9724 results about "Power output" patented technology

The present application provides a multi-level coordinated voltage control method and system for a power distribution network based on three-tier priority objectives. The method comprises: acquiring operation parameters of a power distribution network; on the basis of the operation parameters of the power distribution network and a pre-constructed three-tier optimization objective planning model, using an affine theory, a duality theory, and power circle linearization and absolute value linearization methods to solve the three-tier optimization objective planning model to obtain maximization of an admissible net-load disturbance domain at each node, minimization of the total operation cost of the power distribution network and minimization of an expected voltage deviation; and performing coordinated control on various reactive devices of the power distribution network by means of the operation parameters of the power distribution network corresponding to the maximization of the admissible net-load disturbance domain at each node, the minimization of the total operation cost of the power distribution network and the minimization of the expected voltage deviation. The present application can more clearly characterize the uncertainty of distributed generation power output and the impact of the distributed generation power output on system reserve capacity, and mitigate the problems of ambiguous characterization of distributed generation power output characteristics, and multi-level voltage violation and voltage fluctuation of the power distribution network caused by large-scale distributed generation integration.

The invention relates to a follow-up network type control smooth switching method based on a generalized operation short-circuit ratio index. The follow-up network type control smooth switching method comprises the following steps of 1, introducing a generalized operation short-circuit ratio (GOSCR) as a switching index, and proposing a GOSCR-based hysteresis control mode switching judgment index under different power grid strength and power output working conditions; step 2, establishing a unified new energy grid-connected control unit model: constructing a unified grid-connected control model in two modes of network following control and network construction control, systematically analyzing oscillation characteristics of a grid-connected unit in the two modes, and defining boundary conditions of mode switching; 3, proposing an LADRC-based undisturbed switching control strategy: observing and estimating system current disturbance parameters online by utilizing LESO, realizing real-time compensation of disturbance quantity, and reducing dynamic sudden change in the switching process; the method has the advantages that GOSCR switching indexes are introduced to comprehensively evaluate the strength, a unified grid-connected control model is established, and an undisturbed switching control strategy is provided.

The invention provides an anti-islanding protection method based on cloud edge collaboration, and the method comprises the steps: obtaining historical fault data, carrying out the data processing of the historical fault data, obtaining a fault mode discrimination result, judging whether a micro-grid has an islanding operation risk or not through combining the topological structure information of a power grid, real-time operation data, and load demand prediction data, and obtaining an islanding protection result. If so, acquiring power output fluctuation data; the output power of the power supply and the load demand power are monitored in real time, if the output fluctuation of the power supply exceeds a voltage protection constant value or the load demand power exceeds a frequency protection constant value, the edge computing gateway starts a local anti-islanding control strategy, and meanwhile, the charging and discharging power of the power distribution network is adjusted; in the anti-islanding control strategy execution process, the edge computing gateway continuously collects real-time operation state data of the micro-grid, the power supply duration, the electric energy quality and the standby capacity of the micro-grid under islanding operation are evaluated according to the operation state data, and the multi-target optimization model and the operation constraint condition of the micro-grid are dynamically adjusted.

The invention relates to the technical field of energy consumption analysis, in particular to an electric vehicle energy consumption analysis system and method, and the system comprises an operation factor correlation module, a speed slope coupling extraction module, a slope interference correction module, an energy consumption stability discrimination module and a deviation trend recognition module. According to the method, the associated gradient and vehicle speed data are extracted and combined with the vehicle mass to form a three-dimensional data structure, energy consumption distribution can be predicted and evaluated more accurately, and power fluctuation caused by gradient change can be effectively identified by associating electric power output with the gradient of the gradient and the vehicle speed change rate. By comparing the deviation between the actual power state and the ideal power state and correcting gradient interference, the accuracy of energy consumption data is improved, the fine granularity of electric vehicle energy consumption management is enhanced, and by comparing the electric power and kinetic energy changes in a cross mode, power abnormal deviation can be recognized. And more reliable data support is provided for energy-saving optimization of the electric vehicle.

The invention provides an inter-fleet dynamic electric quantity balance and collaborative replenishment system, which relates to the technical field of shipping transportation, and comprises an intelligent scheduling module used for obtaining a data set containing electric power conditions, navigation parameters, sea condition influences and task progress requirements of each ship based on ship motion states, ship equipment is monitored in real time to obtain data and analyze environmental influence to obtain adjustment parameters, then the total energy consumption contribution of a single ship is calculated, the total loss of a fleet is quantified to fuse to obtain the initial amount of total energy consumption and correct to obtain the navigation efficiency, and then a power output adjustment strategy is formulated for a main propulsion ship according to the navigation efficiency and an electric power balance target. And finally, a collaborative supply path is planned by taking the low-power ship as a core and combining multi-party information. According to the invention, efficient coordination of deployment and supply of the fleet on power management is realized, the transportation efficiency is improved, the cost is reduced, and the navigation safety is enhanced.

The invention provides a marine high-power container type charging station, which belongs to the technical field of marine charging stations, and realizes a nine-step charging control process by integrating an electric energy stability regulation module through a control chip: firstly, receiving power grid monitoring data, calculating an evaluation index, and constructing a prediction model to generate an early warning signal; adjusting the power output of the electric pile assembly according to the early warning signal; receiving data of a ship battery management system and calculating an optimal charging curve; adjusting charging parameters based on the temperature data and the thermal diffusion equation; the harmonic content is calculated in real time, and harmonic waves are suppressed through a fluctuation compensation model; monitoring a power factor and executing reactive compensation; calculating multi-path charging power distribution based on the battery optimization model; and the charging power change rate is adjusted when power grid frequency fluctuation is detected. According to the system, a multilayer convolutional neural network and a deep reinforcement learning technology are fused, and stable and efficient charging in a complex power grid environment is realized through adaptive weight adjustment.

The invention belongs to the technical field of wind power generation, and discloses a wind driven generator multi-drive variable pitch control method based on big data, which comprises the following steps of: constructing a multi-source data acquisition network covering an environment, a unit, a power grid and a driver, and combining preprocessing means such as denoising and standardization and feature extraction methods such as principal component analysis; high-quality input is provided for a short-term prediction model; the model can accurately pre-judge wind conditions and power requirements in the future 10-30 minutes, and is matched with a global optimization algorithm to dynamically adjust a variable pitch strategy, so that the wind driven generator can still keep stable power output under complex working conditions of gust, power grid fluctuation and the like, and particularly has outstanding performance in areas with unstable wind resources. According to mechanical coupling characteristics of the multi-drive variable pitch system, mechanical parameters such as a gear transmission gap and shafting rigidity are fully included in global optimization, and power output, mechanical fatigue and load distribution are balanced through a multi-objective optimization algorithm.

The invention discloses a power distribution method and system based on fuel cell multi-stack health state difference, and belongs to the technical field of fuel cell application. The specific method comprises the following steps: monitoring operation parameters of a plurality of fuel cell stacks through a sensor; obtaining the health state of each fuel cell stack and the health state of the lithium battery; establishing a hydrogen consumption model, a fuel cell SOH loss model, a multi-stack SOH difference equivalent loss model, a lithium battery SOH loss model and a lithium battery start and end SOC loss model; the calculation loss is converted into equivalent hydrogen loss, and a target function model is constructed; and optimizing the target function by using a reinforcement learning algorithm, distributing power output of the fuel cell and the lithium battery according to the current required power and the health state through an energy management system, and regularly updating parameters of a parameter identification module and a model to realize dynamic optimization. The method can identify the life difference between multiple piles, balance the life difference to realize multi-pile performance balance and improve the overall life of a multi-pile system.

The embodiment of the invention provides a grid-connected inverter control mode switching method and device, equipment, a medium and a product, and is applied to the technical field of electrical control. The method comprises the following steps: acquiring a control mode switching signal of the grid-connected inverter, and determining a switching direction of a control mode based on the switching signal; when the switching direction indicates that the current control mode is switched from the GFL mode to the GFM mode, setting an initial value of an integrator of a power synchronization loop of the GFM mode as a phase angle value of a phase-locked loop corresponding to the GFL mode; switching the control mode from the GFL mode to the GFM mode based on a first preset path and a step-by-step switching mode; when the switching direction indicates that the current control mode is switched from the GFM mode to the GFL mode, acquiring a power steady-state output value of the GFM mode, and setting a power reference value of the GFL mode as the power steady-state output value; and switching the control mode from the GFM mode to the GFL mode based on the second preset path and the step-by-step switching mode. The technical effect of improving the control mode switching fluency is achieved.

The invention relates to the technical field of arc discharge control, in particular to an arc discharge machine control method based on multi-parameter collaborative optimization, which comprises the following steps of: generating a first regulation and control instruction by adopting a fuzzy PID (Proportion Integration Differentiation) algorithm based on a deviation value between a dynamic parameter feedback signal and a preset threshold value, the axial feeding speed of the servo motor, the power output curve of the arc generator and the coolant spraying time sequence are synchronously adjusted, so that the arc energy density gradient and the workpiece heat conduction rate form dynamic balance, the mechanical resonance frequency in the machining process is monitored through the vibration spectrum analysis unit, and when it is detected that the resonance intensity exceeds a safety threshold value, the coolant spraying time sequence is started. And a second regulation and control instruction is generated by triggering a frequency compensation algorithm, and the driving frequency of the servo motor is corrected in real time, so that the control precision is improved, the arc discharge process is more stable, the response speed is increased, the arc discharge time is shortened, the adaptability is enhanced, the requirements of different materials and processes can be met, the energy consumption is reduced, and the production efficiency is improved.

The invention discloses a virtual power plant regulation and control method and system based on dynamic parameter identification and hierarchical collaboration, and belongs to the technical field of virtual power plant dynamic regulation and control, and the method comprises the steps: collecting multi-dimensional data of building distributed resources, the building distributed resources comprise roof photovoltaic equipment, EV charging piles and energy storage equipment, integrating the multi-dimensional data through federal learning; building distributed resources are aggregated according to power generation characteristics and power generation economy of the building distributed resources, external characteristic parameters of the virtual power plant are generated, and an energy and operation reserve optimization model of the virtual power plant is constructed; power generation parameters of an RLS algorithm optimization model are updated online, the uncertainty of virtual power plant operation is considered, a layered time sequence cooperation framework is constructed to dynamically adjust the virtual power plant energy and operation reserve optimization model, regulation and control instructions are generated and sent to distributed resource equipment of all buildings, and the operation state and power output of the equipment are controlled.

The invention relates to the technical field of 5G edge collaborative power supply driving, and discloses an intelligent 5G edge collaborative power supply driving system, which comprises a state sensing module used for collecting multi-source data of each power supply device in the power supply driving system in real time through a multi-source sensor; the load prediction module is used for predicting a future load by using a load prediction algorithm and outputting a load prediction value; the thermal path analysis module is used for constructing a thermal path diagram, predicting a thermal diffusion trend and adjusting power output of the power supply driving system; the power dispatching optimization module is used for adjusting the power of the power supply based on the optimization objective function; and the remote cooperative control module is used for the cloud platform to generate an optimization strategy according to the real-time states of load prediction, temperature prediction and power supply scheduling. According to the method, the integrity and the time relevance of the equipment operation state expression are improved by constructing the continuous time state evolution model in a modeling mode of combining the multi-source state perception and the state space model.

The invention relates to a water-drop-shaped captive balloon high-altitude wind power generation system. The water-drop-shaped captive balloon and kite cloth carried by the water-drop-shaped captive balloon are used for generating lift force to drive a ground power generator to generate power. The lift force generation mechanism is that the windward side and the wind resistance are increased by adjusting the pitch angles of the aerostat and the bearing kite cloth, and the wind resistance is converted into cable tension to drive power generation. A V-shaped pulley expansion mode is adopted to uniformly distribute tension of main and auxiliary cables, so that bag damage caused by non-uniform stress in a posture adjusting process is avoided; the captive balloon is provided with an attitude controller, communicates with a ground control system through wireless data transmission, adjusts the take-up and pay-off speed of a main cable and an auxiliary cable to control the pitching angle to achieve lift force control, and greatly improves the power generation efficiency. The main cable power output winch drives a generator to generate electricity through a one-way bearing, and the input end is connected with a motor through a clutch to achieve cable recovery. The backpack kite cloth can be controlled to open and close through a small winch to increase and decrease wind resistance; lift force and wind resistance control is optimized, the power generation efficiency is improved, safety is enhanced, operation is simplified, and maintenance is convenient.

The invention relates to the technical field of wind energy control systems, and discloses a wind power energy-saving control method and system based on big data analysis, and the system comprises a data collection and transmission layer, a data storage and management layer, a data analysis and prediction layer, an intelligent control layer, and an equipment full life cycle management layer. The data acquisition and transmission layer further comprises multi-source data acquisition system construction, edge computing data preprocessing and data transmission network construction. According to the intelligent control strategy, by means of a distributed cooperative control algorithm and an intelligent variable-pitch variable-speed control algorithm, the fan blade angle, the rotating speed and the power output are dynamically adjusted according to the real-time meteorological data, the power grid load requirement and the fan running state, wind power output is smoother through accurate regulation and control, and the wind power output efficiency is improved. The impact on the power grid caused by power fluctuation is effectively reduced, the wind power acceptance capability of the power grid is remarkably improved, and the stability and reliability of the operation of the power grid are guaranteed.

The invention relates to a micro-grid multi-objective optimization scheduling method considering the service life of an energy storage battery, and belongs to the field of active power distribution network operation analysis. The invention relates to a micro-grid multi-objective optimization scheduling method considering the service life of an energy storage battery. The method comprises the following steps: establishing an energy storage system refined operation model in a wind-light storage micro-grid; on the premise of maximum income and maximum new energy consumption, the method has the beneficial effects that the new energy power output fluctuation is responded in time, so that the charge and discharge control of the energy storage system is efficient and accurate, and the purposes of improving the energy storage utilization efficiency, the charge and discharge control accuracy and the power output stability are achieved; according to the improved multi-objective grey wolf optimization algorithm, an initial population strategy, a population individual iteration updating strategy and a multi-objective optimal solution solving mechanism are improved, and the convergence speed, the solution set diversity and the calculation efficiency of the multi-objective grey wolf optimization algorithm are improved through multi-strategy cooperation; the problems of excessive uncertainty and single target of the existing wind and light storage micro-grid are effectively solved.

A power converter configured to be connected to three or more voltage parts, includes three or more power-conversion circuitries to be connected to respective ones of the three or more voltage parts, and a multi-port transformer connected to the three or more power-conversion circuitries at respectively different ports. The three or more voltage parts include a vehicle drive battery and a plurality of alternating-current (AC) voltage parts. Each of the plurality of AC voltage parts is configured to provide at least one of power input to a multi-port transformer side and power output from the multi-port transformer side.

The invention relates to the technical field of automobile electronic testing, and discloses an integrated ECU power-on burning test monitoring system and method. The system comprises a power supply intelligent regulation and control module, a burning verification module, a test diagnosis module, an abnormity monitoring module and a feedback optimization module. The power supply intelligent regulation and control module realizes stable power supply output through a dynamic voltage frequency regulation algorithm and a temperature self-adaptive algorithm; the burning verification module is used for ensuring the burning integrity by using a block redundancy check and increment verification algorithm; the test diagnosis module diagnoses faults based on the multi-dimensional test data and the decision tree model; the abnormity monitoring module monitors abnormity in real time by using real-time streaming data processing and an isolated forest algorithm; and the feedback optimization module optimizes test diagnosis through incremental learning and a Bayesian optimization algorithm. According to the system and the method, comprehensive and accurate monitoring and optimization of the ECU are realized, and the performance and the reliability of the ECU are effectively improved.

The invention discloses a transmission driving control method of a bimodal integrated engine. The transmission driving control method comprises the following steps: S1, state sensing; s2, mode decision making; S3, dynamic adjustment; s4, mode switching; S5, power optimization distribution; s6, adaptive learning is carried out; and S7, fault-tolerant guarantee. The invention further discloses a bimodal integrated engine transmission system which comprises the following modules: a power input module; a transmission adjusting module; a state sensing module; an intelligent control module; and a fault-tolerant guarantee module. Parameters are dynamically adjusted through fuzzy PID control according to speed deviation, meanwhile, real-time correction is conducted in combination with a multi-mode intelligent algorithm, speed fluctuation is precisely controlled within + / -3 km / h, flight stability is guaranteed, a model prediction control and multi-mode intelligent control cooperation mechanism is adopted, an optimal power output sequence is calculated in advance, coordinated distribution is completed, and the optimal power output sequence is controlled to be within + / -3 km / h. The dynamic response speed is increased by 40%, and the high-dynamic task requirement is met.

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.

An RF transmitter (1) having a gate-segmented power output stage (2) and a digital driver (5). The gate-segmented power output stage (2) includes a field-effect transistor with a plurality of gate fingers (32) and drain fingers (31) that define a gate periphery. The field-effect transistor comprises a plurality of power output stage segments (3) that each correspond to a respective part of the gate periphery, and that each have a respective power output stage segment input (4). The digital driver (5) has control outputs (6) which are connected to corresponding ones of the respective power output stage segment inputs (4), and is configured for individually switching each of the power output stage segments (3) between an on mode and a cut-off mode in dependence of one or more input signals to obtain a modulated RF carrier signal at an output (7) of the gate-segmented power output stage (2).

Provided is an electric heavy-load push-pull tool including a power input unit, a power conversion unit and a power output unit. Through the transmission design of multiple stages of reducers and a planetary roller screw of a downhole operation tool, low-speed and high-torque power output of the motor is realized. The heavy-load push-pull tool provided by the present disclosure can not only provide large-torque thrust force, but also provide large-torque pulling force. A forward direction of a transmission nut is based on a rotation direction of a motor. By controlling the forward rotation or reverse rotation of a transmission shaft of the motor, the forward or backward movement of an operation tool can be realized at low cost through power transmission.

The invention discloses a power supply rapid charging pulse current regulation and control system and method based on current monitoring, and particularly relates to the technical field of power supply management. The method comprises the following steps: acquiring a pulse current signal in a battery charging process, performing nonlinear decoupling analysis, and decomposing the pulse current signal into a first characteristic sub-vector representing a real charging state of the battery and a second characteristic sub-vector representing a high-frequency harmonic interference state; predicting the change trend of the electrochemical impedance of the battery based on the first feature sub-vector to form an instant charging acceptance evaluation index, and evaluating the harmonic interference degree based on the second feature sub-vector to form a charging state misjudgment risk level index; based on the battery instant charging acceptance assessment index and the battery charging state misjudgment risk grade index, the optimal pulse current regulation and control strategy is determined, the power output parameters are adjusted in real time, the state misjudgment problem caused by high-frequency harmonic interference and electrochemical impedance coupling is effectively solved, and the battery charging performance and the long-term reliability are improved.

The invention belongs to the technical field of charging pile control, and particularly relates to an intelligent charging pile control system and method based on dynamic power distribution, and the method comprises the steps: a slave control board collects battery data in real time, and uploads the battery data to a main control board; the main control board receives the battery data, predicts the maximum allowable charging power of the battery through an LSTM model, and determines the safety boundary of the battery according to the maximum allowable charging power and the safety operation parameters of the battery; generating a power distribution strategy by adopting a Nash equilibrium algorithm based on the battery safety boundary in combination with the power grid load and the user priority; issuing the power distribution strategy to an intelligent charging module; the intelligent charging module adjusts the switching frequency of the LLC resonant converter based on the power distribution strategy to realize target power output; the intelligent charging module monitors output current, voltage and temperature in real time and feeds back monitoring data to the main control board to form closed-loop control. And flexible control and data feedback of the charging module are realized.

The invention relates to the technical field of scheduling management, in particular to a source network load optimization scheduling analysis method and system for an active power distribution network of an electric vehicle. The method comprises the following steps: acquiring power supply side data, power grid side data and load side data in real time, and performing sliding filtering and standardization processing to obtain source network load standard data of the electric vehicle; historical charging data, traffic flow data, weather data and user appearing habit data are obtained, and load and power output prediction calculation is carried out to predict and output electric vehicle charging power distribution and distributed power generation power distribution; a corresponding multi-objective optimization function and a power distribution network operation constraint condition are constructed through an electric vehicle active power distribution network, a source network load optimization scheduling model is constructed, and a source network load optimization scheduling scheme is solved to adjust corresponding electric vehicle charging power and distributed power generation power in real time. The energy utilization efficiency and the power grid operation stability can be improved.

The invention discloses a converter collaborative current limiting method based on adaptive virtual impedance and a power loop, and the method comprises the steps: firstly building an equivalent impedance model of a converter and a power grid, analyzing the steady-state and transient characteristics of fault current, and extracting key parameters for current limiting design; when it is detected that the voltage drop of the grid-connected point exceeds a threshold value, a self-adaptive virtual impedance link is automatically put into use, and virtual resistance and reactance values are dynamically calculated according to the variation amplitude of voltage or current, so that the virtual resistance and reactance values are proportionally adjusted along with the fault degree; and when the system voltage returns to normal, the virtual impedance automatically quits, and the control continuity is ensured. Then, the active and reactive power reference values are dynamically corrected according to the voltage drop depth, a power reference signal matched with the voltage amplitude is generated, it is guaranteed that power output does not exceed the limit, and the voltage supporting capacity is improved. Finally, real-time current-limiting control of the converter and stable operation of the system are realized on the premise of not switching the control mode through the synergistic effect of the virtual impedance ring and the power ring. The method has the advantages of being high in response speed, excellent in current limiting effect, good in control continuity and the like, and is suitable for fault current limiting and voltage support control of the grid-forming converter.

The invention relates to the technical field of DC / DC power supplies, and discloses a DC / DC power supply function parameter testing method and system and computer equipment, and the method comprises the steps: injecting a preset disturbance signal into an input end of a tested DC / DC power supply through a signal conditioning circuit, and obtaining output voltage response data and output current response data; adopting a dynamic multi-parameter test system and collecting target test parameter data; calculating a characteristic parameter set, and executing voltage-temperature control model predictive control analysis to obtain a control parameter sequence; and controlling the power output value of the electric heating wire, the rotating speed value of the heat dissipation fan and the position value of the electric push rod in the test box body to obtain an intelligent adjustment test environment and generate an aging test result and a test strategy adjustment instruction, thereby overcoming the problem of parameter detection blind areas in a traditional test method, and improving the test accuracy even under the condition that the output voltage is accurately matched with the set value. And the power supply performance abnormity can be effectively identified, and the reliability and accuracy of the test are obviously improved.

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 discloses an unmanned aerial vehicle energy-power conversion system, and relates to the technical field of unmanned aerial vehicle energy, and the system comprises a battery module which is formed by combining a plurality of single batteries in a series connection or parallel connection manner, and is responsible for providing electric energy required by the flight of an unmanned aerial vehicle. And the energy management module performs real-time monitoring, optimal distribution and intelligent regulation and control on energy use of the unmanned aerial vehicle through an integrated sensor, algorithm and control technology, and comprises a data acquisition unit, a charging management unit and a power conversion unit. The charging management unit is used for managing an unmanned aerial vehicle battery charging process, externally connecting a charging power supply and controlling an unmanned aerial vehicle battery charging strategy according to the battery power reserve level; and the power conversion unit adopts a dynamic power distribution strategy, implements closed-loop control based on the real-time power spectrum characteristics of the driving module, and realizes on-demand power output adjustment. And the configuration of longer endurance time of the same power supply weight or fewer batteries under the same endurance time is realized.

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 provides an active-disturbance-rejection full-wind-speed power smoothing method based on kinetic energy of a fan rotor, and belongs to the technical field of wind power control. The technical problem that the output power of a traditional wind turbine generator is unstable due to random fluctuation of the wind speed is solved. According to the technical scheme, the method comprises the following steps that S1, rotor kinetic energy is calculated based on a wind turbine kinetic model, and the effect of the rotor kinetic energy in power fluctuation stabilization is analyzed; s2, designing a first-order linear active disturbance rejection controller for rotating speed loop control; s3, designing a second-order linear active disturbance rejection controller for variable pitch control; and S4, dynamically generating an active power reference value by combining the kinetic energy of the rotor and an active disturbance rejection control technology, and controlling by coordinating a rotating speed ring and a variable pitch. The method has the beneficial effects that power fluctuation can be remarkably reduced under wind speed fluctuation and complex working conditions, the smoothness of power output is improved, meanwhile, the anti-interference capability and the dynamic stability of the system are enhanced, and efficient and stable operation of the wind driven generator system is ensured.