1269 results about "Voltage stability" patented technology

The invention provides an intelligent power distribution load prediction and adaptive scheduling method, and aims to improve the response capability and operation efficiency of a power distribution system to load fluctuation. According to the method, historical load data, real-time meteorological information and user power consumption behavior data of a target power distribution area are collected, a multi-dimensional input data set is established, a short-term load prediction model is constructed based on the data set, and prediction of future load levels of multiple feeder lines and key nodes is achieved. According to a prediction result, potential voltage deviation, load imbalance and equipment overload risks are identified, and a dynamic scheduling control strategy is generated by adopting a rolling horizon optimization method in combination with a power grid topological structure and operation constraints; the strategy comprises control measures such as transformer tap position adjustment and capacitor bank switching; through issuing a scheduling strategy to an equipment control unit, active adjustment is completed before load change, voltage stabilization, load balancing and energy optimization are realized, and intelligent and adaptive operation guarantee is provided for a power distribution system.

The invention relates to the technical field of power distribution network operation management, and discloses a power distribution network capacity-increase-free access system based on dynamic coordination. The system comprises a multi-source data acquisition and fusion unit which acquires multi-source data and fuses the multi-source data to generate dynamic power grid state characteristics; the multi-time-scale collaborative optimization architecture unit is used for constructing a layered architecture to realize optimization of different time scales; the space-time load prediction unit is used for predicting load distribution based on the space-time diagram model; and the multi-target dynamic optimization solving unit is used for constructing a model to solve an optimal scheduling instruction. In addition, a layered communication protocol unit is arranged to guarantee communication, and a voltage stability cooperative control unit and a distributed energy storage cooperative control unit are arranged to improve the stability of the power grid. Through cooperation of multiple technologies, capacity-increase-free access of the power distribution network is realized, the operation efficiency is improved, the loss is reduced, the voltage stability is ensured, and challenges brought by distributed energy and electric vehicle charging are effectively dealt with.

The invention provides a generator excitation regulation and control optimization system based on model prediction, and relates to the technical field of data processing, and the system comprises a data acquisition module which is used for collecting the operation parameters of a generator; the state estimation module is used for calculating a state variable of the generator excitation system and constructing a state space model of the generator excitation system; the model prediction control module is used for calculating excitation current prediction values at multiple moments in the future by adopting a rolling horizon optimization method, and performing dynamic correction based on a prediction error correction mechanism; the optimization calculation module is used for constructing a target optimization function which comprises a generator voltage stability target item and an excitation current adjustment smoothness target item, and calculating an optimal excitation current adjustment amount by adopting a constraint optimization method; the excitation adjusting module is used for controlling a generator excitation system so as to adjust excitation current; according to the invention, the autonomy and accuracy of the generator excitation regulation and control optimization system are improved.

The invention relates to the technical field of electrical equipment monitoring, in particular to a power distribution equipment state intelligent diagnosis system and method, and the system comprises a current fluctuation detection module, a voltage stability evaluation module, a heat effect diffusion analysis module, a fault point positioning module and an equipment state evaluation module. According to the invention, through extracting current change characteristics of key nodes and judging continuity of fluctuation differences, rapid identification of current anomalies can be realized, a voltage distortion region and a distribution range thereof can be positioned in combination with phase deviation and symmetry change of voltage waveforms, and the current anomalies can be identified based on a temperature gradient path and jump point screening. A hot spot diffusion trend and a thermal disturbance boundary can be described, morphological analysis and coordinate labeling are performed on the basis of multiple fault areas, multi-point concurrent positioning is realized, association between thermal disturbance and connection defects is identified in combination with temperature difference and current path change characteristics, fault diffusion trends and state disturbance blocks can be accurately presented, and the accuracy of fault diagnosis is improved. And the power distribution state identification and intervention efficiency is improved.

The invention discloses a game reinforcement learning-based reactive power dispatching method for an electric vehicle to participate in a power distribution network, and the method comprises the steps: collecting node voltage, line current, electric vehicle charging and discharging states and topological information, and constructing graph attention embedding; generating an initial reactive power regulation action in the multi-agent game reinforcement learning framework, and shaping and updating a strategy gradient through double rewards; the improved particle swarm is initialized according to the updating strategy gradient, and a self-adaptive inertia coefficient is set; iteratively searching according to the node voltage sensitivity, the line reactive margin and the expected convergence step number to obtain an optimized particle swarm; the particle speed is mapped to a main strategy network through online cooperative training, the inertia coefficient is synchronously adjusted, a cooperative optimization strategy is output, a reactive power dispatching instruction is generated, and the algorithm is updated in a closed-loop mode according to real-time feedback. According to the invention, rapid and cooperative reactive dynamic scheduling of an electric vehicle group is realized, and the voltage stability and the electric energy quality are remarkably improved.

The invention relates to the technical field of power grid operation and maintenance, and discloses an AI-based reactive compensation optimization method and system, and the method comprises the following steps: integrating the four-dimensional characteristics of traffic flow, power grid load, user preference and equipment health degree, constructing a space-time correlation graph model, achieving the adaptive evolution of a topological structure through dynamic graph convolution and an attention mechanism, and obtaining a reactive compensation optimization model. The method comprises the following steps: fusing spatial-temporal features of a graph neural network with a BiGRU time sequence for modeling, injecting an equipment health degree attenuation factor, generating a charging demand prediction result with uncertainty quantification, expanding an intelligent agent state space, improving a reward function, and embedding equipment health degree constraints in a strategy network and near-end optimization. By sensing the degradation state of the equipment in real time and intelligently adjusting the reactive compensation strategy, the service life of the old equipment is prolonged while the voltage stability is guaranteed, the deviation accumulation effect is effectively inhibited through feedforward-feedback composite control and dynamic rescheduling, and accurate matching of a scheduling instruction and actual output is ensured.

The invention relates to the technical field of charging piles, and provides a DC charging pile voltage stability optimization method and device and a storage medium, and the method comprises the steps: obtaining voltage sequence data of a DC output end of a charging pile, carrying out the segmentation statistics of the voltage sequence data, and obtaining an optimization adjustment matrix and a voltage compensation factor matrix; and performing dynamic adjustment on a preset direct current output control strategy of the charging pile based on the optimization adjustment matrix to obtain an adjusted control strategy parameter, performing compensation calculation on the adjusted control strategy parameter by using the voltage compensation factor matrix to obtain an optimization control instruction, and generating a dynamic voltage stabilization control signal according to the optimization control instruction. An output control strategy is dynamically adjusted by optimizing an adjustment matrix and a voltage compensation factor matrix, an efficient dynamic voltage stabilization control signal is generated, and the problems of response lag and insufficient prediction precision existing in the prior art when a complex voltage fluctuation mode is processed and a future voltage change trend is predicted are solved.

The invention provides a power management chip pin state intelligent detection method and system. The method comprises the steps that temperature difference quantity is input into a gradient heat conduction compensation structure, and compensation current opposite to a temperature gradient vector is generated based on temperature field distribution in the copper-based composite material; superposing and coupling the voltage fluctuation signal and the compensation current to generate a reference voltage signal; performing frequency component separation processing on the reference voltage signal, and extracting electromagnetic interference characteristic data in a frequency component; performing time sequence correlation on the electromagnetic interference characteristic data and the temperature distribution data, dynamically adjusting an interference suppression parameter through closed-loop control, and acting the adjusted interference suppression parameter on an interference suppression process of a reference voltage signal to generate a pin state detection signal; and detecting the pin state of the power management chip according to the pin state detection signal. According to the invention, the pin voltage stability and the state detection precision of the power management chip are improved.

The invention relates to the technical field of electric power reactive power compensation, in particular to a novel static dynamic reactive power compensation device and a control method thereof, and the device comprises a power grid state monitoring module, a reactive power demand calculation module, a compensation control execution module, a harmonic suppression module and a dynamic energy storage support module. According to the method, through real-time extraction and segmented analysis of the dynamic operation parameters of the power grid, accurate perception of the state of the power grid is realized, the transient characteristics of operation are represented, an accurate basis is provided for subsequent compensation, decomposition and classified accumulation of reactive power requirements are realized, inductive and capacitive power differences are accurately identified, the calculation accuracy and efficiency are improved, and load adaptation is optimized; in harmonic suppression, resolution and response speed are enhanced through signal separation and frequency correction, energy storage and compensation linkage is improved through dynamic energy storage optimization, dynamic response capability is enhanced, loss is reduced, voltage stability and power factors are improved, stable guarantee is provided for complex fluctuation scenes, and electric energy is saved.

The invention discloses a power distribution network voltage control method based on distributed photovoltaic active-reactive cooperation, and the method comprises the steps: analyzing the influence of reactive / active injection on voltage stability through building a sensitivity matrix, deducing a critical control gain constraint condition, and guaranteeing the static stability of the voltage of a power distribution network; a photovoltaic inverter active / reactive cooperative control general model suitable for different scenes is constructed based on an intra-day scheduling and real-time control two-stage framework, and control curve parameters of each node photovoltaic inverter are optimized by taking minimization of network loss cost, active reduction cost, inverter voltage regulation cost and voltage deviation as targets in the intra-day scheduling stage; in the real-time control stage, active power reduction and reactive power regulation are dynamically coordinated based on an optimal control curve, reactive power margin is preferentially utilized to suppress voltage rise, and power flow distribution is directly regulated through active power reduction when reactive power capacity is saturated. According to the method, the safety and economy of the power grid are considered, and theoretical support and a practical solution are provided for voltage control of the high-permeability photovoltaic power distribution network.

The invention relates to the technical field of resistance measurement, in particular to a constructional engineering electrical grounding resistance detection system which comprises a path monitoring module, a delay judgment module, a path switching module, an interval regulation and control module and a data correction module. According to the method, the voltage stability is judged by collecting the path node voltage change value and the timestamp and calculating the voltage difference, precision screening is completed in cooperation with path state information, the abnormal fluctuation recognition capability is improved, the delay value change trend is combined with a period difference value comparison mode, and path stability evaluation is assisted; the path switching behavior is combined with feedback response and relay state recognition, the accuracy of channel switching is improved, the sampling interval is dynamically regulated and controlled according to delay change, clearer response characteristics are obtained in combination with waveform displacement mapping operation, the accuracy of data alignment is enhanced, and the accuracy of channel switching is improved. The whole process realizes a closed-loop processing structure of path identification, delay judgment, switching confirmation and waveform correction, and effectively improves the response consistency and time precision of ground resistance measurement.

The invention relates to the technical field of power distribution network operation optimization, and discloses an intelligent power distribution network distributed power scheduling system based on edge computing, which comprises a data acquisition and preprocessing module, an edge computing and local scheduling module, an SDN network control module, an application layer global scheduling module and a scheduling execution and control module, according to the invention, by introducing the edge computing node, local preprocessing of data and generation of a preliminary scheduling strategy are realized, delay of data transmission to the central server is significantly reduced, and the system can dynamically adjust the scheduling priority in combination with the improved particle swarm optimization algorithm, so that the scheduling efficiency is improved. Three core indexes of renewable energy utilization rate, power distribution network loss and voltage stability are comprehensively considered, and a more accurate and efficient local scheduling strategy is generated. And meanwhile, the SDN network control module ensures that high-priority data can still be efficiently transmitted when the network is congested by calculating a link priority coefficient, so that the real-time performance and the reliability of the system are further improved.

The invention relates to the technical field of intelligent control, in particular to an intelligent power optimization scheduling method in a new energy grid-connected scene, which comprises the following steps of: acquiring a current load, a rated load, an adjustment rate and a temperature rise value through a data interface, and normalizing to generate a characteristic parameter matrix; the product of the adjusting rate and the temperature rise value is used for calculating the correlation degree with the aging factor to obtain an elastic weight, extracting the voltage stability and the weight, inputting the voltage stability and the weight into a Dijkstra algorithm to screen a path meeting the safety margin, monitoring the slope of an inertia change curve, adjusting a compensation coefficient through fuzzy PID control, and feeding back the compensation coefficient to correlation degree calculation. According to the method, multi-source parameters are fused through a rule tree structure, the load regulation rate and the aging factor weight are dynamically distributed, bus voltage stability and elastic weight path optimization are combined, a synchronous inertia margin safety correction system is constructed, the system response precision, the state cooperation efficiency and robustness are improved, local optimum is avoided, and the system reliability is improved. And the standby capacity configuration and the frequency advance correction capability are optimized.

The invention relates to the technical field of voltage control, in particular to a time-delay-containing uncertain AC / DC micro-grid DC bus voltage control method, which comprises the following steps: acquiring load change and DC bus voltage fluctuation data, calculating a load current change rate, analyzing the correlation between voltage fluctuation and load change, and performing voltage adjustment. And time delay characteristics in a load state are identified, voltage fluctuation uncertainty factors are identified, and the time delay compensation amount is adjusted to restore the voltage stability. According to the invention, by adopting linear and nonlinear time-delay compensation strategies, the problem of accuracy of time-delay compensation under different load conditions is effectively solved, various load fluctuations and voltage changes can be dynamically adapted, the problem of uncertainty of voltage fluctuations is solved, and the prediction of voltage fluctuations is optimized by dynamically adjusting the weight of uncertainty factors. The over-reaction to sudden voltage change is reduced, the voltage stability is improved, and the operation stability and the voltage regulation precision of the micro-grid are remarkably improved.

The invention relates to the technical field of battery state monitoring, in particular to an intelligent online capacity checking management system for a storage battery pack, which comprises a jump sensing module, a lag correlation module, a starting point confirmation module, a state diagnosis module and a correction output module. According to the invention, the method achieves the precise recognition of a voltage hysteresis effect through the positioning of the difference value of a current sudden change point, the tracking of a fluctuation convergence interval, the construction of a dynamic sudden change buffer structure, the enhancement of the capturing capability of a key change behavior, the extraction of a response time point in combination with the change trend of a voltage derivative, and the combined binding of a charge state based on the voltage difference value and current sampling. The real-time performance and the pertinence of capacity evaluation are improved, the sudden change period is identified through the charge state difference value, a synchronous mark is established through voltage stability analysis, correction mapping is carried out according to the state difference, a capacity correction path with continuity, traceability and dynamic adaptability is formed, and the precision and the stability of capacity management of the storage battery pack are effectively improved.

The present invention relates to the technical field of power distribution networks. Disclosed are a multi-energy storage power distribution network voltage regulation method and apparatus, and a device. The method comprises: establishing a voltage optimization model for a power distribution network, converting an energy storage constraint in the voltage optimization model of the power distribution network on the basis of physical information of an energy storage system, establishing a security model of the energy storage system, forming a training guide model of the energy storage system on the basis of a Markov decision model, training each energy storage system by using a TD3 algorithm, sharing network parameters obtained through training between different energy storage systems, and adjusting the voltage of the power distribution network. By means of optimizing an operation strategy of an energy storage system, the present invention realizes efficient and sustainable operation of the energy storage system. A charging and discharging strategy of the energy storage system is optimized by means of a DRL algorithm having shared parameters, improving voltage stability of the power distribution network, reducing energy loss, improving the overall efficiency of the system, enhancing the collaboration performance of multiple energy storage systems, and optimizing control of the energy storage system in an active power distribution network.

A method for configuring reactive power capacity of a power grid to which offshore wind power is connected on a large scale includes the following steps. Static voltage stability index of substations in a regional power grid to which the offshore wind power is connected on the large scale and at multiple points and relative dynamic voltage drop area index for an N-2 fault are calculated. Thus, weak nodes in voltage stability are located, thereby implementing a prediction of a risk to the voltage stability of the regional power grid. These nodes are used as candidate reactive power compensation configuration nodes. An objective function is established as the minimum total cost of reactive power compensation devices at the candidate reactive power compensation configuration nodes. A multi-type reactive power optimization configuration model including static voltage stability constraints, voltage stability constraints under faulty state, and other constraints is solved.

The invention discloses an electric energy quality comprehensive treatment method and device based on multilevel collaborative optimization, and belongs to the technical field of electric energy quality treatment of an electric power system. According to the method, voltage and current data of a power grid are collected in real time and multi-parameter monitoring is performed; processing the data by using an adaptive wavelet transform and singular value decomposition combined algorithm, extracting disturbance characteristics and identifying the type and position of a disturbance source; a multi-objective optimization function including harmonic suppression, voltage stabilization, three-phase balance and power factor improvement is constructed, a Pareto optimal solution set is solved, and an optimal solution is selected; and according to an optimization result, generating and executing a cooperative control strategy of each type of electric energy quality treatment equipment. Cooperative treatment of various electric energy quality problems and cooperative control of various treatment devices are realized, the accuracy, self-adaptability and economical efficiency of electric energy quality treatment are improved, and the reliability and stability of an electric power system are enhanced.

The invention discloses an asynchronous motor energy-saving monitoring and dynamic early warning method based on situation awareness, and relates to the technical field of asynchronous motor energy-saving monitoring. The method comprises the steps that S1, multi-source heterogeneous data are synchronously collected, power supply parameters and operation parameters are obtained in real time, and a frequency conversion working condition self-adaptive deviation value and a current stability coefficient are obtained; s2, generating four-dimensional situation factors including an overtemperature danger value, a current stability coefficient, a voltage stability coefficient and a frequency deviation value; s3, constructing a fourth-order transmission network, and generating a primary early warning signal based on the path mark combination; s4, calculating the motor efficiency in real time, triggering an efficiency abnormity mark, fusing the efficiency abnormity mark with the primary early warning signal, and reconstructing a final early warning instruction; and S5, executing hierarchical control according to the final early warning instruction. Through multi-parameter coupling and dynamic early warning transmission, the early-stage capture capability of the composite fault is improved, the early warning response delay is shortened, energy-saving optimization and fault protection of the motor are realized, and the method has a remarkable practical value.

The invention discloses a new energy voltage ride-through parameter optimization method considering polymorphic voltage stability constraint, a storage medium and equipment, and belongs to the technical field of power system safety and stability improvement. In order to solve the problem that an existing new energy voltage ride-through parameter optimization method is only suitable for a single fault scene and only aims at a voltage stable form to optimize a new energy voltage ride-through parameter, the invention provides a new energy voltage ride-through parameter optimization method. Based on the dynamic characteristics of the new energy during the power grid fault period, the new energy general control model is established so as to determine the dominant control parameters influencing the dynamic characteristics of the new energy during the high / low voltage penetration period, the influence of the dominant control parameters on the voltage stability under different faults is considered, and the voltage regulation capability and the power regulation cost of the new energy are considered. A new energy voltage ride-through parameter optimization model considering polymorphic voltage stability constraints is established, and the optimization model is solved by adopting a particle swarm optimization algorithm. The method is used for voltage ride-through parameter optimization of new energy.

The invention discloses a multi-new energy station low voltage ride through control parameter coordinated optimization method, system, device and medium, and the method comprises the steps: evaluating the coupling influence degree of the reactive power change of each new energy station on the grid-connected point voltage of other new energy stations in a target region; obtaining the reactive voltage influence coupling degree index value of the grid-connected point of each new energy station; setting the value range of the reactive current proportionality coefficient in the low voltage ride through control of each new energy station based on the reactive voltage influence coupling degree index value of the grid-connected point of each new energy station; and within the value range of the reactive current proportionality coefficient in the low-voltage ride-through control of each new energy station, performing coordinated optimization on the reactive current proportionality coefficient in the low-voltage ride-through control of each new energy station. Compared with the prior art, the overall deviation of the grid-connected point voltage of the multiple new energy stations can be reduced, and the voltage stability of the power grid is improved.

The invention relates to the technical field of electric power operation and maintenance, in particular to a distributed optical storage unit cooperative scheduling control method and system, the method is applied to a distributed optical storage system comprising a plurality of optical storage units, each optical storage unit comprises a photovoltaic unit, an energy storage unit and a load unit, and is configured with a local unit controller. Comprising the following steps: collecting real-time operation data of an optical storage unit, and extracting local collaborative scheduling characteristics and a local DC bus voltage instantaneous fluctuation ratio from the real-time operation data; each unit controller inputs the corresponding local cooperative scheduling feature into a preset local scheduling strategy generation model, and outputs an initial scheduling action instruction set; and performing global voltage stability evaluation on the distributed optical storage system based on the local direct current bus voltage instantaneous fluctuation rates corresponding to all the optical storage units, and calculating to obtain a system voltage fluctuation index. The distributed local control capability and the global collaborative demand are organically fused, and the global voltage stability and the collaborative scheduling consistency are ensured.

The invention discloses a phase modifier unit state monitoring and load control method and system for reactive power compensation of a power grid, belongs to the technical field of automatic control of power systems, and mainly solves the problem that service life loss is aggravated or a control target is single due to the fact that the health state of equipment is not fully considered when an existing phase modifier unit responds to reactive power requirements of the power grid. The method comprises the following steps of: calculating reactive vacancy and extracting vibration frequency band energy characteristics by acquiring voltage and frequency of a power grid as well as vibration, temperature and exciting current signals of a unit in real time, and constructing a health state index HSI in combination with self-adaptive wavelet basis selection; taking minimum equipment life loss and optimal power grid voltage stability as multiple objectives, dynamically generating a load control instruction by adopting a particle swarm algorithm, realizing weight adaptive adjustment based on HSI, and introducing an online life loss model as a constraint condition; finally, reactive power output is adjusted through an excitation system, and cooperative control of power grid stability and equipment life extension is achieved.

The invention relates to an electric power system transient state and voltage stability collaborative prediction method based on a space-time diagram neural network, and belongs to the field of electric power system stability evaluation. The method comprises the following steps: constructing a power grid information adjacency matrix according to a topological structure and electrical parameters of a power system; based on the power grid information adjacency matrix, extracting topological features and time features of the power system from the PMU data; and fusing the extracted topological features and time features, and outputting prediction results of transient state and voltage stability of the power system. According to the method, the accuracy and robustness of stability prediction of the power system can be effectively improved, the real-time requirement is met, and technical support is provided for safe and stable operation of the power system.

The invention relates to a power distribution network voltage partition control method and system based on a hierarchical K-means clustering algorithm, and belongs to the technical field of power distribution system voltage partition optimization. According to the technical scheme, power distribution network node parameters are collected in a self-adaptive partition mode, a node sensitivity coefficient and an eigenvector of an electrical distance are constructed, and then a K-means algorithm is used for fine region division; selecting a dominant node: solving a Jacobi matrix through load flow calculation, extracting a voltage sensitivity coefficient, and selecting a node with the maximum sensitivity as a dominant node in each partition; multi-objective optimization: constructing an optimization model with minimum network loss, minimum voltage deviation and highest voltage stability as objectives; and partition cooperative control: accessing wind power and photovoltaic power to the dominant node, adjusting reactive power output in real time according to an optimization result, and realizing partition autonomy and global cooperation. According to the invention, voltage fluctuation and out-of-limit are inhibited, system network loss is reduced, control efficiency and economy are improved, and the method is suitable for complex topology and high permeability scenes.

The invention relates to the technical field of distributed photovoltaic cluster voltage regulation and control, in particular to a distributed photovoltaic cluster voltage regulation and control method considering net load balancing. According to the method, multi-parameter monitoring equipment is installed at each node to collect data, the net load power is calculated, the space-time distribution characteristics of the net load power are analyzed, key nodes are determined, and a voltage threshold value is set. A comprehensive objective function is constructed by taking voltage stabilization and net load balancing as objectives, a multi-objective optimization model is established, a particle swarm optimization algorithm is adopted for solving, and a result is converted into an inverter regulation and control instruction. And the inverter executes the instruction and feeds back the instruction, the regulation and control effect is evaluated according to the feedback, and closed-loop control is realized. According to the method, the voltage stability of a power supply system is effectively improved, net load balancing is realized, the operation efficiency and reliability of a power system are improved, and technical support is provided for efficient application of a distributed photovoltaic cluster in a power distribution network.

The invention discloses a protocol conversion and protocol self-learning method and system for transformer area optical storage and charging cooperation, and the method comprises the steps: constructing a digital twinborn body of a transformer area optical storage and charging system, carrying out the parallel operation of a protocol agent and a protocol agent in a virtual environment, and achieving the cooperative training of the protocol agent and the protocol agent through a hierarchical reinforcement learning architecture, the protocol agent is responsible for learning a dynamic priority scheduling and compression strategy for heterogeneous protocol messages such as Modbus, CAN and IEC 104, the protocol agent is responsible for learning a power balance and voltage stability control strategy based on photovoltaic output, energy storage SOC and charging load, and the two agents realize cross-domain collaborative optimization through a reward function mutual coupling mechanism. And finally, safely deploying the collaborative strategy obtained by training to the edge control equipment of the physical transformer area. According to the method, the problems of disjunction of protocol conversion and cooperative control, protocol strategy solidification, insufficient cross-domain cooperation and the like in the prior art are solved, and the operation efficiency and the self-adaptive capability of the transformer area optical storage and charging system are improved.

The invention discloses a power adapter parameter dynamic optimization method for prolonging the service life of equipment, and relates to the technical field of adapter performance optimization, and the method comprises the steps: monitoring and recording the output voltage, current and power factor parameter changes of a power adapter in real time under static and dynamic load conditions by using a test instrument; identifying the trend and reason of abnormal parameter change; performing thermal design optimization and temperature management on the power adapter; performing over-power and over-current limitation on the pulses one by one; introducing a power factor correction circuit; optimizing the switching frequency and the design of an output filter; a load line adjusting circuit is introduced, and space is reserved for dynamic ripples when output current jumps; and evaluating whether the optimization effect reaches an expected target or not. By introducing the power factor correction circuit and the load line adjustment circuit, the power factor of the power adapter is improved, the harmonic pollution is reduced, the stability of the output voltage of the power adapter is improved, and the performance of the power adapter under the dynamic load condition is improved.

The invention discloses a response control method for a charging station participating in a low-voltage treatment scene, and the method comprises the following steps: S1, collecting the voltage data of a charging pile, calculating a deviation, and judging a low-voltage grade; s2, when the deviation is in a light interval, outputting reactive power support voltage in proportion; s3, when the deviation exceeds a heavy threshold value, reducing active power and starting V2G feedback; s4, constructing a graph structure model, and extracting inter-node power coupling and user behavior factors; s5, inputting the model into an improved prediction controller, predicting a future voltage state, and optimizing and outputting a current period power control value; s6, power adjustment is executed, and feedback power is slowly output in the V2G stage; and S7, comparing the control deviation with the behavior factor change, updating the node control priority, and feeding back for rolling optimization of the next period. According to the invention, while the charging regulation and control precision is improved, the user experience and the voltage stability are effectively considered, and the method is suitable for various low-voltage treatment scenes.

The invention relates to the technical field of energy storage modules, in particular to a direct-current power supply energy storage system suitable for various household appliances. According to the method, the current change value and the power fluctuation range of the energy storage unit are analyzed, statistics of the power change amplitude and the time interval is combined, the work cycle rule of the energy storage unit is defined, the power difference between the partitions is extracted through the Markov chain model, and real-time balance of the partition power supply state is achieved by gradually optimizing path distribution. And resource waste caused by power fluctuation is effectively reduced. Through gradual correction of a real-time output power value and a target power value, dynamic power matching of an energy storage unit is realized, the flexibility and accuracy of energy distribution are improved, voltage fluctuation trend is monitored and adjusted through a variational mode decomposition technology, voltage stability control of an energy storage module is realized, the reliability of an energy storage system is optimized, and the energy storage efficiency is improved. And the overall stability and the energy utilization efficiency are improved by matching the output power and the frequency adjustment range.