1358 results about "Coordinate control" patented technology

The invention discloses an intelligent air conditioner water chilling unit control method and system based on predictive optimization. Operation data and weather forecast data are collected to establish a dynamic response reference, building cold load sudden change opportunity is recognized, the mismatching relation between magnetic suspension frequency and wet bulb temperature is detected, and energy-saving opportunity recognition data is generated to determine a cooling starting judgment table; identifying equipment response delay characteristics by using a dynamic response reference, extracting time sequence advantage parameters to determine dislocation configuration among multiple pieces of equipment, and generating coordination control parameters; cloud-local transmission delay analysis is carried out according to the coordination control parameters, and a coordination control sequence is generated through buffer opportunity identification and delay compensation; mechanical refrigeration suppression data is generated through energy efficiency mode classification, free cooling potential mining is implemented to form a cold source optimization factor, and an emergency response strategy is generated; an emergency response strategy is used to identify a multi-device linkage trigger critical zone, a prediction correction feedback network is constructed, a global collaborative optimization instruction is generated, and the system operation efficiency and the control precision are improved.

The invention relates to a humanoid robot double-arm body operation method based on reinforcement learning, belongs to the field of robot cooperative control, and is characterized in that a strategy of trajectory block prediction and time integration fusion is used for double-arm cooperative control, and continuity and stability of double-arm operation are improved; in reinforcement learning control, a three-dimensional pose track generation and correction module is introduced, condition generation and denoising correction of a three-dimensional space are carried out on a track block layer, and geometric consistency and naturalness of a generated track are guaranteed; the invention further provides a reinforcement learning optimization framework and a simulation-reality migration process, and through system integration of reward, value guidance and migration processes, strategy deployability and safety are guaranteed; compared with the prior art, the method has the advantages that the naturalness, the collaboration and the success rate of double-arm operation can be remarkably improved, the generalization ability is high, and the good simulation-to-reality migration ability is achieved.

The invention discloses an unmanned aerial vehicle cluster collaborative target hunting method based on brain-like calculation, and belongs to the technical field of autonomous navigation robot and multi-robot coordination control, and the method comprises the steps: obtaining a multi-view image under a camera view, and obtaining the local pose and covariance of each unmanned aerial vehicle; obtaining a global consistent relative pose of the unmanned aerial vehicle cluster according to the local pose and the covariance of each unmanned aerial vehicle; performing state estimation on the target by adopting an unscented Kalman filtering algorithm to obtain target state information under a unified coordinate system; using a Bezier curve generation algorithm to obtain a target motion trail in a section of historical state; the method comprises the following steps: expanding an unmanned aerial vehicle cluster through a centroid extension method to obtain a surrounding queue of target surrounding, solving the minimum surrounding cost by adopting a Gaussian Newton method, and generating a surrounding position of each unmanned aerial vehicle; the motion trajectory of the unmanned aerial vehicle is generated by adopting mixed A * search, and a final surrounding trajectory sequence is generated, so that the accuracy of motion state estimation is remarkably improved.

The invention discloses an intelligent coordination control method and system for power plant equipment based on multi-source heterogeneous data, and belongs to the technical field of intelligent manufacturing and industrial automation, and the method comprises the steps: deploying a multi-mode sensor network in the power plant equipment, collecting the multi-source heterogeneous data in real time, and carrying out the real-time data preprocessing through an edge calculation node; carrying out collaborative modeling on the preprocessed data by adopting a hybrid analysis framework, predicting an equipment state trend, identifying a fault propagation path, positioning a root cause and optimizing a maintenance decision scheme; the equipment failure probability is evaluated through a fault diagnosis result, a grading early warning mechanism is triggered, and a rule base is updated and optimized in combination with a dynamic knowledge base; a three-dimensional model is constructed by using a digital twinning technology to carry out virtual simulation and remote control, and maintenance guidance is carried out through an augmented reality auxiliary technology. According to the method, efficient real-time monitoring and fault prediction are achieved, the fault diagnosis time and the operation and maintenance cost are remarkably reduced by combining the fault tree model and the digital twinning technology, and the equipment operation safety and reliability are improved.

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 embodiment of the invention discloses a double-truck-crane cooperative hoisting monitoring method and device, electronic equipment and a storage medium. The method comprises the steps that component parameters and operation environment parameters of hoisting operation are collected, a reference data model is constructed, and an action expected value and a safety control threshold value are set; state acquisition devices are arranged on the two truck cranes to acquire multi-source data in real time and transmit the multi-source data to a central control system so as to realize synchronous sensing of the two trucks; processing multi-source data based on a data fusion algorithm, and constructing a dynamic attitude model and a collaborative operation state model; a state prediction correction value in the model is compared with the reference data model in real time, deviation is judged, and safety early warning is triggered; and generating a feedback control instruction, dynamically adjusting the hoisting operation, correcting the coordination deviation, and realizing the coordination control of the hoisting posture. In this way, through multi-source data fusion and real-time feedback control, the safety and coordination of cooperative lifting operation of the double truck cranes are remarkably improved.

The invention discloses a coordinated control method and system for comprehensive energy multi-agent coordinated group control and autonomous decision making. The method comprises the following steps: establishing a relaxation strategy of an agent corresponding to a distributed power supply; the main coordinator iteratively solves to determine a global coordination signal according to the global operation data of the integrated energy system and the operation data of each agent by taking the lowest total cost, the highest system energy efficiency, the minimum carbon emission and the minimum global penalty coefficient meeting the global relaxation constraint as a relaxation global optimization target; the minimum operation cost of each intelligent agent, the minimum response deviation to a global coordination signal and the minimum deviation between an actual state and a reference state are taken as control targets; establishing an electricity price autonomous decision-making model of each agent and a response power boundary autonomous decision-making model for a global coordination signal; and based on the control target of each agent, the electricity price autonomous decision-making model and the response power boundary autonomous decision-making model, predicting to obtain a control target value of each agent, and realizing balance between a global optimization target and local autonomy.

The invention discloses a power coupling-considered capacity ratio estimation method for a network-constructing / network-following multi-machine hybrid system, and the method comprises the steps: building a GFL-GFM hybrid system model, combining VSG power coupling small signal modeling and a dynamic relative gain matrix (DRGA), and quantifying the coupling strength of active / reactive power to output voltage; the frequency domain impedance characteristic of the GFM converter is simplified, and an ideal voltage source series equivalent reactance equivalent model is constructed; deriving an analytical relationship between a generalized short-circuit ratio (gSCR) and a capacity ratio based on the model, and generating a ratio formula in combination with engineering boundary sub-scenes to form a ratio range suitable for multiple scenes; for coupling time-varying characteristics, a closed-loop mechanism of'mixed mode-coupling sensing-correction calculation-execution feedback 'is constructed, and the ratio is dynamically adjusted. According to the method, coupling quantization and multi-machine coordination control are deeply fused, the problem that the external characteristic model precision and the multi-working-condition adaptability are difficult to consider under complex environment coupling is effectively solved, and the engineering applicability of the hybrid fan system is enhanced.

The invention provides a large-scale ultrafast charging facility vehicle network interaction-oriented adaptive regulation and control system, and belongs to the technical field of charging pile load scheduling. During working, a multi-source data acquisition and vehicle network state sensing module acquires and fuses multiple types of data, and a vehicle network interaction strategy optimization module generates a strategy according to the data; the power dynamic distribution module distributes power according to a strategy, and the energy storage coordinated control module optimizes energy storage charging and discharging. The real-time response and feedback module quickly responds to a power grid instruction and corrects a strategy, and the security and protocol adaptation module guarantees security and docking. During use, after the terminal is started, fusion data is automatically collected, a regulation and control strategy is generated and executed, instructions are responded and corrected in real time, and safety and cross-platform adaptation are guaranteed in the whole process.

The invention discloses a light passenger car chassis steering stability self-adaptive control method, which comprises the following steps of: collecting multi-dimensional working condition parameters in real time, and covering 12 types of key parameters such as a steering wheel rotating angle and a wheel speed; after preprocessing and abnormity diagnosis, double models are adopted to estimate the dynamic state of the vehicle in parallel; dynamically setting a control target, and realizing personalized control in combination with driver style recognition; the control quantity is optimized through multi-mode strategy dynamic switching and steering and braking system cooperative control; and actuator dynamic compensation, scenarized self-learning and multi-stage fault handling mechanisms are introduced. According to the method, the complex working condition adaptability is improved, the response speed is controlled, the subsystem cooperation efficiency is improved, the system robustness and the long-term use performance are enhanced, and the driving safety of the light bus is effectively guaranteed.

The invention relates to the technical field of large-scale power grid safety guarantee, in particular to an optimal decentralized coordination control method for a micro-grid in a new energy power system, which comprises the following steps of: performing virtual disconnection verification on key power transmission lines one by one based on online acquired power grid topology and real-time line power flow data, and establishing a whole-grid cascade risk quantitative index. According to the method, power grid topology and line power flow data are acquired in real time, virtual disconnection verification of key power transmission lines is implemented one by one, simulation and analysis of a line fault scene are realized, whole-network cascade risk quantitative indexes are established on the basis, and an initial fault line with the maximum threat to system safety is identified by comparing the indexes with a safety threshold value in real time, so that the safety of the system is improved. A fragile line risk contribution list is generated, and high-risk lines are positioned and prevented in advance; and further directly calculating the power adjustment amount required by the micro-grid group according to the risk contribution list, and forming a forward-looking tide dredging instruction set.

The invention discloses a virtual power plant voltage-reactive power coordination control method based on fusion of a graph neural network and deep reinforcement learning, and the method comprises the steps: explicitly introducing a topological structure of a power distribution network into the state representation of deep reinforcement learning in the form of graph data, and extracting and processing the graph structure data through the graph neural network; topological correlation and dynamic interaction among nodes in a power grid are accurately captured; and the extracted graph features and the traditional system state quantity are fused and input to the deep Q network for learning and decision making, so that the optimal coordination control of the distributed resources in the virtual power plant and the power distribution network equipment is output. The virtual power plant voltage-reactive power control method and device aim at solving the problem that in the prior art, perception on a power grid structure is insufficient, and the decision-making precision and robustness of virtual power plant voltage-reactive power control and the adaptability in a large-scale complex power distribution network are remarkably improved.

The invention discloses a flexible load multi-time scale coordination control method for a novel power system, and the method comprises the specific steps: collecting millisecond, minute and hour level flexible loads and environment data, and constructing a multi-time scale load response model; a millisecond-level model and a second-level model are utilized, a real-time operation state is combined, a second-level adjusting instruction is generated, and an execution result is fed back; based on minute-to-hour level prediction data, integrating adjustable load, distributed energy and energy storage constraints, optimizing a load operation plan in a rolling manner, and coordinating source-network load-storage combined operation; and making a long-term load operation plan in combination with day-level and above prediction data, and executing deviation self-adaptive correction according to short and medium periods. According to the method, hierarchical control and collaborative optimization of the adjustable load under different time scales from millisecond level to day level are realized, and the operation stability, the adjustment flexibility and the auxiliary service capability of the novel power system under the high-proportion new energy access condition are remarkably improved.

The invention discloses a power distribution network source load storage coordination control method based on a distributed power supply, electronic equipment and a medium. The method comprises the following steps: acquiring operation data of a power distribution network in real time; judging based on a preset switching condition and the operation data of the power distribution network, if the operation of the power distribution network does not meet the switching condition, controlling the power distribution network to execute a centralized regulation and control strategy, and obtaining an optimal global control strategy, the preset switching condition at least comprising communication interruption; based on the optimal global control strategy, controlling a plurality of sub-regions to execute a first regulation and control instruction, wherein the sub-regions comprise user distributed photovoltaic power generation; if the operation of the power distribution network meets the switching condition, controlling the power distribution network to execute a distributed regulation and control strategy, and obtaining an optimal local control strategy corresponding to each sub-region; and controlling the corresponding sub-region to execute the second regulation and control instruction based on the optimal local control strategy. The centralized regulation and control and the distributed regulation and control are switched through the preset switching condition, so that the response speed and the flexibility can be improved, and the intervention cost can be reduced.

The invention discloses a water-wind-light combined intelligent control system based on multi-energy coordinated control, and belongs to the technical field of multi-energy coordinated control, and the system comprises the steps: firstly collecting environment and operation parameters of wind power, photovoltaic and hydroelectric equipment, and carrying out the preprocessing of the environment and operation parameters to form a structured data set with a space-time label and a dynamic sampling identifier; performing space-time correlation analysis on the data set, constructing a wind power fluctuation and hydropower regulation margin real-time response matrix and a photovoltaic output attenuation and hydropower storage capacity compensation time sequence correlation model, and quantifying the multi-energy dynamic complementation degree and grading the data credibility through a two-dimensional coupling algorithm; then combining with a historical meteorological scene association rule base, constructing a water-wind-light combined power supply capability prediction model containing an energy characteristic mutual compensation coefficient, and generating a hierarchical regulation and control strategy with a space-time priority; and finally, executing a regulation and control strategy to complete parameter adjustment, continuously monitoring through closed-loop feedback, and triggering multi-energy collaborative complementary regulation when the single energy regulation capability is saturated, thereby realizing water-wind-light efficient collaborative control.

The invention discloses a vertical take-off and landing airport air traffic collaborative management method and system. The method comprises the following steps: constructing an airport air model integrating a physical environment, an airspace structure, a natural environment, an aircraft state and observation information; receiving observation information in real time to drive model synchronization; performing prospective multi-dimensional conflict prediction based on the synchronous model and eVTOL dynamics; on the basis of the prediction result, generating a cooperative regulation strategy when multiple constraint conditions are met; converting the strategy into an instruction, issuing the instruction through a security data link, and verifying an execution effect based on observation information to realize closed-loop control; according to the method, the air traffic environment of the vertical take-off and landing airport is systematically and digitally reconstructed through the quintuple model, unified representation of the operating environment and the traffic subject is achieved, the problem of heterogeneous information fusion is solved, the airspace utilization efficiency and safety under high-density operation can be remarkably improved, and the method is suitable for large-scale popularization and application. And the method has strong adaptive capacity to dynamic changes such as wind power disturbance and emergency conditions.

The invention discloses a power distribution network hierarchical cooperative operation control method for distributed photovoltaic local absorption, relates to the technical field of electrical engineering, and aims at solving the problem of local electric energy absorption of a power distribution area containing a large amount of distributed photovoltaic and flexible resources. Global scheduling is carried out on flexible resources and distributed photovoltaic in a transformer substation area on the basis of a day-ahead-day centralized optimization strategy and hierarchical-partition coordination control, an optimization result is applied to a real-time control stage, and optimized scheduling instructions are distributed from top to bottom; and the optimal operation of distributed photovoltaic local absorption in the transformer area is realized. Therefore, by adopting the power distribution network hierarchical collaborative operation control method for distributed photovoltaic local consumption, a novel power system in a complex operation mode can be accurately analyzed, and the photovoltaic consumption capability of the power distribution network is improved by collaborative optimization of flexible resources and an operation scheme of a distributed power supply; and the operation economy of the novel power system is improved.

The invention relates to the technical field of electric power automation control, and discloses a coordinated control system for a generator set, which adopts an intelligent DCS integrated design architecture and performs real-time data interaction with a set decentralized control system through hard wiring or a communication protocol. According to the system, through deep fusion of a model prediction control algorithm and intelligent DCS integrated design, a boiler-steam turbine dynamic coordination model is constructed, and a control instruction is generated in real time in combination with a rolling optimization strategy. By means of the design, the main steam pressure deviation is controlled within + / -0.2 MPa, and the load response rate reaches 1.5% Pe / s and is improved by more than 40% compared with a traditional system. Particularly, under the working condition of deep peak regulation, by means of decoupling control over a fuel-air supply-water supply loop through a neural network algorithm, main steam temperature fluctuation (within + / -5 DEG C) and reheater spray desuperheating water quantity sudden change (the change rate is smaller than or equal to 10 t / min) are effectively restrained, the control quality of a unit under the conditions of variable loads and variable coal quality is remarkably improved, and the control efficiency of the unit is improved. And the high-standard requirement of the power grid on the AGC performance is met.

The invention provides a dual-motor synchronous coordination control method of an electric helicopter, which comprises the following steps: taking a first controller as a main control motor driving system, and establishing a rotor rotating speed reference by adopting a rotating speed loop control strategy; and the second controller serves as a slave control motor driving system, a torque loop control strategy is adopted, and load disturbance is estimated and compensated in real time through the extended state observer. According to the dual-redundancy motor driving system, mechanical coupling interference is effectively inhibited, the fault tolerance of the system is enhanced, a feasible technical scheme is provided for safe flight of the electric helicopter, and the dual-redundancy motor driving system has important application value.

The invention discloses a reclosing coordination control method and system based on electrical parameter measurement, particularly relates to the technical field of intelligent power grid fault processing and security defense, and is used for solving the problem that the reliability of fault judgment is reduced due to the fact that high-proportion distributed photovoltaic is connected to a power distribution network. A harmonic detection signal is injected during a reclosing waiting period, a response signal is measured, harmonic subspace energy distribution is extracted, an energy distribution matrix is constructed to calculate a nuclear norm change rate and form similarity, a system harmonic impedance stable state is judged through double verification, and finally a reclosing decision is executed according to a fault judgment parameter. The method can accurately identify the convergence characteristic of the dynamic process of the system, effectively distinguish the instantaneous fault from the permanent fault, and significantly improve the reclosing success rate and the power supply reliability of the distributed photovoltaic power distribution network.

The invention discloses a multi-valve cooperative control system and method for a choke manifold control box, and the method comprises the steps: collecting process parameters and valve state parameters in real time through a data collection module, calculating the required total throttling pressure drop or total flow through a data analysis module based on the target bottom hole pressure or target flow set by an operator in combination with real-time data, and calculating the total throttling pressure drop or total flow according to the calculated total throttling pressure drop or total flow; the valve role distribution module distributes valve roles according to a preset rule, the instruction generation module distributes total demands to all valves according to a proportion to generate a target opening instruction set, the instruction verification module verifies instruction safety through a safety interlocking matrix, and the valve driving module executes the instruction passing verification. The feedback optimization module calculates the correction based on the actual parameter deviation and performs iterative optimization; the problems of insufficient precision, low response speed and poor safety in multi-valve cooperative control are solved, the precision and reliability of throttling control are improved, valve abrasion is reduced, and the system anti-jamming capability and fault emergency response efficiency during sudden change of working conditions are enhanced.

The invention relates to the field of power grid real-time monitoring and coordination control, in particular to a distributed photovoltaic grid-connected regional power grid real-time monitoring and coordination control system which comprises a sensing acquisition module, a multi-time scale prediction module, a self-evolution calibration module and a distributed coordination control module. The sensing acquisition module acquires wide-frequency-domain electrical quantity and multi-dimensional meteorological quantity through a double-layer sensing network, and restoration data is obtained through anomaly detection; the multi-time-scale prediction module integrates ultra-short-term, short-term and medium-and-long-term prediction, and realizes cross-scale collaboration based on a combined state space model and the like; the self-evolution calibration module performs online compensation on the multi-scale prediction residual error based on deep reinforcement learning; the distributed coordination control module is combined with an improved alternating direction multiplier method and a non-dominated sorting genetic algorithm to obtain an optimal power instruction meeting voltage, frequency and harmonic ternary constraints; according to the invention, the stability of power grid operation in a high-permeability photovoltaic grid-connected scene is improved.

The invention discloses a multi-time scale inertial frequency cooperative adaptive control method for a high-proportion new energy power grid. The method comprises the following steps: S1, establishing a frequency and inertia cooperative control topological structure of a high-new energy penetration power system; s2, acquiring parameters of the power system, constructing a multi-time-scale power system inertia-frequency control interconnection model, and acquiring fast and slow variables of the power system; s3, carrying out fast and slow time scale separation on the multi-time scale power system inertia-frequency control interconnection model to obtain a slow subsystem model and a fast subsystem model; s4, setting a control evaluation index, and optimizing a control parameter based on the control evaluation index; and S5, according to the optimized control parameters, based on the slow subsystem and the fast subsystem, calculating to obtain a control quantity. According to the method, inertia control and load frequency control are effectively integrated, and the overall stability and reliability of a power system under the high and new energy permeation condition are improved.

The invention provides a parallel orientation control method and system for multiple welding devices, and the method comprises the steps: S1, collecting real-time three-dimensional space coordinate data and device attitude angle information from multiple operation devices through a multi-source sensor, and obtaining an initial positioning information set; s2, performing multi-source data fusion processing on the initial positioning information set by adopting a state estimation filtering algorithm to obtain a fused operation equipment position and attitude estimation result; s5, acquiring the corrected accurate equipment positioning information and the three-dimensional geometric model data of the target workpiece, and generating a task allocation scheme of multi-equipment collaborative operation; and S7, simulating a potential motion interference scene among multiple devices according to the preliminary movement path sequence and the device attitude adjustment specification, and judging whether a path conflict condition exists or not. According to the method, the positioning precision and the operation efficiency of multi-device collaborative operation are remarkably improved, path conflicts are effectively avoided, and the stability and the safety of coordinated control in a complex scene are ensured.

The invention discloses a multi-resource coordination control method and system in a new energy high-proportion power grid, and the method comprises the steps: collecting power grid data which comprises new energy output data, meteorological data, power grid load data, power grid operation state data and energy storage system operation parameters; constructing a new energy output prediction model based on the power grid data; calculating quantitative cost based on the power grid data and the new energy output prediction model, wherein the quantitative cost comprises energy storage system cost, power grid operation cost and power grid stability cost; constructing an energy storage charging and discharging strategy model based on the quantitative cost; obtaining an optimal energy storage charging and discharging strategy based on the energy storage charging and discharging strategy model; and constructing a multi-resource cooperative scheduling model based on the optimal energy storage charging and discharging strategy, and outputting a scheduling plan. According to the method, the energy storage depreciation, the power grid loss and the stability risk are quantified into specific cost indexes by calculating the quantitative cost, hidden loss can be avoided, and the operation cost of the system is minimized.

The invention discloses a multi-device and multi-mode centralized control system which comprises an interaction control end and a device assembly. The interactive control end is configured according to three different control modes of centralized control, independent control and hybrid control, in the centralized control mode, a set of main control unit is configured for all devices, and the main control unit comprises a main control server which is used for operating a main control program and coordinating and controlling operation of all device components; in the independent control mode, each equipment component is provided with a corresponding embedded controller, and the embedded controller comprises an embedded control server which is used for operating a main control program for controlling the corresponding equipment component; in the hybrid control mode, one or more independent devices are provided with embedded controllers, the embedded controllers communicate with the main control server, the device assembly comprises a device body and a driving unit, and the device body is driven by the driving unit to achieve various operation functions. According to the invention, centralized control of multiple devices in a large-scale manufacturing factory can be realized.

The invention relates to the technical field of new energy power system operation control, and discloses a new energy station coordination control optimization method, system, equipment and medium based on data real-time transmission and synchronization, and the method comprises the steps: deploying a data collection module at each power generation unit side of a new energy station, and collecting multi-dimensional operation data in real time, converting into a uniform format; constructing a hybrid communication network architecture, monitoring a link state in real time, and triggering redundant path switching; establishing a global unified clock reference, carrying out time synchronization, and adding a timestamp to transmission data; performing preprocessing, anomaly detection and state estimation on the data with the timestamps; based on the preprocessed whole-field synchronous data, constructing an optimal control model, and solving an output distribution instruction of each power generation unit in a preset period; and the optimization instruction is issued to the power generation unit in a closed-loop manner through the low-delay communication network for execution. The method can effectively improve the overall operation efficiency, scheduling compliance and system stability of the new energy station, and has good engineering popularization and application prospects.

The invention provides an intelligent control system and method for a natural cooling heat exchange ventilation unit, and relates to the technical field of energy-saving fans. The optimal operation mode and the natural cooling availability of a target ventilation unit are determined firstly; the expected heat exchange amount of the natural cold side is determined according to the current indoor heat load demand characteristics and the natural cold available capacity, and then a static adjusting instruction of a natural cold side actuator is determined according to the expected heat exchange amount; according to the heat load demand characteristics and the expected heat exchange amount, the mechanical refrigeration demand cold capacity is determined, and then a dynamic adjusting instruction of a mechanical refrigeration side actuator is determined according to the heat exchange efficiency and the mechanical refrigeration demand cold capacity of the mechanical refrigeration unit under the current working condition; and performing coordination control on the actuators on the natural cooling side and the mechanical refrigeration side based on the static adjustment instruction and the dynamic adjustment instruction. According to the scheme, dynamic cooperative control over natural cooling and mechanical cooling of the natural cooling heat exchange ventilation unit under the variable working conditions can be achieved, and therefore the energy efficiency is optimized on the premise that the requirement for indoor thermal comfort is met.

The invention discloses a multi-objective collaborative optimization control method for a new energy station and an energy storage system, relates to the technical field of new energy optimization control, and constructs a collaborative optimization framework with multi-time scale, multi-objective and multi-element deep coupling. The framework realizes joint optimization control of a wind power plant, a photovoltaic power station, an energy storage system and a conventional generator set through a whole process design of target modeling-scene adaptation-algorithm solution-closed loop execution. Formulating a source network storage dynamic interaction strategy based on scene self-adaption, and fully mining the adjustment potential of wind power, photovoltaic, energy storage and conventional generator sets; and solving a real-time optimal control distribution value by adopting a multi-objective optimization algorithm to form closed-loop coordination control. According to the method, the problems of poor adaptability and insufficient source network storage resource collaboration of traditional single target optimization are solved, the new energy consumption rate and the system stability are remarkably improved, and the operation cost is reduced.

The invention discloses an active power and frequency coordination control method for a wind storage combined system, and belongs to the technical field of electric power system operation and control. The problems that in the prior art, due to wind power and load uncertainty, the frequency control effect is poor, energy storage life management is insufficient, and long-term and short-term control instructions are disjointed are solved. The problem is solved by constructing a three-layer collaborative architecture of a predictive optimization layer, a real-time coordination control layer and an execution layer: the predictive optimization layer randomly optimizes and generates a reference state track and a frequency modulation capacity instruction of an energy storage unit based on a scene method; the real-time coordination control layer utilizes a deep reinforcement learning coordination controller, synthesizes frequency deviation, SOC compensation signals and frequency modulation capacity reservation instructions, and dynamically distributes active power of a fan and energy storage; and the execution layer realizes rapid issuing of the instruction. The method is mainly used for active power coordination and frequency support of a wind storage combined system in a high-proportion new energy power system, and can effectively delay energy storage life attenuation while stabilizing frequency fluctuation.