716 results about "Power optimization" patented technology

An object tracking device, such as may be attached to a trailer to track movements of the trailer, may have various communication capabilities, such as cellular / LTE, Wi-Fi and GPS, which each have varying power requirements. A power optimization routine determines which communications should be performed to conserve battery while also providing frequent location updates. The routine determines if another plugged in tracking device is within low power Bluetooth (BLE) range and, if so, communications location via that device. If not, a Wi-Fi scan is performed and a lookup table is consulted for the corresponding location of any identified Wi-Fi access points. If location data cannot be found for an identified Wi-Fi access point, GPS location of the device is determined and the lookup table is updated to include the GPS location of the Wi-Fi access point.

The invention provides a multi-mechanical-arm cooperative control method which comprises the following steps: modeling initial states and virtual constraints of mechanical arms, constructing an overall description of a multi-mechanical-arm cooperative task, decomposing the task into sub-tasks of the mechanical arms based on the description, and optimizing load distribution; the initial state is represented by a quintuple and comprises the position, posture, linear velocity, angular velocity and load information of the tail end of the mechanical arm; the virtual constraints comprise relative positions, relative postures, synchronous speeds, load balancing and power optimization constraints and are used for limiting kinematics and dynamics behaviors of the multiple mechanical arms; on the basis of the initial state and the virtual constraint, load distribution is optimized by utilizing a genetic algorithm, and a smooth trajectory meeting the collaborative task requirement is generated by adopting a quintic polynomial interpolation method; and the pose deviation of the multiple mechanical arms is corrected based on a virtual spring damper by combining trajectory tracking control and collaborative error compensation. According to the method, the movement efficiency and the task completion quality of the multi-mechanical-arm cooperative task are improved.

A GPU cluster computing power optimization architecture oriented to large model training is characterized in that a computing power resource management module is used for monitoring and managing the computing power resource state of each GPU in a GPU cluster in real time, a task scheduling module is used for allocating GPU resources according to the requirements of training tasks, and a computing power optimization module is used for dynamically optimizing the computing power of the GPU cluster in the task execution process. And the result feedback module is used for collecting and analyzing the optimized computing power use condition so as to adjust a subsequent task scheduling strategy. According to the method, the computing power resource state of the GPU cluster is monitored and managed in real time, the GPU resources are dynamically allocated according to the demand of the training task, and the computing power of the GPU cluster is dynamically optimized in the task execution process, so that the computing power utilization efficiency of the GPU cluster is effectively improved, and the training cost is reduced.

The invention relates to the technical field of intelligent control, in particular to an intelligent power optimization scheduling method in a new energy grid-connected scene, which comprises the following steps of: acquiring a current load, a rated load, an adjustment rate and a temperature rise value through a data interface, and normalizing to generate a characteristic parameter matrix; the product of the adjusting rate and the temperature rise value is used for calculating the correlation degree with the aging factor to obtain an elastic weight, extracting the voltage stability and the weight, inputting the voltage stability and the weight into a Dijkstra algorithm to screen a path meeting the safety margin, monitoring the slope of an inertia change curve, adjusting a compensation coefficient through fuzzy PID control, and feeding back the compensation coefficient to correlation degree calculation. According to the method, multi-source parameters are fused through a rule tree structure, the load regulation rate and the aging factor weight are dynamically distributed, bus voltage stability and elastic weight path optimization are combined, a synchronous inertia margin safety correction system is constructed, the system response precision, the state cooperation efficiency and robustness are improved, local optimum is avoided, and the system reliability is improved. And the standby capacity configuration and the frequency advance correction capability are optimized.

The invention discloses a laser power optimization method based on a deep reinforcement learning strategy, belongs to the technical field of simulation and artificial intelligence, and is suitable for a laser powder bed melting (LPBF) process. The method comprises the steps that firstly, an LPBF simulation model is built, a molten pool three-dimensional temperature field distribution model is built through a moving heat source, and the forming process under a laser scanning path is simulated in combination with a part STL file; a state space and a reward function are constructed by extracting state features of a molten pool in real time, and a laser power adjustment strategy is optimized by using a deep reinforcement learning DQN network model. An optimal laser adjustment action is generated by adopting an epsilon-greedy algorithm, and intelligent agent interaction data is stored and updated based on an experience pool, so that network model parameters are optimized, and the stability and uniformity of the depth of a molten pool are ensured. According to the method, internal defects are effectively reduced, the quality and intelligent control level of the LPBF process are improved, and a new thought is provided for efficient and accurate process parameter optimization.

The invention discloses a multi-modal perception and optimization method and system for a low-power-consumption AR device, and the method comprises the steps: collecting multi-modal data, task demands, resource state data and environment data for the AR device; lightweight processing is carried out on the multi-modal neural network model through model pruning, parameter quantification and distillation technologies; inputting the collected data into a lightweight multi-modal neural network model for dynamic reasoning to obtain a multi-modal recognition result; comprising the steps of executing modal adaptive weight acquisition based on task requirements and environment data; executing energy consumption constraint scheduling according to the equipment resource state data, dynamically selecting a reasoning path strategy, and obtaining corresponding modal feature output; multi-modal feature fusion is carried out, task reasoning is completed, and a multi-modal recognition result is obtained; early-leaving control is executed based on a middle-layer confidence coefficient threshold value in the reasoning process; and interactively outputting a real-time multi-mode identification result. According to the invention, energy efficiency and precision balance and multi-mode fusion low-power-consumption optimization can be realized.

The invention provides a wind power plant AGC optimization control method and system based on model prediction control, and relates to the technical field of model control, and the method comprises the steps: building an adaptive prediction model, carrying out the online identification of parameters through a recursive least square method, constructing an active power optimization objective function, solving a control instruction sequence, designing a feedback compensator, and correcting a control instruction. And finally, issuing to each fan to execute and realize closed-loop control. According to the method, the AGC control precision of the wind power plant can be improved, the power fluctuation is reduced, and the power grid friendliness and the adaptability of an AGC control system are enhanced.

The invention relates to a new energy charging pile dynamic scheduling method, system and device and a medium, and the method comprises the steps: collecting the real-time load data of a power grid, user charging request information, battery health state data and historical charging data, and carrying out the classification and aggregation of charging behavior characteristics, and generating behavior grouping labels; demand prediction and power safety boundary calculation are carried out based on a preset power prediction model in combination with the battery health state, and a power prediction result is formed; detecting power grid load conflicts and optimizing power distribution to generate a preliminary scheduling scheme; performing multi-target power optimization adjustment on the conflict points to generate a charging power scheme, and issuing and executing the charging power scheme; and finally, dynamically updating prediction model parameters according to charging pile feedback data. According to the method, the problems of neglect of battery health change, insufficient charging behavior distinguishing, response lag, weak collaborative optimization capability and the like in the prior art are solved, and collaborative optimization of power grid stability, equipment safety and charging efficiency is realized.

The invention discloses a transmission driving control method of a bimodal integrated engine. The transmission driving control method comprises the following steps: S1, state sensing; s2, mode decision making; S3, dynamic adjustment; s4, mode switching; S5, power optimization distribution; s6, adaptive learning is carried out; and S7, fault-tolerant guarantee. The invention further discloses a bimodal integrated engine transmission system which comprises the following modules: a power input module; a transmission adjusting module; a state sensing module; an intelligent control module; and a fault-tolerant guarantee module. Parameters are dynamically adjusted through fuzzy PID control according to speed deviation, meanwhile, real-time correction is conducted in combination with a multi-mode intelligent algorithm, speed fluctuation is precisely controlled within + / -3 km / h, flight stability is guaranteed, a model prediction control and multi-mode intelligent control cooperation mechanism is adopted, an optimal power output sequence is calculated in advance, coordinated distribution is completed, and the optimal power output sequence is controlled to be within + / -3 km / h. The dynamic response speed is increased by 40%, and the high-dynamic task requirement is met.

The invention discloses an energy storage equipment power balance dynamic load automatic regulation and control method and system, and the method comprises the steps: building a charging power optimization model based on an electricity price policy divided in peak and valley time periods, combining historical electricity demand data, and obtaining the optimal charging power in each time period; the method comprises the following steps: constructing a discharge power dynamic adjustment model through operation state parameters acquired by a real-time environment parameter acquisition device in combination with real-time electricity demand data, and acquiring optimal discharge power in each time period; parameters of the two models are optimized, and the load state based on the optimal charging and discharging power is balanced; based on the historical regulation and control data, the overload condition under the abnormal factors is predicted; and on the basis of a power imbalance condition, preferentially ensuring power supply of a key load according to a power distribution priority and executing energy storage equipment state self-checking. The method has the advantages that the charging power and the discharging power are optimized in combination with the electricity price and the real-time requirement, so that the load state is balanced, the energy cost is reduced, and the equipment overload risk is avoided.

The invention provides a circulation power optimization method and system for a three-phase dual-active bridge, and relates to the field of power electronics, the method comprises the following steps: determining an optimal backflow modulation strategy set corresponding to a hybrid modulation group, the hybrid modulation group comprising triangular wave modulation, trapezoidal modulation, synchronous modulation and extended phase shift modulation, the optimal backflow modulation strategy set corresponding to the hybrid modulation group comprises modulation modes and degree-of-freedom relationships corresponding to different transmission power per unit value ranges in different voltage conversion ratio ranges; determining a degree of freedom and power relationship set corresponding to the hybrid modulation group, wherein the relationship set comprises a relationship between the degree of freedom and power of different transmission power per unit values corresponding to each mode; the phase shift angle, the primary side duty ratio and the secondary side duty ratio are determined by combining the real-time voltage conversion ratio and the real-time virtual power per unit value, operation of the three-phase dual-active bridge is controlled, and the method has the advantages of reducing complexity and improving portability and efficiency.

The invention belongs to the technical field of charging pile thermoelectric control, and particularly relates to a high-power liquid cooling charging pile charging power optimization scheduling method and system. The method comprises the following steps: collecting operation state parameters of a plurality of liquid cooling charging piles; identifying a target pile body in a charging state or a standby state; calculating a theoretical energy supply upper limit based on an electrothermal model; combining the vehicle charging demand, the priority, the battery state and the power grid load to construct a power scheduling model and execute distribution; power adjustment and redistribution are carried out when cooling abnormity or connection faults are detected; and finally, a scheduling control instruction is generated and executed in real time. The system comprises an information acquisition module, a state identification module, a model calculation module, a power distribution module, a dynamic adjustment module and an instruction issuing module. According to the invention, efficient, safe and dynamic power scheduling control of the high-power liquid cooling charging pile in a multi-device parallel connection scene is realized.

The invention relates to the technical field of maximum power point tracking, in particular to a solar photovoltaic panel generation power optimization method and system, and the method comprises the following steps: obtaining a photovoltaic panel branch power sequence, constructing a direction change matrix, recognizing a same-direction change section, extracting a difference value, generating a trend labeling table, and comparing power change to screen adjustment nodes. And sending an adjustment instruction to record the difference value, and generating an optimization response table. According to the invention, through regular acquisition of voltage and current of a photovoltaic branch, construction of a power change trend matrix and extraction of power change directions of adjacent moments, trend identification and change modeling are realized, segments with persistent and significant amplitude changes are screened, accidental disturbance is eliminated, and adjustment and judgment accuracy is improved. And comparing the trend segment with a power tracking record, selecting a node with a promotion potential to send an adjustment instruction, recording response feedback, and introducing a quantization threshold and a feedback mechanism to improve the maximum power point identification precision and the power generation efficiency of the system under environmental fluctuation.

The invention provides a power supply optimization model and method for a converter station. The method comprises the steps that firstly, a converter station energy consumption characteristic analysis and electric quantity prediction model is constructed, and the converter station energy consumption characteristic analysis and electric quantity prediction model comprises user converter station energy consumption characteristic analysis based on wavelet transformation and an optimization fast density peak value clustering algorithm and a converter station electric quantity prediction model based on an XGBoost algorithm; then, constructing a wind power and photovoltaic novel power supply output prediction model, and adopting a wavelet decomposition-bidirectional long-short term memory network based on an Attention mechanism as a prediction model; and finally, in combination with the electric quantity prediction model and the novel power supply output prediction model, constructing a power supply combination optimization design model for the converter station. Through the model, the power optimization capability and the use efficiency of the converter station can be improved, electric energy resources are managed and reasonably utilized, and the economical efficiency and the safety of a power system are improved. The power supply optimization model and method for the converter station are innovative and have practical value.

The invention provides a power grid system control method and device, computer equipment and a storage medium in the field of power grid system control, and the method comprises the steps: firstly, determining a dynamic inertia coefficient through integrating node frequency deviation, an energy storage charge state and voltage deviation, calculating inertia compensation power through employing the frequency deviation, precisely capturing the dynamic characteristics of a system, and carrying out the calculation of inertia compensation power; and constructing a target function based on the equivalent impedance matrix and the power deviation, solving a target control sequence under constraint conditions, realizing system power optimization distribution, and further adjusting node power output from multiple aspects by determining composite inertia, synthesizing inertia power, regulating and controlling a power vector and an inertia matrix. And finally, based on energy function construction and partial derivative calculation, generating a control instruction of each node. The steps can effectively deal with system power disturbance, rapidly compensate insufficient inertia, optimize power distribution, maintain system frequency stability, improve safety, reliability and stability of operation of a power system, and enhance adaptability of the system to complex working conditions.

The invention relates to the technical field of electric power dispatching, in particular to an electric power optimization dispatching method and system based on reinforcement learning, and the method comprises the steps: obtaining the operation data, load prediction data and renewable energy output data of an electric power system; based on the operation data, the load prediction data and the renewable energy output data, constructing a power system scheduling optimization model; generating an optimization scheduling strategy based on the power system scheduling optimization model by using a deep reinforcement learning algorithm; calculating the scheduling cost, the environmental influence and the system reliability index of the power system according to the optimal scheduling strategy; and outputting the optimal scheduling strategy and the corresponding scheduling cost, environmental influence and system reliability indexes, so that the operation cost of the power system can be reduced, the utilization rate of renewable energy sources can be improved, and safe and stable operation of a power grid is ensured.

The invention discloses a heterogeneous intelligent computing power optimization management scheduling system for accelerating a large model reasoning task, and relates to the technical field of computing power optimization management scheduling. The video memory fragmentation problem in a long sequence scene is converted into a controllable block migration task, and the performance bottleneck of a traditional video memory exchange mechanism is broken through; based on an operator-level scheduling strategy of a hardware capability fingerprint database, position coding and other compute-intensive tasks are accurately matched with vector instruction set hardware, and resource mismatch loss caused by black-box scheduling is eliminated; an expert selection process is reconstructed by an integer routing and counting sorting algorithm, near-lossless reasoning is realized at a limited node of an instruction set, and the potential value of an old computing power pool is activated.

The invention discloses an Internet of Vehicles vehicle information interaction system based on a YTS engine, and relates to the technical field of vehicle information interaction, a data acquisition module fuses multi-source sensing data, and an AI computing power optimization module adopts a dynamic computing power scheduling strategy; the 3D modeling and physical simulation module ensures the real-time performance of modeling based on an AI-driven 3D environment reconstruction and physical simulation technology; the cloud cooperative computing module processes multi-vehicle data in a distributed manner by using a cloud computing architecture of a YTS engine, optimizes a local model and improves the accuracy of an automatic driving decision; and the intelligent interaction module adaptively adjusts a modal man-machine interaction mode according to the dynamic change of the automatic driving decision, so that the driving experience is improved. The technical bottlenecks of information interaction delay, insufficient 3D modeling precision, poor decision stability and the like of the existing vehicle networking system in a high-speed dynamic environment are broken through; efficient energy consumption management, intelligent cloud optimization and accurate interaction are realized, and the safety and reliability of automatic driving are improved.

The invention discloses a hydroelectric generating set speed regulation control method considering excitation coordination. The method comprises the steps that 1, system state information is collected and preprocessed; step 2, on-line identification of excitation speed regulation coupling characteristics; 3, decomposing a multi-time-scale control target; step 4, designing a self-adaptive multivariable controller; 5, working condition self-adaptive parameter optimization; step 6, intelligently inhibiting the water attack effect; 7, multi-machine cooperative reactive power optimization control is carried out; 8, optimizing and executing a control strategy in real time; the technical problems that excitation speed regulation dynamic coupling, multi-time scale response mismatch, multi-machine cooperative control and the like in a hydroelectric generating set control system cannot be comprehensively solved in the prior art are solved, and the regulation response speed and the system operation safety are improved while the frequency and voltage stability is ensured.

The invention discloses a dynamic load regulation-based DC charging pile power optimization device and optimization method, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining electric energy quality data through extracting a voltage deviation and a total harmonic distortion rate, outputting a prediction value of the electric energy quality data through an electric energy quality prediction model, and calculating a deviation rate; constructing a regional charging network, inputting the regional charging network into the multilayer graph attention network, and outputting a regional electric energy quality evaluation value; a time dimension weight vector is calculated based on the deviation rate, a space dimension weight vector is calculated based on the regional power quality evaluation value, an adaptive weight vector is calculated according to the time dimension weight vector and the space dimension weight vector, a reinforcement learning model is constructed, and an optimal strategy is obtained; the optimal strategy is executed, the actual charging power and the actual charging time period are collected, the total charging amount, the total charging demand, the charging demand response rate and the charging expenditure are calculated and fed back to the reinforcement learning model, the reinforcement learning model is continuously optimized, and the electric energy quality is improved.

The invention relates to the technical field of thermal management optimization, and discloses an active cooling and power optimization system of a plateau turbine generator, which comprises a multi-physics field modeling module, a reinforcement learning decision module, a strategy optimization training module and an edge execution control module, and is used for resolving a temperature field / flow field / stress field coupling state in real time, providing environment dynamic characterization, and optimizing the power of the plateau turbine generator. According to the method, multi-source sensing data is processed, a cooling liquid flow rate and cooling fin angle control instruction is generated, strategy iteration optimization is carried out in a digital twin environment, the physical rationality and energy efficiency optimality of a control algorithm are ensured, a lightweight strategy network is deployed, and sub-second-level real-time response and fault safety protection are achieved. Through intelligent control and multi-physical field cooperation, the temperature adjusting precision and the system response speed are improved, efficient power output of the turbine is kept under different altitudes and climate conditions through a self-adaptive strategy, and a reliable clean energy solution is provided for high-altitude areas in combination with multiple safety protection mechanisms.

The invention provides a computing power optimization method for green energy driving of a data center, and the method comprises the steps: obtaining real-time tidal energy supply data and load demand calculation data, analyzing the matching degree of periodic supply of tidal energy and load fluctuation demand calculation, and determining the peak dislocation time period of tidal energy supply and load demand calculation; evaluating an execution window of a high-energy-consumption calculation task of the data center according to a peak dislocation period of tidal energy supply and calculation load demand, and obtaining an available resource list in an energy sufficient period; if the migrated computing power resource allocation balance degree does not reach a preset balance threshold value, obtaining a current load of a server cluster of the data center, and determining an energy valley period activation condition; and analyzing the mitigation degree of resource mismatching from the available capacity index of the buffer energy, reallocating idle computing power resources according to the mitigation degree, and determining the supplement demand of the tidal energy shortage time period.

According to the large power grid reactive power optimization method and device, the storage medium and the computer equipment provided by the invention, the advantages of the two algorithms are fully exerted through the hybrid chaos quantum particle swarm optimization algorithm and the dimension-by-dimension convex space search algorithm. According to the chaotic quantum particle swarm algorithm, the global search capability and the capability of jumping out of local optimum of a particle swarm are enhanced by utilizing the characteristics of quantum behaviors and chaotic mapping, and the problem of premature convergence of a traditional heuristic intelligent algorithm is avoided. And according to the dimension-by-dimension convex space search algorithm, fine search is carried out on each excellent particle in different dimensions, a local optimal solution is determined, and the search precision and efficiency are further improved. According to the design of the hybrid algorithm, special optimization is carried out aiming at the characteristics of a reactive power optimization problem model, such as variable property difference, constraint complexity and the like, and the technical defects of poor optimization effect and optimization efficiency of an optimization solution algorithm in the prior art are effectively overcome.

The invention provides a photovoltaic power station power optimization distribution method based on ultra-short-term prediction, and relates to the technical field of photovoltaic power station power management, and the method comprises the steps: 1, collecting the operation data and environment monitoring data of a photovoltaic array unit of a photovoltaic power station, and obtaining a unit power abnormality coefficient and a unit stability coefficient; 2, constructing a unit association network of the photovoltaic power station, monitoring and calculating the power interaction degree and the synchronous fluctuation rate between every two adjacent units in the association network, and obtaining a power association matrix and a fluctuation synchronization matrix; 3, according to the short-term power change rate, output compensation proportion prediction and capacity distribution adjustment are carried out, and an output compensation proportion and an adjustable capacity distribution scheme are obtained; 4, based on the power incidence matrix and the fluctuation synchronization matrix, obtaining a cooperative power anomaly coefficient and a system stability coefficient; and 5, generating a power optimization instruction set based on the unit power anomaly coefficient and the unit stability coefficient. And the output stability and the overall efficiency of the photovoltaic power station are improved.

The invention discloses a novel power system primary frequency modulation optimization method based on deep learning, and relates to the technical field of power optimization. The method comprises the following steps: collecting state data of a hydroelectric generating set running in a power grid, constructing a three-dimensional feature tensor, and extracting spatio-temporal features through a TCN-GRU hybrid network; constructing a deep reinforcement learning decision layer, and defining an action space and a reward function; constructing a PID neural network with a time-varying forgetting factor, and outputting a dynamic weight; establishing a dual-time scale updating mechanism; a safety verification module is arranged, and when the system frequency deviation value is larger than a threshold value, the traditional PID mode is switched. Characteristics are extracted from state data of operation of a hydroelectric generating set in a power grid, the time sequence dependency relation of the characteristics is processed through deep learning, the frequency change trend of the power grid is captured, the optimal control strategy is explored by applying the DRL technology, PID controller parameters are adjusted in a dynamic environment, and the optimal control strategy is obtained. And the primary frequency modulation response speed and the control precision of the hydroelectric generating set during power grid frequency fluctuation are improved through self-adaptive control.

The invention discloses a multi-energy fusion system cooperative scheduling method and device in a port dynamic scene, and relates to the field of port energy system optimization scheduling, and the method comprises the steps: constructing a dynamic coupling relation model according to port data; according to the energy system power of each area at the current time point, a multi-objective optimization model is constructed, and objective functions of the multi-objective optimization model include operation cost minimization, total carbon emission minimization, energy storage equipment service life maximization, energy storage equipment power optimization and energy storage equipment power optimization. The constraint conditions of the multi-objective optimization model are power balance, energy storage charge state, electric hydrogen equipment capacity, berth distribution continuity and load demand priority; according to the multi-target optimization model and the port data of each region at the current time point, determining a target collaborative scheduling operation strategy of each region at the current time point, the target collaborative scheduling operation strategy comprising a multi-energy output distribution scheme, a load response instruction and an equipment operation parameter; and executing the target collaborative scheduling operation strategy of each region at the current time point.

The invention belongs to the field of electric power, and discloses a virtual power plant optimization method, device and equipment and a storage medium, and the method comprises the steps: constructing a sliding window for each load equipment of a virtual power plant, and building a feature matrix of the virtual power plant according to the sliding window; obtaining load data of the virtual power plant from the feature matrix, and inputting the obtained load data into a pre-trained power optimization model; wherein the power optimization model takes the operation cost of the virtual power plant and the minimum total amount of energy which is not effectively utilized as a target function, and is used for calculating a target power set conforming to the target function according to the whale algorithm; and optimizing the load equipment of the virtual power plant according to the target power set. Through the application of the multi-layer optimization system and the whale optimization algorithm, the multi-resource coupling efficiency in the virtual power plant is improved, the system operation cost is reduced, and the calculation efficiency of the optimization algorithm is improved.

The invention discloses a super capacitor cyclic charging and discharging energy storage control method and system, and belongs to the technical field of charging and discharging energy storage control, and the method comprises the following steps: setting a to-be-selected model list of a super capacitor, determining an optimal super capacitor configuration scheme in the to-be-selected model list according to a setting demand, and storing the optimal super capacitor configuration scheme in the to-be-selected model list; the charging and discharging constraint conditions of the energy storage equipment and the power transmission equipment are set through the optimal super capacitor configuration scheme; calculating a game equilibrium solution, and outputting a charging and discharging power optimization variable of the energy storage equipment and a power distribution optimization variable of the power transmission equipment; combining the detection data obtained by the sensor with the charging and discharging power optimization variable and the power distribution optimization variable to generate an energy storage equipment charging and discharging control instruction; and performing charging and discharging energy storage control on the energy storage equipment through the energy storage equipment charging and discharging control instruction. According to the method, the limitation of traditional single-index type selection is avoided, and the optimal super capacitor configuration scheme can meet the technical requirements and maximize the economic benefits at the same time.

The invention relates to the technical field of power optimization, and discloses a component-level power optimization method and device of a photovoltaic system. The method comprises the following steps: carrying out classification processing on working state evaluation results, and carrying out maximum power point tracking control on different types of components through a conductance increment method and a three-point comparison method to obtain optimal output power parameters of each component; carrier communication transmission is carried out on the optimal output power parameters of all the assemblies, and working state information of all the assemblies is transmitted to an integrated controller in a sequential relay mode to obtain group cascade optimization control parameters; and performing performance calculation on the group cascade stage optimization control parameters and the component working parameters, and evaluating a system operation state through tracking efficiency calculation and conversion efficiency calculation to obtain a system control parameter optimization result. According to the invention, the efficiency and accuracy of module-level power optimization of the photovoltaic system are improved.

The invention provides a hydraulic cooler heat dissipation power optimization method and system based on air flow, and relates to the technical field of hydraulic system heat dissipation, and the method comprises the steps: obtaining real-time operation parameters, and calculating the actual heat dissipation power; obtaining a target fan rotating speed of the maximum predicted heat dissipation power based on an improved particle swarm algorithm, and when a prediction error is greater than a threshold value, optimizing the model by adopting a deep reinforcement learning algorithm; and a fan rotating speed closed-loop control system is established based on sliding mode variable structure control, and the switching function gain coefficient is adaptively adjusted according to the environment temperature change rate, so that the fan rotating speed is dynamically adjusted. The cooling efficiency of the hydraulic cooler is improved, and the service life of equipment is prolonged.