110 results about "Voltage optimisation" patented technology

The invention relates to a source-containing power distribution network multi-target collaborative voltage optimization control method based on an FACMAC algorithm, and belongs to the technical field of photovoltaic inversion control. According to the technical scheme, a power distribution network physical system is composed of a plurality of feeder lines, a transformer, a line and a plurality of grid-connected photovoltaic inverters, and each inverter can measure operation information such as local voltage and current in real time; the data acquisition and communication system is used for acquiring node operation data and realizing low-delay communication; the multi-agent reinforcement learning control system is composed of a plurality of distributed agents and factorization centralized Critic modules, and whole-network voltage optimization decision can be carried out in training and execution stages. And the execution unit adjusts the reactive power output of the inverter in real time according to the control instruction. According to the method, the whole-network cooperative regulation and control capability is improved, the training efficiency bottleneck in a high-dimensional scene is relieved, the expression capability on a complex nonlinear coupling relationship is enhanced, and efficient, stable and extensible power distribution network voltage optimization control is realized.

The invention discloses a three-phase vehicle-mounted charger hierarchical control method and system based on a battery state, and the method comprises the steps: obtaining the battery state data of a vehicle-mounted power battery, the battery state data at least comprising a battery voltage and a battery charge state; determining a charging mode according to the battery voltage and / or the battery charge state; when the charging mode is a first charging mode, controlling the PFC converter to operate in a constant voltage output mode, and executing voltage following control on the bidirectional DC-DC converter based on the battery voltage; and when the charging mode is a second charging mode, executing voltage following control on the PFC converter based on the battery voltage, and controlling the bidirectional DC-DC converter to operate in a constant-frequency or constant-phase output mode at the same time. By setting hierarchical control strategies under different working conditions, the output voltage of the PFC converter is adaptively adjusted, the voltage regulation transformation ratio under different working conditions is optimized, the overall circuit structure is improved, and the charging efficiency of the vehicle-mounted charger within the full working condition range is improved.

The invention discloses a front-stage and back-stage collaborative optimization control method for a vehicle-mounted V2G bidirectional converter, and relates to the technical field of vehicle-mounted bidirectional converter control. According to the invention, a front-stage totem-pole bridgeless PFC double-closed-loop controller and a rear-stage CLLC resonant converter double-closed-loop controller are designed; secondly, establishing a system joint model, reflecting the dynamic behavior of the bus voltage in the charging and discharging process, designing a corresponding bus voltage optimization method, and adopting an improved particle swarm optimization (IPSO) algorithm to optimize the parameters of a front-stage controller and a rear-stage controller by using a comprehensive cost function in the bus voltage optimization method; and the prediction step length and the weight parameter of the MPC link and the gain of the PI link are optimized in real time, so that the front-stage and rear-stage double-closed-loop control strategy can be more flexible and reliable in application. Through the innovation, the system can adapt to different working conditions in real time, front-stage and back-stage collaborative optimization control is achieved, the overall energy transmission efficiency is improved, and energy loss is remarkably reduced.

A multi-time scale voltage control method for an active distribution network is provided. The method comprises: establishing a voltage optimization approach taking into account a large scale of distributed power supplies to realize cooperative dynamic control in case of a voltage violation generated when the distributed power supplies are incorporated into a distribution network; under a long time scale, establishing a voltage control model for controlling a capacitor bank based on voltage sensitivity analysis to realize drastic voltage regulation in case of the voltage violation by means of reactive power compensation; and under a short time scale, establishing a distributed voltage control model, and considering the problem of voltage violation, solving an optimal control strategy online by fully using active and reactive power outputs of the distributed power supplies to realize quick voltage regulation.

The invention discloses a high-voltage distribution network reactive voltage optimization method, device, equipment and medium based on a time scale, and relates to the technical field of distribution network optimization, and the method comprises the following steps: obtaining the real-time operation state data and prediction information of a high-voltage distribution network, dividing the operation time of the high-voltage distribution network, obtaining the divided data, and calculating the real-time operation state data of the high-voltage distribution network; the division data comprises a long time scale and a short time scale; under a long time scale, based on load prediction and distributed power supply output prediction, a mixed integer programming model is established, and a capacitor bank switching scheme and a transformer tap reference position are obtained; under a short time scale, constructing an optimal power flow model with minimum voltage deviation as a target, and dynamically calling the reactive capacity of the distributed energy converter for real-time correction; and establishing a cross-time-scale coordination control mechanism, triggering advanced re-optimization of a long time scale when the accumulated reactive power regulation quantity under a short time scale reaches a preset reactive power capacity threshold value, and updating an equipment action constraint condition.

The micro-grid multi-time scale voltage optimization method based on deep reinforcement learning comprises the steps of providing a multi-time scale collaborative optimization framework based on deep reinforcement learning for a voltage out-of-limit problem caused by distributed photovoltaic access in a micro-grid; an improved hierarchical DQN (HRL-Dueling DQN) algorithm is adopted to carry out long-time scale optimization control on discrete capacitor equipment, so that the voltage out-of-limit risk is reduced; and performing short-time scale fine adjustment on reactive output of the continuous photovoltaic inverter by using a depth deterministic strategy gradient (DDPG) algorithm to suppress voltage deviation. According to the micro-grid multi-time scale voltage optimization method based on deep reinforcement learning, the real-time performance and robustness of voltage optimization are improved, the voltage out-of-limit risk is effectively restrained, voltage fluctuation is finely adjusted, and reliable guarantee is provided for safe and stable operation of a micro-grid with new energy access.

The invention relates to the technical field of electrochemistry and energy storage, and discloses a battery life and charging efficiency equalization optimization method and system, and the method comprises the steps: extracting the capacity fading rate, internal resistance change trend and temperature sensitivity of a target battery; analyzing the real-time health state of the target battery and the battery chemical model, and establishing a dynamic reference charging curve of the target battery; defining a charging efficiency threshold value and an IDR safety limit value of the target battery; monitoring battery charging data in the charging process of the target battery in real time so as to analyze the current charging efficiency and the instantaneous attenuation rate of the target battery, and establishing a dual-target optimization function of the target battery; and the charging efficiency equalization parameters of the target battery are generated by using the dual-objective optimization function, and the charging efficiency equalization parameters comprise the optimized charging voltage, the optimized charging current and the optimized charging pulse interval, so that the service life of the battery can be prolonged, and the equalization optimization effect of the charging efficiency can be improved.

The invention provides a 10kV feeder reactive voltage optimization control method based on reactive compensation optimization stationing, which relates to the technical field of reactive voltage control, and comprises the following steps: constructing a 10kV feeder reactive optimization configuration model by taking loss reduction and investment cost reduction as targets; solving the reactive power optimization configuration model to obtain a reactive power compensation device optimization stationing scheme of the 10kV feeder line; constructing a 10kV feeder reactive power optimization control model according to the reactive power compensation device optimization stationing scheme; solving the reactive power optimization control model to obtain a reactive power optimization control scheme of the 10kV feeder line; and controlling corresponding equipment of the 10kV feeder line according to the reactive power optimization control scheme. According to the scheme, the reactive voltage control capability of the 10kV feeder line can be improved, and the reliability of stable operation of a power grid is further improved.

The invention relates to the technical field of rare earth electrolytic metallurgy, and discloses a praseodymium-neodymium alloy low-temperature electrolysis cell voltage optimization method and system.The method comprises the steps that multi-parameter coupling analysis is conducted on an electrolytic cell low-temperature electrolysis initial parameter set, and key influence factors are obtained; carrying out correlation analysis on the key influence factors by combining fluctuation characteristics of the cell voltage to obtain an influence weight sequence; determining a collaborative adjustment scheme of the polar distance and the current density according to the weight sequence; the cell voltage is synchronously adjusted based on the collaborative adjustment scheme, and accurate control over the two is achieved; the operation parameters of the electrolytic cell are collected and integrated to obtain a feedback parameter set, the feedback parameter set is fed back to a correlation analysis link to optimize the key influence factor identification precision, and finally a cell voltage optimization scheme is obtained. The technology can accurately identify the key influence factors, dynamically adapt to the operation change of the electrolytic cell, improve the stability of the cell voltage, reduce energy consumption and cost, and improve the efficiency of cell voltage optimization. The production efficiency and the product quality are ensured.

The invention relates to the technical field of electric vehicle driving control, and particularly discloses a driving control system and method for intelligent transfer equipment. The driving control method is used for solving the driving control problems of large current tracking error, high torque ripple and noise vibration, unstable regeneration and poor low-speed micro-climbing and ramp restarting response caused by sampling and inverter time lag, parameter drift, bus fluctuation and degradation faults of a six-phase permanent magnet synchronous motor of intelligent transfer equipment under the complex working condition of a port. The system comprises an AT-AGV, a PMSM, a six-phase inverter, a DC bus, a controller and a planning module. Through midpoint sampling, two-beat equivalent time-delay modeling and limited deadbeat voltage optimization, and in combination with xy harmonic compensation, online parameter identification, three-section regeneration trajectory and fault-tolerant SVPWM, robust stability and high-efficiency recovery under low-overshoot rapid torque establishment, low ripple and low NVH, bus fluctuation and fault are realized; accurate current tracking and smooth traction are realized during micro-crawling and ramp restarting.

A power distribution network voltage cooperative control method considering a cluster division-optical storage all-in-one machine comprises the following steps: step 1, combining an electrical modularity index, a balance index and a membership index to construct a cluster comprehensive division index; 2, taking the comprehensive division index as a convergence condition for dividing a cluster region, and solving an optimal cluster division result of the power distribution network by utilizing an improved spectral clustering algorithm; 3, with the minimum power distribution network node voltage deviation punishment cost, the minimum network loss cost and the minimum power generation loss cost of the optical storage all-in-one machine as the target, a power distribution network voltage optimization model containing the optical storage all-in-one machine is constructed; and 4, based on the optimal cluster division result, solving the power distribution network voltage optimization model of the optical storage all-in-one machine through a cluster voltage optimization control strategy, optimizing active power, reactive power and voltage of the power distribution network, and improving power flow distribution of the whole network. The voltage regulation task sharing and power balance are realized, the voltage regulation effect is more reasonable, and the voltage out-of-limit problem under the random fluctuation of the source load power can be effectively solved.

The invention belongs to the technical field of voltage optimization, and particularly relates to a reactive voltage optimization control method suitable for a large-scale new energy collection area, and the method comprises the steps: constructing a node power distribution network topology based on a power system simulation platform, and obtaining a training label; mapping the power distribution network into a graph structure containing nodes and edges; executing graph convolution operation through a graph neural network, extracting single-moment spatial topological features, and arranging the single-moment spatial topological features according to a time sequence to form a spatial topological feature sequence; performing spatial-temporal dynamic modeling on the spatial topological feature sequence by using an improved Mamba model, and outputting a spatial-temporal time sequence feature sequence; introducing an attention mechanism, and outputting a reactive power prediction value of each controllable device as a control instruction of power distribution network dispatching; and outputting a control instruction of actual power distribution network dispatching by using the trained graph neural network and the improved Mama model. According to the invention, under the condition of ensuring voltage safety and reactive constraint, the voltage out-of-limit quantity is reduced, and the voltage qualification rate is improved.

The invention discloses a low-carbon demand response and partition hierarchical scheduling system and method based on cloud side-end collaboration, and belongs to the technical field of carbon emission reduction resource response capability modeling and interaction mechanisms, and the system comprises a physical layer, a terminal layer and a platform layer. According to the method, firstly, a node carbon potential modeling method under a dynamic carbon emission factor is researched, multi-type flexible resource models of an electric vehicle, an air conditioner load, an electric hydrogen load, energy storage and the like are constructed, a multi-time-scale multi-element resource response capability evaluation method is formed, and a multi-target demand response interaction mechanism and a cluster regulation and control method considering low carbon and high reliability are provided; secondly, researching a supply and demand cooperation partition interaction low-carbon regulation and control technology in a steady state, and proposing a hierarchical partition cooperation active scheduling and reactive voltage optimization method; and finally, realizing low-carbon demand response and partitioned hierarchical scheduling of different dimensions by relying on a cloud side regulation and control framework.

The invention provides a reactive voltage optimization method and device, computer equipment and a storage medium, and belongs to the field of power grid optimizing.The method comprises the steps that a reactive voltage mathematical model of a regional power grid in each time period is established, and parameters are initialized; the improved Golden litsea rotundifolia algorithm comprises the step of introducing a self-adaptive decline factor for regulating and controlling escape energy and a mutation strategy based on a Cauchy distribution function for updating the positions of male Golden litsea rotundifolia individuals; obtaining an initial active power loss value, a node voltage deviation and investment cost data of each time period; and constructing an objective function, carrying out iterative optimization on the mathematical model through an improved Golden Litsea Algorithm to obtain an optimal solution of the objective function in each time period, and optimizing the operation of the power grid based on the optimal solution of the objective function in each time period. Therefore, through the synergistic effect of the adaptive energy factor and the Cauchy variation and the greedy strategy screening, the method has remarkable advantages in the aspects of reducing the active power loss, improving the voltage stability, controlling the investment cost and the like.

The invention relates to the technical field of power distribution network control, and discloses a whole county photovoltaic cluster voltage control method, system and device based on an improved differential evolution algorithm, and a medium, and the method comprises the steps: calculating electrical data between nodes in a power distribution network, and carrying out the preprocessing of the electrical data; performing cluster division through modularity optimization, and setting a target function of cluster division; and constructing a cluster voltage control model by improving a differential evolution algorithm in each cluster and taking photovoltaic inverter residual capacity reactive compensation and distributed photovoltaic active power reduction as control variables, and performing voltage optimization control of the power distribution network. According to the method, the influence of active power and reactive power is comprehensively considered, adaptive clustering of the power distribution network is realized, and the calculation speed is higher; the differential evolution algorithm is improved, so that better optimization precision and calculation stability are achieved; on the basis of cluster voltage control of the virtual balance nodes, the problem that calculation errors are too large due to the fact that global coordination is ignored by independent control is avoided to a certain extent.

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.

The invention discloses an electrical engineering automatic monitoring system and method, relates to the technical field of electrical engineering monitoring, and forms a core architecture of the electrical engineering automatic monitoring system through cooperation of three algorithm units. A plurality of influence factors such as the current fluctuation value, the total harmonic distortion rate and the humidity of air in the environment where the electrical equipment is located are comprehensively considered, the final output power P3 is obtained through calculation, the operation efficiency of the equipment in the actual environment can be accurately evaluated, efficiency reduction caused by equipment aging or load imbalance can be found, and the reliability of the equipment is improved. And the power output of the plurality of electrical devices is optimized by adjusting the load impedance Z or voltage V, so that the plurality of electrical devices in the monitored electrical engineering can maintain the maximum energy efficiency in different load states, the monitoring period of the plurality of electrical devices can be adjusted, and the energy efficiency of the plurality of electrical devices can be effectively improved under limited monitoring resources. The electrical equipment with low energy efficiency is emphatically monitored and maintained, and the monitoring efficiency of the automatic monitoring system of the electrical engineering is improved.

The invention discloses a multi-reactive-power-source static and dynamic optimization control method and system based on source-network cooperation, and the method comprises the steps: S1, building and solving a unified optimization model for fusing multiple types of reactive power sources at a power grid side and a power supply side based on ultra-short-term prediction data, and generating a switching plan, a reference curve and a voltage optimization target interval in a future time period; s2, performing simulation check on the optimization plan by using a power grid digital twin model, and if a transient voltage safety standard is violated, triggering re-optimization taking transient safety as a constraint; s3, based on the optimization target and real-time measurement, a tracking adjustment mode or an emergency cooperative control mode is adaptively executed according to the disturbance level; and S4, equipment execution and feedback updating. Through static and dynamic closed-loop coordination and source network resource global optimization, the problems of static and dynamic separation and insufficient source network coordination in traditional control are solved, and the economical efficiency, the safety and the adaptive capacity of the high-proportion renewable energy power grid are remarkably improved.

The invention discloses a power distribution network disturbance self-healing voltage optimization control method, device and system based on multi-agent deep reinforcement learning. The power distribution network disturbance self-healing voltage optimization control method comprises the following steps: modeling a distributed power distribution network voltage optimization problem as a partition Markov decision model; the partition Markov decision model is solved through a deep reinforcement learning MGPER-MATD3 model, reactive power of each photovoltaic inverter in the distributed power distribution network is obtained, and voltage optimization of the power distribution network is completed; wherein the deep reinforcement learning MGPER-MATD3 model performs sampling according to the priority of each experience during experience sampling, and the priority of each experience is related to the TD error, the experience rare degree and the experience reward feature value. The method has the advantages of good voltage control effect, high experience utilization efficiency and the like, and is suitable for the field of distributed power distribution network reactive voltage control and the like.

The present application relates to a multi-stage coordinated voltage optimization method for a power distribution network optical storage system, which divides the voltage regulation strategy into three stages: 1) using an analytical method for local reactive power compensation of a photovoltaic inverter; 2) selecting the reactive power output of the photovoltaic inverter as a consistency variable based on filter consistency, and performing distributed reactive power regulation of the photovoltaic inverter; 3) selecting the state of charge variation of the energy storage system as a consistency variable based on filter consistency, and performing distributed active power regulation of the energy storage system; this method can fully utilize the voltage regulation resources of the distributed photovoltaic-energy storage system to control the voltage, while solving the voltage out-of-limit problem, and as much as possible reducing the total active power output of the photovoltaic system due to participation in voltage control, and reducing the overall voltage regulation cost of the energy storage system, and at the same time, this method can well adapt to the normal changes of the information-physical topology of the power distribution system, and has better voltage regulation effect and adaptability compared with the traditional single control mode single-stage voltage regulation strategy.

The invention discloses a power distribution system adaptive operation method considering differentiated demands based on joint learning. The method comprises the following steps: 1, establishing a reconfigurable soft switch model; 2, constructing a combined learning agent based on a soft actor-commentator network and a dual deep Q network; 3, performing off-line learning on the reconfigurable soft switch in the established joint learning agent to realize rapid and accurate decision of the reconfigurable soft switch in a time-varying environment; and 4, proposing an adaptive control strategy under the support of the reconfigurable soft switch, including three control modes of loss reduction control, voltage optimization and demand response, and designing an adaptive switching mechanism capable of flexibly switching among multiple modes. Differential regulation and control requirements of the power distribution system are considered, and the flexibility and adaptability of the system are improved by using the reconfigurable soft switch, so that adaptive operation of the power distribution system is realized.

The invention provides a power distribution network reactive voltage optimization method based on mathematical analysis, and relates to the technical field of power distribution network control, and the method comprises the steps: S1, building a control model of a distributed active power distribution network reactive power compensation device according to a physical model and control characteristics of the distributed active power distribution network reactive power compensation device; s2, establishing a multi-optimization objective function which takes the minimum network loss as a core and takes voltage quality into account, and establishing operation constraint conditions; s3, based on the obstacle function conversion constraint and gradient / Hessian matrix optimization, designing a solving algorithm model; and S4, integrating the control model, the multi-objective optimization function and the operation constraint condition into an active power distribution network reactive voltage coordination control optimization model, calling a solution algorithm model for solution, and outputting an optimization control scheme of the reactive power compensation device. According to the scheme, the problems that a traditional distribution network optimization model does not fully fuse the characteristics of the distributed active reactive power compensation device, and a multi-dimensional target and complete constraint are not designed for the active power distribution network can be solved.

The invention discloses a network-transformer-load voltage cooperative control method based on multi-agent deep reinforcement learning, and the method comprises the steps: constructing a three-layer power distribution network environment of a power distribution network side, a transformer side and a load side, and setting agents in the three layers; establishing a power distribution network voltage optimization control model, controlling actions of a plurality of equipment at three layers of a power distribution network side, a transformer side and a load side, and taking minimization of voltage deviation, network loss and action cost as a target function; modeling a network-variable-load three-layer collaborative voltage optimization control problem into a Markov decision process, and defining a corresponding action space and a state space; optimizing a global optimal strategy of each agent by using value decomposition of implicit communication; and training and solving the agents by using a multi-agent near-end strategy optimization algorithm to realize collaborative optimization among the agents so as to realize global voltage collaborative control. According to the method, voltage optimization control of the power distribution network is realized, and the operation stability, the calculation efficiency, the convergence speed and the global optimality of the power grid are ensured.

The invention discloses a high-fidelity rapid ion transport device. The device comprises an upper computer module, an FPGA data processing module, a digital-analog voltage conversion module, a voltage amplification module, a low-pass filtering module and an ion trap module which are connected in sequence, the invention also discloses a voltage optimization method for high-fidelity rapid ion transport, the speed of ion movement is controlled through a sin2 function, and the method provided by the invention calculates the optimal voltage value of each transport electrode under the transport track of ions through a quadratic programming algorithm; the moving process of ions has the characteristics of slow start, constant-speed middle section and slow stop, and the smooth movement can effectively avoid excitation of a motion mode and reduce the heating effect; the optimal voltage value of each transport electrode avoids additional heating, improves the efficiency of quantum calculation, and meets the high fidelity requirement of the ion logic gate.

A supply voltage may be set using a local voltage regulator, such as a Digital Linear Voltage Regulators (DLVR). A DLVR may include a compensator, and the performance of the compensator may be affected by a dropout (DO) voltage. To improve the performance of a compensator, a number of compensator calculations may be pre-calculated to reduce the complexity of remaining real-time computations and enable compensator calculations to be completed within a single DLVR clock cycle. A DLVR may include a sense filter, and the DLVR transfer function (TF) may be modified using dynamic shaping of open loop gain and pole locations of a sense filter. The DO range associated with the DLVR TF may be changed according to a monitored DO(t) to reduce the sensitivity of a domain VMIN on dropout, which reduces power consumption, increases performance, and enables simplification of test flows.

The application discloses a kind of multi-energy distribution network voltage control methods, comprising the following steps, define local voltage control rule;Analysis distributed power output causes voltage fluctuation upper and lower limit;Build global voltage control function;Algorithm is solved to objective function, the local voltage control rule is acquisition control point voltage u, determine whether to satisfy the following constraint, set the reactive power output of voltage compensation device.The multi-energy distribution network voltage control method, considering the uncertainty fluctuation of distributed power supply, improve the accuracy of voltage fluctuation analysis, formulate the localized voltage adjustment rule and global voltage optimization function, realize passive control and active control combination, reduce fluctuation rate, using interval number to express the uncertainty of distributed power supply, less computing resource demand, control reaction speed is fast.

A GCIB apparatus that can change the energy of ions to be irradiated onto a substrate without changing the electrode arrangement of the GCIB apparatus that have an extraction electrode arrangement optimized for a specific voltage or a permanent magnet type magnet that effectively removes singly charged monomer ions at that voltage, or the magnetic field strength of the permanent magnet type magnet. A separated high voltage power supply that generates a positive or negative high voltage in addition to the first high voltage power supply, the second high voltage power supply and the third high voltage power supply, and a separated high voltage application circuit that applies a positive or a negative separated high voltage supplied from the separated high voltage power supply to the ground electrode of the extraction electrode and the ground electrode portions of the one or more electrostatic lenses.

This application relates to the field of power system technology, and in particular to a distributed optimization method for reactive power and voltage in a multi-zone flexible interconnected AC / DC hybrid distribution network based on cluster partitioning. By constructing distributed optimization models for reactive power and voltage within and between clusters, an improved particle swarm optimization algorithm (PSO) is used to enhance the global search capability of the model. Furthermore, the advantages of the alternating direction multiplier method (ADMM) for distributed solving are combined to improve solution efficiency and effectiveness, thereby effectively optimizing the reactive power and voltage of a multi-zone flexible interconnected AC / DC hybrid distribution network with a high proportion of distributed renewable energy. The aim is to solve the problem of reactive power and voltage optimization in such a network.

This invention discloses a hierarchical control method and system for a three-phase on-board charger based on battery state. The method includes: acquiring battery state data of the on-board power battery, the battery state data including at least battery voltage and battery state of charge; determining a charging mode based on the battery voltage and / or battery state of charge; controlling the PFC converter to operate in a constant voltage output mode when the charging mode is a first charging mode, while simultaneously performing voltage following control on the bidirectional DC-DC converter based on the battery voltage; and performing voltage following control on the PFC converter based on the battery voltage when the charging mode is a second charging mode, while simultaneously controlling the bidirectional DC-DC converter to operate in a constant frequency or constant phase output mode. This invention adaptively adjusts the output voltage of the PFC converter by setting hierarchical control strategies under different operating conditions, optimizes the voltage regulation ratio under different operating conditions, improves the overall circuit structure, and enhances the charging efficiency of the on-board charger across the entire operating range.

The application discloses a flexible on-load voltage regulating transformer multi-target cooperative control system and method based on hour-level load prediction, which comprises the following steps: a node parameter acquisition module acquires electrical parameters of each node of a power distribution network; a load prediction module outputs a load prediction sequence in the future time; a multi-target optimization module generates an optimal control optimization strategy according to a cooperative optimization model; an upper prediction optimization layer makes an hour-level optimization decision on tap position and capacitor group switching; a lower real-time adjustment layer outputs a voltage dynamic compensation amount and generates a minute-level equipment adjustment instruction; and an execution module coordinates and controls flexible on-load voltage regulating transformer (FOLTC) tap switching, capacitor group switching and converter action, so that multi-target optimization of voltage deviation, reactive power compensation and equipment loss is realized. The application is suitable for voltage optimization, reactive power compensation and loss control in a conventional load fluctuation scenario of a power distribution network.