210 results about "Load leveling" patented technology

The invention discloses a charging pile system management method based on dynamic load balancing, and particularly relates to the technical field of charging pile management, and the method comprises the steps: collecting time domain scheduling data of a charging pile side, a power grid side and a distributed renewable energy output device side, carrying out the fusion filtering, and obtaining a station-level state vector; generating a station-level prediction sequence of the charging pile in the next control period based on the station-level state vector and a rolling prediction model; an inner-layer controller reads the station-level state vector and the station-level prediction sequence, establishes a power prediction optimization model and solves the model, and outputs an expected station-level power trajectory and station-level power redundancy; a result output by the inner-layer controller is received, and a station-level power envelope curve and an energy storage charging and discharging set value are comprehensively solved and transmitted; the slope upper limit of the station-level power envelope curve is contracted based on the oscillation amplitude, the new slope upper limit is substituted into the next solution, the subsequent power change rate is limited, and the problem of unstable power exchange between the charging station and the superior power grid is effectively solved.

The invention discloses a storage battery capacity attenuation trend prediction method, and belongs to the technical field of storage battery prediction. By collecting voltage, current and temperature data of each monomer in real time and combining historical capacity attenuation and internal resistance growth data, the method identifies a voltage and capacity difference value, evaluates cyclic stress non-uniform distribution, and determines a current sharing proportion and a load unbalance degree. Identifying an abnormal mode of new battery overload and aged battery deep discharge, constructing a mixing abnormal working condition identification mode, if the unbalance degree exceeds the standard, adaptively adjusting the charging and discharging time and the current switching frequency, establishing a load balance control framework, predicting the capacity attenuation rate and the residual cycle index of each monomer, and determining the capacity matching degree and the life matching degree; finally, a comprehensive residual life estimation value and a credibility interval are generated through fusion; the performance balance of the mixed battery pack is remarkably improved, the overall service life is prolonged, and the method is suitable for real-time monitoring of a battery management system.

The invention discloses a charging pile dynamic load balancing scheduling system based on reinforcement learning, and the system comprises a data collection module which comprises 5G communication units installed at the charging piles, and is used for obtaining the power of the charging piles, vehicle battery parameters and power grid load data in real time; the reinforcement learning decision module maps the data in the data acquisition module into a state vector through a feature extraction network, and generates a power distribution strategy by adopting a PPO + DQN hybrid algorithm; the communication protocol module realizes state synchronization and control instruction transmission between the charging piles based on an OCPP2.0 standard; the execution control module converts the control instruction into a PWM control signal, and adjusts the output power of each charging pile in real time; through the reinforcement learning decision module, the system can analyze the charging pile power, the vehicle battery parameters and the power grid load data in real time, generate an accurate power distribution strategy, ensure accurate matching of the charging pile power and the electric vehicle demand power, and thus significantly improve the charging efficiency.

The invention discloses an air conditioner supply and demand collaborative optimization scheduling method, which relates to the technical field of air conditioner supply and demand scheduling, and comprises a sensing prediction module, an acquisition comparison module, a deviation correction driving module, a control adjustment module, a cold source control module, a strategy adjustment module and an anomaly detection module. When the abnormality detection module detects that the air conditioner terminal breaks down, the control adjusting module enhances the power of the peripheral equipment to fill the cooling capacity gap, if the water chilling unit breaks down, the cold source control module starts the standby unit to switch the cooling capacity output path, and when the cooling capacity is insufficient, the control adjusting module is in linkage to reduce the air volume of the low-priority area to preferentially guarantee cooling of the core area; meanwhile, a strategy adjusting module automatically matches a predefined strategy chain according to the fault type, for example, global average temperature control is switched when temperature control fails, a load balancing strategy is triggered to be distributed to a standby unit when equipment is overloaded, and the strategy is gradually rolled back to the optimal strategy based on historical data after the fault is repaired; and strategy conflicts and equipment fluctuations are avoided through progressive improvement of cold energy supply.

The invention provides a charging pile power distribution method and system based on dynamic load balancing, and the method comprises the following steps: obtaining the local state information of each charging pile, the state information at least comprises the charging queuing length, the current vehicle charge state, the current load power, the battery health state, the predicted residence time, the electricity price information and the power grid load state; based on the historical charging behavior, the electricity price fluctuation and the traffic flow data, the vehicle access frequency, the electricity price trend and the power demand of each charging pile in the future time period are predicted through a time sequence neural network; a prediction model is constructed; according to the method, forward-looking power planning is realized by entering a multi-dimensional prediction mechanism; the power distribution efficiency is improved by adopting a local collaborative game; the self-adaption and generalization ability is realized through reinforcement learning, and the robustness of a scheduling strategy in multiple scenes is enhanced; through a multi-target scheduling index, the electricity price, the battery life, the waiting time and the power grid load are optimized at the same time.

Examples described herein relate to circuitry to access a request to perform operations written to a single queue. In some examples, the circuitry is to allocate the operations from the single queue to multiple queues associated with multiple accelerators based on load data of the multiple accelerators. In some examples, at least one of the multiple accelerators is associated with at least two queues.

The invention relates to the technical field of solid waste incineration automatic control, and discloses a solid waste incineration equipment automatic control method and system.The method comprises the steps that real-time data of solid waste components in a hearth are collected, and an initial multi-dimensional parameter set is obtained through preprocessing; calculating a balance point of the heat value and the water content through fuzzy reasoning; when the balance point exceeds a threshold value, parameter interaction influence is predicted, and a multi-dimensional control vector is generated; performing data fusion and historical data matching in combination with real-time incineration process indexes to obtain an enhanced load distribution adjustment factor; carrying out weight comparison based on the adjustment factor and the balance point, judging a load balance influence index, and carrying out matching correction if the index exceeds a compatible threshold value; updating a load distribution scheme according to a correction result, and generating a final operation strategy; and a control instruction is sent through a feedback cycle mechanism and is continuously optimized, so that closed-loop control is realized. The device can adaptively treat dynamic changes of solid waste components, and the incineration efficiency and stability are improved.

The invention discloses a power supply circuit and electrical equipment. The power supply circuit comprises a three-phase power supply and three rectification and inversion modules, wherein each rectification and inversion module comprises a first input end, a second input end and an output end; the first input ends of the three rectification inversion modules are respectively connected to three phase lines in a three-phase power supply in a one-to-one correspondence manner; second input ends of the three rectification inversion modules are connected to a neutral line in the three-phase power supply; the output ends of the three rectification inversion modules are respectively connected to three loads in a one-to-one correspondence manner; and the rectifying and inverting module is used for sequentially rectifying and inverting the input voltage and then supplying power to corresponding loads. According to the invention, the three-phase power supply is divided into three independent single-phase power supply paths for three loads, single-phase rectification is carried out on each phase independently, the voltage level is reduced, a low-voltage-level device can be selected, and the cost is reduced; the three-phase load is balanced, the harmonic dispersion is realized, and the harmonic pollution is reduced; fault area isolation is realized, and maintenance difficulty and maintenance cost are reduced.

The invention discloses a dynamic load balancing method and device for an intelligent charging pile management system, and the method comprises the steps: obtaining the operation parameters of a target area transformer corresponding to the intelligent charging pile management system in a current control period, so as to obtain the real-time total load of the target area transformer in the current control period; dynamically calculating the real-time available capacity of the transformer based on the rated power and the real-time total load of the transformer in the target station area; calculating the predicted available capacity of the transformer in the next control period through a load prediction model based on the historical power data and the external environment variables; and according to the real-time available capacity and the predicted available capacity, dynamically adjusting the operation power of the plurality of accessed charging piles, and expanding the number of the accessible charging piles in a load trough period. By fusing the real-time power grid state and the multivariable predicted future load demand, the dynamic fine adjustment of the power of the charging pile and the prospective expansion of the off-peak access amount are realized, the safety of the power grid is ensured, and the capacity utilization efficiency of the transformer is remarkably improved.

The invention relates to a low-voltage power distribution network inter-station power mutual aid method and system. The method comprises the following steps: monitoring the load state of each transformer area of the low-voltage power distribution network and the low-voltage side current of the transformer area; when an overload transformer area is detected, searching the adjacent transformer area meeting a load capacity margin condition and a low-voltage side current margin condition as a target light-load transformer area, and if the adjacent transformer areas are both overloaded, starting an orderly power utilization three-level progressive transfer scheme; and determining a target unloading load according to a transfer strategy, controlling the section switch to execute switching operation, and switching the target unloading load from the overload platform area to the target light-load platform area. According to the invention, load balance between stations can be effectively optimized, the power supply safety and reliability are improved, and the method has the characteristics of low investment cost, high automation degree and strong adjustment capability.

The invention discloses an automobile charging pile charging allocation method based on dynamic load balancing, and the method comprises the following steps: S1, collecting the real-time operation data of a charging pile, and storing the real-time operation data in a charging pile load database; s2, constructing a charging scheduling optimization model, and setting an optimization target and a constraint condition; s3, a charging power distribution scheme is initialized, population individuals are generated by adopting a doliolaria group algorithm, and a fitness value is calculated; s4, running the dogvessel squirt group algorithm for optimization, adjusting the power by the navigator according to the optimal solution, and updating the power distribution scheme by the follower; s5, population diversity is calculated, and if the proportion of low-diversity individuals is lower than a threshold value, differential evolution algorithm optimization is triggered; s6, the fitness values are compared, and an optimal charging power distribution scheme is selected; and S7, scheduling the charging power in real time and monitoring the state by applying the optimal charging power distribution scheme. According to the invention, charging power balanced distribution is realized through an intelligent optimization algorithm, user waiting time and power grid load fluctuation are reduced, and charging station operation efficiency is improved.

The invention relates to the technical field of flywheel energy storage, in particular to a flywheel energy storage controller for a data center and a control method, and the controller comprises a flywheel rotating speed dynamic regulation and control module, an energy storage and release optimization module, a load balancing evaluation module, a real-time monitoring and feedback module and an overall efficiency evaluation module. According to the invention, energy storage and release can be optimized in real time, the energy utilization rate of the system can be improved, energy storage and release conditions can be accurately judged, control of a driving motor can be optimized, and the driving motor can be timely adjusted and maximized in energy output and storage according to load changes by accurately monitoring the rotating speed of the flywheel and dynamically regulating and controlling the rotating speed of the flywheel; energy conversion efficiency is remarkably improved, energy loss is reduced, stable operation of the system is ensured, a load distribution path is optimized through load balancing, load balance is ensured, control parameters are adjusted through real-time feedback, load matching efficiency is improved, energy consumption is reduced, and the overall efficiency of the flywheel energy storage system is remarkably improved.

The invention discloses a power equipment anomaly detection method and device, equipment and a storage medium. According to the dynamic graph division method based on computational load awareness, partition layout of load balance is achieved, the problem of load imbalance between processing nodes is remarkably reduced, hotspot bottlenecks caused by high-frequency equipment concentration are avoided, the overall throughput and parallel processing capacity of a system are improved, and the performance requirement for real-time processing of high-frequency data is met. And a constructed partition-unit-device multi-level anomaly detection mechanism triggers the detection task of each level as required, so that the system resource overhead is effectively reduced, and the overall detection efficiency is improved. By analyzing the abnormal propagation path in the electrical topology and combining the spatial position information of the equipment, the abnormal region boundary expression is generated, so that the conversion from point-level identification to chain-level analysis and region-level visualization is realized, the operation and maintenance personnel can quickly position and respond, and the interpretability of the abnormal result and the practical value of scheduling are improved.

The invention discloses an analysis method and system for energy conservation and loss reduction of transformers, and the method comprises the steps: collecting the standby state data of a transformer group, recognizing a high-loss unit, and predicting the parameter drift trend of the high-loss unit; acquiring an electromagnetic impact strength signal in a working state; the parameter drift trend and the electromagnetic impact strength signal are fused, and the collaborative imbalance risk is evaluated; and according to a risk assessment result and a risk cause, dynamic voltage regulation and load balancing optimization are adaptively executed. The cross-working-condition correlation analysis and closed-loop control are carried out on the transformer group serving as the power supply core, so that the standby no-load loss can be effectively reduced, the equipment aging can be delayed, the operation cost can be reduced, the long-term stability and reliability of the power supply system can be ensured, and the method can be widely applied to the industrial fields of pulsed electric fields, precision manufacturing and the like.

The invention, which relates to the technical field of the new energy vehicle power supply system, discloses a dynamic load balancing power supply system for a new energy vehicle charging pile cluster, comprising a data acquisition module, a load prediction and decision module, a dynamic power distribution execution module and a safety protection and communication module. The data acquisition module is used for monitoring three-phase electrical parameters, temperature and electric energy quality data of the charging pile cluster in real time; the load prediction and decision module is connected with the data acquisition module and is used for predicting a load trend based on historical and real-time data and generating a dynamic regulation and control strategy; the dynamic power distribution execution module is connected with the load prediction and decision module, accurate prediction and rapid dynamic regulation and control of the charging load can be achieved, the problems of three-phase imbalance, transformer loss, harmonic pollution and the like of a power distribution system caused by randomness and volatility of charging behaviors are effectively solved, and the intelligent graded safety protection capability is achieved.

The invention relates to a transformer area group optimization method and device, computer equipment and a readable storage medium. The method comprises the following steps: obtaining voltage, current and frequency of each edge node in a to-be-optimized transformer area group, photovoltaic output data of the transformer area group, energy storage charge and discharge state data and electric vehicle charging power data, and presetting various operation constraints set for the transformer area group; and inputting the voltage, the current, the frequency, the photovoltaic output data, the energy storage charge and discharge state data, the electric vehicle charging power data and the various operation constraints into a pre-constructed multi-target optimization model, obtaining optimization strategies meeting the various operation constraints, and performing optimization guidance on the operation of the transformer area group according to the optimization strategies. The transformer area group level multi-objective optimization task is calculated through the cloud high-performance computing cluster, so that an optimization strategy which is more adaptive to the current transformer area group is obtained, edge setting is controlled according to the optimization strategy to execute optimization output, and the load balance and operation robustness of the transformer area group are ensured.

The invention relates to the technical field of power electronics, and particularly provides a double-rectification single-inversion redundant uninterruptible power supply system and a control protection method thereof, and the system comprises a power distribution system, a variable current power module and a control protection system. The power distribution system has double AC inputs, a common DC bus and AC outputs. The converter module comprises three converters, two of which serve as rectifiers and are connected in parallel with the direct current bus, and the other one serves as inverter output. The control method comprises the following steps: in a double-path load balancing mode, realizing power equalization through composite voltage control; in a single-path operation mode, fault seamless switching is realized based on load feedforward and adaptive slope control; in a power distribution mode, triggering according to capacity margin and cooperatively adjusting a droop coefficient based on an optimization target to realize power redistribution; and complete real-time fault protection logic is realized. According to the invention, uninterruptible power supply with high reliability and high electric energy quality is realized, and active regulation and control of power are realized.

The invention relates to the technical field of artificial intelligence, in particular to a load balancing method and device, electronic equipment, a storage medium and a program product, and the load balancing method comprises the steps: obtaining traffic characteristics of communication links between different nodes in a data block migration process of a distributed storage system; determining a load distribution mode of each communication link based on the flow characteristics of each communication link; based on the flow characteristics and the load distribution mode of each communication link, determining hotspot nodes and corresponding service priorities thereof; determining a target hotspot node based on the access frequencies, the data transmission quantities, the node loads and the corresponding service priorities of all the hotspot nodes; and based on the processing performance, the network state and the storage capacity of the node, screening out a target low-load node meeting a preset condition from the distributed storage system, and migrating data of the target hotspot node to the target low-load node. According to the invention, the network flow distribution and node load balancing state can be dynamically optimized.

The invention provides a multi-zone-area ordered charging scheduling method and system based on a simulated annealing genetic algorithm, and the method comprises the steps: generating an initial population through employing a real number coding mode, and enabling a population individual to represent a charging scheduling scheme; constructing a fitness function in the genetic algorithm by taking load balancing and network loss minimization as optimization targets; selecting individuals by adopting a roulette selection and elitism strategy, and reserving a plurality of individuals with the highest fitness to enter the next generation by adopting a self-adaptive crossover mutation operation; taking the optimal individual of the current population as an initial solution, and executing simulated annealing operation; after optimization of a preset number of iterations, an optimal charging scheduling scheme is output, and the load rate, the voltage and the charging pile state of each transformer area are monitored in real time; and when abnormality is detected, charging power distribution is dynamically adjusted, and the load of the overload area is transferred to the low-load area. Through fusion of simulated annealing and a genetic algorithm, multi-zone-area charging scheduling is optimized, load balancing and network loss minimization are realized, and the stability and dynamic adaptability of a power grid are improved.

The invention relates to the technical field of electric vehicle charging infrastructure intelligent management, and discloses a charging pile load balancing method and system based on a neural network, and the method comprises the steps: analyzing the state, environment and user behavior data of a charging pile through a multi-mode auto-encoder network; generating a unified scene representation; recognizing a typical scene by using a variational auto-encoder and a density clustering algorithm; decomposing the charging load balancing knowledge into cross-scene shared knowledge and scene specific knowledge; automatically generating a neural network architecture adaptive to the current scene based on the scene similarity; using a meta reinforcement learning algorithm to train a meta strategy network to generate a load balancing decision; scene smooth transition is realized through gradual model switching; according to the method, multi-scene adaptation, knowledge sharing and smooth switching are realized, the problems of system redundancy, knowledge isolation, scene switching performance fluctuation and the like in a traditional method are effectively solved, and the operation efficiency of the charging infrastructure is improved.

The invention relates to the technical field of electrical measurement, in particular to an intelligent cable management system and method for a high-voltage direct-current converter station, and the system comprises a data analysis module, a path evaluation module, a current shunting module, an anomaly detection module and a load mapping module. According to the method, on the basis of collecting node temperature, current and current-carrying capacity data, the heat capacity utilization rate and the redundancy rate are jointly calculated to improve the margin quantification precision, path endpoint deviations are dynamically compared based on the margin, path states are sorted in combination with the current-carrying capacity, and the problem of uneven heat capacity distribution is solved; a shunting instruction is generated according to a path current-carrying ratio and a redundancy rate to improve load balance, abnormal nodes are identified through sudden change of a temperature rise rate and a current change rate, loads are remapped in combination with the redundancy rate and the shunting instruction, thermal capacity evaluation, path sorting, shunting control and abnormal reconstruction closed-loop control are performed, and thermal safety management and response sensitivity are enhanced.

This application relates to a point-to-point load balancing method, apparatus, device, storage medium, and program product, applied to a first node in an enhanced Ethernet network. The method includes: generating a load balancing task when establishing a service network connection between the first node and a second node; when executing the load balancing task, if the second node is idle, configuring a network test connection between the first node and the second node with a lower priority than the service network connection; performing a load balancing test on the first node and the second node through the network test connection; if the test result shows an unbalanced load, establishing a new network test connection between the first node and the second node, and performing the load balancing test again, until a target network test connection with a load-balanced result is obtained; and replacing the service network connection between the first node and the second node with the target network test connection. This method can achieve point-to-point load balancing in an online environment without affecting existing service traffic.

The application relates to an active equalization system for a BMS and relates to the technical field of battery management.The system comprises a state evaluation module, which is used for performing state evaluation on each battery cell in the BMS to generate a state evaluation index of each battery cell; a path optimization module, which is used for optimizing an energy transmission path according to the state evaluation index of each battery cell to generate a path optimization result; a current equalization module, which is used for calculating an adaptive equalization current based on the state evaluation index of each battery cell and the path optimization result; a loss compensation module, which is used for performing efficiency loss compensation on the BMS based on the adaptive equalization current and the path optimization result to determine a corresponding efficiency loss compensation coefficient; and an active equalization module, which is used for generating an equalization control signal for the BMS based on the efficiency loss compensation coefficient, the path optimization result and a time attenuation factor.The system can improve the accuracy and efficiency of BMS load balancing.

The invention discloses a cluster load balancing method and device, a storage medium, a program product and computer equipment, and the method comprises the steps: determining the node state information of each server node in a cluster, and enabling each server node to be controlled by a plurality of controllers; determining node role information of each server node; and adjusting the control relationship between each server node and the plurality of controllers at least based on the node state information and the node role information, so that the server nodes can be controlled by utilizing the controllers, a load balancing algorithm does not need to be deployed in the server, and the load balancing efficiency is improved. And the control relationship between the controller and the server nodes can be dynamically adjusted according to the node state and the node role information of each server node, so that the load balance of the cluster in the actual operation process is improved, the cluster performance is improved, and in addition, the distribution reasonability of the control relationship between the controller and the server nodes can also be improved.

The invention relates to the technical field of electric vehicle charging facility intelligent control, and discloses a charging pile load balancing method and system based on reinforcement learning. The system comprises a digital twinborn simulation module, a data sensing and processing module, a state characterization module, a double-track decision module, a baseline strategy unit, a shadow learning agent, a safety verification and execution module, a double-path reward calculation module, a shadow experience learning and verification module and an online deployment and continuous learning module. Through the technical path of simulation pre-training, shadow evaluation, safety verification and continuous learning, the problem of high deployment risk of reinforcement learning in a real system is solved, safe and smooth transition from simulation to reality is realized, absolute safety and continuous evolution of system operation are ensured, and the method is suitable for popularization and application. A prospective technical architecture is constructed to cover future scenes, and dynamic collaborative optimization of multiple targets such as charging efficiency, fairness, economy and power grid safety is realized.

The application discloses a circuit and method for improving transient load response of constant voltage power supply, which comprises a voltage regulator and a load balancing structure. The method comprises: when the external load current is in a stable state, the charging diode is in a reverse cut-off state; when the external load current is in an increasing state, the output load voltage of the voltage regulator is in a falling state, the charging diode is in a conducting state, and the energy storage capacitor performs voltage compensation processing on the voltage regulator; and when the external load current is in a decreasing state, the output load voltage of the voltage regulator is in a rising state, and the voltage regulator performs charging processing on the energy storage capacitor. The application can improve the transient load response of the constant voltage power supply, reduce the voltage fluctuation amplitude and accelerate the response speed. The application can be widely applied to the technical field of power supply circuits.

The invention discloses a high-reliability UPS redundant power supply control system, and relates to the field of UPS power supply control. Comprising the steps that a redundant power supply topology module adopts an N + X redundant structure to guarantee power supply continuity, a multi-parameter real-time detection module dynamically collects unit operation and battery state data, an intelligent load balancing control module dynamically adjusts power distribution, a rapid fault isolation and switching module achieves non-interruption switching, and a multiple safety protection module restrains fault expansion. The battery pack intelligent management module optimizes charging and balancing, the remote monitoring and operation and maintenance module supports local and remote management, and the system state self-diagnosis module automatically locates faults and generates reports. The system is excellent in power supply continuity and stability, accurate in load balancing, rapid in fault response and low in energy consumption; the system is high in environment adaptability, supports intelligent operation and maintenance and emergency expansion, is safe and compliant in operation, and comprehensively meets the high-reliability power supply requirements of key scenes.

The application relates to a charging power distribution method based on an intelligent gateway, the intelligent gateway and a storage medium. The intelligent gateway is used for being connected with multiple charging piles and forming a charging network. The method comprises the following steps: determining a candidate charging pile meeting a preset power adjustment condition as a target charging pile according to a candidate charging pile set, the candidate charging pile set comprising multiple candidate charging piles, and the candidate charging pile being a charging pile in a charging state in the charging network; acquiring the maximum available charging power of the candidate charging pile set; and calculating the target charging power of the target charging pile according to a load balancing algorithm and the maximum available charging power. The maximum available charging power of the charging network and the target charging pile are determined by the intelligent gateway, and the distribution power of the target charging pile is calculated by the intelligent gateway according to the load balancing algorithm, so that the charging pile only needs to perform a charging task according to the distribution current, the load balancing function and the charging function of the charging pile are decoupled, and the design of the charging pile is simplified.