1407 results about "Power efficient" patented technology

The invention discloses an intelligent power grid optimal scheduling method and system based on multivariate energy storage cooperative scheduling, and relates to the technical field of power grid optimal scheduling, and the method comprises the following steps: building a prediction model based on first data, generating prediction data, coupling energy storage characteristic parameters of different types of energy storage equipment with the prediction data, and obtaining a prediction model; establishing a multi-energy collaborative scheduling model; dynamically screening the energy storage scheduling strategy set based on a preset real-time performance evaluation index to generate an optimal strategy subset; according to the optimal strategy subset, performing differentiated charging and discharging control instructions on the energy storage equipment cluster; and collecting second data in the charge and discharge control process, calculating a deviation value between the second data and the prediction data, converting the deviation value into a feature vector, inputting the feature vector into a preset incremental learning algorithm, and optimizing parameters of the multi-energy collaborative scheduling model. Layered screening is implemented in combination with real-time performance evaluation indexes, and it is ensured that the optimal scheduling scheme can be rapidly selected in different time periods and under the uncertain disturbance condition.

The invention discloses a system for testing an electromagnetic shielding coupler of a testing transformer based on intelligent sensing, and relates to the technical field of electrical equipment testing, and the system comprises a flexible distributed intelligent sensing array which is composed of a plurality of high-density miniature electromagnetic sensors and a temperature-vibration composite sensing unit, a non-uniform topological structure is embedded into a seam and an insulating interface area on the surface of the electromagnetic shielding coupler. According to the test system of the test transformer electromagnetic shielding coupler based on intelligent sensing, through dynamic electromagnetic field reconstruction of the flexible distributed intelligent sensing array and multi-band interference coupling modeling, the limitation of a traditional static test is broken through; and real-time capture and multi-dimensional decoupling of shielding effectiveness attenuation characteristics in a complex alternating electromagnetic environment are realized. Compared with a traditional method, the method has the advantages that the shielding effectiveness dynamic quantization error is reduced, and the engineering guidance value of test data is improved.

The invention provides a dynamic configuration computing resource management method and system based on a power wireless terminal, and the method comprises the steps: obtaining a real-time operation state data set and a power business demand data set of the power wireless terminal, and carrying out the multi-dimensional feature fusion processing of the real-time operation state data set and the power business demand data set; the method comprises the steps of generating a resource adaptation feature set, performing real-time strategy matching processing on the resource adaptation feature set based on a preset resource adaptation rule base, generating a computing resource configuration strategy set, performing adjustment operation on computing resources of the power wireless terminal, and generating a resource allocation verification result and terminal operation efficiency feedback data. And continuously comparing the terminal operation efficiency feedback data with a preset efficiency optimization threshold value, and carrying out iterative updating processing on the resource adaptation rule base according to a comparison result. According to the invention, accurate matching between computing resource allocation and power business demand fluctuation can be realized.

The invention discloses a fan inertia support evaluation and control method considering power grid inertia space-time distribution. Based on wide area measurement system data, a power grid inertia space-time distribution model is constructed, inertia contribution characteristics of different areas are analyzed, and the influence of extra-high voltage direct current transmission on inertia distribution is considered. And calculating the inertia response power of the fan, quantifying the support contribution of the virtual inertia of the fan based on the frequency change rate and the regional equivalent inertia, and establishing an inertia support efficiency evaluation index system. A dynamic feedback control strategy is put forward, a reinforcement learning or adaptive gain adjustment method is adopted in combination with the real-time distribution condition of the inertia of the power grid, the virtual inertia control parameters of the fan are optimized, and the response accuracy is improved. Based on a wide-area coordination optimization method, a fan inertia support scheduling strategy is optimized, and the system frequency stability is improved. The method can effectively improve the frequency supporting capability of the new energy power grid, improves the new energy consumption level, enhances the operation safety of the power grid, and is suitable for a high-proportion new energy grid-connected smart power grid.

The invention discloses an industrial internet of things real-time monitoring and predictive maintenance system based on digital twinning, and relates to the technical field of industrial digital twinning operation and maintenance, the system comprises a multi-modal data acquisition module, a sensor network is deployed, and edge calculation preprocessing is carried out; the digital twinning construction module is used for constructing a high-precision model by fusing a physical law and deep learning; the real-time monitoring module is used for detecting abnormity by using a space-time diagram neural network; the predictive maintenance module is used for optimizing a maintenance strategy in combination with a probabilistic algorithm; and the man-machine interaction module supports AR / VR and brain-computer interface operation. In addition, the system integrates functions of block chain security, energy management and the like, and realizes full-life-cycle intelligent management of equipment. The operation and maintenance efficiency of the industrial equipment is greatly improved. The data acquisition precision reaches the nanoscale, and the early warning time is advanced to 72 hours; the maintenance cost is reduced, and the equipment availability is improved; the AR interaction enables the operation efficiency to be improved and the training period to be shortened. And meanwhile, energy consumption reduction is realized.

The invention relates to the technical field of battery management, and discloses a battery management system with an active monitoring function, which comprises a processor, a battery state monitoring module, an active equalization control module, a performance evaluation module and a fault early warning module. The battery state monitoring module collects various parameters of a battery at different stages, and generates an abnormal signal by using a dynamic threshold algorithm. The active equalization control module adjusts an equalization strategy based on a fuzzy logic algorithm, and a fuzzy rule base can be updated. And the performance evaluation module tracks and evaluates an equalization process, analyzes equalization efficiency and quality, and optimizes an equalization strategy accordingly. The fault early warning module displays abnormal signals in a grading mode and carries out early warning, and the processor is further connected with the aging feature extraction module and the thermal runaway prediction module to achieve battery life prediction and thermal runaway early warning. According to the invention, comprehensive monitoring, efficient equalization control, accurate performance evaluation and reliable fault early warning of the battery are realized, the safety of the battery is improved, and the service life of the battery is prolonged.

The invention relates to the technical field of electric power management and control, and discloses an electric power BIM type data GIS discount classification method and system, and the method comprises the following steps: 1, constructing an electric power industry exclusive classification system and semantic mapping, and building a three-stage classification frame based on a GIS and an electric power industry standard; 2, unifying a space coordinate reference and a topological relation, and verifying topological consistency between components and between the components and topography and roads through a GIS (Geographic Information System) tool; 3, multi-scale classification and dynamic LOD model generation are carried out, and LOD model details are automatically switched according to a GIS scale; 4, performing data format conversion and intelligent lightweight processing, performing lossless compression on key components, and performing simplified compression on secondary components; and step 5, performing classification verification and dynamic updating mechanism, and comparing whether BIM and GIS base map position classification is correct or not, thereby realizing data synchronization. According to a dynamic classification framework and an intelligent lightweight strategy in the power industry, the BIM model efficiently adapts to the spatial analysis requirement of the GIS, and the data fusion and application efficiency is improved.

The invention belongs to the technical field of optical storage cooperative control, and discloses a new energy grid-connected optimization control method based on an energy storage system, which comprises the following steps: collecting a power grid impedance fluctuation amplitude, a harmonic distortion rate and environment temperature data, obtaining an initial SOC value, correcting the SOC value, calculating an adjustable power margin and a response delay parameter of a photovoltaic unit, and calculating the optimal power grid impedance fluctuation amplitude and the optimal power grid impedance fluctuation amplitude. Generating an efficiency responsibility table; constructing a response cluster, generating a frequency modulation task package, and further generating a three-dimensional efficiency mapping table; the inertia demand is dynamically calculated in combination with the power grid frequency change rate, a responsibility transfer strategy is automatically activated in a high-altitude and high-temperature scene, and an efficiency instruction set is generated; analyzing response deviation by simulating typical disturbance of a high-altitude weak power grid scene, positioning a deviation source in combination with historical data, and dynamically adjusting an efficiency weight correction factor; and updating a responsibility allocation rule of the efficiency responsibility table, disrupting the low-efficiency cluster, and recombining the high-adaptability unit.

The invention relates to the technical field of photovoltaic power station management, and discloses a photovoltaic power station Internet of Things intelligent management and cloud edge collaborative optimization system. The system comprises an operation data acquisition module which is used for acquiring power station data in multiple dimensions, quantitatively identifying an abnormal operation state and generating a management early warning signal; the equipment state evaluation module is used for extracting equipment characteristics and dynamically evaluating the self-maintenance capability to obtain a state evaluation reference value; the edge processing module is used for monitoring parameters such as data traffic and predicting and analyzing edge processing efficiency to obtain an edge calculation evaluation value; the cloud edge collaboration module is used for jointly analyzing collaborative optimization efficiency and generating a regulation and control signal; the optimization parameter generation module is used for generating equipment maintenance and computing power distribution parameters; the system further comprises an authority control module and a dynamic adjustment module. According to the system, intelligent management and cloud edge collaborative optimization of the photovoltaic power station are realized, the operation efficiency and reliability of the power station are improved, and the safety and dynamic adjustment capability are enhanced.

The invention discloses a ship main power and auxiliary power real-time switching method and system based on large model optimization, and relates to the technical field of ship power system control, and the method comprises the steps: collecting the operation state data and environment parameters of a ship main power system and an auxiliary power system in real time through a multi-mode sensor array; and performing space-time alignment and noise suppression processing on the operation state data and the environment parameters, inputting the feature subset into a lightweight large model inference engine, generating a multi-target optimization decision set, executing a main and auxiliary power dynamic switching control sequence in a preset time window according to the multi-target optimization decision set, and outputting the main and auxiliary power dynamic switching control sequence. And generating a self-optimization log including a switching efficiency evaluation report and a model iteration suggestion based on the deviation degree of the switching feedback data and a preset performance index. According to the ship main and auxiliary power real-time switching method and system based on large model optimization, stable operation of a ship power system is guaranteed, and meanwhile the service life of equipment is prolonged.

The invention relates to the technical field of energy management and intelligent scheduling, provides a photovoltaic energy storage motor collaborative scheduling optimization system based on multi-modal data, and constructs a collaborative scheduling system integrating data intelligent processing, high-precision prediction and multi-objective optimization by fusing photovoltaic module, energy storage equipment, motor and power grid side multi-modal data. Data quality is improved through Z-score cleaning and Kalman filtering, accurate prediction of photovoltaic power, load demand and energy storage aging is realized by using a long-short-term memory neural network and an Arrhenius equation, and a multi-target collaborative scheduling strategy including economic, reliable and environmental benefits is constructed based on a particle swarm optimization algorithm. Dynamically adjusting photovoltaic output distribution, energy storage charging and discharging and a motor speed regulation ratio; systematic innovation of data value deep mining, multi-target dynamic balance, equipment full-life-cycle management and renewable energy source efficient consumption is realized, and the intelligent level and comprehensive operation efficiency of the micro-grid are remarkably improved.

The invention relates to the technical field of automatic control, in particular to an automatic control method of a BOG low-temperature recovery system for a storage tank for an LNG (liquefied natural gas) ship, which comprises the following steps: acquiring BOG pressure fluctuation data and temperature difference trend, analyzing pressure rise rate and cooling efficiency change, identifying a pressure peak value and a cooling lag section, screening a low-efficiency section and marking an optimization unit. And identifying an energy matching degree, analyzing energy loss distribution, identifying an over-limit node and a fault signal, and obtaining an energy and fault synchronous control table. According to the system, through real-time data monitoring and intelligent analysis, the BOG recovery process can be accurately controlled, energy circulation and cooling treatment can be optimized, the efficiency of a low-temperature recovery system can be improved, BOG pressure and temperature difference changes can be monitored, pressure fluctuation and cooling lag can be analyzed, continuous improvement of condensation efficiency can be ensured, control parameters can be accurately adjusted, energy loss is avoided, and the energy efficiency of equipment can be improved; energy consumption and equipment fault management are optimized, and efficient and safe operation of the low-temperature recovery system is ensured.

The invention discloses a commercial building energy monitoring and intelligent control method and device and a storage medium, and belongs to the technical field of building intelligent control, and the method comprises the steps: collecting data, building a nonlinear mapping relation, and generating an energy consumption demand prediction tensor; injecting an adversarial disturbance sample, and evaluating the robustness of the prediction model; in combination with the energy consumption baseline, performing cross confirmation and correction on the prediction data exceeding the threshold value; performing attribution analysis on the corrected energy consumption sequence to generate an energy consumption attribution map; adjusting the solution of a multi-objective optimization function according to the atlas, and generating an optimal cooperative control strategy; and the comprehensive efficiency is used as a reinforcement learning reward, strategy parameters are iteratively updated, and a control knowledge base is formed. According to the method, a closed-loop control framework integrating robust demand prediction, dynamic attribution analysis, collaborative optimization decision and a self-evolution strategy is adopted, intelligent regulation and control of building energy consumption can be realized, and the long-term adaptive optimization capability is improved.

The invention relates to the technical field of photovoltaic power generation. The invention provides a photovoltaic module cooling method, system and device and a storage medium, and the method comprises the steps: collecting the working condition parameter data of a photovoltaic module and the environment parameter data in a corresponding range of the photovoltaic module in real time, and obtaining a basic data set; based on the basic data set, a relation model between the working condition parameters and the environmental parameters of the photovoltaic module is established, the relation model is utilized to predict the temperature change trend and the output power change condition of the photovoltaic module under different environmental conditions, and a prediction result is obtained; and starting a liquid cooling, air cooling and phase change material collaborative cooling mechanism according to the prediction result, continuously monitoring the working condition parameters of the photovoltaic module in the cooling process, and dynamically adjusting and optimizing the cooling mode and the corresponding parameters according to the monitoring result. The problems that an existing photovoltaic module lacks an effective temperature adjusting means, the output power is obviously reduced when the working temperature rises, and the overall efficiency and economic benefits of a photovoltaic power generation system are reduced are solved.

The invention discloses a power generation efficiency optimization method for a floating photovoltaic power generation system. The method comprises the following steps: analyzing the motion response of a floating body in a marine environment, and determining the boundary condition of the floating body swinging under the action of waves; based on hydrodynamic analysis software AQWA, a hydrodynamic analysis model of the floating body is built, and the pitching angle of the floating body is verified; in combination with an inclined surface irradiance calculation model, an improved particle swarm algorithm is adopted for optimization, and the initial inclination angle of the photovoltaic panel when the photovoltaic panel receives solar radiation energy to the maximum extent is solved; in combination with different wave conditions of a certain sea area, simulation software is used for performing dynamic simulation on the floating body and the assembly, the influence of different environmental conditions on the power generation efficiency is analyzed, and the effectiveness of the scheme is verified. According to the method, the optimal initial angle is determined by optimizing the initial mounting inclination angle of the assembly, fully considering the movement condition of the floating body under the action of waves and combining the influence of different wave conditions, the radiation quantity received by the assembly is maximized, and the defects in the aspects of laying design, dynamic adaptability and power generation efficiency in the prior art are overcome.

The invention relates to the technical field of power system operation or management, in particular to a partial discharge monitoring strategy optimization method and system based on dynamic resource allocation, and the method comprises the steps: constructing a three-stage monitoring system comprising a sensor node, a sink node and a cloud processing center, firstly initializing monitoring parameters, and then obtaining system state information periodically or in a triggering manner, calculating the risk level of each monitoring point in combination with a dynamic risk evaluation model; constructing an efficiency-maximized resource allocation optimization model based on risk levels and resource constraints, solving an optimal scheme by adopting an improved multi-target particle swarm algorithm, and issuing the optimal scheme to each node to adjust monitoring behaviors to form closed-loop optimization; and meanwhile, model parameters are dynamically updated through an online learning mechanism. According to the method, dynamic matching of risks and resources is realized, the monitoring accuracy and the resource utilization rate are improved, the adaptability of the system to the equipment state and the environment change is enhanced, and the method is suitable for partial discharge monitoring scenes of various power equipment.

The invention discloses an off-line function verification system for an intelligent electric actuating mechanism, and the system comprises an off-line function verification unit which achieves the data interaction through the connection with a system signal terminal unit of the intelligent electric actuating mechanism, so as to achieve the off-line function verification function of the intelligent electric actuating mechanism. The offline function verification unit is an integrated module and comprises a man-machine interaction module, a host control module, an interface module, an instruction control module, a control power supply control module and a power supply control module; the problems that a large amount of manpower and time are consumed, the test process is tedious, the work efficiency of the original test process is low, human errors and test record detection errors are prone to occurring, data analysis depends on manpower, and the error risk is increased due to the fact that a traditional test method at the present stage consumes operations such as frequently changing used tools and modifying wiring in the test process are solved.

The invention relates to the technical field of electric workover rig thermal management, and discloses a workover pump truck electric drive thermal management method and system, and the system comprises a multi-source temperature collection module, an operation state monitoring module, a thermal management analysis module, a strategy matching engine module, a dynamic execution control module, a feedback optimization module, and a prediction decision module. The method comprises the following steps: acquiring environment temperature, motor temperature, battery temperature and cooling liquid temperature data; obtaining motor power, battery current and a vehicle operation mode; generating a thermal management demand level based on the temperature and the operating parameters; matching natural cooling, air cooling or liquid cooling strategies according to grades; cooling operation is executed, the temperature change rate is calculated in real time, and the cooling intensity is dynamically adjusted; when the threshold value is exceeded, liquid cooling cooperative control is started; predicting a thermal load trend through an LSTM model, generating a dynamic cooling plan, and optimizing an execution priority; and a thermal management efficiency evaluation report is output, so that the operation reliability and energy efficiency of the electrically-driven workover pump truck are improved.

The invention relates to the field of digital twinborn technology and industrial equipment intelligent operation and maintenance, and discloses an electric power screen cabinet heat dissipation efficiency improvement and thermal management comprehensive analysis system, which comprises an offline modeling module for constructing a thermal order reduction model with efficient calculation through a model order reduction method based on high-fidelity CFD simulation data; the real-time data acquisition module is used for acquiring real-time power consumption, sparse sensor temperature and an expected load curve; the online state estimation module is used for fusing physical prediction of the thermal reduced-order model with real-time sparse measurement data and reconstructing a global three-dimensional temperature field of the power screen cabinet in real time; and the prospective scheduling analysis module is used for taking the current global temperature field as an initial condition, driving the thermal reduced-order model to perform prospective deduction by utilizing an expected load curve, and calculating and outputting a future thermal margin to early warn a thermal risk. According to the invention, comprehensive real-time sensing, prospective risk prediction and self-adaptive correction of the thermal state of the electric screen cabinet are realized, and thermal management is converted from passive response to active prevention.

The invention discloses a ship power system optimization control method and system based on a simulated annealing algorithm, and relates to the technical field of ship power control, and the method comprises the steps: obtaining operation parameters and historical energy consumption data in real time, and constructing a multi-objective optimization function of dynamic weight distribution; generating an initial temperature parameter and a solution set in combination with the navigation state and the environment data; a neighborhood search strategy disturbance solution set is improved, a new solution is evaluated by using a dynamic acceptance probability function, temperature parameters are adaptively adjusted for iterative optimization, and an optimal control parameter combination is output; and an adjustment instruction set is generated after multi-dimensional efficiency verification, and a propulsion device, a generator set and an energy storage module are cooperatively controlled, so that global energy consumption optimization is realized. According to the method, through data driving and intelligent algorithm fusion, energy efficiency and environmental adaptability are improved, and stable operation under complex working conditions is guaranteed. According to the ship power system optimization control method and system based on the simulated annealing algorithm, the energy efficiency level and the environmental adaptability of the ship power system are improved.

The invention belongs to the technical field of intelligent electric meters, and discloses an intelligent electric meter system with an abnormal power consumption behavior recognition function. The system is composed of a data acquisition module, an electric energy quality monitoring module, a data preprocessing module, a power utilization behavior feature extraction module, a short-time behavior monitoring module, an anomaly detection and diagnosis module, a behavior trend analysis module, an alarm and visualization module and a remote collaborative management module. Through deep mining and learning of historical power utilization data of a user, the system can accurately grasp power utilization habits, time period change rules and load characteristics of the user, along with dynamic evolution of power utilization conditions, the system can automatically optimize an anomaly detection threshold value, limitation brought by a traditional fixed threshold value is abandoned, and through the personalized and adaptive design, the power utilization efficiency of the user is improved. The system can exert the optimal efficiency in different regions and different types of users, and the application range and the practicability of the system are remarkably improved.

The invention discloses a dynamic efficiency optimization management and control method for a wind-light hydrogen storage system, which comprises the following steps of: acquiring operating parameters of an electrolytic bath based on a water electrolysis hydrogen production simulation framework of an alkali liquor medium, and analyzing joint probability distribution and correlation coefficients of historical wind-light data through a wind-light photovoltaic synergistic effect model; constructing a system load accommodating setting model; when the frequency or voltage fluctuation of the power grid is detected, starting a quasi-synchronous electric element regulation and control framework PSEERA, and adjusting the output power through a droop control equation; the comprehensive performance indexes are optimized by adopting a Ling-tuned chaos infinitesimal search (SACMESA) algorithm, and main control factors are extracted through a multi-element optimization evolution (BKEAMOEA) algorithm analyzed according to key elements to carry out multi-objective optimization; implementing a scattered point type cooperative control strategy including bottom-layer equipment control, middle-layer energy management and upper-layer system scheduling, and realizing cross-level dynamic optimization; the peak regulation and frequency modulation capability, the safety and the stability of the power grid are remarkably improved.

The invention discloses a charging pile power adjusting method based on edge calculation, and the method comprises the following steps: S1, collecting the operation data of a charging pile, and carrying out the preprocessing of the operation data; s2, mapping the charging piles to different areas by adopting an MAP-Elite algorithm, and calculating an efficiency evaluation value of each area; s3, generating a power adjustment strategy of the charging pile through an A3C algorithm; s4, adjusting the power output of each charging pile in real time according to a region division result in combination with the charging pile load and the power grid state; s5, issuing a power scheduling instruction to each charging pile through an edge computing node, and dynamically correcting a power regulation strategy; and S6, continuously updating a region division result, and issuing a power scheduling instruction again through the edge computing node to ensure that the power output of the charging pile realizes load balancing. According to the method, the charging pile power adjustment is optimized through edge calculation and the A3C algorithm, and the real-time performance, the accuracy and the power grid load balance are improved.

The invention discloses a power transmission line inspection abnormity early warning method based on multi-mode perception and digital twinning, and belongs to the technical field of power transmission line monitoring. The problems that in the prior art, inspection efficiency is low, multi-source data utilization is insufficient, early warning lags behind and closed-loop control is lacked are solved, and efficient on-site processing and feature fusion of data are achieved by deploying a multi-modal sensing terminal cluster and a layered edge computing node; a digital twinborn model fusing multi-physical field simulation and data-driven AI is constructed at a cloud end, so that virtual-real interaction and deep analysis of a physical mechanism and real data are realized; the health state of the power transmission line is evaluated based on the deep analysis result, multi-stage early warning can be generated accordingly, a reverse control instruction is triggered, closed-loop control from state perception and intelligent diagnosis to active intervention is finally formed, and the intelligent level and operation and maintenance safety efficiency of power transmission line inspection are remarkably improved.

The invention discloses a multi-source cooperative power distribution network fault self-recovery and recovery optimization method, system and device and a storage medium, and relates to the technical field of power system power distribution networks, and the method comprises the steps: collecting the electrical quantity and parameters of each node in real time, and obtaining system state information through data processing; detecting faults by using a multi-criterion algorithm, and determining types and severity; determining a fault section according to a result and minimizing isolation; a collaborative recovery strategy is formulated by integrating multiple sources, and network reconstruction and switching operation optimization are carried out; and finally evaluating a recovery effect, and combining with a historical data feedback adjustment strategy to improve the recovery efficiency. The fault recovery capability of the power distribution network can be remarkably improved, the fault isolation is fast, the load recovery rate is high, the recovery time is short, the economic loss is reduced, the self-healing of different fault scenes is strong, and the unification of technology and economic benefits is realized.

The invention provides a conduction oil boiler system optimization control method and system based on an adaptive algorithm. The method comprises the steps of data acquisition, data preprocessing, model training, parameter optimization and real-time control. The system comprises a sensor network, a data processing module, a model training module, an optimization calculation module and a control execution module. Boiler operation data are collected in real time through a sensor network, a combustion boiler natural gas dynamic prediction model and a circulating pump efficiency optimization model are constructed after preprocessing, parameters such as the number of boilers, the circulating pump frequency and the oil supply temperature are optimized through an adaptive differential evolution (ADE) algorithm, and finally closed-loop adjustment is achieved through a control execution module. According to the method, the data-driven modeling technology and the self-adaptive optimization capability are utilized, so that system parameters can quickly respond to process demand changes, the heat efficiency is remarkably improved, the energy consumption is reduced, the temperature fluctuation is reduced, the operation and maintenance cost is optimized, and the operation performance and the economical efficiency of the heat conduction oil boiler system are comprehensively improved.

The invention belongs to the field of networking type energy storage systems, and discloses a state of charge balance control method for a networking type energy storage unit and related equipment, and the method comprises the steps: obtaining a real-time SOC value and charging and discharging power, dynamically judging a preset working condition, determining an optimization target, and carrying out the optimization of a state of charge balance based on an optimization feedback coefficient. And the power is superposed in a power instruction to compensate the power fluctuation of the network-forming type energy storage unit so as to realize SOC balance control. The characteristics of the synchronous generator are simulated through the virtual synchronous generator model, so that the energy storage unit better adapts to the power grid; working conditions are judged according to real-time SOC and power, and different optimization targets are made for different working conditions, so that the control strategy is more targeted; a feedback coefficient is optimized to compensate power fluctuation, and the power is accurately regulated and controlled. By adopting the method, the problem of inaccurate regulation and control under a complex working condition of a traditional static balancing method can be effectively solved, the contradiction between the system efficiency and SOC balancing can be balanced, quick regulation can be realized when the SOC is close to a limit value, overcharge and overdischarge are avoided, safe and stable operation of a power grid is ensured, and the overall efficiency of an energy storage system is improved.

The invention relates to the technical field of nuclear reactor safety, and particularly discloses a reactor waste heat export, recovery and comprehensive utilization system and method based on a heat pipe, and the system comprises a nuclear energy generation system, a secondary loop energy conversion system, a primary loop waste heat discharge system, a secondary loop waste heat discharge system, and a waste heat recovery and comprehensive utilization system. According to the waste heat recycling system, the heat pipe can be used for heat transfer and storage under the peak regulation operation condition, the nuclear heat use efficiency is remarkably improved, the requirements of various types of energy sources such as electricity and heat are met, and passive export of reactor core waste heat can be achieved through multi-natural-circulation coupling cooperative operation under the condition of a whole-field outage accident. By means of the efficient heat transfer characteristic and the modularization characteristic of the heat pipes, independent operation of the heat pipes can be achieved, the problem of single-point failure of the system can be avoided, meanwhile, separation of a reactor coolant and a cooling medium of a waste heat removal system can be achieved, the inherent safety of a nuclear reactor is remarkably improved, and the requirements of multi-purpose scenes are met; and the purpose of fully utilizing resources is achieved.

The invention relates to the technical field of heat dissipation of energy storage converters, and discloses a PCS inverter intelligent partition liquid cooling heat dissipation system, which comprises a differential partition liquid cooling module, and realizes accurate on-demand heat dissipation based on a dynamic priority algorithm of component load, temperature gradient and function importance. The multi-mode intelligent switching module is integrated with a prediction model based on a time sequence convolutional network and can be actively and automatically switched among a natural cooling mode, a liquid cooling circulation mode and a refrigeration enhancement mode; and the multi-parameter flow control module is used for dynamically adjusting the flow of each subarea through a micro proportional valve and a speed-adjustable pump according to parameters of components, cooling liquid and a system. According to the method, the optimal balance between the heat dissipation efficiency and the system energy consumption is realized by utilizing the advantage that the TCN efficiently processes the time sequence data, the operation reliability and the service life of the PCS inverter under different working conditions are remarkably improved, and the method is suitable for scenes such as household use, industrial and commercial use and energy storage power stations.

The invention relates to an urban rail transit station ventilation and air conditioning system cooperative control method, device and equipment based on dynamic energy efficiency optimization and a medium. Constructing an energy efficiency model of the ventilation and air conditioning system by combining the energy efficiency curved surface characteristics of the water chilling unit and the cubic law power consumption characteristics of the frequency conversion equipment; an intelligent optimization algorithm is adopted, a dynamic scaling temperature feasible domain of chilled water of a water chilling unit and a frequency interaction limitation domain of frequency conversion equipment are constructed, the feasibility of a temperature set value is kept, meanwhile, coupling constraints in the frequency conversion equipment are decoupled, and an optimal parameter combination is solved; and the optimal parameter combination is issued to an execution layer, the actual energy efficiency ratio deviation of the system is monitored in real time, and energy efficiency model parameter re-calibration or iterative optimization is triggered according to a deviation threshold. Therefore, the overall energy efficiency of the ventilation air conditioning system is stably improved, and efficient operation under the dynamic working condition is achieved.