1428 results about "Electrical load" patented technology

An energy management system can comprise a housing configured to receive energy storage assemblies within an interior region of the housing via one or more openings on a surface of the housing. One or more guide tracks positioned within the interior region can mechanically and electrically couple to the energy storage assemblies. One or more switches can electrically couple the energy storage assemblies in series to increase a voltage provided from energy storage assemblies to an electrical load and can electrically couple the energy storage assemblies in parallel to facilitate charging the energy storage assemblies from an energy source.

The invention relates to a dynamic load distribution method, device and equipment based on a vehicle body controller and a medium, and the method comprises the steps: collecting the mechanical load, electrical load and thermal load data of a vehicle body in real time through a distributed sensor network, and generating multi-source heterogeneous initial load data; performing standardization processing on the initial load data, and constructing a three-dimensional data cube type standard load data set; comprehensive characterization features are extracted through feature space mapping, and a fusion load feature set containing a threshold range is dynamically generated in combination with historical data; constructing a load correlation model based on the fusion features, and mapping to generate a steering and braking control parameter combination; and according to the real-time load threshold cross-border state, dynamically adjusting the task priority, allocating real-time calculation core resources to the chassis system, and generating an optimal allocation scheme. According to the method, the problems of multi-source load data fusion failure, resource allocation rigidity and safety task response delay in the prior art are solved, and dynamic load optimization of the vehicle body control system under complex working conditions is realized.

The invention relates to the technical field of state monitoring, and discloses a tower inclination state monitoring method and system. The method comprises the following steps: performing multi-field digital coupling modeling on an electric power tower to obtain a digital twinborn model and calculating an electrical load-structure response sensitivity matrix; collecting a multi-modal sensing characteristic data set including tower inclination angle time sequence data, insulator chain offset, tower body micro-vibration spectrum and foundation settlement gradient; performing time-delay correlation analysis on the electrical event and the structure response to obtain an electrical-structure mapping matrix; tensor decomposition processing is carried out through a multi-mode inclination feature fusion network, and tower inclination mode fingerprints are obtained; and carrying out abnormal separation processing on the tower foundation area, the connection area and the upper structure area, and outputting structural inclination and non-structural inclination judgment results. According to the invention, the accurate mapping relation between the electrical load and the structure response is realized, the false alarm rate is effectively reduced, and the accuracy and timeliness of early warning are improved.

The invention relates to the technical field of electrical load management, and discloses an electrical load spatial-temporal distribution modeling and adaptive optimization regulation and control system. The system comprises a load monitoring module, an optimization decision-making module, an exception handling module, a communication coordination module and a data tracing module. The load monitoring module collects load data by using a space-time correlation analysis technology; the optimization decision-making module adopts a dynamic clustering algorithm to generate a regulation and control strategy; the exception handling module corrects load exception in a graded manner based on a multi-scale coordination mechanism; the communication coordination module realizes instruction interaction through a hierarchical routing protocol; and the data tracing module generates a topology storage index by using a multi-dimensional feature coding algorithm. The system can accurately collect load data, reasonably distribute power, effectively process load abnormity, guarantee communication stability, facilitate data tracing, improve the operation efficiency and reliability of an electric power system, and meet the requirement of a modern electric power system for electrical load management.

A vehicle having a primary mover, a charging system, multiple electric loads, and a controller. The charging system includes a charge storing device and an alternator. The alternator is configured to convert mechanical energy generated by the prime mover into electrical energy to charge the charge storing device. The multiple electrical loads are electrically coupled to the charging system via a power distribution module. The controller is communicably coupled to the charging system and is configured to receive an indication that an electrical output of the charging system is unable to provide sufficient electrical energy to each electrical load in the electrical loads. The controller is also configured to provide a control signal to the power distribution module in response to the indication. The control signal is configured to cause the power distribution module to decouple at least one of the electrical loads from the charging system.

The invention relates to the field of intelligent early warning, and particularly discloses a composite home fire early warning system combined with smoke and gas monitoring, which is characterized in that firstly, multi-parameter acquisition is started by taking temperature abnormity as a trigger condition, and gas concentration, smoke diffusion and electrical load data are synchronously acquired; local feature extraction is carried out on the time sequence of each parameter; and then establishing an association relationship among the parameters through a cross-time-sequence dynamic message passing fusion mechanism. And finally, fusing the multi-dimensional time sequence characteristics, inputting the fused multi-dimensional time sequence characteristics into a risk grading model, and judging the current risk grade in combination with a historical fire evolution rule. According to the scheme, through time-space correlation analysis of multi-dimensional parameters, daily environment fluctuation (such as cooking fume) and real fire initial characteristics can be effectively distinguished, the method can adapt to risk characteristics of different house type structures, and upgrading from passive alarm to intelligent pre-judgment is achieved.

The invention discloses a distributed robust optimization scheduling method and system for a solar photovoltaic photo-thermal comprehensive utilization system, and belongs to the field of comprehensive utilization of renewable energy sources. The method comprises the steps of obtaining an electrical load and a thermal load based on an integrated load prediction model; obtaining typical sunlight photovoltaic and photo-thermal based on scene clustering analysis; based on photovoltaic, photo-thermal, electrical load and thermal load, an objective function and a constraint condition for optimal scheduling are set for the solar photovoltaic photo-thermal comprehensive utilization system, and based on the objective function and the constraint condition, a two-stage distribution robust optimal scheduling model is obtained by combining a comprehensive demand response mechanism; and solving the two-stage distribution robust optimization scheduling model by adopting a column and constraint generation algorithm to obtain a two-stage distribution robust optimization scheduling result. The method obviously improves the utilization rate of renewable energy sources, reduces the operation cost and carbon emission of the system, and is suitable for building energy systems in solar energy-enriched areas.

The invention discloses a non-intrusive load decomposition method and system based on an xLSTM-Transform model, and is applied to the technical field of non-intrusive load decomposition. XLSTM is constructed by combining sLSTM (scalar LSTM) and mLSTM (matrix LSTM), so that the processing capability of the model on time sequence data is enhanced; a causal convolution module is added behind an input layer, a sliding window in a fixed direction of the causal convolution module can more accurately extract a local load mode of an electric appliance while ensuring causality, and future information leakage is avoided; a self-attention mechanism in a Transform architecture is introduced to establish an xLSTM-Transform combination model, historical data is adopted to train the model, and finally, the model is used to perform electric appliance load decomposition. According to the method, a multi-scale electrical load mode can be effectively processed, and the problem of long-term dependence faced by a traditional time sequence model is solved.

The invention discloses an electricity stealing detection method and device for a low-voltage distribution network, an intelligent electric energy meter, equipment and a medium, and the method comprises the steps: collecting the electricity utilization information and internal temperature state data of a detected electric energy meter in real time, and respectively generating corresponding real-time electricity utilization load change trend data and temperature change trend data; detecting each electric device loaded by the detected electric energy meter, and identifying the type of each electric device; predicting the electrical load of the tested electric energy meter according to the identified type of each electric device; according to the predicted electrical load of the tested electric energy meter, generating corresponding predicted electrical load change trend data; and according to the real-time electrical load change trend data, the real-time temperature change trend data and the predicted electrical load change trend data in the same time period, judging whether the detected electric energy meter has an electricity stealing behavior or not. According to the invention, the electricity larceny behavior in the low-voltage power distribution network can be quickly and accurately identified, the electricity larceny position is accurately positioned, and electricity larceny prevention is effectively realized.

The invention relates to a virtual power plant optimization operation method and system based on data center shared energy storage and load space-time migration, and the method comprises the steps: quantifying the electric energy utilization efficiency of a data center according to the power consumption of IT equipment, the power consumption of refrigeration equipment and the power consumption of other auxiliary equipment in a data center power consumption model; in the load space-time migration model, delay processing time calculation is carried out on batch processing loads, and the load migration amount across the data centers is calculated among the data centers; dynamically distributing the energy storage capacity of each data center in the shared energy storage model, and sharing the energy storage investment cost by adopting a Shapley value; in the double-layer optimization model, the upper-layer model generates an electricity price signal and a demand response instruction according to the wind and light output prediction data, the real-time electricity price of the power grid and the initial load demand of each data center, and transmits the electricity price signal and the demand response instruction to the lower-layer model; and the lower-layer model feeds back the obtained data center response and the electrical load to the upper-layer model. Compared with the prior art, the method has the advantages of high collaboration, high efficiency, high consumption and the like.

The invention provides an electrical load data privacy protection method and system, a storage medium and electronic equipment, and is applied to the technical field of electrical data processing. Actual electrical load data of a user and noise data of an energy storage device are collected in real time, and charging and discharging behaviors of the energy storage device are controlled through electricity price linkage; the optimization noise is superposed to the actual power utilization load data to generate target power utilization load data meeting the power grid transmission limitation, the energy storage charging and discharging noise is utilized to cover the real power utilization mode of the user, the energy storage charging and discharging behavior is controlled through electricity price linkage to guarantee the economic benefit, and the dynamic balance of the privacy intensity and the energy storage economical efficiency is achieved.

The invention relates to the technical field of vehicle power supply management, in particular to a vehicle load power supply distribution method, device and equipment and a storage medium, according to the scheme, a vehicle using scene at the current moment is determined by analyzing vehicle operation data, and the load levels of a plurality of electric appliances in use are determined by combining a vehicle using scene-load level mapping table; and a power supply distribution scheme is formulated, on-demand power supply for each electricity load of the vehicle is realized, and meanwhile, a user is allowed to customize load grade setting of a vehicle using scene. According to the scheme, through reasonable distribution of power supply, the power supply proportion of each load can be adjusted in real time according to the vehicle use scene on the premise of ensuring normal operation of key functions of the vehicle, so that the operation efficiency and stability of the vehicle are improved, it can be ensured that each electricity load obtains proper power supply in different scenes, the energy utilization efficiency is improved, and the energy consumption is reduced. The personalized electricity demand of the user is met, the driving experience of the user is improved, and the intelligent level of the vehicle is improved.

The invention discloses a power grid dispatching method based on emergencies and a graph structure. The method comprises the following steps: establishing a baseline load prediction model by collecting historical electrical load data; recognizing emergencies influencing the power utilization demand in real time; and in combination with real-time power consumption behavior data, constructing a time-varying power consumption disturbance factor, and dynamically correcting load prediction. Geographic distribution and historical power generation data of new energy power stations such as wind power and photovoltaic power stations are acquired, a power generation graph structure model is constructed, the power generation amount is independently predicted by adopting a long-short-term memory network, and a prediction result is corrected in combination with a graph structure. And based on the corrected load and power generation prediction, considering the capacity and the energy state of the energy storage system, optimizing multi-time-sequence-scale energy scheduling, and formulating an energy storage charging and discharging and generator set operation scheme. According to the method, the dynamic influence of emergencies and the spatial relevance of new energy are fused, the load prediction precision and the new energy power generation prediction accuracy are remarkably improved, more reasonable power grid dispatching is achieved, and the safety and stability of a power grid are enhanced.

A load control device for regulating an electrical load includes: a support component including a connection portion; a knob frame including a gripping portion and an elastic portion, the gripping portion at least partially surrounding the support component and configured to drive the knob frame to rotate in response to a rotational force acting thereon, the elastic portion configured to move toward the connecting portion in response to the knob frame moving in a first direction, thereby undergoing elastic deformation; a first circuit board component; a switch trigger component; a switch component coupled to the first circuit board component, for controlling the load in response to triggering and releasing the triggering from the switch trigger component; and an adjustment component coupled to the first circuit board component, for adjusting the load in response to the rotation of the knob frame. The load control device simultaneously realizes rotation and pressing functions of the knob frame. A system including the load control device is also disclosed.

The invention provides a real-time three-dimensional city modeling and dynamic updating method and system based on deep learning. According to the method, building structure static load parameters of a target building are obtained, a micro-seismic sensor array is deployed at a node of a bearing wall and a frame beam column, and stress wave propagation path data are collected at a preset sampling frequency. And synchronously receiving electrical load data uploaded by the intelligent electric meter, and extracting a current abrupt change characteristic section in a time window corresponding to the abnormal vibration event. Building structure static load parameters, stress wave frequency domain energy distribution and the phase angle offset of the current abrupt change characteristic section are input into the deep learning model, the association rule of structural mechanical response and electrical load disturbance is coupled, and a three-dimensional model updating instruction is generated. And dynamically adjusting the grid vertex coordinates of the target building in the initial three-dimensional city model based on the instruction, and overlapping and displaying the space influence range boundary of the current abrupt change feature segment. The technical scheme provided by the invention is based on real-time three-dimensional city modeling and dynamic updating of deep learning.

The invention relates to the technical field of building electrics, and particularly provides a control method and system of an optical storage direct flexible system. The control method comprises the following steps: acquiring power load data of a target building within a preset time; acquiring illumination meteorological data in the preset time; obtaining a power load prediction curve according to the illumination meteorological data and the power load data; based on the power load prediction curve, constructing a control relationship between the direct current bus voltage at the grid-connected part of the optical storage direct-flexible system and the total grid-connected power; and based on the control relationship, performing power regulation according to the voltage change condition of the direct current bus. According to the invention, when the real-time power deviates from the predicted optimal load value, such as the inflection point, the optical storage direct-current flexible system can automatically deploy the power of the energy equipment connected to the direct-current bus. And system power response is completed according to the flexible capacity potential of the equipment, so that the system operation state tends to be economically optimal.

A load control device for regulating an electrical load includes: a support component including a connection portion; a knob frame including a gripping portion and an elastic portion, the gripping portion at least partially surrounding the support component and configured to drive the knob frame to rotate in response to a rotational force acting thereon, the elastic portion configured to move toward the connecting portion in response to the knob frame moving in a first direction, thereby undergoing elastic deformation; a display assembly disposed on a side surface of the knob frame facing away from the support component; a circuit board component disposed adjacent the display assembly; a switch component disposed on the circuit board component, for controlling the load in response to triggering and releasing the triggering; a switch trigger component for applying the triggering and releasing the triggering to the switch trigger component; and an adjustment component for adjusting the load in response to the rotation of the knob frame. The load control device allows for a more flexible arrangements of the switch trigger component, and makes the structure more reliable. A system including the load control device is also disclosed.

The invention discloses a household electrical load optimization scheduling method and system based on an MAPPO algorithm. The method comprises the following steps: (1) collecting multi-source heterogeneous data from a user side and an external environment and carrying out preprocessing; (2) estimating the future equipment use probability of the user by collecting historical equipment use data of the user, and outputting an equipment behavior prediction vector; (3) acquiring real-time electricity price information, performing short-term electricity price trend prediction by using the time sequence prediction model based on a historical electricity price sequence, and outputting an electricity price prediction sequence; (4) generating a scheduling strategy by the MAPPO network based on the equipment behavior prediction vector and the electricity price prediction sequence, and outputting a corresponding scheduling result; and (5) sending a scheduling result to the home gateway, controlling the equipment, collecting an execution feedback result in real time, updating internal parameters of the MAPPO network, and realizing strategy iteration and adaptive adjustment of the MAPPO network.

The invention relates to the technical field of photovoltaic energy supply, in particular to a distributed photovoltaic self-consistent energy supply system for an expressway. The system comprises an analysis and prediction module, a strategy making module, a fault diagnosis module and an optimization scheduling module. The photovoltaic power generation power, the energy storage charge and the load operation power are predicted through the analysis and prediction module, the corresponding control strategy is formulated by using the strategy formulation module, and the unified intelligent coordination control is performed on the photovoltaic power generation, the battery energy storage, the load power utilization load and the power grid according to the prediction result and the control strategy. The power supply proportion of battery energy storage, load electrical load and a power grid can be dynamically adjusted according to the predicted photovoltaic power generation power, the power generation power of a photovoltaic array, the charge state of an energy storage system and the power of the electrical load are monitored in real time by using a fault diagnosis module, and the power supply proportion of the power grid is determined according to a neural network model for fault determination. And the fault type and the fault probability are determined, and fault early warning and diagnosis are carried out.

The invention relates to the technical field of electric energy meters, in particular to an intelligent electric energy meter with a power saving function. A communication module; the power supply module generates power according to the temperature difference between the surface temperature of the electric energy meter chip and the air temperature in the electric energy meter so as to provide electric energy for the communication module; and the control module is used for respectively adjusting the monitoring sampling mode of the detection module from a low-frequency sampling mode to a low-frequency full-frequency composite sampling mode according to the fluctuation ratio of the electrical load, and adjusting the data transmission mode of the communication module from a periodic transmission mode to an encrypted transmission mode based on the power generation voltage ripple of the power supply module. The signal transmission frequency band of the communication module is adjusted to be a low frequency band; wherein the alternating transmission frequency of single transmission of the periodic transmission mode is determined according to the average power generation voltage of the power supply module. The working stability of the electric energy meter is improved.

An example system includes a DC / DC converter interposed between a battery pack and an electrical load of a vehicle, the DC / DC converter having a first power supply phase and a second power supply phase; and a controller, including: a power request circuit structured to interpret a power request for the electrical load; a power provision circuit structured to determine a current value for each of a plurality of power supply phases; and a power command circuit structured to provide a phase power command value in response to the current value for each of the plurality of power supply phases, the phase power command value including a duty cycle command for each of the plurality of power supply phases; and wherein each of the power supply phases are responsive to the phase power command value to provide pulse width modulated (PWM) electrical power to the electrical load.

The invention discloses a regulation and control quota distribution method and system fusing subjective and objective multi-dimensional features. The method comprises the following steps: acquiring electrical load data, historical response behavior data and subjective response intention information of a user; firstly, a Ward system is used for clustering, users are clustered according to active power, then a primary clustering center is used as an initial clustering center of secondary clustering, FCM clustering is carried out, and a typical load curve of the users is described based on a secondary clustering method; load prediction is carried out based on an NARX neural network, a predicted load curve is compared with a typical load curve, and adjustable potential is calculated; constructing a DR feature data set; and the DR feature data set is fused with an entropy weight method and an analytic hierarchy process to obtain a combined weight, a fuzzy relation matrix is constructed, a user comprehensive score is quantified, and a comprehensive response potential score of the user is formed.

An apparatus may control the power delivered from an AC power source to an electrical load, and may comprise a controllably conductive device. The apparatus may also comprise a controller that may be operatively coupled to a control input of the controllably conductive device. The apparatus may also include a first wireless communication circuit operable to communicate via a first protocol and to join a first wireless communication network operable to communicate via the first protocol. The first wireless communication circuit may be in communication with the controller. The controller may be operative to determine a first condition for communicating via the first protocol. The controller may also be operable to control the first wireless communication circuit to join the first wireless communication network upon the first condition being satisfied.

The invention relates to a data center equipment type selection method based on electrical and heating ventilation load linkage, and belongs to the technical field of data center infrastructure design. The model selection method comprises the steps of setting basic data, obtaining the number of machine rooms and the number of IT devices in a single machine room, obtaining the total power of the IT devices, setting an energy efficiency index set value, obtaining the project power consumption scale, obtaining the total cooling load, selecting models of air conditioners at the tail ends of the machine rooms and substations, selecting models of water chilling units, selecting models of heat exchangers, selecting models of cooling water pumps and chilled water pumps and selecting models of cooling towers. And obtaining the total power of the air conditioner, obtaining an energy efficiency index accounting value, and carrying out PUE verification. According to the method, through closed-loop iteration of electrical load-heating and ventilation load-PUE, model selection and quantity selection of all key equipment are completed at a time; professional splitting, repeated adjustment and modification and capacity redundancy caused by traditional serial design are avoided, the design period is remarkably shortened, and initial investment is reduced.

The invention discloses a distributed power grid dispatching management and optimization method, and relates to the technical field of power grid automation, and the method comprises the steps: collecting multi-dimensional power grid data in a system, carrying out the data preprocessing of the multi-dimensional power grid data, carrying out the electrical load prediction of the initial multi-dimensional power grid data through an LSTM neural network, and carrying out the optimization of the electrical load. Performing energy storage SOC state real-time evaluation on the electrical load data based on a DTW dynamic time warping load identification model, performing state judgment on power grid equipment according to an evaluation result, constructing a multi-target optimization model by using mixed integer second-order cone programming, performing power grid constraint adjustment by using the multi-target optimization model, and performing power grid constraint adjustment on the basis of a power grid constraint result. And performing real-time power generation plan adjustment by using an improved particle swarm algorithm to obtain target electric power output of the power grid, and performing power grid energy storage control optimization according to the target electric power output. And the prediction accuracy of the new energy output and the load demand is obviously improved.

A facility for predicting on site electric power requirements for maintaining a fully electric fleet of vehicles in a mining operation to ensure sustainable electric operations, comprising a server comprising communications module configured to receive data and to transmit load management instructions; plurality of electrical charging stations; plurality of electric vehicles configured to be recharged by a selected electrical charging station; wherein the server comprises processor coupled with non-volatile memory, the non-volatile memory storing processing instructions that, when executed by the processor, causes the processor to simulate a total electrical power demand of the mine site due to the plurality of cabled mine equipment and the plurality of electric vehicles for each electric vehicle and each charging station within the facility over a prescribed period thereby to simulate and determine an energy storage capacity requirement of the charging stations of the facility through the prescribed period; thereby to forecast the electrical load management needs of the facility through the prescribed period.

A trailer test module of a vehicle is electrically connected between a trailer tow electrical connector of the vehicle and a wiring harness of the vehicle that carries electrical signals for a trailer such that when the electrical connector of the trailer is attached to the trailer tow electrical connector, the trailer tow electrical connector is between the trailer test module and the electrical connector of the trailer. The trailer test module includes circuitry that replicates an electrical load of the trailer.

A current control module is for controlling an in-rush current resulting from the initiation of an electrical connection of a battery to an electrical load, wherein a metal-oxide-semiconductor field-e

The invention discloses an engineering building detection method and system, and relates to the technical field of building fire detection and early warning. Through systematic area division and multi-dimensional sensor network monitoring, environment and equipment operation data of a key area can be collected in real time; and obtaining and evaluating a layout closeness index Ji, a cable overload coefficient Oi, an electrical load change index Li and a toxic gas leakage coefficient Ci of the ith monitoring area, thereby improving the early warning capability of the fire risk. Through combination of smoke diffusion analysis and fire risk index evaluation, the fire risk can be accurately judged, and a fire control instruction can be triggered in a targeted manner. Besides, based on a smoke diffusion index correction mechanism, the system can dynamically adjust the operation states of the spraying equipment and the ventilation facility, so that fire control precision and resource saving are realized, and the fire safety and emergency response efficiency of the building are effectively improved.

The invention discloses an electrical system load prediction method based on a data center, and the method comprises the steps: obtaining electrical parameters, environmental parameters and historical operation data of each subsystem through a data collection module, carrying out the normalization and time sequence reconstruction of the collected data, and forming standardized time sequence data; a combined neural network model comprising a convolutional neural network (CNN), an improved long-short term memory (LSTM) network and an attention mechanism is utilized to extract local fluctuation, mutation, frequency and statistical characteristics, and capture of data long-term dependence and key time sequence information is realized, so that an electrical load prediction value is output, and finally real-time regulation and control of a data center electrical system are realized. The method can effectively improve the prediction precision and response speed, and provides reliable technical support for the full life cycle management of the data center.