1203 results about "Energy control" patented technology

The present invention relates to a production and energy system based on a new energy-powered submerged arc furnace, and a related control method. The production and energy system comprises a power transmission and distribution system, a renewable energy power generation system, an energy storage system, and a submerged arc furnace-based production system. The power transmission and distribution system is respectively connected to the renewable energy power generation system, the energy storage system, and the submerged arc furnace-based production system. The production and energy system further comprises an energy control system. The energy control system comprises: an integrated regulation and control system used for performing integrated regulation and control on the production and energy system by means of the power transmission and distribution system; a renewable energy power generation control system used for controlling operation of the renewable energy power generation system; an energy storage control system used for controlling operation of the energy storage system; and a submerged arc furnace-based production control system used for controlling operation of the submerged arc furnace-based production system. Compared with the prior art, the present invention has the advantages of optimizing energy utilization, achieving environmental protection and low-carbon development, improving the stability and reliability of the energy system, etc.

The invention discloses an intelligent management system and method applied to a radio frequency energy output device, and belongs to the technical field of intelligent management of the radio frequency energy output device.An integrated sensor array is deployed on a radio frequency host and a hand tool electrode, and load voltage, output current, electrode temperature and tissue impedance are synchronously collected; a dynamic thermal impedance collaborative analysis model is constructed after time synchronization alignment, a three-dimensional thermal field simulation map is generated, and a thermal accumulation trend is predicted; a double-layer control framework is constructed, a first control layer generates a dynamic power adjustment rule base based on a preset treatment target and a safety threshold value and outputs an instruction, a second control layer receives the instruction and adjusts output parameters in real time, and a strategy is adjusted by combining a model prediction result in the execution process; meanwhile, the operation state is continuously monitored, a fault classification model is constructed for anomaly detection, a fault source is positioned, and a visual report is generated; and finally, constructing a mapping relation based on historical data, and generating an energy control scheme adapted to individualized requirements through iterative optimization.

Disclosed herein are systems and methods for energy management. A system, such as a vehicle, includes a plurality of energy storage units that include a supercapacitor and an electrochemical battery. The system includes an energy controller that tracks historical power draw from the plurality of energy storage units over time in power tracking data, and that identifies a power draw based on the power tracking data. The energy controller switches between a first configuration and a second configuration based on the identified power draw. The first configuration is configured for drawing power from the electrochemical battery and disconnecting from the supercapacitor, while wherein the second configuration is configured for drawing power from the supercapacitor and disconnecting from the electrochemical battery.

The invention provides a stirring friction machining device and a welding, material adding and energy control method. The stirring friction machining device comprises a spinning sleeve and a rotation driving assembly, a rotating spinning screw is installed in the spinning sleeve, the rotation driving assembly drives the spinning screw to rotate through a cutter handle, and a stirring component is arranged at the bottom end of the spinning screw; the outer side of the spinning sleeve is coated with an electromagnetic resistance composite coil with adjustable current and frequency, a feeding port and / or a shredding port are / is formed in the side wall of the spinning sleeve, and variable magnetic field intensity is excited through the electromagnetic resistance composite coil according to the electromagnetic induction principle; according to the invention, accurate adjustment of heat input of a tool is realized by adjusting current in a staying starting stage, the problem that a feeding channel is blocked by hardening of materials after short staying during material adding or welding is solved, and composite stirring of metal in a deposition edge area is realized by adjusting the magnitude and frequency of the current as a dominant electromagnetic force. And meanwhile, the temperature distribution characteristics of the acting area are locally adjusted, the interface bonding strength is improved, and the residual stress of the additive component is relieved.

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 present invention discloses a laser metal curved surface automatic focus processing system and method thereof, including: a multimodal data fusion device, a curved surface detection module with a secondary tracking structure, integrated 3D structured light vision and vibration sensing equipment; a dynamic focus correction module, configured with an adaptive energy control unit and a beam shaping component; a nonlinear motion control console, including a six-degree-of-freedom parallel mechanism motion platform, and its servo control system integrates a time-space phase compensation algorithm; a core control unit, the core control unit has an embedded fusion algorithm model, and establishes a nonlinear mapping between material removal efficiency η and multivariable dynamic parameters. The processing quality level of the present invention is improved through the multi-source data fusion of 3D structured light and vibration sensing, the curvature recognition accuracy is improved, and the focus offset is reduced in combination with the axial vibration compensation mechanism, thereby effectively improving the processing quality.

The invention discloses an energy regulation and control method for a digital intelligent low-carbon building, and relates to the technical field of energy intelligent control, and the method comprises the following steps: constructing a multi-source feed-forward sensing link, unifying data collection timestamps, fusing people counting data, access control record data and wireless local area network probe data, and obtaining a multi-source feedback sensing link; passenger flow step fingerprints capable of reflecting instantaneous passenger flow change characteristics are generated and used for feedforward input of follow-up dynamic response; and performing crowded wave dynamic tracking based on passenger flow step fingerprints, analyzing a crowd movement track in combination with feedforward input, positioning a high-concentration air quality risk area, outputting a risk arrival time and intensity curve, and generating a feedforward ventilation time history input prediction link. According to the method, people flow changes are sensed and predicted through multi-source feedforward, a ventilation strategy is actively deployed, self-optimization is achieved in combination with feedback correction and closed-loop evaluation, and energy consumption is reduced and the service life of equipment is prolonged while air health and safety are guaranteed.

The invention discloses a self-adaptive full-band efficiency correction method for an LIBS (laser-induced breakdown spectroscopy). The method comprises the following steps: for each laser pulse, inputting collected spectral data into a trained physical information deep network to obtain a current plasma temperature; when the current plasma temperature is not in a preset ideal temperature interval, predicting by using a physical information deep network to obtain an optimal laser energy adjustment sequence; wherein energy adjustment is performed according to the optimal laser energy adjustment sequence, so that a future temperature sequence can be converged to a preset ideal temperature interval; controlling an energy controller of the laser to emit a next laser pulse according to the optimal laser energy adjustment sequence to obtain a stable plasma; and after the stable plasma is obtained, collecting target spectral data, and outputting a full-band efficiency calibration curve according to the target spectral data. And high-precision and high-robustness full-band efficiency correction can be realized in a complex and changeable real environment.

The invention belongs to the technical field of power supply, and discloses a source network load storage power supply-based energy control method and system and a storage medium, and the method comprises the steps: determining a first photovoltaic power generation amount and a first power consumption demand amount of a prediction day at a set moment based on first historical data and prediction data, calculating a difference value between a first available power consumption amount and the first power consumption demand amount, and when the difference value is greater than a first preset value, establishing a cost objective function based on the capacity of the energy storage device, the difference value, the operation loss and maintenance loss of the energy storage device, and the electricity price cost of each time period in the prediction day, and charging the energy storage device based on the charging time and the charging electric quantity obtained by solving the cost objective function. And periodically determining a second photovoltaic power generation amount and a second power consumption demand amount in each set time period, and carrying out charging and discharging control on the energy storage device according to a source-grid-load-storage cooperative power supply mechanism. According to the method, the source network load storage power supply system can be comprehensively optimized through accurate prediction and control.

The invention relates to the technical field of data center energy control, and discloses a data center cooling system dynamic control method adaptive to equipment aging, and the method comprises the steps: firstly constructing an energy consumption model for controlling a data center cooling system based on equipment parameters and an energy conservation principle; in order to solve the problem of performance degradation possibly caused by long-term operation of equipment, the method monitors operation parameters of key equipment such as a cooling tower, a heat exchanger, a water chilling unit and an air conditioner terminal in real time; according to the method, an equipment energy consumption model is iterated in a rolling manner by using a long-short term memory (LSTM) network model in deep learning, so that the energy consumption model for control is dynamically updated, the prediction precision is improved, and finally efficient model prediction control is realized. The method is not only suitable for the design and construction of a new data center, but also suitable for the transformation and upgrading of an existing data center, and has wide application prospects and practical values.

The invention belongs to the technical field of intelligent energy control, and particularly relates to an energy equipment control method based on regional people number distribution, which comprises the following steps: acquiring real-time people number distribution data, environmental parameter data, historical energy consumption data and historical people flow characteristic data, and predicting regional people number distribution data in different time periods; based on historical energy consumption data, historical people flow characteristic data, real-time people number distribution data and environmental parameter data, a dynamic energy demand model is constructed, regional people number distribution data in different time periods are combined, a people number density thermodynamic diagram is generated, regional energy equipment load change is predicted, and a regional energy demand model is established according to the load change and the people number density thermodynamic diagram. A differential control strategy is generated, and operation parameters of the energy equipment are dynamically adjusted; and regulating and controlling the energy equipment according to the differentiated control strategy, synchronously collecting regulated and controlled regional environment feedback data and equipment energy consumption data, and generating an energy efficiency evaluation report. Therefore, the problems of high response delay, limited parameter matching precision and the like in the prior art are solved.

The invention provides an RNN-A-ECMS diesel-electric hybrid power ship energy control method considering battery life attenuation. The method comprises the steps that a diesel-electric hybrid power ship model is established; an optimized energy management strategy based on a PMP algorithm, wherein the optimized energy management strategy comprises a PMP energy management strategy considering optimization of battery life attenuation; a self-adaptive multi-target energy management strategy based on an RNN recurrent neural network comprises an online energy management strategy combining the RNN recurrent neural network and an A-ECMS self-adaptive equivalent fuel consumption minimum energy management strategy, training of a prediction model RNN recurrent neural network of a battery charge condition SOC reference trajectory, and optimization of control parameters by combining a PSO algorithm and a PMP algorithm. According to the method, the hamiltonian function is improved by establishing the battery semi-empirical life loss model, adding the penalty factor related to the battery life in the PMP energy management strategy and fully considering the influence of the battery output current rate on the battery life loss, so that the multi-objective optimization of the battery life attenuation and the fuel consumption of the diesel engine set is realized.

An electric-thermal integrated energy control method is provided. The method comprises predicting renewable energy and multivariate loads in an integrated energy system based on a pretrained SA-PSO-BP neural network; constructing an objective function of the integrated energy system, and adding power network constraints and heat network constraints for optimal scheduling; and obtaining an optimal solution of the objective function by means of a SA-PSO algorithm based on prediction results of the renewable energy and the multivariate loads, and controlling the integrated energy system according to the optimal solution of the objective function; wherein, a training process of the SA-PSO-BP neural network comprises: training a BP neural network by means of a feature training set, and iterating and updating weights and thresholds in the BP neural network in the training process by means of the SA-PSO algorithm to obtain the SA-PSO-BP neural network.

A catheter system (100) for treating a treatment site (106) within or adjacent to a vessel wall (108A) of a blood vessel (108), or within or adjacent to a heart valve, includes an energy source (124) and a system controller (126). The energy source (124) generates a plurality of energy pulses. The system controller (126) includes a processor that controls the energy source (124). The system controller (126) is configured to implement an energy control algorithm to control both of (i) a pulse frequency of the plurality of energy pulses, and (ii) an energy level for each of the plurality of energy pulses that are generated by the energy source (124). As the pulse frequency of the plurality of energy pulses increases, the energy level for each of the plurality of energy pulses decreases.

The invention discloses a polar region water area temperature difference energy control method and a power generation device, and belongs to the technical field of temperature difference energy power generation. The control method comprises the steps that under the condition that the net output power of the power generation device is maximum, the optimal parameter sequence of different cold and heat source working conditions is determined; constructing an MPC prediction model by taking the rotation speed of a working medium pump and the rotation speed of an expansion machine as input parameters and the evaporation temperature and the condensation temperature as output results according to the incidence relation between the pressure of the cycle working medium and the evaporation temperature and the condensation temperature; constructing a target function according to the optimal parameter sequence and an output result of the MPC prediction model; and based on the MPC prediction model and the target function, the MPC prediction model is iteratively updated by adjusting the rotating speed of the working medium pump and the rotating speed of the expansion machine, the optimal rotating speed of the working medium pump and the optimal rotating speed of the expansion machine are determined, and the operation parameters of the power generation device are controlled. The operation parameters of the power generation device are adaptively adjusted through the working conditions of the cold and heat sources, so that the net output power of the power generation component is maximum, and the power generation efficiency is improved.

The invention relates to the technical field of power grid interaction, in particular to a two-way energy control method and intelligent equipment, and aims to solve the problem of how to perform charging and discharging control on two-way energy equipment according to power grid load information and peak and valley electricity prices on the basis of an optimal cost principle. The method comprises the steps that the initial electric quantity of an energy storage module is acquired; acquiring historical electrical load information of a set time period; and predicting a power control curve of a set time period according to the initial electric quantity, the historical electric load information and the expected electric quantity based on a cost optimal principle. On the basis of the predicted power control curve, charging and discharging control is performed on the energy storage module through the bidirectional energy equipment, so that the stability of the power grid can be enhanced, the power supply condition of the power grid can be optimized, the electric charge expenditure of the user can be reduced, the user experience can be improved, income can be brought to the user, and the return on investment of the user can be improved. And by adopting a dynamic programming algorithm, the operation speed is improved, and engineering realization is more convenient.

The invention belongs to the technical field of laser welding, and discloses a multi-parameter cooperative control laser welding method and system.The method comprises the steps that a laser energy control model is established based on material strength-thickness-gap ternary coupling, and the output power of an optical fiber laser is controlled; coaxial three-layer gradient gas is adopted for protection of a welding area, preheating before welding and double-temperature-zone gradient annealing after welding are adopted, the output power of an optical fiber laser and the three-layer gradient gas protection flow are regulated and controlled in real time through combination of multiple sensors and a control algorithm, the temperature of a heat affected zone is monitored in real time, and a cooling nozzle is controlled for cooling. And continuous parameter optimization is achieved through machine learning, and multi-parameter control laser welding is achieved.

The invention discloses a source-grid-load-storage integrated coordinated management and control system for an intelligent micro-grid, and relates to the technical field of intelligent micro-grids and comprehensive energy regulation and control. The system comprises the following components: a multi-energy-flow data acquisition unit, a multi-energy-flow coupling modeling and optimizing unit, a multi-energy-flow gradient utilization execution unit, a mode self-adaptive switching unit and a main control unit, according to the invention, through a multi-energy flow coupling modeling and optimization unit, an electric-thermal-gas multi-energy flow coupling model comprising a heat supply network transmission loss calculation sub-model is constructed, a renewable energy consumption constraint sub-model is additionally arranged, and an improved hybrid particle swarm optimization algorithm is adopted to carry out dynamic optimization solution, so that an optimal scheduling strategy is generated; according to the strategy, the consumption rate of renewable energy sources is increased, electricity-heat gradient utilization and efficient configuration are achieved through a heat energy distribution priority regulation and control mechanism and efficiency optimization control of the waste heat recovery module, and dependence of heat loads on electric energy is remarkably reduced.

The embodiment of the invention provides an energy recovery circuit, which comprises a front-end energy storage circuit, a switching circuit, a controller, a transformer, an energy absorption circuit, an energy transfer circuit and an energy control circuit, and is characterized in that when the switching circuit enters a turn-off state, the energy absorption circuit absorbs current flowing through a primary side coil of the transformer for storage; and the energy control circuit controls the energy transfer circuit to transfer the energy absorbed by the energy absorption circuit to the front-end energy storage circuit for storage. Therefore, according to the energy recovery circuit, when the switching circuit enters a turn-off state, the energy absorption circuit can prevent the current flowing through the primary side coil from forming peak voltage on the switching circuit, and high-voltage protection is carried out on the switching circuit; and the energy recovery circuit also adopts the energy transfer circuit to transfer the energy absorbed by the energy absorption circuit to the front-end energy storage circuit, so that the recovered energy is continuously transferred to the secondary coil, and the conversion efficiency of the power supply is improved.

The invention relates to an energy management method and system for an intelligent micro display terminal. The method comprises the following steps: acquiring operation state parameters of the micro display terminal; inputting the operation state parameters into a preset energy consumption evaluation model, and calculating the operation state parameters based on the preset energy consumption evaluation model to obtain a current energy consumption level; calling a corresponding energy control strategy according to the current energy consumption level, and dynamically correcting execution parameters of the energy control strategy based on the state change of the environment where the micro display terminal is located and the user interaction behavior change to obtain a corrected energy control strategy; adjusting operation parameters of the micro display terminal according to the corrected energy control strategy; and acquiring response indexes of the micro display terminal after the operation parameters are adjusted, and feeding back the response indexes to a preset energy consumption evaluation model to optimize the decision process of the next round of energy control strategy. The method has the effect of improving the reliability of the equipment.

A meter socket adapter (MSA) for connecting a battery energy storage system (BESS) to a household microgrid through a meter combo load center. The MSA includes: a housing matching an electricity meter and a meter socket in the meter combo load center; a plurality of power conductors that connect among the electricity meter, the meter socket, and BESS; a meter interconnection device (MID) switch for connecting a utility grid or one or more distribution energy resource (DER) devices to supply power to household loads in the household microgrid; a MID driving circuit for driving the MID switch to close or open; and a thermal management circuit couple with a plurality of temperature sensors disposed at each high current connection point to prevent overheat.

Disclosed herein are systems and methods for energy management. A system, such as a vehicle, includes a solar cell that generates energy in response to receiving light. The system includes an energy controller, which includes a processor and memory, that predicts an optimal time period for charging an energy storage unit based on information tracking discharging of the energy storage unit over time. The system includes trickle charging circuitry that provides the energy to the energy storage unit during the optimal time period, and the energy storage unit that stores the energy and discharges the energy to power at least one component, such as a vehicle propulsion mechanism.

The invention discloses an intelligent heat pump control system based on PV / T optical storage direct drive, and particularly relates to the technical field of energy control, which realizes real-time acquisition and comprehensive analysis of PV / T assembly output power, irradiation signals and phase change material temperature change data by adopting a data acquisition module, a control processing module and a heat pump output module; the method comprises the following steps: performing quantitative evaluation on discontinuous fluctuation and thermal response interference in a signal by adopting a fuzzy logic algorithm, Hilbert-Huang transform and a local phase space reconstruction and chaos dynamics model so as to identify a thermal coupling risk of nonlinear abrupt change, and generating an adjustment instruction according to a difference value between a phase change response fluctuation frequency and a preset reference frequency; the heat pump output module adjusts the working medium flow and the compression ratio in real time according to the adjusting instruction, synchronous matching of the phase change heat storage device and the PV / T assembly is achieved, and the energy density and the temperature control precision of the system are effectively improved.

The invention discloses a measurement, identification and control integrated rapid energy control device used for network construction type new energy power generation, and relates to the technical field of power system stability control, the measurement, identification and control integrated rapid energy control device comprises a data acquisition unit, a disturbance analysis unit and a control execution unit, the data acquisition unit acquires a first operation parameter and extracts a first characteristic parameter; the disturbance analysis unit analyzes the characteristic parameters to obtain disturbance information and a source position, and constructs a disturbance characteristic library; and the control execution unit calculates compensation parameters, generates a control strategy, forms a hierarchical coordination control structure and issues an adjustment instruction. According to the invention, a complete closed loop from measurement to control is constructed, so that rapid and accurate energy control can be carried out on various disturbances, and the stability and anti-disturbance capability of the network construction type new energy power generation system are effectively improved.

The invention provides a common-antenna zero-power-consumption Internet of Things terminal energy management system based on radio frequency energy taking, which comprises a radio frequency antenna, a radio frequency energy taking module, a rectification energy storage module, a low-frequency wake-up module, a backscatter communication module and an MCU control unit, in the MCU control unit, a single-chip microcomputer controls the on-off of a power supply of a sensor through a sensor power supply control pin; a power supply of the sensor and a radio frequency switch of the backscatter communication module are switched on only in a wake-up state, and only power supply to a wake-up pin is reserved in the single chip microcomputer; through a three-layer architecture of energy capture, energy control and energy application, the terminal equipment is completely separated from battery dependence. The system constructs a closed-loop energy management system for energy collection, storage, scheduling and dormancy, the scheme is suitable for the field of passive Internet of Things with strict requirements on power consumption, cost and deployment flexibility, and a feasible solution is provided for an Internet of Things terminal.

The invention discloses an enhanced SVG low voltage ride through method, and belongs to the technical field of electrical automation equipment, and the method comprises the steps: carrying out the three-phase voltage synthesis of a self-built voltage amplitude and a self-built voltage phase, and obtaining a current reference value through an electromagnetic square; according to the self-built voltage phase and the current reference value, a bridge arm differential mode voltage modulation wave is obtained through inner loop current control; obtaining a main circuit commutation actual value of the enhanced SVG, and obtaining a bridge arm common-mode voltage modulation wave through circulating current suppression control in combination with the self-built voltage phase; actual energy and rated energy of the enhanced SVG are obtained, and an energy modulation signal is obtained through an energy control ring; and after modulation is carried out according to the bridge arm differential mode voltage modulation wave, the bridge arm common mode voltage modulation wave and an energy modulation signal, a switching signal is generated to control the enhanced SVG, and low voltage ride through is realized. According to the invention, the problem that the enhanced SVG fails or is off-network under the condition of low voltage can be solved.

The invention discloses a motion planning method for a heterogeneous robot, and relates to the technical field of robots. The method comprises the following steps: step 1, subspace definition and differential homeomorphic mapping establishment; step 2, dynamic situation field construction; step 3, geometric motion generation; and 4, controlling system energy. According to the method, universal adaptation of robot systems with different configurations can be realized, the algorithm reuse rate is greatly improved, the development cost is remarkably reduced, efficient obstacle avoidance can be realized in a dynamic environment, and the method has excellent real-time performance and wide applicability and robustness.

The invention relates to the technical field of ship energy control, and provides an energy efficiency optimization scheduling method for a marine SOFC heat recovery system, and the method comprises the steps: dynamically distributing an SOFC waste heat path through a heat energy distribution optimization algorithm according to the type of a current ship operation condition, and for a high-load condition, taking a propulsion power demand as a priority target, and carrying out the optimal scheduling of the SOFC waste heat path; the SOFC waste heat is preferentially guided into a ship power position to assist in propelling; for a medium load working condition, SOFC waste heat is distributed to auxiliary propulsion and life service energy according to a preset proportion, and the preset proportion is finely adjusted according to real-time requirements; and for a low-load working condition, the SOFC waste heat is preferentially introduced into life service energy, and the distribution sequence and proportion are dynamically adjusted according to environmental parameters. A machine learning classification model matched with a multi-threshold rule is introduced for working condition recognition, and recognition of different working conditions is achieved; by constructing a dynamic heat energy scheduling mechanism, dynamic optimal allocation of SOFC waste heat among different energy utilization paths is achieved, and the flexibility and adaptability of heat energy utilization are improved.

A method for energy control can include receiving an operating point of an energy conversion device at a telematics interface of a data processing system, the telematics interface configured to receive information from the energy conversion device. The method can include generating, by the data processing system, a digital twin of the energy conversion device, the digital twin having a virtual operating point. The method can include predicting, by the data processing system, a future load associated with the energy conversion device. The method can include modeling a predicted response to the future load based on the digital twin, the predicted response corresponding to the virtual operating point. The method can include conveying, to the energy conversion device via the telematics interface, an adjustment to the operating point of the energy conversion device based on the predicted response to the future load.

A method for energy control can include receiving an operating point of an energy conversion device at a telematics interface of a data processing system, the telematics interface configured to receive information from the energy conversion device. The method can include generating, by the data processing system, a digital twin of the energy conversion device, the digital twin having a virtual operating point. The method can include predicting, by the data processing system, a future load associated with the energy conversion device. The method can include modeling a predicted response to the future load based on the digital twin, the predicted response corresponding to the virtual operating point. The method can include conveying, to the energy conversion device via the telematics interface, an adjustment to the operating point of the energy conversion device based on the predicted response to the future load.