6258 results about "Power station" patented technology

The present application relates to the technical field of power plant optimization, and discloses a thermal power plant auxiliary power system optimized dispatching method and system considering a wind-solar-storage system, and a device and a storage medium. The method specifically comprises: collecting thermal power plant auxiliary power system data, and establishing an auxiliary power system multi-objective function on the basis of the thermal power plant auxiliary power system data and a wind-solar power generation cluster model in an auxiliary power system; introducing constraint penalties and constraint conditions to the auxiliary power system multi-objective function, and constructing a thermal power plant auxiliary power system optimized dispatching model; and processing the thermal power plant auxiliary power system optimized dispatching model by using a dynamic learning factor-based particle swarm algorithm to obtain an optimized dispatching result, and completing thermal power plant auxiliary power system optimized dispatching on the basis of the optimized dispatching result. According to the present application, the optimal interactive output among wind turbine units, photovoltaic units, energy storage units, and a generating set can be determined on the basis of the optimized dispatching result, auxiliary power system low-carbon optimized dispatching is implemented, and the problem in the prior art of lacking dispatching in which new energy and thermal power plant auxiliary loads are integrated for analysis is solved.

The invention relates to the technical field of power station operation and maintenance, and discloses a power generation equipment intelligent operation and maintenance method and system based on model autonomous selection, and the method comprises the steps: obtaining the multi-mode operation and maintenance data of a photovoltaic power station, and generating a multi-mode operation and maintenance data set; inputting each modal data of the multi-modal operation and maintenance data set into a corresponding module for feature extraction; inputting the extracted feature vectors into a contrast learning network for cross-modal alignment, and outputting an executable decision result by combining a retrieval enhancement generation technology with a knowledge graph; and constructing a privacy protection training framework through federated learning, inputting an executable decision result into a digital twin system for strategy verification, and generating a trained multi-modal large model for the photovoltaic power station to select a corresponding module in the trained multi-modal large model based on the feature data for real-time monitoring. According to the invention, the problems of poor accuracy and untimely reaction in the traditional operation and maintenance process of the photovoltaic power station are solved, and the operation and maintenance of the power station can be carried out timely and accurately.

The invention provides an intelligent optimization method and system for a revenue mode of a commercial energy storage power station, and relates to the technical field of energy storage power stations. Establishing a multi-dimensional life loss coefficient matrix by analyzing the health state data of the energy storage equipment; a candidate charging and discharging scheduling strategy is generated based on a deep reinforcement learning model, and a double-constraint optimization charging and discharging strategy with predicted income and life loss as a reward function is adopted; and finally generating a control instruction to realize optimized operation of the energy storage power station. According to the method, equipment life loss and economic benefits are comprehensively considered, and long-term benefit maximization of the energy storage power station is realized.

The invention discloses a photovoltaic power station intelligent remote operation and maintenance method and system based on relay protection, and the method comprises the steps: collecting the multi-mode characteristic quantity of a photovoltaic array region relay protection device in real time through a distributed optical fiber sensing network, so as to construct a dynamic protection characteristic matrix; performing feature decoupling on the dynamic protection feature matrix by adopting a space-time convolution adversarial encoder to generate a fault feature vector set; inputting the fault feature vector set into a physically constrained LSTM-GAN hybrid prediction model, and predicting a multi-dimensional failure probability tensor of the key relay protection node in a future preset duration; generating an optimal operation and maintenance strategy decision tree based on the multi-dimensional failure probability tensor; and analyzing the operation and maintenance strategy decision tree through a remote execution gateway driven by the digital twinborn, and synchronously updating the topological connection relationship of the digital twinborn bodies of the power station. According to the embodiment of the invention, the operation and maintenance efficiency and reliability of photovoltaic power station protection can be improved.

The invention provides a power grid evolution behavior modeling method and system based on dynamic digital twinning, and relates to the technical field of intelligent power grid digital twinning. According to the method, interaction characteristics of power station output, user load response and an energy storage scheduling strategy are captured in real time through a digital twin interface, behavior coupling factors containing complementarity and conflict weight are formed, and a power grid response relation and a multi-scale demand elastic curved surface are constructed by using an implicit tensor fusion technology and nonlinear projection. And through the steps of virtual behavior anchor point disturbance and the like, offset is separated to identify an unbalance region, an evolution trajectory is generated, an optimal collaborative path is extracted, and the weight of a behavior coupling factor is dynamically adjusted, so that rapid convergence of a global collaborative stability domain is realized. The method covers a behavior coupling factor generation module and the like, supports various operations, effectively solves many problems in power grid resource interaction, and improves dynamic adaptability and cooperation efficiency of power grid evolution modeling.

The invention discloses a risk scheduling method for a water-wind-solar complementary system, and the method comprises the steps: collecting historical data and power grid topological parameters, accessing global and regional numerical weather forecast data, employing a coupling model, fusing meteorological grid data with a historical power station output sequence, and generating hourly reservoir incoming water amount and wind-solar power probability prediction results. According to the predicted time sequence and the generated scene, calculating the scene probability based on the generated scene; according to the prediction time sequence, using a quantification method to obtain a peak regulation risk quantification value; calculating power grid power flow distribution and critical clearing time according to the generation scene and the power grid topological parameters, and calculating a system stability margin; a multi-target optimization model is constructed according to a peak regulation risk quantized value after splitting and a system stability margin, the system stability margin is introduced as an optimization target, the overall stability of the system is improved, a meteorological-hydrological-output three-mode feature mapping method is provided, and meteorological feature extraction of a key grid region is enhanced through an attention mechanism.

The embodiment of the invention provides a new energy optical power correction method and system based on weather abrupt change prediction. The method comprises the following steps: identifying a cloud layer type contained in a target area, and obtaining a cloud layer type coverage rate; if the cumulonimbus coverage rate exceeds a first threshold value, predicting a cloud layer movement path in combination with the cloud layer movement vector field to obtain a first cloud layer coverage change sequence; if the layer cloud coverage rate exceeds a second threshold value, an irradiance attenuation coefficient is predicted based on the cloud layer thickness in the meteorological data and the solar altitude angle, and a second cloud layer coverage change sequence is obtained in combination with the ground aerosol optical thickness and the corrected irradiance attenuation coefficient; and monitoring real-time operation data of the power station, combining the first / second cloud layer coverage change sequence, predicting a matching relationship between a cloud layer moving direction and a power station power reduction area by adopting a double-flow Transform architecture, calculating a power fluctuation range corresponding to each power station power reduction area, dynamically adjusting power grid operation parameters of a target area, and determining the power grid operation parameters of the target area. The power grid stability is ensured.

The invention discloses an energy storage power station fire early warning method and system based on multi-parameter fusion, and the method comprises the following steps: collecting the temperature, characteristic gas concentration, cell expansion force, voltage fluctuation and environment temperature and humidity data of a lithium battery of an energy storage power station in real time through a distributed sensor, and carrying out the cleaning, denoising and standardization processing of the collected parameters, temperature and gas concentration monitoring values are corrected through an environment temperature and humidity compensation algorithm, abnormal data caused by environment interference are eliminated, the preprocessed data are input into a preset intelligent early warning module, and the model is based on a random forest algorithm. Through multi-parameter fusion and intelligent algorithm deep analysis, in combination with environment compensation, interference elimination, early warning accuracy improvement, graded early warning and linkage response, full-stage accurate disposal is achieved, timeliness is enhanced, sensor redundancy, multi-cabin cooperation and other mechanisms guarantee reliability, a closed loop from monitoring to disposal is formed, and the fire risk and loss are effectively reduced.

The invention belongs to the technical field of power station boiler waste heat recycling energy-saving equipment and relates to a plate-and-shell type coal economizer. The plate-and-shell type coal economizer comprises a corrugated plate heat exchange core body (1), a heat exchanger shell (2), a first and a second water inlet / outlet water tank (3, 3') and a first and a second exhaust gas inlet / outlet pipeline (4, 4'), wherein the corrugated plate heat exchange core body (1) is mounted in the heat exchanger shell (2), the first and second water inlet / outlet water tanks (3, 3') are respectively arranged at the upper and lower ends of the front side of the corrugated plate heat exchange core body (1), and the first and second exhaust gas inlet / outlet pipelines (4, 4') are respectively arranged at the upper and lower sides of the corrugated plate heat exchange core body (1). The plate-and-shell type coal economizer solves the problem that the length of a water passage needs to be increased to solve the problem of unapparent water heating due to the great difference between an exhaust gas density and a water density, and meanwhile, a corrugated passage at the exhaust gas side enables the exhaust gas flow turbulivity to be increased and the heat transfer efficiency to be improved; the blockage due to ash accumulation happens rarely, even if the ash is accumulated, the cleaning is easy to finish.

The invention discloses a lithium battery thermal runaway control and safety response strategy system, which realizes the real-time monitoring and prediction of the internal temperature and voltage state of a battery through thermal-electric coupling modeling and multi-sensing data fusion, and realizes the real-time monitoring and prediction of the internal temperature and voltage state of the battery based on a model prediction control and threshold enhancement strategy. And triggering graded safety response in the early stage of thermal runaway. The system adopts a multi-stage response mechanism including measures of primary early warning, active cooling intervention, emergency power-off fire extinguishing and the like, a gas suppression path and cooling mode switching function is designed, and heat diffusion and spreading and toxic gas harm are effectively suppressed. Compared with a method depending on complex digital twinning or deep learning, the method is simple in structure, rapid in response and low in data dependence, has good system stability and engineering applicability, and can be widely applied to the field of lithium battery safety management of electric vehicle battery packs, energy storage power stations and the like.

The invention relates to a boiler combustion optimization control system of a thermal power plant, relates to the technical field of thermal power generation control, and aims to solve the problems of inaccurate multi-source data perception, slow control response and insufficient multi-target collaborative optimization capability under fire coal quality fluctuation and load change. The system comprises a data perception and fusion module, a working condition self-adaptive identification module, a multi-target dynamic optimization decision module and a distributed execution control module, and through multi-source data acquisition and feature extraction, depth time sequence mode identification, multi-target optimization model solving and distributed coordination control, the multi-target dynamic optimization decision module and the distributed execution control module are subjected to multi-source data fusion. Dynamic balance of boiler heat efficiency improvement and nitrogen oxide emission reduction is achieved, and adaptability and control precision of the system under complex working conditions are enhanced.

The invention discloses an intelligent operation and maintenance scheduling and resource optimization method and system for a new energy power station, and belongs to the technical field of data processing and management.The method comprises the steps that real-time power generation data, equipment sensor data, weather forecast data, a power grid scheduling instruction and electricity market electricity price information of the new energy power station are obtained; a unified data matrix is generated through space-time alignment processing to execute bidirectional feedback prediction, and a corrected power generation plan is generated; a cooperative scheduling decision is generated through a multi-target dynamic balance algorithm, and a personnel dispatching scheme and a material allocation path are generated through real-time path planning; and generating a historical decision data set based on the execution result and the actual execution deviation, and adjusting prediction parameters and scheduling parameters through an incremental learning model. A closed-loop optimization method combining data fusion, collaborative prediction, multi-target scheduling and adaptive learning is adopted, global collaborative optimization of multiple factors such as power generation, operation and maintenance, energy storage and markets can be achieved, and the economic benefit and the intelligent level of overall operation of a power station are improved.

The invention relates to the technical field of new energy intelligent decentralized control, and discloses a distributed photovoltaic power station intelligent monitoring method and system, and the method comprises the steps: collecting electrical state parameters, and carrying out the normalization processing; performing anomaly detection and island detection based on a multi-dimensional deviation fusion mechanism; generating a power support parameter through a self-adaptive reactive power injection mechanism; under the island mode, loading voltage frequency double-droop combined control; and after the main network is recovered, grid-connected reconnection is realized based on a phase progressive phase locking mechanism. Compared with the prior art that multi-dependence centralized master station judgment and scheduling cannot realize fault isolation and independent operation in time especially under the condition that main network communication is abnormal or frequency fluctuation occurs in a far-end island edge node, the technical problem that fault isolation and independent operation cannot be realized in time in the prior art is solved. Autonomous monitoring of an individual power station level and stable operation of an island are realized, and the stability of the system in a weak network or island operation scene is improved.

The invention relates to a single battery early-stage thermal runaway early warning method based on operation parameter fusion and isolated forest algorithm optimization, and the method achieves the closed loop of early perception, precise recognition and real-time early warning of the thermal runaway of a battery through the dual-engine drive of LSTM time sequence feature enhancement and isolated forest dynamic optimization. The hysteresis limitation of traditional BMS threshold value alarm is broken through, and an early warning node is advanced before the thermal runaway chain reaction is started; the problem of abnormal monomer positioning in a large-scale battery cluster is solved, and high-efficiency detection of large-order-of-magnitude monomer / cabinet hierarchy is realized; an active safety protection system of the energy storage power station is constructed, and a landing technical support is provided for intelligent operation and maintenance.

The invention provides an energy storage power station and power grid collaborative complementary regulation method and system, and relates to the technical field of smart power grids, comprising the steps of obtaining disturbance components by performing multi-scale decomposition on power grid frequency and voltage data, and constructing a distributed model prediction control framework to optimize energy storage resources in groups; and designing a self-adaptive dynamic programming controller to evaluate the system state and output the optimal charging and discharging power, and responding to the topological change of the power grid in real time. According to the invention, collaborative optimization configuration of energy storage resources can be realized, the power grid stability is improved, the service life of energy storage equipment is prolonged, and the system operation cost is reduced.

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.

System and method embodiments are described for solar panel protection against severe environmental impacts. A tracker controller may be configured to rotate a solar tracker comprising a group of solar panels to a safe position to diminish hail impact during a hailstorm. Such a rotation may be implemented using one or more motors to drive arc gears coupled to a pair of supporting purlins that support the group of solar panels. The tracker may comprise a bumper rail placed on the top when the tracker is at the safe position for hail impact absorption. Implementing solar panel protection provides a practical solution to protect solar panels from damage. Thus, the need for costly repair or replacement may be avoided, minimized, or decreased. As a result, the economic efficiency of solar power plant operations may be improved significantly.

The invention provides a blast suppression method and system for an underground cavity of a pumped storage power station. The method comprises the following steps: configuring and deploying a multispectral gas sensor array; carrying out gas explosion limit calculation and risk assessment; calculating the risk level of explosion by using the prediction model; performing graded explosion suppression operation by using a graded explosion suppression execution module; combustible gas is guided to enter an inerting area by utilizing a space flow guide optimization module; and the ignition source blocking module is used for cooperative control to block an ignition source. The technical problems of low mixed gas explosion risk prediction and monitoring precision, low explosion suppression efficiency, slow response speed and relatively high operation and maintenance cost are solved.

The invention relates to the technical field of energy storage power station fire monitoring, and discloses a fire detection and early warning method and system. The method comprises the steps that real-time monitoring signals are acquired and preprocessed, and after a preliminary abnormal index is determined, changes of temperature and gas concentration are analyzed and evaluated to obtain a fire risk pre-judgment score; if the preset risk threshold value is exceeded, potential fire source area distribution is judged according to a pre-established dynamic fire feature database, fire source positioning coordinates are obtained, and consistency is verified; abnormal points of the temperature and the gas concentration are classified, a fire initial symptom set is determined, the dynamic fire feature database is updated, judgment is conducted again, and the accuracy of early warning signals is determined; and if the accuracy is higher than the preset accuracy threshold, generating a comprehensive risk assessment report, obtaining a fire identification result, extracting an optimized feature vector from the fire identification result, and generating a fire source region distribution map and an abnormal early warning signal. According to the invention, fire source positioning can be realized, the false alarm and missing alarm rate is obviously reduced, and the emergency response speed is improved.

The invention discloses an automatic protocol matching system for intelligent gateway sensor equipment of a power distribution station house. The automatic protocol matching system comprises a protocol feature deep analysis unit, a graph convolutional neural network topology construction unit, an equipment protocol suitability evaluation unit, a real-time data transmission monitoring unit, an abnormal protocol correction unit and a protocol matching result output unit. The system analyzes sensor protocol characteristics, combines power station house sensor deployment and transmission path construction graph models, evaluates protocol suitability, monitors transmission data, corrects abnormal protocols, and finally outputs a matching scheme. The graph convolutional neural network and the power station room data transmission detection model are utilized to realize automatic protocol matching, improve the fit degree of adaptation and an actual scene, form an exception handling closed loop, reduce interaction failures, improve the data transmission efficiency and stability of the power distribution station room, and meet the requirement of intelligent reconstruction.

The invention discloses a network construction type energy storage capacity optimal matching method based on a frequency response model, and belongs to the technical field of gas turbine power plant financial supervision and artificial intelligence, and the method comprises the steps: S1, quantifying the power vacancy of a typical station under an extreme short-circuit fault, determining a virtual inertia constant and a virtual droop coefficient of network construction type energy storage, and carrying out the calculation of the virtual inertia constant and the virtual droop coefficient; setting an initial output coefficient and adjusting a step length; s2, constructing and integrating a system frequency response model based on the inertia and frequency modulation characteristics of the conventional unit, the wind turbine generator and the network construction type energy storage; s3, applying power disturbance to the system, gradually increasing an initial output coefficient and performing simulation, and recording the maximum frequency deviation, the maximum frequency change rate and the quasi-steady-state frequency deviation; and S4, when the maximum frequency deviation, the maximum frequency change rate and the quasi-steady-state frequency deviation are respectively smaller than or equal to set threshold values, the energy storage capacity proportion corresponding to the current initial output coefficient is the optimal proportion. According to the method, the problem of accurately measuring the optimal configuration proportion of the network construction type energy storage in the new energy station is solved.

The invention relates to the technical field of new energy storage regulation and control, and discloses an electricity-hydrogen coupling energy storage system and method for a new energy field station. The system comprises a feature analysis unit, a topology construction unit, a regulation and control domain positioning unit and a decision optimization unit. The feature analysis unit receives operation condition information of the new energy station, and generates a condition feature vector according to real-time load fluctuation, a historical output curve and an equipment operation database by using a deep feature extraction network; the topology construction unit constructs an energy topological graph for distributed energy storage equipment and hydrogen production equipment in the electricity-hydrogen coupling system, dynamically updates a graph structure and a node state based on a real-time monitoring mechanism, and deploys the graph structure and the node state in a heterogeneous computing framework; a regulation and control domain positioning unit positions a matched regulation and control domain in a dynamic representation learning mode according to the working condition feature vector; and the decision optimization unit utilizes a multi-source decision fusion algorithm to screen regulation and control instructions in a regulation and control domain and generate a scheduling sequence, so that the adaptability and the operation efficiency of the system to working conditions are improved.

The invention relates to a power plant temperature control system safety protection device and system. The power plant temperature control system safety protection device comprises a redundant temperature acquisition module, a temperature control safety processing module, an actuator control protection module, an intelligent strategy recovery module and a communication and alarm module. Through multi-point temperature data acquisition, abnormal trend intelligent identification and local linkage control, the device realizes quick response and protection control of temperature control abnormity of a key area of a boiler thermodynamic system. The system supports data interconnection with a DCS platform and an alarm analysis platform, has local independent operation and remote centralized management capabilities, and is suitable for temperature control safety protection of high heat load areas such as boilers of coal-fired power plants.

The invention provides an energy storage power station optimization operation mode decision-making method and system, and relates to the technical field of energy storage power station optimizing.A hybrid prediction model is constructed to realize high-precision decomposition prediction of power load, and meanwhile, the internal resistance of a battery is estimated in real time by adopting a recursive least square method; and the battery capacity and internal resistance parameters are dynamically corrected in combination with a temperature compensation mechanism. Through health state multi-index fusion evaluation, self-adaptive distribution of charging and discharging power is achieved, and compared with the prior art, the problem that a traditional static model cannot adapt to complex environment changes is solved. The battery capacity fading risk can be predicted in advance by introducing a double-compensation mechanism of an environmental influence index and an electric power influence index. According to the scheme, the response speed and economical efficiency of energy storage in a high fluctuation load scene are remarkably improved, and a reliable dynamic optimization decision support system is provided.

The invention relates to the field of an energy storage frequency modulation system, in particular to a thermal power frequency modulation power optimization method for an energy storage cluster cooperating with a Saggowski new energy base, and aims to solve the problem that the frequency modulation enthusiasm of an energy storage cluster power station in a high-proportion new energy power system is reduced due to limited frequency modulation capability caused by power-capacity constraint. Thermal power generating unit compensation output is designed to reserve frequency modulation capacity for an energy storage cluster power station, meanwhile, sufficient coefficients related to the residual frequency modulation capacity of the thermal power generating unit are introduced, a thermal power generating unit frequency modulation power distribution model is designed, and the equipment loss condition of the unit is reduced while the operation cost of the thermal power generating unit is reduced. The SOC overall balance target, the cycle life and the economical efficiency target of each energy storage power station are considered, preference decision making is carried out on the SOC balance target of the energy storage power station to ensure that the frequency modulation capacity of the energy storage power station is sufficient, and the frequency modulation capacity of each energy storage power station is fully utilized while the frequency modulation cost of the energy storage cluster is reduced.

The invention discloses a method for effectively studying and judging false alarm of a fire alarm system in a transformation power station in a multi-dimensional mode, and the method comprises the steps: collecting and fusing multi-channel sensor data, such as smoke concentration, temperature, humidity, electromagnetic field intensity, grounding state and the like, and constructing a scenarized data set in combination with an environment label and an operation and maintenance log; through multi-level feature processing such as normalization, wavelet denoising and dimension reduction, the data quality and discriminability are improved; according to the method, the space-time diagram neural network is used for achieving multi-period environment subarea and typical signal mode division, multi-period fusion confidence judgment is conducted on alarm signals based on the fuzzy set theory and Bayesian reasoning, dynamic judgment of false alarms and real alarms is achieved, and the accuracy and robustness of alarm judgment in the complex environment are effectively improved.

The invention relates to the technical field of industrial Internet of Things equipment fault diagnosis, and discloses a power plant equipment fault diagnosis method and system based on the Internet of Things, and the method comprises the steps: obtaining an equipment working condition monitoring data set, executing coupling analysis and abnormal triggering collection, and obtaining a multi-equipment association state data set; performing node analysis and path distribution based on the data set to form a fault diffusion path scheme; carrying out risk calculation and trend reasoning to obtain a real-time fault risk assessment result; according to an evaluation result, searching an optimal parameter combination from a preset parameter library and generating a parameter instruction set; after the instruction set is executed, initial operation state data are collected, delay evaluation and parameter correction are carried out, and final operation state data are obtained; and performing difference analysis and risk scoring according to the state data, adjusting a path weight, and updating a fault diffusion path scheme. The method can solve the problem of fault diagnosis lag.

The invention discloses an energy storage battery health feature extraction and state evaluation method based on transfer learning. The method comprises the following steps: S1, constructing a source domain health feature library and pre-training a model; according to the method, dependence on complete cyclic data is broken through, high precision and robustness are still achieved under the conditions of data sparsity and working condition difference, and the method is suitable for intelligent operation and maintenance and predictive maintenance of an energy storage power station. And meanwhile, common incomplete and partial charge and discharge data fragments under actual working conditions can be directly utilized for feature extraction and state evaluation, dependence on complete charge and discharge cycles is avoided, and the application scene of the data driving method is greatly widened.

The invention relates to the technical field of wind-solar storage power station operation control, and discloses a wind-solar storage multi-energy complementary power station cooperative operation method and system. The method comprises the following steps: constructing a wind-light fluctuation rate phase plane, defining a coupling stable elliptical region and generating a multi-source coupling state tag; an energy storage response performance plane is constructed, a dynamic dead zone threshold parameter is calculated, a dynamic respiration hexagonal boundary is established, and the penetration depth is calculated to generate an energy storage effective action instruction; and constructing a virtual energy gravitational potential energy field by utilizing an energy storage effective action instruction, calculating virtual potential energy heights of independent physical nodes and nonlinear flow resistance characteristics of a virtual energy transmission pipeline, and analyzing a virtual potential energy height difference and a virtual energy flow velocity to obtain a final power distribution scheme. Precise classification of fluctuation modes is achieved through geometric topology, the dynamic breathing hexagon is utilized to consider battery life and stability control requirements, the problem of lagging of multi-target optimization calculation is solved based on the physics field theory, and the operation efficiency of a power station and the friendliness of a power grid are remarkably improved.

The invention relates to the technical field of photovoltaic power adjustment, and discloses a photovoltaic power automatic adjustment method and system. The method comprises the steps of collecting real-time parameters of an inverter through distributed communication, performing multi-dimensional evaluation sorting based on equipment efficiency, load rate and operation time, executing adaptive power distribution fusing edge calculation and cloud optimization, generating standardized instruction time-sharing issuing, and storing control logic by using a block chain. And an encrypted link is established to realize bidirectional data interaction between the power station and the dispatching system to form a closed-loop control system. According to the invention, high-efficiency, precise and reliable dynamic power adjustment of the inverter level is realized.