164 results about "Load leveling" patented technology

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 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 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 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.

The invention discloses an electrochemical energy storage system operation optimization method based on load prediction, relates to the technical field of electrochemical energy storage, and realizes dynamic optimization of a system by comprehensively utilizing historical load data, real-time data and an intelligent algorithm. The method comprises the following steps: firstly, acquiring historical electrical load data associated with a periodic day from a cloud database, constructing a historical load curve graph and calculating an average historical load curve graph; then, on the basis of the real-time electrical load data of the current period day, historical extreme point matching and scaling adjustment are combined, and a predicted load curve graph of a future time period is generated; the discharge power of the electrochemical energy storage system is optimized, regulated and controlled according to the prediction result, it is ensured that the system effectively discharges at the load peak, and meanwhile overload is avoided; according to the method, the operation strategy of the energy storage equipment is dynamically adjusted through combination of historical data and real-time data, the system operation efficiency and the load balancing capacity are effectively improved, and the method is suitable for a complex and changeable power utilization environment.

The invention provides a new energy power station redundancy scheduling management method and system, and the method comprises the steps: deploying a main switch and a standby switch in a control center of a new energy power station, and enabling the main switch to achieve the switching with the standby switch based on a dynamic link detection mechanism; for target equipment of a new energy power station, a main uninterruptible power supply and a standby uninterruptible power supply are provided, the main uninterruptible power supply and the standby uninterruptible power supply are respectively provided with an independent battery pack, a static switch is adopted to realize switching between the main uninterruptible power supply and the standby uninterruptible power supply, and loads of the main uninterruptible power supply and the standby uninterruptible power supply are allocated through load balancing; monitoring the operation state of the target equipment of the new energy power station through the control center; and when the running state of the target equipment shows to be abnormal, triggering a redundancy switching mechanism through the control center, and switching the abnormal target equipment to the preset standby equipment, so that resources can be reasonably allocated, the equipment overload can be avoided, and the resource utilization rate can be improved according to the real-time link state and the equipment load condition.

The present application relates to electric vehicle charging scheduling technical field, especially to distributed charging pile group dynamic load balancing and power optimization allocation system and method, including: cloud coordinator obtains the space correlation data between piles containing geographical distance and traffic similarity and fuses with historical and weather data, inputs into the spatiotemporal graph neural network prediction model, outputs probability distribution prediction value and prediction confidence interval and issues, edge control node constructs multi-objective optimization model based on prediction uncertainty, generates dynamic allocation table and backup scheme under capacity constraint to minimize charging time and maximize renewable energy consumption as the target, issues allocation table to execute power allocation and obtains actual power feedback, when the actual power exceeds the confidence interval range, switch backup scheme to preferentially reduce flexibility load. The present application effectively improves the deficiency that single-point prediction lacks defense, realizes reliable global dynamic load balancing under the premise of guaranteeing the safe operation of power grid transformer.

The application discloses an analysis method and system for energy saving and loss reduction of a transformer, comprising: collecting standby state data of a transformer group, identifying a high-loss unit and predicting a parameter drift trend thereof; acquiring an electromagnetic shock intensity signal in a working state; fusing the parameter drift trend and the electromagnetic shock intensity signal, evaluating a risk of synergistic disorder; and adaptively performing dynamic voltage regulation and load balancing optimization according to a risk evaluation result and a risk cause. Through cross-condition correlation analysis and closed-loop control on the transformer group as a power supply core, the application not only can effectively reduce standby no-load loss and delay equipment aging, reduce operation cost, but also can ensure long-term stability and reliability of a power supply system, and can be widely applied to industrial fields such as pulse electric fields and precision manufacturing.

This invention relates to the interdisciplinary field of new energy battery health management and artificial intelligence, specifically to a new energy power battery SOH estimation method based on physically guided hierarchical spectral hybrid experts: Incremental capacity physical features are extracted from battery charge-discharge time-series data, and the prior distribution of degradation stages is obtained through clustering; singular value spectral decomposition is performed on the weight matrix of the pre-trained Transformer model, freezing the basic components and dividing the residual spectral space into multiple spectral experts; a hierarchical gated network is constructed with upper-layer radial basis function-based physical routing and lower-layer Softmax-based data-driven routing, and the expert outputs are weighted and fused; a joint loss function including mean square error, monotonicity penalty, route alignment, and load balancing is designed for efficient parameter fine-tuning; finally, the fine-tuned model outputs the SOH estimation result. This invention improves the estimation accuracy and physical consistency throughout the entire life cycle by decoupling the macroscopic degradation stage from local electrochemical fluctuations, and is suitable for online monitoring of the health status of power batteries.

A system employing a modular containerized energy-storage systems (ESS) and power cabinet control system allows for long duration uninterruptible power supply (UPS) capabilities for battery and electrochemical storage devices modular containerized ESS and power cabinet control system. Long duration uninterruptible power supply (UPS) capabilities for battery and electrochemical storage devices may be realized. Embodiments of a modularized energy storage system can comprise a first power cabinet configured to function as a primary power and load balancing appliance. The first power cabinet can comprises power electronics including a DC-AC inverter, transformer, and frequency regulator; a set of one or more energy storage systems, which may be battery-based.

The invention discloses a charging pile distribution system based on dynamic load balancing, and relates to the technical field of intelligent scheduling of new energy automobile charging facilities. Updating the dynamic load characterization information to a central database of the charging pile distribution system; generating a charging pile distribution scheme; and updating the charging service scheduling state. According to the method, the problem that pile position idling and local overload coexist due to traditional static distribution is effectively avoided, and the utilization rate of charging facilities and the operation stability of a power grid are improved; the robustness and service continuity of the system in a sudden high-concurrency scene are enhanced, so that more efficient, reliable and safe charging experience is provided for users, and high-quality development of related infrastructures of new energy automobiles is powerfully supported.

The invention relates to a service bus design and implementation method based on a micro-service architecture, and belongs to the field of micro-services. According to the invention, load balancing and regularized forwarding are supported through the dynamic routing module, the centralized bottleneck is avoided, and the routing efficiency can be effectively improved and the request forwarding delay can be reduced by combining the real-time updating of the service registration center and the dynamic capacity expansion and shrinkage of the adaptive micro-service; the functions of fusing, degrading, current limiting and gray release are integrated, dependence on a third-party component is not needed, the system complexity is reduced, and the service iteration period is shortened; and calling chain tracking is realized based on Trace ID, and log and index analysis is combined, so that the fault positioning time is shortened from an hour level to a minute level, and the operation and maintenance efficiency is remarkably improved.