1377 results about "Power loss" patented technology

The invention relates to the technical field of vehicle charging station operation and maintenance management, and particularly discloses a new energy vehicle charging station operation and maintenance management system and method based on the Internet of Things, and the method comprises the steps: collecting high-frequency harmonic characteristic data in a charging process in real time, and generating harmonic-impedance spectrum combined monitoring data; extracting dynamic attenuation gradient data of the line characteristic impedance; when the dynamic attenuation gradient data is lower than a preset critical safety threshold value, triggering a charging mode switching instruction; the charging piles are controlled to be switched into a charging mode, the phase difference parameters of the multiple charging piles are dynamically optimized, and a phase difference dynamic adjustment instruction set is generated; and finally, self-adaptive correction of the impedance compensation amount is carried out through the MEC equipment, and by self-adaptive correction of the impedance compensation amount, the system can maintain that the overall impedance is constantly greater than a critical safety threshold value, and power loss and equipment damage caused by impedance mismatch are avoided. The real-time feedback mechanism is helpful for improving the energy efficiency of the system, reducing the operation cost and ensuring the long-term reliability of the charging infrastructure.

The invention relates to the technical field of power grid data analysis, in particular to a power consumer anomaly detection and risk assessment method, system and device based on spatial-temporal feature fusion and a storage medium. The method comprises the following steps: acquiring multi-source power consumption data of various power consumers, constructing a space-time correlation feature extraction model, and establishing a user behavior space-time coupling relationship through time sequence analysis and spatial distribution characteristic analysis; constructing a multi-dimensional risk mapping model based on a long short term memory network and a convolutional neural network, establishing a nonlinear mapping relation from feature information to a risk assessment result, and identifying an abnormal power consumption behavior; establishing a distributed node credibility evaluation system, and performing credibility quantitative analysis on the data nodes in combination with a fuzzy comprehensive evaluation method; and based on an abnormal detection result and credibility evaluation, intelligent analysis and dynamic early warning of the electric charge management and control risk are realized. The time-space coupling characteristic of power consumption data can be fully mined, and high-precision identification of abnormal power consumption behaviors and accurate detection of non-technical power loss are realized.

The invention discloses a fan variable pitch anti-stall control method, device and system based on temperature scheduling, and the method comprises the steps: calculating a minimum anti-stall pitch angle under different environment temperatures and fan output powers, and taking the minimum anti-stall pitch angle as an optimal pitch angle amplitude limiting value; constructing a corresponding relation among the environment temperature, the fan output power and the optimal pitch angle amplitude limiting value; in the operation process of the controlled fan, the temperature of the environment where the controlled fan is located and the output power of the controlled fan are collected in real time; and according to the environment temperature and the fan output power which are collected in real time and the stored corresponding relation, the real-time optimal pitch angle amplitude limiting value is obtained, so that the real-time pitch angle of the controlled fan is controlled to be lower than the real-time optimal pitch angle amplitude limiting value. According to the method, the operation safety of the unit can be improved, the control stability is improved, and the power loss can be minimized while stall is prevented.

The invention discloses a high-voltage discharge circuit suitable for a storage battery power supply and an electric flame stove. The high-voltage discharge circuit comprises an inversion driving module, a boosting transformer T1, at least three groups of active bridge rectifier modules and discharge modules corresponding to the active bridge rectifier modules. Through combination of driving of the active bridge rectifier module, voltage / current double-closed-loop feedback and real-time PWM signal output control of the control chip U1, current difference of each discharge branch is eliminated, the problem of uneven branch current is solved, and ring current and reactive power loss between branches are eliminated; a diode in a voltage doubling rectifying circuit and a bleeder resistor of a resonance circuit in the prior art are abandoned, an active bridge type rectifying module is adopted for driving, a PWM signal is dynamically adjusted through the low ESR characteristic of a capacitor C1 and closed-loop control, extra power loss is avoided, the working temperature of the circuit is controlled to be below 85 DEG C, and the service life of the circuit is prolonged. And the continuous service life of components in the circuit is prolonged.

The invention discloses a transmission mechanism and an all-terrain vehicle using the transmission mechanism, the transmission mechanism comprises a linkage assembly, an output shaft, two sets of transmission parts, a sensor module and a controller, the linkage assembly is connected with the output shaft, and the two sets of transmission parts are arranged on the two sides of the linkage assembly respectively; each transmission part comprises a first transmission part, a second transmission part, a supporting part, a first friction part, a second friction part, an extrusion module and a driving unit, the first transmission parts are connected with the output shaft, the second transmission parts are rotationally connected with the supporting parts, and the first friction parts are connected with the first transmission parts; the second friction piece is connected with the second transmission piece and abuts against the first friction piece, the extrusion module abuts against the first friction piece or the second friction piece, the driving unit is connected with the extrusion module, and the controller is electrically connected with the driving unit and the sensor module. According to the transmission mechanism, the power loss is reduced, the all-terrain vehicle is helped to escape, and the control stability of a driver is improved.

The invention discloses a vehicle-mounted power supply short-circuit heat prediction method based on big data analysis, and the method comprises the following steps: collecting real-time operation data, and storing the data in a data cache, thereby obtaining a cache time sequence signal; filtering the cached time sequence signal, and extracting a basic feature sequence; constructing an RC network model based on the basic feature sequence, and converting a fractional order operator into an equivalent integer order model; performing parameter estimation on the equivalent integer order model to generate a thermal prediction model; taking the thermal prediction model and the basic feature sequence as joint input and output power loss features; inputting the power loss characteristics into a quantile regression forest model to form a heat prediction result; comparing the heat prediction result with a heat threshold value and a temperature rise threshold value, and outputting a control trigger signal; and calling parameters in a control strategy library according to the control trigger signal to generate a control strategy. According to the invention, vehicle-mounted power supply short-circuit heat prediction is realized.

The invention relates to the technical field of power distribution network fault self-healing, in particular to a power distribution network fault self-healing method based on intelligent distributed feeder automation, which comprises the following steps of: acquiring a permanent fault signal of a feeder section of a power distribution network and an isolated network topology; based on the updated network topology, obtaining a real-time load value weight of each load node in the non-fault power-loss area and an output upper limit of the distributed power supply; an optimal power supply recovery path is determined based on distributed calculation by taking the maximum recovery total load value weight as a target, and the determination of the path needs to meet the output constraint of the available distributed power supply; and controlling the interconnection switch and the section switch on the path to execute switching-on and switching-off operations, forming a new power supply network, completing self-healing, and verifying the power flow distribution in the newly formed power supply network. After power supply is restored, the states of voltage and current are verified through load flow calculation, and voltage out-of-limit is improved through reactive power output adjustment of the distributed power supply without manual intervention.

The invention discloses a virtual impedance compensation-based active-disturbance-rejection control method for a dual-active-bridge converter, and the method comprises the following steps: firstly designing a virtual impedance compensation strategy, connecting an equivalent virtual conductor in parallel with an output capacitor, converting the equivalent virtual conductor into a duty ratio injection amount, and achieving the active counteracting of the negative impedance of a constant-power load and the reconstruction of the damping of a port; an unstable pole of the right half plane is made to move back to the left half plane, and extra power loss is not introduced; an active disturbance rejection control strategy based on virtual impedance compensation is designed, residual uncertainty after virtual impedance compensation is uniformly regarded as total disturbance online observation and feedforward offset, and therefore rapid and smooth voltage tracking is kept under parameter perturbation and external disturbance. Under the working conditions of voltage reference step and load abrupt change, the improved active disturbance rejection control strategy realizes the effects of no overshoot, shortest setting time and minimum oscillation by enhancing disturbance observation and virtual impedance compensation, and has better damping and disturbance rejection performance.

The invention provides a day-ahead reactive power optimization method based on a graph convolutional network and a conditional value-at-risk strategy. The method comprises the following steps: firstly, utilizing Monte Carlo simulation to generate a plurality of joint scenes of wind power and load uncertainty, extracting a typical operation scene through a K-means clustering method, and establishing a multi-target random reactive power optimization model taking active power loss and node voltage deviation as optimization targets; secondly, based on the optimization model, constructing a weighted adjacency matrix fused with power grid physical parameters, and enhancing the recognition capability of the graph convolutional network on the relationship between the nodes; then, a graph convolutional network model is trained to establish efficient mapping between a power system operation state and a reactive power regulation scheme, and rapid strategy generation is realized; and finally, performing cross-scene evaluation on the performance of each scene strategy and the voltage stability risk based on the conditional value-at-risk, and screening out a day-ahead optimization strategy with the best robustness. According to the method, the calculation efficiency and strategy robustness of day-ahead reactive power optimization are effectively improved, and the method is suitable for a power system with high-proportion new energy access.

The invention discloses a method for improving the toughness of a power distribution network containing an energy storage type flexible soft switch in a fault state, and belongs to the field of power systems and automation of the power systems, and the method comprises the steps: extracting time sequence data and fault information of a distributed power supply and a load; according to the method, an integrated optimization model integrating fault isolation, multi-stage dynamic maintenance, network reconstruction and energy storage type flexible soft switch cooperation is constructed, minimization of whole-process power loss and branch and device loss is taken as a target, and multiple constraints including time sequence balance, device operation, maintenance resources and decoupling power flow are taken into consideration. Wherein the energy storage type flexible soft switch realizes cross-period energy time shifting through an embedded energy storage system; and a mixed integer second-order cone programming algorithm is adopted to carry out rolling solution, and island division, a maintenance sequence and a device operation strategy are synchronously optimized in each stage. According to the method, global coordination and space-time energy management of the multi-stage recovery process can be realized, and the power supply recovery capability and toughness of the power distribution network under the extreme fault are remarkably improved.

The invention provides a gear wind resistance power loss prediction method and system, computer equipment and a medium, and belongs to the field of aero-engine power transmission, and the method comprises the steps: obtaining physical parameters of a to-be-predicted gear; according to the physical parameters of the to-be-predicted gear, gear structure parameters are extracted, tooth surface torque parameters and fluid mechanics parameters are calculated, and the gear structure parameters, the tooth surface torque parameters and the fluid mechanics parameters of the to-be-predicted gear are consistent with the same type of parameters in the sample space in sequence and dimension; the gear structure parameters, the tooth surface torque parameters and the fluid mechanics parameters of the to-be-predicted gear are input into a pre-trained power loss prediction model, the wind resistance power loss of the to-be-predicted gear is obtained, and the power loss prediction model is obtained through a sample space training deep learning model. Compared with numerical simulation, the method has the advantage that the power loss prediction efficiency is remarkably improved.

The invention relates to a heat dissipation analysis and optimization method for an IGBT module, and the method comprises the steps: obtaining the electrical parameters and working conditions of the IGBT module, and calculating the total power loss during the operation of the IGBT module based on the electrical parameters and working conditions. Based on the total power loss and the physical structure of the IGBT module, a thermal resistance network model of a semiconductor junction region and a cooling environment and a Cauer type thermal impedance model based on dynamic characteristics are established. Inputting the total power loss into the thermal resistance network model and the Cauer type thermal impedance model, calling a finite element simulation algorithm to solve a temperature distribution field of the IGBT chip, and determining the total equivalent thermal resistance. And when the junction temperature or the total equivalent thermal resistance of the IGBT chip does not meet the set index, positioning the heat dissipation bottleneck and generating a heat dissipation optimization strategy. And executing a heat dissipation optimization strategy, and updating the thermal resistance network model and the Cauer type thermal impedance model after executing the heat dissipation optimization strategy so as to update the temperature distribution field and the total equivalent thermal resistance.

The invention discloses a servo connecting rod high-frequency swing mechanism, which belongs to the technical field of bearing processing equipment, and comprises an adjusting piece and a swing head which are arranged on the same side outside a bracket, one end of the transmission shaft is connected with the adjusting piece; one end of the swinging shaft is connected with the swinging head; the two ends of the connecting rod are eccentrically arranged relative to the transmission shaft and the swing shaft respectively and rotationally connected with the adjusting piece and the swing head respectively; the adjusting piece is used for adjusting the eccentric distance of the connecting rod relative to the transmission shaft in the radial direction. The belt link is omitted through direct-drive transmission, the servo motor coupler directly drives the transmission shaft, power loss is reduced, and the swing frequency and stability are improved.

The invention discloses a prefabricated branch fireproof cable with a copper metal shell and a preparation method of the prefabricated branch fireproof cable. The prefabricated branch fireproof cable comprises a copper metal main shell, the bottom of the copper metal main shell is communicated with a copper metal sub-shell, a main cable is installed in the copper metal main shell, and a branch cable is installed in the copper metal sub-shell; the main cable and the branch cable both adopt copper core conductors, and copper has high conductivity, so that resistance in a current transmission process can be reduced, electric energy loss can be reduced, electric energy transmission efficiency can be improved, and stable and efficient transmission of electric power to each electric device can be ensured; the nanoscale magnesium oxide insulation powder filled in the copper metal main shell and the copper metal branch shell has excellent insulation performance, can effectively isolate electrical connection between the main cable and the branch cable and the external environment and between the main cable and the branch cable and other conductors, prevents electric leakage and short circuit accidents, and improves the electrical safety of a cable system.

The invention belongs to the technical field of power supply regulation and control, and particularly relates to a computing power center-oriented flexible power supply system comprehensive regulation and control method, device and equipment and a medium, and the method comprises the following steps: collecting computing power center power supply data; calculating a sub-index based on the power supply data, and calculating a main index corresponding to the sub-index; calculating a comprehensive evaluation index based on the main index and the sub-indexes of the main index; and regulating and controlling power supply of the computing power center based on the comprehensive index. According to the method, detailed indexes such as the calculation power loss rate, the key load supply guarantee duration and the unit calculation power energy consumption are provided, comprehensive and systematic evaluation from a macroscopic power grid to alternating current and direct current equipment in a calculation power center is achieved, a more comprehensive guiding effect is achieved on a subsequent regulation and control scheme, and a more detailed regulation and control method can be made.

The invention relates to a power supply module, electronic equipment and a preparation method of the power supply module, the power supply module comprises a substrate, a power supply chip, a first electronic device and a second electronic device, the power supply chip is arranged in the substrate, the first electronic device is arranged on the surface of one side of the substrate along the thickness direction of the power supply module, and the second electronic device is arranged on the surface of the other side of the substrate. The first electronic device is electrically connected with the power supply chip, the side, away from the substrate, of the first electronic device is used for being electrically connected with the circuit board, the second electronic device comprises an inductor, the inductor comprises a magnetic core and a magnetic packaging part, the magnetic core is electrically connected with the power supply chip, and the magnetic core is packaged on the surface of the side, away from the first electronic device, of the substrate through the magnetic packaging part. The power supply module with the structure occupies a small area, can shorten a power routing path, reduce parasitic power routing path, reduce power loss, reduce an electric stress risk, can reduce the overall thickness of the power supply module, and improves the layout space of the first electronic device and the second electronic device, thereby facilitating the improvement of the power supply performance of the power supply module.

The invention discloses a power grid intelligent power flow control method based on an improved cuckoo search algorithm. The method comprises the following steps: firstly, constructing a mathematical model of an AC-DC hybrid system, analyzing the characteristics and interaction of each device in the system, and determining a composite function taking the minimization of power generation cost, active loss and generator voltage deviation as targets according to an operation target; and then, decomposing the complex objective function into a plurality of sub-problems through hierarchical processing, and realizing respective solving. In hierarchical calculation, an improved cuckoo algorithm is adopted for optimization in a solution space, and an optimal power flow distribution and control strategy is obtained. And finally, intelligent layered power flow control of the alternating-current and direct-current hybrid system is realized according to an optimization result. According to the invention, the system response speed and the regulation efficiency are obviously improved, and the effect in the aspect of optimized operation is outstanding; compared with the traditional Cuckoo search CS algorithm and NSGA algorithm, the cost consumption is respectively saved by 2.21% and 4.49%; the experimental model is low in weekly average cost and high in power transmission efficiency, the power transmission efficiency is effectively improved, and the loss cost is reduced.

The invention discloses a permanent magnet synchronous motor model prediction control method based on multi-target Pareto optimum, which comprises the following steps: measuring two-phase current, motor rotating speed and rotor position of a permanent magnet synchronous motor, converting the two-phase current into d-axis current and q-axis current according to the rotor position, and converting an alternative voltage vector of an inverter into d-axis voltage and q-axis voltage at the same time; inputting the d-axis current, the q-axis current, the d-axis voltage, the q-axis voltage and the rotating speed of the motor into a current prediction model of the permanent magnet synchronous motor, and predicting a future current state value; establishing a multi-target optimization model including torque ripple, current total harmonic distortion, switching frequency and power loss, calculating by using a multi-target particle swarm algorithm in combination with non-dominated sorting and congestion distance to obtain a Pareto optimal solution set, obtaining an optimal weight factor, and automatically adjusting target weights according to motor operation states under different operation conditions; and constructing a cost function based on the optimal weight factor, and selecting a voltage vector enabling the cost function to be minimum as optimal control output.

The invention belongs to the technical field of valves, and discloses a single-action pneumatic actuator control system which comprises a gas supply gas circuit, a pneumatic pilot valve, a first electromagnetic valve, a pilot loop and a delayed discharge loop. And the delayed discharge loop is configured to discharge the air pressure in the control air cavity, and after a preset time, the air pressure is gradually reduced to be below a set value so as to cut off the pneumatic pilot valve. During power failure in a whole platform range, the time-delay discharge loop can enable the whole control system to automatically switch into a pure pneumatic time-delay mode, so that the situation that the BDV immediately enters an open state due to power loss of the first electromagnetic valve is avoided, and the environmental adaptability and long-term operation reliability of the control system under extremely severe environments such as an ocean platform and flammable and explosive environments are remarkably improved; the safety performance is improved.

The invention relates to the field of energy storage and reactive compensation capacity optimization configuration, in particular to a new energy pooling station energy storage and reactive compensation configuration method based on big data analysis. The method comprises the following steps: firstly, acquiring data, respectively obtaining a sensitivity coefficient of active power and a sensitivity coefficient of reactive power through a disturbance response sampling linearization identification method, and introducing a dynamic active-reactive decoupling voltage improvement evaluation algorithm based on sensitivity weighting to obtain a voltage improvement amplitude of a node; then, based on the voltage improvement amplitude of the node, constructing a target function; and carrying out iterative solution on the constructed objective function, and outputting the optimal active power regulation quantity of each energy storage unit and the optimal reactive power regulation quantity of each reactive compensation device. The technical problems that the sensitivity coefficient is difficult to update in real time, the calculation scale is huge, the response speed of real-time optimization scheduling, unnecessary active loss and battery life loss are seriously influenced, and the physical balance of the voltage improvement amplitude between the nodes is ignored are solved.

The invention discloses a topology state monitoring-based high-voltage distribution network self-healing model construction method and system, equipment and a medium. The method comprises the steps of obtaining main network topology structure data, equipment operation state data and typical wiring mode data; based on topological structure data and typical wiring mode data, a main network self-healing model including bus self-healing, buscouple self-healing and total station self-healing types is constructed by adopting a graph theory adjacency matrix, and the charging and discharging states of the model are periodically polled and judged according to equipment operation state data. And the linkage analysis of the topology power-off state and the bus voltage abnormity is fused before and after the self-healing process is started, and the self-healing action logic is locked through multiple conditions, so that the problem that the traditional technology is lack of standardized modeling, dynamic state evaluation and linkage analysis is solved, the strategy reusability and the fault power recovery efficiency are improved, the misoperation risk is reduced, and the reliability of the system is improved. And the safety and reliability of the operation of the main network are ensured.

The invention relates to the technical field of electronic equipment, in particular to a power supply fault diagnosis device of a server system and a server wherein the power supply fault diagnosis device of the server system comprises a first programmable controller, a first baseboard management controller, a first power supply unit, a second power supply unit and an anomaly detection and switching unit. By arranging the anomaly detection and switching unit, the standby power supply module can be triggered in time when the first power supply unit of the server is abnormal, continuous power supply of the first programmable controller and the first substrate management controller is ensured, and an out-of-band management function is kept uninterrupted; therefore, real-time detection, recording and reporting of faults can still be realized under the condition of STBY power supply faults, the technical problem that power supply faults cannot be diagnosed due to power loss of the BMC and the CPLD in the prior art is effectively solved, and the power supply reliability, the fault diagnosis coverage rate and the maintainability of a server system are remarkably improved.

The invention provides a PC power supply load adjusting method and device based on AI prediction and a medium, and the method comprises the steps: continuously collecting a power supply signal of a PC power supply output end, a processor operation state signal and a graphic part working state signal, generating load time sequence data, separating power consumption signatures corresponding to different calculation tasks after mode recognition, obtaining a power consumption signature set, and carrying out the calculation of the power consumption signature set. Inputting a pre-trained load prediction model, obtaining a load intensity change profile in a future power supply period through time sequence analysis processing, carrying out feedback calibration on the load intensity change profile and the current double-circuit power supply state information of the PC power supply, generating a power supply power distribution strategy, and outputting the power supply power distribution strategy. And adjusting working points of a primary conversion path and a secondary conversion path in the PC power supply according to the power distribution strategy of the power supply, so that the output power of the power supply is dynamically matched with the load change. According to the invention, unnecessary power loss is reduced, so that stable operation of a PC system is guaranteed, and refined and dynamic adjustment of the power supply load is realized.

The invention relates to the technical field of energy storage scheduling optimization, and discloses a hybrid energy storage output optimization scheduling method, system and device, a medium and a product. Constructing a hybrid energy storage output optimization model by taking the operation balance state of the hybrid energy storage device and the power distribution network as a constraint condition, converting the hybrid energy storage output optimization model into a Markov decision process, determining a state space, an action space and a reward function in the Markov decision process, and determining the output of the hybrid energy storage device on the basis of the Markov decision process. A deep reinforcement learning algorithm is adopted to train the charging and discharging strategy of the hybrid energy storage device to obtain the optimal charging and discharging strategy of the hybrid energy storage device, so that the purposes of voltage stabilization, power loss minimization and economic operation of the energy storage system can be achieved, and the scheduling accuracy and reliability of hybrid energy storage are improved.

The invention relates to the technical field of electronic transformers, and discloses a passive wireless high-reliability electronic transformer data reporting method, system and device and a medium, and the method comprises the steps: employing different reporting strategies for different types of transformer nodes when the residual electric quantity is lower than a threshold value; and the timeliness of data reporting and the power consumption are effectively balanced. For a mutual inductor node undertaking a core parameter monitoring task, basic report data is sent at the lowest report frequency, stable transmission of core data is ensured, and excessive power consumption is avoided at the same time; and for non-core parameter monitoring task nodes, temperature, voltage and current data are sent at a preset reporting frequency, so that the electric quantity loss is further reduced while important data are reported. And through a mode of temporarily storing other collected data, unnecessary data transmission is avoided, and the use efficiency of the electric quantity is improved. The reliability and the stability of the system are improved.

The invention discloses an intelligent self-adaptive solar power supply system and an energy management method, and relates to the technical field of solar intelligent power supply. A multispectral dynamic capture module improves the spectrum utilization rate and the photoelectric conversion efficiency; the global environment sensing module accurately predicts shadow and weather; the multi-mode MPPT cooperative control module reduces the power loss under the shadow; the heterogeneous energy storage cooperative scheduling module optimizes energy storage distribution; the intelligent load interaction module realizes accurate load management; the edge intelligent decision center realizes high-efficiency energy decision and safety guarantee, and all the modules work cooperatively, so that intelligent adaptive management of the solar power supply system is realized. Through multi-module innovation and cooperation, the performance of a solar power supply system is remarkably improved, the power generation efficiency is improved, environmental changes are accurately coped, power loss is reduced, energy storage and load management are optimized, efficient energy decision is achieved, safe operation of the system is guaranteed, the operation and maintenance cost is reduced, and intelligent development of solar power supply is promoted.

Techniques are described herein for improving the burst operation of flyback power converters. The techniques provide a burst mode that delivers voltage pulses to a switching element with decreased power losses (e.g., greater switching efficiency). The efficiency of the power converter can be improved by capping the number of pulses to be delivered in a given burst packet and instituting a fixed delay to limit the power delivery. The pulses may be delivered during valleys of the signal at the switching terminal of the switching element to reduce switching losses. Additionally, a frequency clamp circuit is also provided to ensure the switching frequency is not above a predetermined threshold frequency. A device designed to implement the improved burst operation includes a burst control circuit, a pulse control circuit, a delay control circuit, and a latch.

A battery capacity analysis and predication method and system for a storage battery, relating to the technical field of data processing. The method comprises: acquiring operation data of a target storage battery within a preset time period, wherein the operation data comprises a voltage sequence, an internal resistance sequence, a temperature sequence, and a charge and discharge cycle number sequence; on the basis of the operation data, determining time intervals corresponding to a plurality of operation conditions; on the basis of operation sub-data within each time interval, calculating a temperature influence factor, a power loss factor, and a cycle aging factor within each time interval; acquiring a battery capacity attenuation amount of the target storage battery within each time interval; on the basis of the battery capacity attenuation amount, temperature influence factor, power loss factor, and cycle aging factor within each time interval, generating a capacity attenuation amount curve of the target storage battery; and on the basis of the capacity attenuation amount curve, predicting battery capacity in the next time period.

A method is for adaptively controlling a charging module that converts three-phase AC input to DC output with improved power factor correction. The method includes performing Clarke and Park transforms on input three-phase AC voltages to obtain d-q axis components, measuring and transforming boost inductor currents to obtain current axis components, generating control values through current-loop and voltage-loop modules based on target and measured values, and synthesizing PWM signals to control switching operations. The method features adaptive phase angle computation using a proportional-integral controller that responds to load variations in real-time. The control system maintains high power factor under varying load conditions by continuously adjusting the phase reference based on the q-axis voltage error. This approach minimizes power losses, reduces harmonic distortion, and ensures stable operation across the full operating range of the charging module while maintaining unity power factor.

The invention discloses a short-time voltage fluctuation suppression method, device and equipment based on partition fluctuation evaluation, and relates to the technical field of power system control. The method comprises the following steps: under a minute-level time scale, taking the comprehensive minimization of active power loss, voltage fluctuation in adjacent time periods and reactive power regulation cost as a target, constructing a multi-period reactive power voltage optimization control model, and obtaining a reference voltage and a reference reactive power control quantity; a power system is partitioned through a modularity-based reactive voltage control robust partitioning algorithm, and influence areas which are mainly influenced by new energy fluctuation are identified on the basis of partitioning. And constructing a partition fluctuation evaluation index and setting a partition optimization event triggering mechanism. And under a short-time scale, constructing a subarea reactive voltage optimization control model based on quadratic programming by utilizing a subarea space decoupling technology, and executing subarea coordination optimization. And correcting the partition reactive voltage optimization control model through a model correction method based on multiple linear regression.