149 results about "Load management" patented technology

The invention belongs to the technical field of power equipment, and discloses a smart energy system based on a power electronic transformer and a monitoring method, and the method comprises the steps: installing a sensor; equipment operation data is collected in real time, and environment monitoring equipment collects environment data; the data processing module preprocesses the collected data; the abnormity monitoring module judges whether an abnormal condition exists or not; the fault detection module judges whether the system has a fault hidden danger or has a fault; the energy scheduling control module formulates an energy scheduling strategy; the electric energy quality collaborative optimization module ensures that the electric energy quality of the whole system meets the standard; and the load management unit manages different types of loads according to the hierarchical load control strategy. According to the invention, abnormal conditions and potential fault hidden dangers can be found; intelligent energy scheduling can be realized, energy utilization efficiency can be improved, electric energy quality can be cooperatively optimized, hierarchical load management can be realized, and key load power supply can be guaranteed; and a hierarchical load control strategy is adopted to carry out differential management on loads.

The invention relates to the technical field of power equipment, in particular to an intelligent thermal management system for a cleaning robot, which comprises a state sensing unit, a risk assessment unit, a thermal state calculation unit, a data decision unit, an execution unit and a feedback correction unit, and is characterized in that the state sensing unit is used for acquiring internal temperature and humidity and key component temperature; the risk assessment unit calculates the characterization values of the dew formation trend and the overheating trend; the thermal state calculation unit quantifies the overall thermal state of the system; the data decision-making unit determines the actual adjustment amount of the heating and cooling power according to the parameters; the execution unit comprises a heating module, a cooling module and a load management module and correspondingly adjusts output power. And the feedback correction unit corrects the thermal state according to the operation deviation. According to the invention, through multi-parameter fusion decision and dynamic power adjustment, collaborative management of heat dissipation and condensation prevention is realized, and the working reliability of the cleaning robot in a severe environment is improved.

The invention provides a virtual power plant demand side response and load management method and system, and relates to the technical field of power system scheduling, and the method comprises the steps: obtaining a demand side response request and a historical response track of each distributed energy source; performing time-frequency domain decomposition on the historical response trajectory, extracting a transient component and a steady component, constructing a dynamic response model containing the current output power and the load deviation cumulant, and calculating load deviation distribution through Kalman filtering; a Markov decision process is constructed based on load deviation distribution, an optimal power distribution strategy is solved by adopting a value iteration algorithm, and a response execution scheme is generated; collecting a power change sequence after execution of each distributed energy source, converting the power change sequence into a load recovery demand vector, constructing a recovery capacity constraint cone based on the current available adjustment margin of the power grid, and solving an optimal projection point; and performing hierarchical division according to the optimal projection point to obtain a staged recovery sequence. According to the method, the accuracy of virtual power plant demand response and the coordination of load recovery are improved, and the power grid regulation pressure is reduced.

This invention discloses a power grid intelligent dispatch decision-making system and method based on multi-source heterogeneous data fusion, belonging to the field of power grid intelligent dispatch technology. The specific steps of the power grid intelligent dispatch decision-making method based on multi-source heterogeneous data fusion include: Step 1, collecting multi-source heterogeneous data, constructing a meteorological feature vector set, predicting power grid load changes, and outputting load forecast values; Step 2, fusing load forecast values ​​and equipment health coefficients to construct a dual-risk collaborative analysis matrix, generating risk entropy values ​​and mapping them to standard risk models; Step 3, according to the standard risk models, performing generator output pre-adjustment, energy storage charging and discharging control, and interruptible load management in a tiered manner, and coordinating microgrid resources through blockchain. This invention avoids the one-sidedness of single-risk factor decision-making by constructing a dual-dimensional risk collaborative decision-making architecture, orthogonally decomposing demand-side forecast risk and equipment health risk into multiple state modes.

The invention relates to the technical field of power dispatching, and discloses a power supply system dispatching method and a power supply system thereof, and the power supply system comprises a monitoring module, a dispatching module, a protection module, a data prediction module, an energy storage module, a remote control and communication module, an artificial intelligence and adaptive dispatching module, a power quality monitoring module and an intelligent load management module. The power quality monitoring module collects harmonic wave and voltage fluctuation data in real time, gives an alarm immediately when the harmonic wave and voltage fluctuation data exceed the standard, and pushes governance suggestions; and the scheduling module is matched to adjust the non-linear load operation time, so that quality problems are reduced from the source. Scene features are identified through deep learning, and an emergency strategy is automatically generated; and the scheduling module receives feedback in real time and corrects the strategy. The protection module accurately locates a fault point through multi-sensor cross validation and automatically cuts off a fault loop; the remote control module pushes fault information to the operation and maintenance terminal in real time, remote diagnosis is supported, and the on-site inspection frequency is reduced.

The invention discloses an intelligent self-adaptive solar power supply system and an energy management method, and relates to the technical field of solar intelligent power supply. A multispectral dynamic capture module improves the spectrum utilization rate and the photoelectric conversion efficiency; the global environment sensing module accurately predicts shadow and weather; the multi-mode MPPT cooperative control module reduces the power loss under the shadow; the heterogeneous energy storage cooperative scheduling module optimizes energy storage distribution; the intelligent load interaction module realizes accurate load management; the edge intelligent decision center realizes high-efficiency energy decision and safety guarantee, and all the modules work cooperatively, so that intelligent adaptive management of the solar power supply system is realized. Through multi-module innovation and cooperation, the performance of a solar power supply system is remarkably improved, the power generation efficiency is improved, environmental changes are accurately coped, power loss is reduced, energy storage and load management are optimized, efficient energy decision is achieved, safe operation of the system is guaranteed, the operation and maintenance cost is reduced, and intelligent development of solar power supply is promoted.

The invention provides a load management data processing method in a power grid construction supervision project, and the method comprises the steps: carrying out the matching analysis of power fluctuation feature data and a pre-established load feature model, determining a corresponding load type if the matching degree meets a preset condition, and obtaining a multi-dimensional power decomposition result; carrying out quantitative calculation on the harmonic content of each electrical load based on the multi-dimensional power decomposition result, judging whether the harmonic content exceeds a standard limit range or not, and obtaining a to-be-treated harmonic component set; according to correlation analysis of the to-be-treated harmonic component set and the reactive power component, if the reactive power component is dominant, determining a compensation processing priority, and generating a targeted reactive power compensation strategy scheme; and carrying out classification processing on the load characteristics by adopting the reactive compensation strategy scheme, and if a classification result shows a high harmonic pollution characteristic, judging that the load type is a non-linear load type, and obtaining a load characteristic identification result.

The invention discloses a feed processing load management method based on a staged strategy gradient algorithm, and relates to the technical field of industrial automation control, and the method comprises the steps: collecting a feed processing dynamic state set, and obtaining a standardized decision input vector through preprocessing; based on the standardized decision input vector, dynamically adjusting equipment parameters through a two-factor cooperative compensation algorithm, and obtaining an equipment control instruction; executing an equipment control instruction, and generating an efficiency feedback data packet by monitoring equipment energy consumption change and output material granularity in real time; a load management decision model is constructed, and optimization strategy parameters are obtained by combining the efficiency feedback data packet and adopting a staged strategy gradient algorithm; and updating the load management decision model based on the optimization strategy parameters, and generating a closed loop verification report through load management verification. According to the method, the load management decision model is constructed through a quantum annealing mechanism, the local optimal limitation of traditional reinforcement learning is broken through, and global optimization efficiency jump is realized.

The invention discloses a lunar dust protection system for a low-energy particle detector. The lunar dust protection system comprises a protective cover, a nitrogen blowing opening, a pressing device, an unfolding device, a power supply subsystem and a control subsystem, wherein the protective cover and the nitrogen blowing opening are integrally designed; the protective cover covers the head of the sensor and is consistent with the head of the sensor in shape; the nitrogen blowing port blows nitrogen into the sensor in a ground test stage to prevent pollution; the blocking cap is used for blocking before the satellite is launched to prevent dust pollution; when the system is in a protection state, the pressing device presses the protective cover and the head of the sensor, and the protective cover is released through thermal cutting according to an instruction of the control subsystem; the unfolding device is matched with the pressing device to achieve unfolding of the protective cover, and the protective cover is locked after being released to a designated position. The power supply subsystem supplies power to the pressing device according to an instruction of the control subsystem; the control subsystem periodically collects state monitoring parameters and transmits the state monitoring parameters to the load management unit, controls power-on and power-off of the pressing device according to a ground remote control instruction, and executes unfolding operation of the system.

The invention discloses a load management-oriented intention identification and electric quantity data analysis method and device, and belongs to the technical field of electric power system analysis. According to the method, feature extraction is performed on the input text of the load management personnel through the preset natural language processing method, intelligent analysis is performed on the load electric quantity data in linkage, modal component decomposition is performed on the first historical electric quantity data, short-time fluctuation and long-term tendency features of new energy can be accurately captured, and the new energy management efficiency is improved. The method comprises the following steps: carrying out prediction on electric quantity data, carrying out feature division in combination with the prediction load data to obtain first historical electric quantity feature data and second historical electric quantity feature data, carrying out time sequence feature analysis to obtain periodic feature data and abrupt change electric quantity data in an electric power system, and generating electric quantity probability distribution data through a preset large model. According to the method, the load fluctuation of the power system can be quantified, so that the more accurate electricity consumption valley time period and electricity consumption peak time period of the power system can be obtained, and support is provided for novel load management work of a power grid company.

An electric power and electric quantity balancing method for a clean energy base containing a high-energy load comprises the steps that firstly, the fluctuation rule of the high-energy load is recognized based on the historical operation condition of the high-energy load, and a high-energy load model is established; secondly, according to the operation mechanism of the clean energy base, establishing an output model for each new energy unit in the base, and predicting the output and load demand of the new energy units in combination with weather forecast and historical data; then, according to the characteristics of a high-energy load and a conventional load, establishing a load management system, introducing a demand response mechanism, constructing a flexible scheduling resource platform considering different time scales by relying on the load management system and an adjustable power supply in a base, and setting priorities and adjusting costs for all adjustable resources in the flexible scheduling resource platform; and different adjustment resources can be reasonably selected in the scheduling process of the power grid. And finally, establishing a high-energy load-containing optimal scheduling model considering electric power and electric quantity balance, and ensuring that the electric power and electric quantity balance on the two sides of the source load can be realized in the scheduling of the power grid on different time scales.

The invention relates to a hybrid plug connector system (100) for transmitting power and data. The plug connector system has a plug (110) with a plug / cable interface (110a) for wired data and energy transmission, and a plug interface (112) for wired energy transmission and wireless data transmission. The plug connector system (100) also has at least one complementary socket (120) with a socket interface (122) which is designed to be complementary to the plug interface (112). The plug connector system allows energy and data to be transmitted between devices (consumers and generators) connected to the plug / cable interface (110a) and the socket / cable interface (126), wherein the radio plug wireless transmitter and / or receiver (114) and the radio socket wireless transmitter and / or receiver (123) allow wireless transmission with a high bandwidth and short range, in particular in the 60 GHz band. As a result of the wireless data transmission within the plug connector system, the fragile and error-prone data contacts of the plug connection which are otherwise necessary are dispensed with, as a result of which the connector mechanism can have a simpler design and the data connection is more reliable. As a result, the use of hybrid connections between generators and consumers of high power is simplified and allows plug 'n' play, for example for load management or current direction reversal in electrical systems.

A charging load management method oriented to a pulse charging method, belonging to the field of lithium-ion batteries. The charging load management method comprises: a staggered sequencing method for different charging pulses, a pulse current waveform adjustment method, a grouping and staggered sequencing method for a series of pulses having equal periods, a grouping and staggered sequencing method for a series of pulses having unequal periods, and an overall coordination method for charging pulses. A method for grouping and temporally sequencing different pulses can reduce the total charging current of all loads and the fluctuation amplitude thereof without reducing an individual charging load. When applied to a battery pack consisting of a plurality of cells, the method of the present invention can suppress the overall charging power fluctuation of the battery pack; and when applied to a plurality of electric vehicles, the method of the present invention can suppress the charging power fluctuation of an electric vehicle charging station, and assist the vehicle charging station in participating in power grid demand-side response, thereby facilitating an enhancement in the stability of a power grid.

The application discloses a low-carbon energy dispatching system of wisdom park source network load storage cooperation, relates to the wisdom park energy dispatching technical field, and the system includes the data acquisition module and gathers the distributed power output, the energy storage charge and discharge state and the load power demand time series data; the prediction analysis module is associated with the power output data and the weather forecast data, generates the short-term output prediction curve; the net load calculation module calculates the difference value of supply and demand, generates the net load fluctuation curve; the energy storage dispatching module plans the energy storage charge and discharge plan by overall planning the peak valley arbitrage and the frequency modulation standby capacity; the load management module generates the load reduction instruction containing the reduction time length and the compensation price by combining the adjustable load response potential and the net load low valley period. The system can improve the energy storage utilization efficiency and the power grid stability, reduce the influence of load reduction on the park power consumption, realize the wisdom park energy low carbonization and fine collaborative scheduling.

The application provides a production capacity and energy consumption collaborative load management system for pneumatic conveying and weighing feeding, relates to the technical field of energy consumption collaborative load management, and comprises: a device initialization module, which is used for marking a powder pneumatic conveying collaborative control production line as a target production line, acquiring initial production capacity of the target production line, and performing initialization configuration of operation parameters of each operation device belonging to the target production line; a coefficient construction module, which is used for extracting adjustment steps corresponding to each operation device from the initialization configuration after the initialization configuration is completed, so as to construct a plurality of collaborative response coefficients; and a compensation management module, which is used for determining a load abnormality degree of the target production line according to energy consumption and load state of the target production line, and judging whether to perform synchronous compensation management on each operation device based on the initialization configuration, so that the system has self-adaptive optimization capability, can dynamically adjust control strength under the conditions of working condition fluctuation, material property change and pipe network resistance change, and realizes balance and stability between production capacity targets and energy consumption control.

The application discloses a base station load balancing method and device, a storage medium and an electronic device, and relates to the technical field of communication. The method comprises the following steps: predicting the distribution characteristics of network service demand of a target scene in a target time period based on historical service data; adjusting the resource distribution of the base station according to the distribution characteristics; receiving a target service combination in the target time period, wherein the target service combination comprises M services and the priority of each service, and M is an integer greater than or equal to 1; based on the resource distribution of the base station, adjusting the target model according to the target service combination and a target algorithm, and generating a load balancing strategy for the target service combination based on the adjusted target model. The application solves the technical problem that the traditional load balancing algorithm is inefficient and cannot realize active load management when the base station load balancing scheme in the prior art faces rapid changes and burst conditions.

The present application relates to the technical field of power equipment, and more particularly to an intelligent thermal management system for a cleaning robot, comprising a state sensing unit, a risk assessment unit, a thermal state calculation unit, a data decision unit, an execution unit and a feedback correction unit, the state sensing unit collects internal temperature, humidity and key component temperature; the risk assessment unit calculates dew condensation trend and overheating trend representation values accordingly; the thermal state calculation unit quantifies the overall thermal state of the system; the data decision unit determines the actual adjustment amount of heating and cooling power based on the above parameters; the execution unit includes heating, cooling and load management modules, and adjusts the output power accordingly; the feedback correction unit corrects the thermal state according to the operation deviation. Through multi-parameter fusion decision and dynamic power adjustment, the present application realizes the collaborative management of heat dissipation and dew condensation prevention, and improves the working reliability of the cleaning robot in harsh environments.

The invention provides a multi-energy complementary dynamic load management method, system and device and a storage medium, and relates to the technical field of power systems, and the method comprises the steps: predicting the power consumption and generating capacity of a park and energy equipment in the park in a preset time period in combination with historical power consumption data and environment information of the park; calculating the sum of the residual stored electric quantity of the energy equipment and the electric quantity of the generated energy, and when the sum of the electric quantity is smaller than the electric quantity, calculating the electric quantity difference between the electric quantity and the sum of the electric quantity; according to the regional characteristics of the park, generating a load guarantee coefficient of the park; generating an initial power scheduling plan of the park according to the load guarantee coefficient; and obtaining charging and discharging characteristics of the energy storage equipment, adjusting the initial power scheduling plan according to the charging and discharging characteristics, and generating a target scheduling plan. The method has the technical effects that the specific pertinence of the generated scheduling scheme is ensured, and complex and changeable power consumption scenes can be effectively dealt with.

A load management system for a wind turbine involves: a lifting device mounted in a nacelle and connectable to a load through a lifting line; and a slew frame connectable to the load when the load is at the nacelle, the slew frame operable to change an orientation of the load when the load is connected to the slew frame and the lifting device. The load management system can further have a taut wire system. The taut wire system involves a frame mounted up-tower in a nacelle, the frame having a plurality of interconnected beams mounted on both longitudinal top beams of the nacelle, the interconnected beams having a wire connector positioned outside the nacelle transversely beyond a sidewall of the nacelle. The taut wire system further has a down-tower tensioning subsystem and at least one tensioned guide wire connected to the frame at the wire connector.

Embodiments of the inventive concept measure the amount of electrical power being consumed in one or more houses or buildings, before the utility meter or meters. These measurements are used by a smart load manager (SLM) apparatus, in addition to information about the maximum capacity of the electrical lines that are being measured, to maximize the number of electric vehicle supply equipment (EVSE) units that can be installed in the one or more buildings, and maximize the amount of power that is available for electric vehicle (EV) charging at any given time.

The invention relates to the technical field of power system load management, and discloses a multi-element load collaborative optimization regulation and control method, which comprises the following steps: acquiring multi-element load data through a monitoring and data acquisition device, and representing a multi-element load nonlinear regulation behavior; a core optimization variable is determined by adopting a quasi-inverse Harris eagle optimization algorithm, a regulation and control scheme on that day is updated in combination with a historical scheme, real-time data, electricity price and load prediction, and the QOHHO algorithm gives consideration to nonlinear characteristics of multiple loads and communication delay; a single-step improved QOHHO algorithm is adopted to generate a day-ahead regulation and control plan; and in consideration of the power exchange penalty cost caused by the load deviation, modeling communication delay by adopting a preset method, and compensating a load adjustment dead zone and a rate constraint deviation through the optimized TDOF controller. The method is applied to the power distribution network containing high-proportion renewable energy sources, collaborative optimization scheduling of industrial, commercial and resident multi-element loads is achieved, and the power balance capacity, the renewable energy source consumption rate and the operation economy of the power grid are improved.

The present application relates to the technical field of power system management, in particular to a kind of power system load management method and system based on blockchain technology, in one specific embodiment, the present application provides a kind of power system load management method based on blockchain technology, management method includes: according to distribution structure, distribution area is divided into multiple distribution blocks, obtains the distribution data and link relationship of each distribution block;According to distribution data, the real-time load of distribution block is obtained, and according to real-time load, the block that needs to be adjusted is filtered out, to obtain the block to be adjusted, the load gap of the block to be adjusted is calculated;According to link structure, and according to initial compensation scheme, compensation load is calculated;According to compensation load and load gap, initial compensation scheme is adjusted, to obtain final compensation scheme.

The application provides a multi-strategy intelligent load management device based on two transformers, which comprises a master control module, a sub-control module, a sectional switch module and a tie switch module, the master control module is in communication connection with the sub-control module, the tie switch module is connected with the master control module, and the sectional switch module is connected with the sub-control module. The master control module and the sub-control module are in communication connection through a Can bus, the master control module receives voltage and current values and switch states sent by the sub-control module, sends control commands to the sub-control module, and the sub-control module sends the commands to corresponding sectional switches. The application can unload the three-level loads of two bus sections twice respectively under the condition that incoming line one and incoming line two are separately operated. Also, the application can manage the three-level loads of two bus sections uniformly and unload them twice when a single incoming line is used to supply two bus sections.

The invention discloses a three-in-one carrier rocket power supply and distribution energy management system, and the system comprises a first-stage power management module which is used for managing the first-stage power supply and distribution energy management of a rocket; the second-stage power management module is used for managing second-stage power supply and distribution energy of the rocket; the power supply and distribution energy management comprises control single machine management, measurement single machine management and load management; the ground launch control module is used for respectively controlling the first-stage power supply management module and the second-stage power supply management module, and is respectively connected with the first-stage power supply management module and the second-stage power supply management module through RS422 or Ethernet interfaces; and the ground power supply module is used for providing electric energy for the first-stage power supply management module and the second-stage power supply management module respectively, and is electrically connected with the first-stage power supply management module and the second-stage power supply management module respectively.

The invention discloses a panoramic load management system and method for a power demand side, and the method comprises the steps: collecting multi-source data, and carrying out the processing of the collected multi-source data; generating a thermodynamic diagram and load curve linkage visualization result taking a municipal administration map as a base map, and outputting a targeted load adjustment suggestion; combining the adjustable load potential data with power supply gap pre-judgment data dispatched and issued in a power grid data platform to generate a multi-scene cooperative regulation and control trigger signal; based on the cooperative regulation and control trigger signal and the load regulation suggestion, screening a target regulation and control resource, and issuing a corresponding regulation and control instruction to a user side terminal; and acquiring execution data of the regulation and control instruction, completing post effect evaluation based on a preset evaluation index, and iteratively calibrating a regulation and control strategy according to an evaluation result. The method has the advantages that accurate load regulation and control and new energy consumption improvement are realized, and the power grid and load change are dynamically adapted.

This invention belongs to the field of intelligent industrial load management technology, specifically relating to a load identification and control method and system based on edge AI and online self-learning. It includes the following steps: collecting multi-source heterogeneous raw load data through an edge intelligent terminal deployed on the user side; inputting the load feature data into a lightweight deep learning model deployed on the edge intelligent terminal; initiating an online self-learning process based on the confidence level; generating and executing an optimal control strategy for specific electrical equipment based on a preset finite state machine and multi-objective optimization model, according to the identification results and received power grid control instructions; and uploading the model parameters and control effect data locally on the edge intelligent terminal to a cloud master station for global model aggregation and updating. This invention achieves millisecond-level real-time accurate load identification by deploying a lightweight CNN-LSTM hybrid model on the edge.

The invention discloses a charging pile frequency conversion load management method and system based on artificial intelligence. The method comprises the steps of data acquisition, data preprocessing, clustering objective function design, iterative solution design, hyper-parameter search and charging pile frequency conversion load management. The invention belongs to the technical field of data processing, and particularly relates to a charging pile frequency conversion load management method and system based on artificial intelligence. Dimension disasters are reduced by adding projection subspace items, clustering result sparsity is constrained based on introduction of 21 norms and reconstruction items, and noise influences are reduced; a mutation strategy is designed on the basis of combining individual advantages and population advantages, and the convergence speed is increased; the mutation position is subjected to crossover operation through a crossover strategy, so that the individual position diversity can be reserved; and the final position is selected based on greedy, so that the search efficiency is improved, the optimal hyper-parameter combination is finally realized, and the clustering efficiency and precision are improved.

The invention discloses an iron and steel industry power demand prediction method and system based on multi-source factors, and relates to the technical field of data processing and big data analysis, and the method comprises the steps: collecting the historical environment, port throughput, logistics, power demand and iron and steel industry chain associated data of a target region; obtaining multi-source factor standard subdata through space-time alignment, preprocessing and monthly granularity division; through correlation analysis and trend chart construction, screening a first electricity consumption correlation factor, and determining an electricity consumption correlation coupling factor pair and short-term accidental and long-term stable correlation dynamic factors; a prediction model is constructed based on fitting of a time function and a periodic function, data are collected in real time, correction is carried out in combination with sudden factors such as extreme weather and policy adjustment, and a steel industry power demand prediction value is output. The method has the advantages that deep fusion of multi-source factors is achieved, prediction is accurate and reliable, and effective support can be provided for power grid load management and production planning of the iron and steel industry.

One aspect of the present disclosure provides a method. The method comprises the steps of: transmitting, by an SMF, a first message including a request for information related to a load of a specific UPF to an NF; receiving, by the SMF, a second message including information related to a load of specific traffic of the specific UPF from the NF; determining, by the SMF, an overload of the load of the specific UPF on the basis of the information related to the load of the UPF; and performing, by the SMF, UPF reselection on the basis of the determination.

A method for optimizing the network load in at least one predefined sector of an electrical network of at least one network operator using a prediction model for load management, wherein there is a network operator interface to the network operator and the data and information from model calculations and individual data from users of a service provider are used, wherein individual communication is performed at least with the users that can provide power in the sector.