6512 results about "Control mode" patented technology

The invention relates to the technical field of micro-grid control, in particular to a multi-layer distributed micro-grid control system and method based on edge cloud collaborative lightweight reinforcement learning. The method comprises the following steps: a cloud management unit trains a DQN model by using historical environment data, generates a lightweight DQN model through pruning and quantification, issues the lightweight DQN model to an edge node unit, and formulates day-level and weekly-level optimization scheduling strategies based on a function layering + layering cooperation strategy; the edge node unit monitors electrical quantity and environmental parameters in real time through a sensor, deploys a lightweight DQN model to monitor abnormal conditions and trigger local early warning and emergency strategies, and makes a fast decision by using the lightweight DQN model; the micro-grid subgroup units collect data of the edge node units. According to the invention, through close cooperation of the edge node layer, the micro-grid subgroup layer and the cloud management layer, a distributed cooperative control mode of edge node real-time decision, subgroup cooperative management and cloud global optimization is realized.

The present invention provides an inverter control method, device, computer equipment and storage medium, belonging to the field of power electronics. The method comprises: determining a frequency control equation of the inverter under a grid-connected condition when the inverter is in a VSG control mode; determining a relationship between a virtual electromotive force of a virtual synchronous generator in a reactive state and an output voltage of the inverter; constructing a voltage-current double closed-loop control structure of the inverter under the VSG control mode, and determining a current reference value and a voltage reference value of the virtual synchronous generator under dq axes through the frequency control equation and the relationship; constructing a fuzzy control rule of a virtual damping coefficient and a virtual inertia according to the current reference value and the voltage reference value; and controlling the virtual damping coefficient and the virtual inertia through the fuzzy control rule, thereby improving the stability and performance of the inverter during the grid-connected process, reducing the harmonic disturbance of the power system, and helping to improve the adaptability and reliability of the entire photovoltaic power generation system.

The invention relates to a follow-up network type control smooth switching method based on a generalized operation short-circuit ratio index. The follow-up network type control smooth switching method comprises the following steps of 1, introducing a generalized operation short-circuit ratio (GOSCR) as a switching index, and proposing a GOSCR-based hysteresis control mode switching judgment index under different power grid strength and power output working conditions; step 2, establishing a unified new energy grid-connected control unit model: constructing a unified grid-connected control model in two modes of network following control and network construction control, systematically analyzing oscillation characteristics of a grid-connected unit in the two modes, and defining boundary conditions of mode switching; 3, proposing an LADRC-based undisturbed switching control strategy: observing and estimating system current disturbance parameters online by utilizing LESO, realizing real-time compensation of disturbance quantity, and reducing dynamic sudden change in the switching process; the method has the advantages that GOSCR switching indexes are introduced to comprehensively evaluate the strength, a unified grid-connected control model is established, and an undisturbed switching control strategy is provided.

The invention relates to the field of robot control, and provides a humanoid robot control system and method based on reinforcement learning, and the humanoid robot control system comprises a first control subsystem and a second control subsystem. The first control subsystem comprises a strategy reasoning module, a state conversion module and a robot control module; the second control subsystem comprises a data acquisition module and a driving control module; the data acquisition module is used for finishing timestamp alignment and abnormal value filtering of sensor data and transmitting the data to the state conversion module; the state conversion module is used for fusing multi-source sensor data and constructing a time sequence state feature containing a real-time measurement value and historical time sequence information; the strategy reasoning module is used for generating a multi-joint angle target value of the robot according to the time sequence state characteristics provided by the state conversion module; and the robot control module is used for analyzing the multi-joint angle target value output by the strategy reasoning module, selecting a control mode and generating a control command comprising a parameter adjustment instruction.

The invention belongs to the technical field of industrial automation control, and particularly relates to a dynamic optimization control method and system for multi-component batching of a paste making machine, and the method comprises the steps: generating a characteristic waveform spectrum corresponding to a residue molecular structure; dynamically generating electric lubricating medium parameters containing the electroosmotic liquid concentration and the release rate; triggering a low-frequency vibration module of the material layer to generate vibration waves, and enabling the solution and the vibration waves to form a molecular-level synergistic stripping effect; calculating a nanoparticle dispersity index, and generating an inverse gradient mixing instruction when the index exceeds a threshold value; activating a hot vortex ring in a spiral flow guide convex pattern area and establishing a directional heat energy migration channel; and verifying the dynamic equilibrium state of the temperature of the paste core area and the viscoelasticity of the target formula, and triggering a phase lock control signal to terminate the parameter adjustment chain when the energy consumption rate reaches a stable interval. According to the invention, the problems that the transition period during formula switching is prolonged and the phase differences among the execution mechanisms are difficult to match in real time due to a traditional staged regulation and control mode are solved.

The invention discloses a thermal power generating unit deep peak regulation control system, and relates to the technical field of thermal power generating unit deep peak regulation, the thermal power generating unit deep peak regulation control system comprises a deep peak regulation control center, and the deep peak regulation control center is in communication connection with a data acquisition module, a load prediction module, a working condition monitoring module, a distributed control module and a safety monitoring early warning module. By introducing a distributed control architecture and bat algorithm optimization, each subsystem of the thermal power generating unit is divided into a plurality of local control units, independent and cooperative control is realized, the flexibility and the response speed of the thermal power generating unit in the deep peak regulation process are remarkably improved, and the reliability of the thermal power generating unit is improved. Distributed control ensures that each subsystem can quickly adjust operation parameters according to actual load requirements, and the global optimization capability of the bat algorithm further improves the self-adaptability and decision-making efficiency of the system in a complex working condition, so that the problem of response lag in a traditional centralized control mode is effectively solved.

The invention belongs to the technical field of power system stability control, and particularly discloses an inverter network following and constructing control mode adaptive adjustment method and system, and the method comprises the steps: obtaining a current power grid state parameter in real time; a multi-scale fusion depth evaluation model is constructed and trained; inputting the current power grid state parameters into the trained multi-scale fusion depth evaluation model, and outputting current power grid state evaluation indexes; according to the current power grid state evaluation index, adaptively selecting a control mode, wherein the control mode comprises a network following type inverter dominant mode, a network constructing type inverter dominant mode and a transition mode; and according to the selected control mode, verifying whether the power grid state satisfies a constraint condition, and if not, carrying out global optimization on the control parameters of the inverter. According to the invention, the dynamic stability and disturbance response capability of the power system can be improved through the adaptive adjustment of the network-following type and network-constructing type control modes of the inverter.

The invention relates to the technical field of communication machine room energy conservation, in particular to a temperature energy-saving control method and system for a communication machine room, and the method comprises the steps: obtaining multi-region temperature and humidity and equipment load data in the machine room, inputting a pre-training model to obtain a cabinet heat dissipation risk level, judging an environment state through combination with an incidence matrix, entering an operation mode if a requirement is satisfied, and entering a control mode if a requirement is satisfied. If not, a local temperature control adjusting mechanism is triggered; the method comprises the following steps: calculating a refrigeration efficiency ratio according to the air volume of ventilation equipment, air conditioner power consumption, real-time electricity price and the like, generating a candidate scheme set by using a dynamic weight multi-target optimization algorithm in combination with factors such as equipment life, and selecting a target scheme through virtual verification; and according to air conditioner waste heat and cabinet heat dissipation and storage heat, a heat energy distribution strategy is dynamically generated by using a deep Q network reinforcement learning algorithm. Therefore, the problems that in the prior art, energy is wasted, cost is high, heat dissipation requirements of all areas are difficult to accurately adapt, the service life and stability of equipment are affected, and fault risks are increased are solved.

The invention discloses a wind power plant dual-mode dynamic active wake flow control method and system and a medium, and the method comprises the steps: taking the maximum power output, power balance and fatigue load stabilization as optimization targets for solving and obtaining an optimal control instruction if a current control mode is a power efficiency improvement mode; otherwise, taking the maximum power output as an optimization target to solve and obtain an optimal control instruction; issuing the optimal control instruction to the virtual wind power plant, and delaying to obtain a delayed control instruction; if the mode is a power efficiency increasing mode, directly issuing to a real wind power plant; and if the mode is a power tracking mode, solving a final optimal control instruction by taking power reference tracking, control reference tracking and fatigue load stabilization as optimization targets, and issuing the final optimal control instruction to the real wind power plant. The method aims at weakening the influence of the wake effect in the wind power plant through dynamic active wake control, improving the generating capacity, maintaining the fatigue load balance and bringing higher economic benefits to operators.

The invention provides an intelligent pressure management system, method and terminal for geological sequestration of carbon dioxide, and the system comprises the steps: collecting the pressure data of the interior and peripheral key positions of each geological reservoir in real time through a pressure monitoring subsystem, and then carrying out the data preprocessing and feature extraction through a data processing and analysis subsystem; and predicting the reservoir pressure change trend in a specific time period in the future. If the predicted reservoir pressure change trend pressure is too high, high-risk related information is transmitted to the intelligent regulation and control subsystem, and the intelligent regulation and control subsystem regulates and controls the reservoir pressure by regulating the flow of a carbon dioxide injection well and a production well or starting a pressure release device according to a preset strategy and dynamically regulates a regulation and control mode according to pressure data. Meanwhile, the communication and monitoring subsystem transmits system operation data to a remote monitoring terminal in real time, so that real-time monitoring is facilitated. Real-time, accurate and automatic regulation and control of the pressure of the geological reservoir are achieved, the stability and safety of the geological reservoir in the carbon dioxide injection process are guaranteed, the geological storage effect of the carbon dioxide is improved, and various negative effects caused by too high pressure are avoided.

The invention discloses a remote monitoring and control method based on an intelligent engineering management system, and relates to the technical field of the Internet of Things, and the method comprises the steps: carrying out the data collection of an engineering object, carrying out the weight adjustment of multi-modal data through a dynamic weight distribution algorithm, and carrying out the fusion, and obtaining an engineering state feature vector; constructing a prediction model by using historical data and the engineering state feature vector, and calculating and analyzing dynamic characteristics through phase-space reconstruction and a Lyapunov exponent to obtain a chaos prediction model; performing anomaly prediction on the engineering object, calculating a Lyapunov index and identifying an abnormal mode to obtain an anomaly prediction result; performing fault-tolerant analysis on the abnormal prediction result and the network state by using an AI model, predicting a fault risk, switching a control mode, and obtaining a fault-tolerant operation state; and analyzing the monitoring data by using an AI algorithm to generate a control instruction. According to the method, the data acquisition quality is adaptively optimized, so that the reliability under complex working conditions is improved.

The invention discloses a slurry pump control method and related equipment, and relates to the technical field of seawater desalination, and the method comprises the following steps: obtaining sensor data of a slurry pump; data analysis is conducted on the sensor data, and the working condition abnormal index of the slurry pump is determined; determining a target control instruction corresponding to the working condition abnormal index based on the working condition abnormal index and a preset threshold interval; and based on the target control instruction, the slurry pump is controlled to execute corresponding target regulation and control operation. Real-time monitoring and intelligent judgment of the operation state of the slurry pump are achieved, early warning can be triggered at the initial stage of equipment abnormity, the operation parameters can be adjusted in a self-adaptive mode, the response delay problem of a traditional single control mode is effectively avoided, and the operation reliability and safety of the equipment are improved.

The embodiment of the invention provides an excavator handle control system for remote operation, a handle control method based on a Hall sensor is adopted, non-contact rocker data sensing is achieved, the influence of contact and movement abrasion on the service life of a handle is reduced, and the excavator handle control system has the advantages of being high in sensitivity and good in performance; the handle mechanism is more flexible to operate, and the service life of the handle is prolonged; a two-hand cooperative control mode is adopted, and two three-degree-of-freedom handles are used for simulating the driving environment of a real excavator, so that a good remote control effect is achieved, and the feeling of presence of an operator in the remote operation process is improved; the operation habits of a driver are fully considered, the walking and working of the excavator are respectively controlled through combination of different degrees of freedom of the left hand handle and the right hand handle, an operating lever of the real excavator is simulated in the aspects of structure, motion degree of freedom and working range, and the man-machine interaction environment of excavator driving is restored.

The invention provides a DLH-IHBA-based adaptive cycle engine control law optimization method, and belongs to the technical field of aero-engine control, and the method comprises the steps: building an adaptive cycle engine model, and determining an optimization variable based on the adaptive cycle engine model; according to the control mode of each self-adaptive cycle engine, constraint conditions and an objective function based on optimization variables are determined, the control modes comprise a steady state control mode and a transition state control mode, and the steady state control mode comprises a minimum fuel consumption control mode, a maximum thrust control mode and a minimum turbine front temperature control mode; and based on the objective function and the constraint condition, optimizing the control rule of each control mode by adopting a multi-dimensional learning prey strategy fused with a badger optimization algorithm to obtain an optimal control variable, and adopting Tent mapping as initial particle swarm position mapping. According to the scheme, the convergence speed of engine control rule optimization is increased, and the optimization effect and the engine performance are improved.

The embodiment of the invention provides a grid-connected inverter control mode switching method and device, equipment, a medium and a product, and is applied to the technical field of electrical control. The method comprises the following steps: acquiring a control mode switching signal of the grid-connected inverter, and determining a switching direction of a control mode based on the switching signal; when the switching direction indicates that the current control mode is switched from the GFL mode to the GFM mode, setting an initial value of an integrator of a power synchronization loop of the GFM mode as a phase angle value of a phase-locked loop corresponding to the GFL mode; switching the control mode from the GFL mode to the GFM mode based on a first preset path and a step-by-step switching mode; when the switching direction indicates that the current control mode is switched from the GFM mode to the GFL mode, acquiring a power steady-state output value of the GFM mode, and setting a power reference value of the GFL mode as the power steady-state output value; and switching the control mode from the GFM mode to the GFL mode based on the second preset path and the step-by-step switching mode. The technical effect of improving the control mode switching fluency is achieved.

The invention relates to the technical field of electric vehicle and power grid interaction control, in particular to a vehicle network interaction cooperative regulation and control method, which realizes cloud platform strategy generation and issuing, edge terminal deployment and data acquisition, local control mode switching and power redistribution by building a hierarchical control framework. Constructing a space-time fusion prediction model to perform dynamic load prediction and strategy generation; designing a multi-modal data interaction mechanism, and standardizing data transmission; an abnormal state co-processing mechanism is established to quickly deal with faults; and user side interaction optimization is implemented, and the user is guided to charge reasonably. Experiments show that according to the scheme, the load rate of the transformer in the area can be reduced by 18%, the charging cost of a user is reduced by 23%, the fault response time is shortened to 1 / 5 of that of a traditional scheme, the power grid safety and the charging efficiency are effectively balanced, the charging experience of the user is improved, and coordinated development of the electric vehicle and the power grid is promoted.

The invention relates to a PFC-PWM (Pulse Width Modulation) dual-mode control method for a high-voltage direct-current power supply with a wide output voltage range. The output current of the high-voltage direct-current power supply is detected in real time, the critical threshold value of the load impedance is mapped into the hysteresis interval of the output current, the theoretical boundary of mode switching is obtained, and the control mode is dynamically adjusted. In order to further guarantee smooth switching of the two control modes, hysteresis control logic is designed, and undisturbed smooth switching of the PFM mode and the PWM mode is achieved. Based on an energy equivalence principle and a frequency consistency constraint of a critical load SWL working point, a current threshold criterion and a parameter pre-synchronization strategy are constructed, seamless connection of a mode switching process in a frequency domain and an energy domain is ensured, a transient oscillation risk is remarkably reduced, and a guarantee is provided for continuous and stable output of a full-range load working condition.

The invention relates to the technical field of information fusion, in particular to a multi-modal information fusion control system and method, and the system comprises a target difference evaluation module, a modal replacement judgment module, a modal lag recognition module, a modal weight reconstruction module and a control instruction output module. According to the method, dynamic adjustment and optimization of the control system are achieved by comprehensively evaluating the difference of the execution parameters in the control period, tiny changes in task execution can be reflected in detail by calculating the path coordinate difference value, the category variation frequency and the offset variation amplitude, and then the control strategy is accurately adjusted; the adaptive capacity and decision precision of the system to the environment change are improved, the control mode more suitable for the current task environment can be switched to in time by evaluating the output error and the matching score between the modes, and the resource allocation is optimized and the response speed and the processing efficiency are improved by identifying the abnormal response mode and reallocating the mode weight.

The invention discloses a curved surface mapping track optimization and multi-modal force control method for complex wall surface polishing. The method can efficiently meet the polishing requirement of a complex curved surface. A polishing track is determined by introducing a projection technology, a path optimization model is constructed, and multi-node force fusion attitude control is combined, so that interpolation point calculation and attitude change and position planning matching are realized, and a robot polishing path including a polishing position, an attitude and adjustment parameters is effectively generated. According to the multi-mode force control method, a force control mode is dynamically adjusted by combining radial height state switching of state classification of convex and concave path sections; multi-node fusion attitude control is further introduced, the surface contact force of a sand disc is accurately tracked, the tail end attitude of the robot is dynamically adjusted to follow the curved surface characteristics, the problems of irregular curved surfaces, unstable force control and the like in the traditional grinding technology are solved, and the method can be widely applied to automatic efficient grinding operation in the fields of buildings, ships and the like; and efficient and high-precision grinding of the complex curved surface is achieved.

The invention discloses a throat lozenge production auxiliary material adding control system, and belongs to the technical field of data modeling and analysis, and the throat lozenge production auxiliary material adding control system comprises a threshold matrix establishment module which is used for constructing a dynamic threshold matrix; the data acquisition module is used for acquiring current production data; the data analysis module is used for generating a mode selection signal according to a comparison result of the current production data and the dynamic threshold matrix; the mode selection module is used for activating a corresponding control mode based on the mode selection signal, and the control mode comprises a conventional mode, an adjustment mode and an exception handling mode; and the control execution module is used for executing a control instruction corresponding to the control mode and sending the verified control instruction to the auxiliary material adding execution mechanism. According to the method, intelligent association and control strategy optimization of multi-source heterogeneous data are realized through data modeling and iterative optimization of an algorithm level.

The invention discloses a wearable exoskeleton man-machine cooperation real-time control method based on artificial intelligence, and relates to the technical field of exoskeleton robots, and the method comprises the steps: recognizing an exoskeleton scene in power grid maintenance, analyzing the demand degree, and constructing a maintenance demand sequence; determining a collaborative analysis scene based on the maintenance demand sequence; performing quantitative analysis on the man-machine confrontation event in the collaborative analysis scene to obtain a collaborative deviation value and generate a collaborative analysis signal; constructing a data set according to signal extraction features, and predicting man-machine cooperative control behaviors by using a genetic algorithm; finally, the exoskeleton is controlled in real time according to prediction, the man-machine cooperation degree is judged, and the control mode is switched when necessary. The exoskeleton man-machine cooperation performance is improved, the cooperation problem can be found in time, the control strategy is optimized, and it is guaranteed that electric power overhaul work is conducted efficiently and safely.

The invention discloses a resource adjustment method and system based on power grid frequency deviation, and relates to the technical field of AGC control, and the method comprises the following steps: obtaining environment state data, and constructing an environment state vector; initializing a control agent according to a preset control mode, taking the environment state vector as input, training the control agent through a reinforcement learning reward function, and outputting a strategy action; dynamically switching the strategy action into a control strategy of the AGC controller based on a soft switching and strategy interpolation mechanism; a model prediction control method is adopted, and the power set value of the AGC control unit is optimized in a rolling mode; according to the method, the trained control agent is integrated into the AGC controller through reinforcement learning, so that the AGC controller can dynamically switch the control strategy of the AGC controller, self-adaptive selection of control actions is realized, and the power grid frequency is controlled to be stable by correcting the set value of the unit in the AGC controller and improving the control precision.

The invention discloses a fusion preview PID transverse control method and system for port and mine automatic driving, and belongs to the technical field of automatic driving, and the method specifically comprises the steps: obtaining a reference trajectory point and vehicle pose data, and calculating a nearest point of a vehicle relative to a reference trajectory and the curvature of the nearest point; switching a straight line control mode and a turning control mode through a dynamic smooth transition factor; calculating a preview distance according to the curvature, the vehicle speed and the wheelbase, and obtaining the pose of the preview point; transverse and course errors are calculated, and PID control is carried out to obtain feedback output; combining feedforward control output, and obtaining a final steering control command according to weight fusion; and vehicle pose and track changes are monitored in real time, and adjustment and control are performed. According to the method, stable automatic driving control of the vehicle under different road conditions is realized, and the driving safety and comfort are improved.

The invention discloses a digital control system and method of a VI source, and relates to the field of VI source control. A main loop controller and a clamping loop controller are separately arranged in a main controller, and seamless switching between the main loop controller and the clamping loop controller is realized in cooperation with a monitor; the main loop controller is responsible for accurately outputting voltage / current under a normal working condition, and the clamping loop controller is specially used for overload protection. The two controllers are provided with programmable adjusting parameters, and the response speed and the steady-state precision are dynamically optimized according to load characteristics. The monitor adopts a periodic scanning mechanism, monitors an output signal in real time and intelligently switches a control mode. According to the invention, the four-quadrant operation capability is realized, the hundred microsecond level protection response speed is realized while high-precision output is maintained, and the problems of response hysteresis and parameter solidification existing in a traditional simulation protection circuit are effectively solved.

The invention discloses a belt type roasting machine operation control method and system. The method comprises the steps that roasting machine operation control parameters including the material level, the fan rotating speed and the air bellow temperature target value are set; collecting the blanking amount, the machine speed, the material level and the temperature measurement values of the roasting section air bellow and the soaking section air bellow in real time; calculating a speed target value based on the blanking amount change rate and the material level error, and dynamically adjusting the speed of the trolley; and a fuzzy control strategy or a regular control strategy is selected according to the blanking fluctuation state, the historical control mode and the fan-speed matching degree. In the fuzzy control mode, slope characteristics are extracted through temperature curve fitting, fuzzy decision is carried out in combination with temperature errors, and the fan rotating speed adjustment amount is output; and in the rule control mode, the fan rotating speed adjustment amount is determined according to a predefined rule table. And finally, the fan target rotating speed is updated according to the fan rotating speed adjustment amount, and the rotating speeds of the main induced draft fan and the backheating fan are adjusted according to the fan target rotating speed. The stability of thermal parameters of the belt type roasting machine can be improved.

The invention relates to the technical field of resource optimization aggregation regulation and control, and particularly discloses an optimization aggregation regulation and control method and system capable of adjusting load resources, and the method comprises the steps: analyzing the environment state of a to-be-adjusted region, determining a resource aggregation regulation and control deviation factor, analyzing the power grid state and power grid weakness characteristics of the to-be-adjusted region, and determining a resource aggregation regulation and control deviation factor; and obtaining a resource aggregation regulation response factor, determining a resource aggregation regulation response priority category and a response duration category of the to-be-regulated region, obtaining regulation scale data of the to-be-regulated region, and judging whether load peak shifting and load power correction need to be performed on the to-be-regulated region or not. The problems that a traditional centralized regulation and control mode is difficult to adapt to the flexibility and complexity of distributed loads, and an existing regulation and control method is insufficient in consideration of load characteristics and influence characteristics and cannot achieve accurate regulation are solved. The utilization rate of power generation equipment is increased, and efficient utilization of energy is promoted. And new energy power generation fluctuation can be smoothed, and the access proportion of renewable energy sources can be improved.

The invention relates to the technical field of automatic spraying, and particularly discloses a spraying system based on multi-modal vision and artificial intelligence self-correction, comprising a multi-modal vision acquisition module for acquiring color image information, depth information and infrared feature information; the control module is used for generating a spraying sensing model according to the information of the multi-modal visual acquisition module; the control module carries out spraying area identification, track planning and spraying parameter decision making based on the spraying sensing model; the spraying execution module is used for spraying; and the feedback self-correction module iterates the spraying parameter decision in the control module based on a reinforcement learning algorithm according to the difference between the actual coating state information and the expected state. The control method based on multi-modal vision and artificial intelligence self-correction is applied to a spraying system based on multi-modal vision and artificial intelligence self-correction. The scheme is used for solving the problems that the spraying quality of complex workpieces is not high due to the single sensing dimension of an existing spraying system, and the production flexibility is poor due to the rigid control mode.

The invention relates to the technical field of boiler combustion control, in particular to a boiler combustion multi-target collaborative optimization control method and system. The method comprises the following steps: firstly, acquiring coal quality parameters in real time through a laser-induced breakdown spectrum analyzer, carrying out qualitative and quantitative analysis on spectral data by utilizing a machine learning algorithm, constructing a boiler combustion digital twinborn model fusing a combustion mechanism model and a data driving model, and predicting combustion characteristics, NOx generation trends and coking risks under different blending schemes; and then, dynamically generating an optimization instruction combination according to a prediction result of the digital twinborn model, establishing a safety interlocking mechanism while executing the optimization instruction, and automatically switching to a manual control mode when detecting that the working condition fluctuates severely or the model prediction deviation exceeds a preset threshold value. According to the method, the problems of coal quality information lag and untimely combustion adjustment caused by traditional manual sampling are effectively solved, and the economical efficiency, the safety and the environmental protection property of boiler operation are improved.

The invention discloses a converter collaborative current limiting method based on adaptive virtual impedance and a power loop, and the method comprises the steps: firstly building an equivalent impedance model of a converter and a power grid, analyzing the steady-state and transient characteristics of fault current, and extracting key parameters for current limiting design; when it is detected that the voltage drop of the grid-connected point exceeds a threshold value, a self-adaptive virtual impedance link is automatically put into use, and virtual resistance and reactance values are dynamically calculated according to the variation amplitude of voltage or current, so that the virtual resistance and reactance values are proportionally adjusted along with the fault degree; and when the system voltage returns to normal, the virtual impedance automatically quits, and the control continuity is ensured. Then, the active and reactive power reference values are dynamically corrected according to the voltage drop depth, a power reference signal matched with the voltage amplitude is generated, it is guaranteed that power output does not exceed the limit, and the voltage supporting capacity is improved. Finally, real-time current-limiting control of the converter and stable operation of the system are realized on the premise of not switching the control mode through the synergistic effect of the virtual impedance ring and the power ring. The method has the advantages of being high in response speed, excellent in current limiting effect, good in control continuity and the like, and is suitable for fault current limiting and voltage support control of the grid-forming converter.

The invention relates to the technical field of power supplies, and discloses a high-precision power supply control method based on a digital signal processor, which comprises the following steps of: acquiring data such as voltage, current, temperature, load parameters and the like of a power supply at a high frequency of kHz-level sampling frequency by using a high-precision sensor, transmitting the data to a DSP (Digital Signal Processor), and removing noise interference by applying wavelet transform. Sample quality is improved through high-frequency data acquisition and wavelet denoising, nonlinear and time-varying characteristics of a power supply system are identified by using a lightweight CNN and an LSTM, a reinforcement learning dynamic optimization control strategy is combined, self-adaptive switching between traditional control and an intelligent algorithm is realized through working condition identification, and the power supply system control method based on the LSTM is realized. The problems that noise interference influences data quality, system characteristic recognition is inaccurate, complex working condition control strategy optimization is insufficient, and output precision is unstable due to the fact that a control mode cannot be switched in a self-adaptive mode in an existing power supply can be effectively solved, and control precision, efficiency and device loss can be considered. And the stable operation requirement of the high-precision power supply under multiple working conditions is met.