determine the appropriate frequency adjustment range.

[0007] Based on the determined frequency adjustment range, the voltage is dynamically calibrated to obtain a preliminary stable voltage output curve.

[0008] Based on the initially stable voltage output curve, monitor the achievement of fluctuation stability conditions. If the relevant indicators of fluctuation stability conditions are not met, optimize the recovery process of instantaneous voltage drop and determine the adjusted voltage stability parameters.

[0009] Obtain the system load distribution status at the shutdown time point, analyze the impact of the response delay after shutdown on the duration of fluctuation, adjust the voltage control logic of the main output channels, and determine the final voltage stabilization adjustment scheme.

[0010] Furthermore, the acquisition of the secondary output channel's shutdown signal triggers the formation of an initial voltage fluctuation dataset, including:

[0011] After the secondary output channel is turned off, the voltage timing data of the main output channel is collected according to the preset time window and sampling interval, and the steady-state voltage value before the turn-off is recorded as the reference value for the start of fluctuation.

[0012] The voltage time series data is differentially calculated to determine the moment when the voltage change rate exceeds the threshold. The difference between the voltage value at that moment and the fluctuation start reference value is extracted as the fluctuation start value. The maximum deviation value in the subsequent sampled data is recorded as the fluctuation peak amplitude.

[0013] Based on the fluctuation start value and the fluctuation peak amplitude, combined with the shutdown time point, a triplet data containing the shutdown time, fluctuation start value, and fluctuation peak amplitude is generated;

[0014] By repeatedly collecting and calculating the triplet data through multiple load switching events, and arranging them in the order of shutdown time, an initial voltage fluctuation dataset is generated.

[0015] Furthermore, based on the initial voltage fluctuation dataset, the impact of shutdown energy dissipation on the main output channel is analyzed, and specific parameters of the fluctuation interference source are determined, including:

[0016] Extract the initial fluctuation value and peak amplitude from the initial voltage fluctuation dataset, calculate the product of the difference between the initial fluctuation value and the peak amplitude and the equivalent capacitance value of the main output channel, generate an energy dissipation index, and determine the influence coefficient of the turn-off energy dissipation.

[0017] Based on the influence coefficient, the time interval for the voltage to recover to a steady state is calculated, the duration of the fluctuation is generated, the spectral distribution of the fluctuation signal is obtained through fast Fourier transform, the frequency characteristics of the fluctuation are extracted, and a dynamic trend curve is generated.

[0018] The specific parameters of the source of fluctuation interference are determined based on the dynamic trend curve.

[0019] Furthermore, based on the influence coefficient, the time interval for the voltage to recover to a steady state is calculated, the duration of the fluctuation is generated, the spectral distribution of the fluctuation signal is obtained through a fast Fourier transform, the frequency characteristics of the fluctuation are extracted, and a dynamic trend curve is generated, including:

[0020] Based on the influence coefficient, the time interval from the onset of voltage fluctuation to its recovery to a steady state is identified as the fluctuation duration. A fast Fourier transform is performed on the voltage sampling data within the fluctuation duration to generate a spectral distribution, and the frequency component with the largest amplitude is extracted as the fluctuation frequency characteristic. Voltage drop amplitude data are arranged in chronological order of sampling time to construct a time-domain sequence of instantaneous voltage drops. The moving average method is used to calculate the rate of change of voltage drop amplitude at adjacent moments, generating the dynamic trend curve.

[0021] Furthermore, the step of acquiring real-time data on the shutdown frequency range and post-shutdown response delay of the secondary output channel, and determining the appropriate frequency adjustment range, includes:

[0022] Based on the specific parameters of the source of the fluctuation interference, the number of shutdown events per unit time is calculated, a shutdown frequency range is generated, and the time interval from the triggering of the shutdown signal to the voltage recovery to steady state is recorded as response delay data.

[0023] Based on the shutdown frequency range and the response delay data, the switching judgment threshold and trigger delay parameter are adjusted to generate the frequency adjustment range.

[0024] Furthermore, the step of dynamically calibrating the voltage based on the determined frequency adjustment range to obtain a preliminary stable voltage output curve includes:

[0025] Based on the frequency adjustment range, the voltage recovery rate is calculated, and dynamic characteristic parameters are generated;

[0026] Based on the dynamic characteristic parameters, real-time voltage feedback values ​​are collected, and the voltage is calibrated using a proportional-integral control method to generate a preliminary stable voltage output curve.

[0027] Furthermore, the process of obtaining the system load distribution status at the shutdown time point, analyzing the impact of the response delay after shutdown on the duration of fluctuations, adjusting the voltage control logic of the main output channels, and determining the final voltage stabilization adjustment scheme includes:

[0028] Based on the voltage stability parameters, the current value and power ratio of each load branch at the turn-off time are collected to generate system load distribution status data.

[0029] Based on the system load distribution status data, calculate the correlation between the response delay time after shutdown and the duration of fluctuation, and determine the degree of influence of the response delay on the duration of fluctuation.

[0030] The off-energy dissipation data is processed by a low-pass filter to retain low-frequency components and generate an interference suppression dataset.

[0031] Based on the interference amplitude distribution of the interference suppression dataset, the proportional-integral control parameters of the main output channels are adjusted, and the final voltage stabilization regulation scheme is generated by combining the frequency characteristics of the source of fluctuation interference and the load effect.

[0032] Furthermore, the method also includes: obtaining system response data after the secondary output channel is turned off through the final voltage stabilization adjustment scheme, and determining the voltage stability guarantee parameters under long-term operation.

[0033] Furthermore, the process of obtaining system response data after the secondary output channel is turned off through the final voltage stabilization adjustment scheme, and determining the voltage stability guarantee parameters under long-term operation, includes:

[0034] Based on the voltage stabilization regulation scheme, the voltage change sequence after the secondary output channel is turned off is collected.

[0035] Calculate the peak amplitude of fluctuations and the recovery rate to generate system response data;

[0036] Based on the system response data, the correlation between the peak fluctuation amplitude and the recovery rate is analyzed, and the operating data is monitored to determine the voltage stability assurance parameters for long-term operation.

[0037] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0038] This invention discloses a power supply control method for a prefabricated capsule-type energy storage system. By real-time monitoring of the impact of load switching, voltage fluctuation data is acquired when a shutdown signal is triggered, and the impact of shutdown energy dissipation on the main output is analyzed. Based on the duration and frequency characteristics of the fluctuations, the dynamic trend of instantaneous voltage drops is calculated, and the parameters of the source of fluctuation interference are determined. This invention reduces the shutdown frequency by adjusting load switching control parameters and uses a proportional-integral control algorithm to dynamically calibrate the voltage, optimizing the recovery process of instantaneous voltage drops. Finally, by analyzing the correlation between the peak amplitude of fluctuations and the recovery rate, and continuously monitoring operating data under stable fluctuation conditions, the voltage stability guarantee parameters for long-term operation are determined, effectively solving the problem of voltage fluctuations in the main output channel caused by the shutdown of the secondary output channel, and improving the stability and reliability of the system. Attached Figure Description

[0039] Figure 1 This is a flowchart of a power supply control method for a prefabricated cabin-type energy storage system according to the present invention.

[0040] Figure 2 This is a schematic diagram of a power supply control method for a prefabricated cabin-type energy storage system according to the present invention.

[0041] Figure 3 This is another schematic diagram of a power supply control method for a prefabricated cabin-type energy storage system according to the present invention. Detailed Implementation

[0042] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] In traditional UPS systems, mains power or battery power is converted by the UPS main unit's internal circuitry before output. However, there is no distinction between primary and secondary output channels, making it impossible to effectively control the power supply time of the UPS main unit's load. The technical solution of this invention improves upon this by implementing hierarchical output channels and achieving precise control of the power supply time of the secondary output channels through internal logic circuitry, without requiring additional optional accessories. This logic circuitry is only effective when the UPS main unit is in battery operation mode; when the mains power is normal, it does not limit the load power supply time. Once the UPS switches to battery power mode, the user can set the power supply time of the secondary output channels through the function selection interface on the front panel of the UPS main unit. For example, according to standard requirements, battery management systems and communication equipment require at least 2 hours of power supply time, while automatic fire alarm systems and fire-fighting linkage controllers require 3 hours of power supply assurance. Taking a mains power supply or battery connection for load 1 and load 2 as an example, the automatic fire alarm system and fire-fighting linkage controller (considered load 1) are connected to the main output channel to ensure continuous operation in battery-powered mode. Simultaneously, the battery management system and communication equipment (considered load 2) are connected to the secondary output channel of the UPS host, and the secondary output channel is set to supply power for 2 hours via the host. In this configuration, after the secondary output channel has supplied power for the preset 2 hours, the UPS host will automatically shut down the secondary output channel to ensure that the main output channel can continuously supply power to critical loads such as the automatic fire alarm system and fire-fighting linkage controller for up to 3 hours. This hierarchical control not only improves the efficiency of power resource utilization but also ensures that the power supply to the most critical equipment is prioritized in emergencies.

[0044] This embodiment first monitors the shutdown signal triggering of the secondary output channel. When the UPS main unit switches to battery operation mode and the secondary output channel reaches the preset power supply time, a shutdown signal is generated to shut down the secondary output channel, thereby achieving effective management of the load power supply time. Figure 1-3 A power supply control method for a prefabricated cabin-type energy storage system may specifically include:

[0045] S101: Obtain the shutdown signal trigger of the secondary output channel to form the initial voltage fluctuation dataset.

[0046] Upon detecting the secondary output channel shutdown signal, the voltage of the main output channel is continuously sampled at a preset sampling interval within a preset time window before and after shutdown to obtain voltage time-series data before and after shutdown. The steady-state voltage value before shutdown is recorded as the fluctuation initiation reference value. Differential calculations are performed on the collected voltage time-series data. When the voltage change rate at multiple consecutive sampling points exceeds a preset threshold, it is determined as the start of fluctuation. The difference between the voltage value at this moment and the steady-state reference value is extracted as the fluctuation initiation value. The maximum deviation value in subsequent sampling data is monitored to determine the fluctuation peak amplitude. Based on the fluctuation initiation value and fluctuation peak amplitude, combined with the corresponding shutdown time information, a triplet data set containing the shutdown time, fluctuation initiation value, and fluctuation peak amplitude is constructed. The above acquisition and calculation process is repeated for multiple load switching events to obtain multiple sets of triplet data. The obtained multiple sets of triplet data are organized and arranged in order of shutdown time to form an initial voltage fluctuation dataset recording the fluctuation initiation value and fluctuation peak amplitude of the main output channel at different shutdown time points.

[0047] For example, during load switching in a power system, the shutdown of the secondary output channel can cause significant electromagnetic interference to the primary output channel. This interference stems from the release of energy stored in the inductor in the circuit at the moment the switch is turned off, as well as the resonance effect formed by parasitic capacitance and inductance.

[0048] Specifically, when the system detects a turn-off signal from the secondary output channel, the voltage monitoring program needs to be started immediately. The selection of the preset time window is crucial; it is typically set to 10 milliseconds before turn-off and 50 milliseconds after turn-off. This time range can fully capture the entire voltage fluctuation process. The sampling interval setting needs to satisfy the Nyquist sampling theorem to ensure accurate reconstruction of the voltage waveform's changing characteristics. The steady-state voltage value before turn-off is used as a reference and obtained by averaging multiple samples to reduce the influence of random noise.

[0049] In one possible implementation, the differential calculation process is achieved by dividing the voltage difference between adjacent sampling points by the time interval. When the rate of voltage change exceeds a preset threshold, it indicates that the circuit begins to exhibit a transient response. This threshold needs to be determined based on the system's rated voltage and allowable fluctuation range, and is typically set to 2-3 times the rated voltage change rate. The initial fluctuation value reflects the degree of impact in the initial stage of the interference, while the peak fluctuation amplitude represents the maximum intensity of the interference.

[0050] It should be noted that the construction method of triplet data establishes a correspondence between time and voltage fluctuation characteristics. Each data set contains complete transient event information: the shutdown time identifies the timestamp of the event, the fluctuation initiation value quantifies the initial impact of the interference, and the fluctuation peak amplitude reflects the maximum degree of interference. This data organization method facilitates subsequent statistical analysis and pattern recognition. In practical applications, data acquisition from multiple load switching events can cover different operating conditions.

[0051] For example, voltage fluctuations are relatively small under light load conditions, while electromagnetic interference is more significant under heavy load conditions due to the larger current. By collecting data under different load conditions and at different switching times, a comprehensive database of voltage fluctuation characteristics can be established.

[0052] Preferably, the temporal arrangement of the dataset not only facilitates tracing the fluctuations at a specific moment, but also enables the discovery of the evolution of fluctuation characteristics over time.

[0053] For example, the fluctuation characteristics of equipment in the early stages of operation may differ from those after long-term operation. This difference reflects the effects of factors such as component aging and increased contact resistance. By comparing data from different periods, the degradation trend of system performance can be assessed, providing a basis for preventive maintenance.

[0054] S102. Based on the initial voltage fluctuation dataset, analyze the impact of shutdown energy dissipation on the main output channels and determine the specific parameters of the source of fluctuation interference.

[0055] The initial value and peak amplitude of each shutdown event are extracted from the initial voltage fluctuation dataset. The difference between the peak amplitude and the initial value is calculated, and this difference is multiplied by the equivalent capacitance of the main output channel to obtain an energy dissipation index. Based on the ratio of this index to the rated power of the main output channel, the influence coefficient of shutdown energy dissipation on the main output channel is determined. For this influence coefficient, the time interval from the onset of the voltage fluctuation to its recovery to a steady-state value is identified as the fluctuation duration. A Fast Fourier Transform is performed on the voltage sampling data within this time interval to obtain the spectral distribution of the fluctuation signal, and the frequency component with the largest amplitude is extracted as the fluctuation frequency characteristic. Based on the fluctuation duration and fluctuation frequency characteristic, the voltage drop amplitude data are arranged in the sampling time order to construct a time-domain sequence of instantaneous voltage drops. The rate of change of voltage drop amplitude between adjacent moments is calculated using a moving average method with a preset window width, resulting in a dynamic trend curve reflecting the voltage recovery process. Based on the dynamic trend curve, the first derivative of each point on the curve is calculated as the slope feature, and the second derivative is calculated as the curvature feature. Combined with the fluctuation duration value, the dominant frequency component value, and the influence degree coefficient value, specific parameters including interference intensity, action time, and frequency components are determined.

[0056] For example, the calculation of energy dissipation metrics involves the energy release process of energy storage elements in a circuit. When the secondary output channel is turned off, the magnetic field energy stored in the inductor and the electric field energy stored in the capacitor are released instantaneously, and this energy conversion generates voltage fluctuations on the primary output channel. The difference between the peak amplitude and the initial value of the fluctuation directly reflects the intensity of this energy release, while multiplying by the equivalent capacitance value converts the voltage change into an actual energy value.

[0057] Specifically, the equivalent capacitance includes the output filter capacitor, parasitic capacitance, and the equivalent capacitance at the load end. These capacitors undergo a charging and discharging process at the moment of turn-off, and the energy dissipation index quantifies the energy change during this process. The influence factor is normalized by the ratio to the rated power, making systems of different power levels comparable.

[0058] For example, in a 1000W power supply system, if the energy dissipation index reaches 50 joules, then the impact factor is 0.05, indicating that 5% of the rated power is dissipated during the transient process.

[0059] In one possible implementation, the duration of fluctuations is identified using a threshold-based method. Starting from the initial moment of the fluctuation, the voltage value is continuously monitored. The fluctuation is considered to have ended when the deviation between the voltage values ​​at multiple consecutive sampling points and the steady-state value is less than a preset threshold. This duration reflects the system's damping characteristics and recovery capability. The Fast Fourier Transform (FFT) converts the time-domain voltage fluctuation signal to the frequency domain, revealing the various frequency components contained within the fluctuation. The dominant frequency component typically corresponds to the circuit's inherent resonant frequency, which is closely related to the inductance and capacitance parameters.

[0060] It should be noted that the moving average method can smooth out high-frequency noise and highlight the main trends when processing voltage dip data. The selection of the preset window width needs to balance the smoothing effect and time resolution, and is usually taken as 1 / 10 to 1 / 5 of the fluctuation period. By calculating the rate of change between adjacent time points, the speed of voltage recovery can be obtained. The dynamic trend curve exhibits a typical exponential decay characteristic, with a fast recovery speed in the early stage and gradually flattening out in the later stage. The slope characteristic reflects the instantaneous speed of voltage recovery; the absolute value of the slope is large in the early stage of the fluctuation and gradually decreases over time. The curvature characteristic describes the rate of change of the recovery speed; positive curvature indicates that the recovery speed is slowing down, and negative curvature indicates that the recovery speed is accelerating. These characteristics are related to parameters such as the circuit's damping coefficient and quality factor.

[0061] Preferably, these characteristic parameters are combined to form a multi-dimensional interference description system. The interference intensity is quantified by the degree of influence coefficient, the duration of action is characterized by the duration of the fluctuation, and the frequency components are represented by the values ​​of the dominant frequency components.

[0062]

[0063] I represents the interference intensity, α represents the influence coefficient, and A max A represents the maximum amplitude value. min A represents the minimum amplitude value. ref β represents the reference amplitude, t represents the decay constant, and t represents the time variable.

[0064]

[0065] f d This represents the frequency value corresponding to the dominant frequency component, k represents the index of the frequency component with the largest amplitude, and f s This represents the sampling frequency, and N represents the number of FFT transform points. This formula is used to determine the specific values ​​of the main frequency components in a voltage fluctuation signal.

[0066] S103. Obtain real-time data on the shutdown frequency range and response delay after shutdown of the secondary output channel, and determine the appropriate frequency adjustment range.

[0067] Based on the interference intensity and frequency components in the specific parameters of the source of fluctuation interference, the number of times the secondary output channel is turned off within a preset time window is monitored. The number of turn-off events per unit time is calculated to obtain the turn-off frequency. The minimum and maximum values ​​of the turn-off frequency within multiple time windows are statistically analyzed to form a turn-off frequency range. Simultaneously, the time interval from the triggering of the turn-off signal to the voltage recovering to a steady-state value is recorded as the post-turn-off response delay. If the upper limit of the turn-off frequency range exceeds a preset frequency threshold, the current switching judgment threshold, switching trigger delay, and load priority weight parameters are identified. The turn-off operation frequency is reduced by increasing the value of the switching judgment threshold or extending the value of the switching trigger delay, resulting in an adjusted control parameter combination. Based on the turn-off operation frequency generated after the operation of the adjusted control parameter combination, combined with the corresponding post-turn-off response delay, the voltage fluctuation data of the main output channel is re-acquired and the interference intensity value is calculated. When the interference intensity value is lower than the preset interference threshold and the upper limit of the turn-off frequency range does not exceed the frequency threshold, the interval between the minimum and maximum values ​​of the current turn-off operation frequency is determined as the appropriate frequency adjustment interval.

[0068] For example, monitoring the shutdown frequency involves accurately counting the switching actions of the secondary output channels. In practical power systems, the secondary output channels perform important functions of load distribution and power regulation, and their shutdown actions can generate electromagnetic interference and affect the stability of the primary output channels.

[0069] Specifically, the preset time window is usually selected as 1 second or 10 seconds, during which the number of shutdown events is counted.

[0070] For example, if 50 shutdown events are detected within a 10-second window, the shutdown frequency is 5Hz. By monitoring multiple consecutive time windows, the range of shutdown frequency variation can be obtained. When the system load fluctuates significantly, the shutdown frequency may vary between 2Hz and 8Hz, forming a shutdown frequency range. This statistical method can reflect the dynamic characteristics of the system.

[0071] In one possible implementation, the response delay after shutdown is measured from the moment the shutdown signal is triggered until the voltage of the main output channel recovers to 95% of its steady-state value. This delay includes the circuit's inertial response, the control loop's settling time, and the filter's settling time. A longer response delay implies poorer dynamic performance of the system and is prone to cumulative effects during frequent switching.

[0072] It should be noted that adjusting control parameters is a key means of reducing the frequency of shutdowns. The switching judgment threshold determines when load switching is triggered; increasing this threshold can reduce unnecessary switching actions.

[0073] For example, increasing the current deviation threshold from 5% to 8% can avoid frequent switching caused by minor fluctuations. The switching trigger delay is the waiting time after the switching conditions are met; extending this time can filter out transient interference. Load priority weights affect the switching order of different loads; proper settings can reduce the number of switching operations for critical loads. The verification process after adjustments evaluates the improvement effect through actual operation. Newly acquired voltage fluctuation data reflects the system performance after parameter adjustments. The recalculation of interference intensity values ​​uses the same method as the initial analysis, quantifying it through voltage fluctuation amplitude and energy dissipation. This before-and-after comparison provides a direct evaluation of the effectiveness of the adjustment measures.

[0074] Preferably, determining the appropriate frequency adjustment range requires simultaneously meeting two conditions: the interference intensity is below an acceptable level, and the shutdown frequency remains within a reasonable range. This range represents a balance between system performance and switching flexibility. While a shutdown frequency that is too low reduces interference, it may slow down load regulation response; conversely, a shutdown frequency that is too high will exacerbate electromagnetic interference problems. Through iterative adjustments and verification, the determined frequency adjustment range provides quantitative guidance for optimized system operation, minimizing the impact of interference on the main output channels while ensuring load management flexibility.

[0075] S104. Based on the determined frequency adjustment range, the voltage is dynamically calibrated to obtain a preliminary stable voltage output curve.

[0076] Based on the off-operation frequency data within the frequency adjustment range, the voltage recovery time series at the corresponding time is extracted. The fluctuation recovery rate is obtained by calculating the ratio of voltage change per unit time to time. By fitting the relationship between the recovery rate and time, the dynamic characteristic parameters of the fluctuation recovery rate are determined. Based on the dynamic characteristic parameters, real-time voltage values ​​are obtained at the sampling points of the main output channel as feedback values. The deviation between the feedback value and the rated voltage is calculated, and the load current change is monitored simultaneously. The influence coefficient of the fluctuation on the load is evaluated based on the product of the deviation value and the load current change. For the influence coefficient and the voltage deviation, a proportional-integral control algorithm is used to calculate the calibration quantity. The proportional term is obtained by multiplying the current deviation value by a preset proportional coefficient, and the integral term is obtained by multiplying the accumulated historical deviation values ​​by a preset integral coefficient. The proportional term and the integral term are added together as the control output quantity. The output voltage value of the main output channel is adjusted by the control output quantity, and the voltage sampling value sequence during the adjustment process is recorded. When the voltage fluctuation amplitude is less than a preset stability threshold within multiple consecutive sampling periods, the voltage value sequence of that period is connected to form a preliminary stable voltage output curve.

[0077] For example, the fluctuation recovery rate is calculated based on the dynamic process of voltage returning to steady state from a disturbance state. After the disturbance caused by load switching ends, the voltage of the main output channel gradually recovers to its rated value. This recovery process exhibits non-linear characteristics, with a faster initial recovery speed followed by a gradual slowdown.

[0078] Specifically, by performing differential operations on the voltage recovery time series, the instantaneous recovery rate at each moment can be obtained.

[0079] For example, in the first millisecond after shutdown, the voltage might recover at a rate of 2V per millisecond; by the tenth millisecond, the recovery rate might drop to 0.5V per millisecond. This rate variation reflects the system's damping characteristics and energy dissipation process. By fitting these rate data points with an exponential or polynomial function, characteristic parameters describing the dynamic changes in the recovery rate, such as the time constant and the decay coefficient, can be obtained.

[0080] In one possible implementation, acquiring real-time voltage feedback values ​​requires the support of high-speed sampling circuitry. The sampling frequency is typically set to at least 10 times the switching frequency to ensure accurate capture of transient voltage changes. Monitoring the load current is equally important, as current variations directly reflect the dynamic demands of the load. The impact factor is calculated by comprehensively considering both the absolute value of the voltage deviation and the magnitude of the load current change; their product reflects the actual impact of the power disturbance.

[0081] It should be noted that proportional-integral (PI) control is a classic feedback control method. The proportional term provides a fast response; its magnitude is proportional to the current error, enabling rapid reduction of steady-state error. The integral term eliminates steady-state error by accumulating historical errors to generate control action. The preset proportional and integral coefficients need to be tuned based on the system's dynamic characteristics, typically using the step response method or frequency response method. An excessively large proportional coefficient can cause system oscillation, while an excessively small coefficient results in a slow response; an excessively large integral coefficient can cause overshoot, while an excessively small coefficient leads to slow elimination of steady-state error. The control output is calculated as the algebraic sum of the proportional and integral terms. This output acts on the power regulation circuit, changing the output voltage by adjusting the duty cycle or conduction angle of the switching transistor.

[0082] For example, when the voltage is detected to be lower than the rated value, the control output is positive, and the duty cycle is increased to boost the output voltage; conversely, the duty cycle is decreased when the voltage is higher than the rated value.

[0083] Preferably, the voltage output curve is formed after adjustments over multiple control cycles. A preset stability threshold is typically set to 1% or 2% of the rated voltage. When voltage fluctuations within this range for 10 or more consecutive sampling cycles, the system is considered to have reached a preliminary stable state. Connecting the voltage sampling values ​​from these stable periods in chronological order creates a curve that visually reflects the adjustment effect of the control algorithm and the dynamic performance of the system, providing a basis for subsequent optimization and adjustments.

[0084] S105. Based on the initially stable voltage output curve, monitor the achievement of fluctuation stability conditions. If the relevant indicators of fluctuation stability conditions do not meet the standards, optimize the recovery process of instantaneous voltage drop and determine the adjusted voltage stability parameters.

[0085] Three indicators—fluctuation amplitude, fluctuation period, and steady-state error—are extracted from the initially stabilized voltage output curve. The ratio of fluctuation amplitude to rated voltage is calculated as the fluctuation rate, and the number of fluctuations per unit time is counted as the fluctuation frequency. The fluctuation rate, fluctuation frequency, and steady-state error are combined to form an evaluation index group for fluctuation stability conditions. If any indicator in the evaluation index group exceeds a preset stability threshold, the component parameter values ​​used to suppress transient interference during turn-off are obtained, including the capacitance value of the parallel capacitor at the output terminal, the resistance value of the series resistor, and the voltage rise slope control time. The transient energy is absorbed by increasing the capacitance value of the parallel capacitor, and the oscillation decay rate is changed by adjusting the resistance value of the series resistor. Based on the adjusted component parameter values, the recovery curve of the instantaneous voltage drop process is re-acquired, and the time required from the start of the drop to recovery to the steady-state value is measured as the recovery time. The maximum overshoot during the recovery process is calculated, and the recovery time and overshoot are combined as recovery characteristic data. By comparing the recovery characteristic data before and after optimization, when the recovery time is shortened and the overshoot is reduced to within the preset range, the current combination of parallel capacitor value, series resistor value, and voltage rise slope control time value is determined as the adjusted voltage stability parameters.

[0086] For example, extracting the fluctuation characteristics of the voltage output curve involves multi-dimensional analysis of the time-domain signal. The fluctuation amplitude reflects the maximum deviation of the voltage from its steady-state value and is obtained by calculating the difference between peaks and troughs. The fluctuation period is determined by identifying the time interval between adjacent peaks or troughs, reflecting the system's oscillation characteristics. Steady-state error refers to the constant deviation between the actual output and the expected value after the system reaches stability.

[0087] Specifically, volatility calculation normalizes the volatility amplitude, making systems with different voltage levels comparable.

[0088] For example, a 0.5V fluctuation in a 24V system and a 4.5V fluctuation in a 220V system both have a fluctuation rate of approximately 2%. The statistical analysis of fluctuation frequency requires setting an amplitude threshold; only fluctuations exceeding this threshold are counted to avoid misclassifying noise as valid fluctuations. This evaluation system, comprised of these three indicators, comprehensively reflects voltage stability.

[0089] In one possible implementation, a capacitor connected in parallel at the output acts as an energy buffer. When the load suddenly decreases, the excess energy is absorbed by the capacitor; when the load suddenly increases, the capacitor releases its stored energy, mitigating transient voltage changes. The capacitor value needs to balance transient response and steady-state ripple; too small a value cannot effectively suppress transient interference, while too large a value will reduce the system's dynamic response speed. Series resistors form an RC damping network, and their resistance determines the oscillation decay rate.

[0090] It should be noted that voltage rise rate control reduces transient impacts by limiting the rate of voltage change. This soft-start mechanism is widely used in switching power supplies, gradually increasing the duty cycle or conduction angle to smoothly raise the output voltage. The control time setting needs to consider the load's capacity and the system's response requirements, typically ranging from milliseconds to hundreds of milliseconds. Measuring the recovery characteristics requires precise capture of the transient process. The recovery time starts from when the voltage drops below a preset threshold and ends when the voltage recovers and stabilizes within the allowable deviation range. This time includes the control loop's response time, the switching delay of the power devices, and the settling time of the filter network. The maximum overshoot refers to the maximum magnitude by which the voltage exceeds the steady-state value during the recovery process; excessive overshoot may damage sensitive loads.

[0091] Preferably, parameter optimization is an iterative process. Initial parameters are set based on theoretical calculations and empirical values, and the effect is evaluated through actual testing. If the recovery time is reduced from 50 milliseconds to 30 milliseconds, and the overshoot is reduced from 15% to 8%, it indicates that the parameter adjustment direction is correct. The determined combination of voltage stability parameters must not only meet stability requirements but also take into account cost and reliability factors.

[0092] S106. Obtain the system load distribution status at the shutdown time point, analyze the impact of the response delay after shutdown on the duration of fluctuation, adjust the voltage control logic of the main output channel, and determine the final voltage stabilization adjustment scheme.

[0093] Based on the adjusted voltage stability parameters, including the values ​​of parallel capacitors and series resistors, the current and power ratio of each load branch are monitored at the turn-off time. The power distribution ratio between the primary and secondary loads is recorded to obtain system load distribution status data reflecting the load distribution characteristics. Based on this system load distribution status data, the turn-off response delay time under different load configurations is calculated. The correlation between the response delay time and the corresponding fluctuation duration is calculated to obtain the proportional coefficient that increases the fluctuation duration by one unit of time in response delay, thus determining the degree of influence of response delay on fluctuation duration. For the original data sequence of the influence value and turn-off energy dissipation, a low-pass filter is used to remove high-frequency noise components, retaining low-frequency components reflecting the actual interference characteristics. The filtered data is arranged in chronological order to form a filtered interference suppression dataset. Simultaneously, the proportional-integral control parameters of the main output channel are adjusted according to the interference amplitude distribution in this dataset. By combining the adjusted proportional-integral control parameters with the frequency characteristics and intensity variation patterns of the source of fluctuation interference, as well as the degree of influence of fluctuations on different types of loads at different times, a comprehensive adjustment parameter set including proportional-integral control parameters and voltage stability parameters is constructed, and the final voltage stability adjustment scheme is determined.

[0094] For example, monitoring the system load distribution status is a key step in understanding the impact of voltage fluctuations. In a power system, different loads have different electrical characteristics and levels of importance. Primary loads typically refer to critical equipment or high-power electrical equipment, whose power supply stability directly affects system operation; secondary loads are equipment whose power supply can be temporarily interrupted or degraded.

[0095] Specifically, the power distribution ratio is calculated by measuring the current of each branch and multiplying it by the corresponding voltage to obtain the power value, and then calculating the percentage of the power of each branch to the total power.

[0096] For example, in an industrial control system, controllers and sensors, as the primary loads, may account for 30% of the total power, while auxiliary equipment such as indicator lights and fans, as secondary loads, may account for 70%. This power distribution directly affects the impact of shutdown operations on the system.

[0097] In one possible implementation, correlation analysis between response delay and fluctuation duration reveals the intrinsic relationship between the system's dynamic characteristics. Response delay comprises three parts: detection delay, control decision delay, and execution delay. When response delay increases, the system cannot compensate for voltage dips in a timely manner, leading to a prolonged fluctuation duration. Statistical analysis of a large amount of measured data reveals an approximately linear relationship between the two, with the proportionality coefficient reflecting the system's dynamic response capability.

[0098] It should be noted that the low-pass filter plays a smoothing role in signal processing. The power dissipation data from the shutdown process contains real interference signals and high-frequency noise, with the high-frequency noise mainly originating from switching actions, electromagnetic interference, and other factors. The low-pass filter allows signals below the cutoff frequency to pass through while attenuating signals above the cutoff frequency. The choice of cutoff frequency needs to strike a balance between preserving effective information and removing noise, and is typically set to one-tenth to one-fifth of the system bandwidth. The filtered interference suppression dataset provides more accurate interference characteristic information. Based on the distribution of interference amplitudes in this data, it can be determined whether the system has resonance at a specific frequency or abnormal interference under certain operating conditions. The adjustment of the proportional-integral control parameters is based on these characteristics: when low-frequency large-amplitude interference is detected, the integral coefficient is increased to improve steady-state accuracy; when high-frequency small-amplitude disturbances exist, the proportional coefficient is decreased to avoid amplifying noise.

[0099] Preferably, the construction of the integrated adjustment parameter set considers optimization objectives across multiple dimensions. The frequency characteristics of the source of fluctuation interference determine the frequency bands that need to be suppressed, and the intensity variation pattern reflects the time-varying characteristics of the interference. Different types of loads have different sensitivities to voltage fluctuations: precision instruments are sensitive to instantaneous drops, while heating equipment is more concerned with average power. The final voltage stabilization adjustment scheme, by balancing these factors, maximizes the overall system efficiency and stability while ensuring the power supply quality of critical loads.

[0100] S107. By using the final voltage stabilization adjustment scheme, obtain the system response data after the secondary output channel is turned off, and determine the voltage stability guarantee parameters under long-term operation.

[0101] Based on the comprehensive regulation parameter set in the final voltage stabilization regulation scheme, the voltage change sequence of the main output channel is collected after the secondary output channel is turned off. The difference between the lowest point of voltage drop and the steady-state value is recorded as the peak fluctuation amplitude. Simultaneously, the time change rate from the lowest point to the steady-state value is calculated to obtain the fluctuation recovery rate, acquiring system response data including timestamps, peak amplitude, and recovery rate. Based on the system response data, the peak fluctuation amplitude and the corresponding fluctuation recovery rate are statistically analyzed using a scatter distribution. The correlation coefficient between the two is calculated through linear regression. If the absolute value of the correlation coefficient is greater than a preset threshold, a significant correlation is determined between the peak amplitude and the recovery rate, obtaining a proportional factor describing their relationship. For the proportional factor and different shutdown signal triggering scenarios, including overload protection triggering, timed switching triggering, and fault isolation triggering, voltage fluctuation characteristic parameters are recorded for each scenario. Under the premise that the fluctuation stability condition is met, operating data is continuously collected within a preset time period, and the distribution characteristics of voltage deviation, fluctuation frequency, and recovery time are statistically analyzed. By extracting the time series trend of the distribution characteristics, the change law of parameters with operating time is identified. The parameter value range with stable trends and meeting performance indicators is determined as the voltage stability guarantee parameter under long-term operation.

[0102] For example, the acquisition of system response data reflects the actual effect of the voltage stabilization regulation scheme. The peak amplitude of the fluctuation directly reflects the severity of the transient interference. In the voltage sequence obtained through continuous sampling, the lowest point usually appears within a few milliseconds after shutdown. The greater the difference between this lowest point and the steady-state value, the more severe the impact on the system and the greater the influence on the load.

[0103] Specifically, the calculation of the fluctuation recovery rate involves the concept of the time derivative. Starting from the lowest voltage point, the system gradually recovers to its steady-state value through feedback control, a process exhibiting nonlinear characteristics. The initial recovery rate is relatively fast, but gradually slows down as the system approaches its steady-state value. The recovery rate reflects the system's dynamic response capability and the effectiveness of the control algorithm. The recording of timestamps gives each data point temporal characteristics, facilitating subsequent correlation analysis.

[0104] In one possible implementation, linear regression analysis reveals the intrinsic relationship between peak amplitude and recovery rate. Scatter plots of a large amount of measured data show that larger peak amplitudes tend to correspond to slower recovery rates, because the system requires more energy and time to compensate for larger biases. The correlation coefficient is calculated using the Pearson correlation coefficient method; a strong correlation is considered to exist when its absolute value exceeds 0.7. The scaling factor is obtained from the slope of the regression line, quantifying the proportional relationship between the two.

[0105] It should be noted that different shutdown triggering scenarios have different characteristics. Overload protection triggering typically occurs when the load current exceeds a set threshold, resulting in a more severe voltage drop. Timed switching triggering involves planned switching according to a preset schedule, allowing the system ample preparation time and resulting in relatively smaller voltage fluctuations. Fault isolation triggering is an emergency action upon detecting an anomaly, with characteristics falling between the former two. The characteristic parameters recorded for each scenario provide a classification basis for subsequent optimization. Continuous monitoring is crucial for capturing the long-term trends of the system. Voltage deviation reflects steady-state accuracy, the number of fluctuations reflects system stability, and recovery time represents dynamic performance. The distribution characteristics of these parameters are obtained through statistical methods, including statistics such as mean, variance, and kurtosis. The preset duration is typically chosen to be one month or longer of continuous operation to cover various operating conditions and environmental changes.

[0106] Preferably, time series trend extraction employs methods such as moving averages or exponential smoothing to filter out short-term fluctuations and identify long-term trends. The variation of parameters over operating time may exhibit three patterns: stable and unchanged, slow degradation, or periodic fluctuations. Stable and unchanged performance indicates good system performance; slow degradation suggests the need for preventative maintenance; periodic fluctuations may be related to environmental factors or load patterns. The ultimately determined voltage stability assurance parameters include not only the numerical range but also the permissible limits of the variation trend, providing a quantitative guarantee for the long-term stable operation of the system.

[0107] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit the scope of one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the scope of protection of one or more embodiments of this specification.

Claims

1. A power supply control method for a prefabricated cabin-type energy storage system, characterized in that, The method includes: The shutdown signal of the secondary output channel is obtained to trigger the initial voltage fluctuation dataset. The initial voltage fluctuation value and peak amplitude are extracted from the initial voltage fluctuation dataset. The product of the difference between the initial fluctuation value and the peak amplitude and the equivalent capacitance value of the main output channel is calculated to generate an energy dissipation index and determine the influence coefficient of the turn-off energy dissipation. Based on the influence coefficient, the time interval from the start of the voltage fluctuation to the recovery to a steady state is identified as the fluctuation duration. A fast Fourier transform is performed on the voltage sampling data within the fluctuation duration to generate a spectral distribution, and the frequency component with the largest amplitude is extracted as the fluctuation frequency characteristic. The voltage drop amplitude data are arranged in the order of sampling time to construct a time-domain sequence of instantaneous voltage drops. The moving average method is used to calculate the rate of change of voltage drop amplitude between adjacent time points to generate a dynamic trend curve. Based on the dynamic trend curve, the specific parameters of the source of fluctuation interference are determined. The process involves acquiring real-time data on the shutdown frequency range and post-shutdown response delay of the secondary output channel, and determining the appropriate frequency adjustment range. This includes: calculating the number of shutdown events per unit time based on the specific parameters of the source of the fluctuation interference, generating the shutdown frequency range, and recording the time interval from the shutdown signal trigger to the voltage recovery to a steady state as response delay data; and adjusting the switching judgment threshold and trigger delay parameters based on the shutdown frequency range and the response delay data to generate the frequency adjustment range. Based on the determined frequency adjustment range, the voltage is dynamically calibrated to obtain a preliminary stable voltage output curve. Based on the initially stable voltage output curve, monitor the achievement of fluctuation stability conditions. If the relevant indicators of fluctuation stability conditions are not met, optimize the recovery process of instantaneous voltage drop and determine the adjusted voltage stability parameters. Obtain the system load distribution status at the shutdown time point, analyze the impact of the response delay after shutdown on the duration of fluctuation, adjust the voltage control logic of the main output channels, and determine the final voltage stabilization adjustment scheme.

2. The power supply control method for a prefabricated cabin-type energy storage system according to claim 1, characterized in that, The acquisition of the secondary output channel's shutdown signal triggers the formation of an initial voltage fluctuation dataset, including: After the secondary output channel is turned off, the voltage timing data of the main output channel is collected according to the preset time window and sampling interval, and the steady-state voltage value before the turn-off is recorded as the fluctuation start reference value. Differential calculations are performed on the voltage time series data to determine the moment when the voltage change rate exceeds the threshold. The difference between the voltage value at that moment and the fluctuation start reference value is extracted as the fluctuation start value, and the maximum deviation value in the subsequent sampled data is recorded as the fluctuation peak amplitude. Based on the fluctuation start value and the fluctuation peak amplitude, combined with the shutdown time point, a triplet data containing the shutdown time, fluctuation start value, and fluctuation peak amplitude is generated; By repeatedly collecting and calculating the triplet data through multiple load switching events, and arranging them in the order of shutdown time, an initial voltage fluctuation dataset is generated.

3. The power supply control method for a prefabricated cabin-type energy storage system according to claim 1, characterized in that, The step of dynamically calibrating the voltage based on the determined frequency adjustment range to obtain a preliminary stable voltage output curve includes: Based on the frequency adjustment range, the voltage recovery rate is calculated, and dynamic characteristic parameters are generated; Based on the dynamic characteristic parameters, real-time voltage feedback values ​​are collected, and the voltage is calibrated using a proportional-integral control method to generate a preliminary stable voltage output curve.

4. The power supply control method for a prefabricated cabin-type energy storage system according to claim 1, characterized in that, The process of obtaining the system load distribution status at the shutdown time point, analyzing the impact of the response delay after shutdown on the duration of fluctuations, adjusting the voltage control logic of the main output channels, and determining the final voltage stabilization adjustment scheme includes: Based on the voltage stability parameters, the current value and power ratio of each load branch at the turn-off time are collected to generate system load distribution status data. Based on the system load distribution status data, calculate the correlation between the response delay time after shutdown and the duration of fluctuation, and determine the degree of influence of the response delay on the duration of fluctuation. By processing the power dissipation data through a low-pass filter, low-frequency components are preserved to generate an interference suppression dataset. Based on the interference amplitude distribution of the interference suppression dataset, the proportional-integral control parameters of the main output channels are adjusted, and the final voltage stabilization regulation scheme is generated by combining the frequency characteristics of the source of fluctuation interference and the load effect.

5. The power supply control method for a prefabricated cabin-type energy storage system according to claim 1, characterized in that, The method further includes: obtaining system response data after the secondary output channel is turned off through the final voltage stabilization adjustment scheme, and determining the voltage stability guarantee parameters under long-term operation.

6. The power supply control method for a prefabricated cabin-type energy storage system according to claim 5, characterized in that, The process involves obtaining system response data after the secondary output channel is turned off through the final voltage stabilization adjustment scheme, and determining the voltage stability guarantee parameters for long-term operation, including: According to the voltage stabilization regulation scheme, the voltage change sequence after the secondary output channel is turned off is collected, the peak fluctuation amplitude and recovery rate are calculated, and system response data is generated. Based on the system response data, the correlation between the peak fluctuation amplitude and the recovery rate is analyzed, and the operating data is monitored to determine the voltage stability assurance parameters for long-term operation.

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