92 results about "Stability constraints" patented technology

The invention discloses a defect detection method and system based on joint distribution optimization and structural knowledge guidance. The method comprises the following steps: analyzing an equipment structured document to construct an equipment physical topological graph containing a physical connection relationship; processing the topological graph by adopting a graph convolutional network to generate a structured feature vector, and extracting a sensing feature vector of the multi-modal sensing data; constructing a knowledge-enhanced attention model, and guiding a perception feature vector to perform cross-modal fusion by using a structured feature vector as a key and a value; and training the model through a combined optimization target containing joint distribution divergence loss and Lipschitz stability constraint so as to ensure the consistency and physical authenticity of the multi-modal features in the shared semantic space. According to the method, the defect detection accuracy can be remarkably improved, and the root cause diagnosis report with physical interpretability is generated.

The invention provides an injection molding machine mold opening and closing parameter optimization method and system oriented to energy conservation and efficiency improvement, and relates to the technical field of industrial process intelligent control. The method comprises the steps that mold temperature, hydraulic pressure and vibration signals are collected in real time through a multi-source sensor, and a composite optimization target of a dynamic energy consumption coefficient and a process stability index is constructed; an improved multi-target genetic algorithm is adopted to iteratively generate a candidate parameter set meeting energy consumption and stability constraints, and the algorithm fuses hydraulic system safety boundary dynamic constraints and an adaptive variation strategy; a parameter adjustment effect is rehearsed through a digital twin model, and motion track deviation is calibrated online in combination with a recursive least square method, so that parameter dynamic injection is realized; and triggering abnormal working condition recovery and material self-adaptive optimization based on a closed-loop feedback mechanism. The system comprises a multi-source data acquisition module, a composite optimization calculation module, a dynamic parameter injection module and a closed-loop feedback control module, and the real-time performance and generalization ability are improved through an FPGA hardware acceleration optimization engine and a transfer learning algorithm.

The invention relates to the technical field of power system control, in particular to a distributed power supply parameter adaptive control method, system and device based on an improved grey wolf algorithm and a medium. Obtaining distributed power supply parameters to construct a VSG model; establishing an objective function by taking the rotational inertia and the damping coefficient as optimization variables and setting stability constraints; adopting an improved grey wolf algorithm comprising a Tent chaotic mapping initialization population, a nonlinear convergence factor, an adaptive weight strategy and a Levy flight mechanism to optimize parameters; and the rotational inertia and the damping coefficient are dynamically adjusted according to the angular velocity change rate to realize parameter adaptive control. The problems that a traditional grey wolf algorithm is prone to falling into local optimum, VSG parameter optimization lacks systematicness, and fixed parameters are difficult to adapt to changing working conditions are solved, parameter optimization precision is improved, coordinated optimization of rotational inertia and damping coefficients is achieved, the system can intelligently adjust control parameters according to the disturbance degree, system power and frequency oscillation is effectively restrained, and the system performance is improved. And the grid connection stability and dynamic response performance of the distributed power supply are improved.

The invention provides a dexterous hand dynamics modeling method and equipment. The method comprises the following steps: constructing a data set according to historical state data of the dexterous hand at multiple moments and corresponding historical moments; constructing a linearization model of the dexterous hand according to the dimension raising function; based on the data set, training the linearization model according to a preset loss function to obtain a kinetic model of the dexterous hand; the preset loss function comprises an evolution error term, a reconstruction error term, a controllability discrimination term and a stability constraint term; the evolution error term is used for representing an error between a dimension raising state and a state evolved by a Kupman operator, the reconstruction error term is used for representing an error generated by comparing a result obtained by raising dimension and then reducing dimension of the state with the state before dimension raising, and the controllability judgment term is used for representing a controllability condition of the linearization model; the stability constraint term is used for representing the stability condition of the linearization model; according to the method provided by the invention, the modeling precision and universality can be improved.

The invention relates to the technical field of structure optimization, and discloses a collision resistance optimization method and system for a rail vehicle energy absorption structure, and the method comprises the steps: determining corresponding simulation parameters influencing energy absorption and a multi-dimensional collision mechanical response data set of the energy absorption structure; constructing a multi-dimensional collision response prediction model; defining a mode stability constraint based on a decoding result of the image feature extraction network, defining a load stability constraint based on a decoding result of the curve feature extraction network, and defining an index matching constraint based on an output result of the multi-dimensional collision response prediction model; based on the mode stability constraint, the load stability constraint and the index matching constraint, a multi-target crashworthiness optimization model of the energy absorption structure is constructed, and solving is carried out to obtain a Pareto solution set; solving an optimal feasible solution of the Pareto solution set, and taking the optimal feasible solution as a collision resistance optimization result of the target energy absorption structure; the method solves the problem that an existing crashworthiness performance optimization method is low in optimization result reliability.

The invention relates to a robust cooperative control method for yaw stability of a distributed angle module electrically-driven vehicle. The method comprises the following steps: acquiring driving state data of a corner module vehicle; according to the transverse stability constraint condition and the transverse dynamical model, a nominal control model is constructed, the nominal control model is a part related to the determined parameters in the transverse dynamical model, and the target of the transverse stability constraint condition is that the error of the yaw velocity and the error of the lateral velocity tend to zero; constructing an uncertainty control model, wherein the uncertainty control model is a part related to the uncertain parameters in the transverse dynamic model; driving state data are input into the nominal control model and the uncertainty control model, and yaw moment control parameters are determined; and based on the yaw moment control parameter and the rear wheel steering control parameter, performing stability cooperative control on the angle module vehicle. By adopting the method, the yaw stability of the angle module vehicle can be improved.

The invention relates to an unmanned aerial vehicle trajectory tracking control method based on model prediction and preset performance constraint, and the method proposes to introduce Lyapunov stability constraint into a model prediction control framework and integrate a preset performance control mechanism, thereby achieving the unification of performance constraint and system stability analysis. Comprising the following steps: establishing a nonlinear system model based on unmanned aerial vehicle dynamics; position errors and attitude errors are defined, a preset performance function is constructed, and errors with performance constraints are converted into unconstrained errors through error normalization and nonlinear transformation; establishing a model prediction optimization problem on the premise of considering input saturation and stability constraints; designing an auxiliary control law based on the transformation error to construct a stability constraint; it is proved that the control strategy can ensure that errors meet preset performance constraints and system local asymptotic stability. According to the invention, stable and reliable trajectory tracking control of the unmanned aerial vehicle system can be realized, and the method has high tracking precision and good dynamic performance.

The invention discloses a high-proportion new energy microgrid optimization operation method considering small interference stability constraint. The method comprises the following steps: 1) generating key droop control parameters; 2) training the input convex neural network by using the key droop control parameters to obtain a convexity transformation model of the small interference stability constraint and the state matrix; (3) constructing a micro-grid real-time optimization model by taking minimization of system network loss and minimization of frequency as targets; 4) solving the micro-grid real-time optimization model to obtain a droop control coefficient; 5) converting the droop control coefficient into a stability index by using a convexity transformation model, and judging whether the stability index Zn is greater than or equal to ximin or not, if so, outputting the droop control coefficient, otherwise, optimizing the droop control coefficient by using a droop control parameter optimization model, and outputting the optimized droop control coefficient; according to the method, the stability of the system is guaranteed, efficient consumption of new energy is realized, the overall operation efficiency of the micro-grid is improved, and the calculation complexity is reduced.

The invention provides an optimal primary frequency control method based on reinforcement learning. The method comprises the following steps: establishing a dynamic model of a power system; designing a frequency control strategy based on a deep reinforcement learning algorithm; the system frequency deviation is used as a reward signal, and a control strategy is trained through interaction with a power system; lyapunov function stability constraints are introduced into the reinforcement learning algorithm; a neural network is adopted to carry out parameterization design on the controller; discretizing the frequency dynamic equation, and proposing a multi-node collaborative optimization target; and deploying the trained control strategy to an actual power system, and adjusting control parameters in real time through an online optimization technology to adapt to the dynamic change of the system operation state. By means of the method, the frequency control accuracy and stability of the power system are improved, and the global optimal performance of frequency control of the whole power grid is achieved in actual deployment.

The invention discloses an AGC adjustment method and system based on multi-objective optimization, and the method comprises the steps: obtaining the output, load demands and new energy output information of each generator set in a real-time operation database of a power system, and obtaining the response capability and cost data of an adjustable load through a demand side management system, the input is used for constructing a multi-objective optimization model; aiming at the constructed high-dimensional nonlinear non-convex multi-objective optimization model, adopting an improved particle swarm optimization algorithm for solving, and improving the convergence speed and the global search capability of the algorithm by introducing an adaptive inertia weight and a diversity retention mechanism to obtain a Pareto optimal solution set; and selecting an optimal frequency modulation scheme from the obtained Pareto optimal solution set according to safety and stability constraints and frequency modulation performance requirements of the power system, determining output adjustment amount and adjustment time of each generator set, and generating a corresponding automatic power generation control instruction.

The invention discloses a new energy consumption capability assessment method and system based on network security and stability constraints, and relates to the technical field of new energy consumption capability assessment, and the method comprises the steps: building a new energy consumption model; analyzing new energy output volatility; performing scene reduction; and calculating the new energy consumption capability. According to the new energy consumption capability evaluation method based on the network security and stability constraints, the randomness of new energy output is fitted by adopting discrete probability distribution, and new energy grid-connected multi-scene state simulation is realized; meanwhile, the probability degree distribution function value in the corresponding scene is calculated and obtained, the efficiency of calculating the new energy consumption capability is improved, and the calculation amount is reduced.

The invention discloses a space-ground integrated resource scheduling method and system, electronic equipment and a medium, and the method comprises the steps: training strategy parameter prediction models of all agents based on a first loss function and a second loss function, and outputting a new strategy parameter set after the training is completed; the new strategy parameter set is used as a current strategy parameter set in a transfer learning stage, so that a strategy parameter set after transfer updating is calculated, and a third loss function is constructed; constructing a stability constraint term according to the migrated and updated strategy parameter set and the current strategy parameter set, and adding the stability constraint term to a third loss function to obtain a total objective function; circularly training the strategy parameter prediction model and the transfer learning stage until the total objective function is converged to a preset threshold value, and obtaining a target strategy parameter set output by the transfer learning stage; and deploying the target strategy parameter set to the space-ground integrated network. According to the invention, the real-time performance of resource scheduling can be realized and the reliability of resource scheduling can be improved.

The invention discloses an SCR denitration ammonia escape and NOx intelligent control method based on machine learning. The method comprises the following steps that boiler operation data are collected and preprocessed; constructing a feature input tensor set; inputting the feature input tensor into a prediction model based on a neural control differential equation, and outputting a predicted NOx and ammonia escape concentration sequence in combination with a Koopman observable space and an energy gating mechanism; constructing a candidate ammonia injection distribution set meeting the valve constraint based on the prediction result; multi-objective optimization is carried out under emission limitation and ammonia injection stability constraint, and an optimal ammonia injection distribution vector is generated; projecting to a feasible region barrier set and then generating an ammonia spraying execution instruction; and collecting feedback data for incremental training and model adaptive updating. The ammonia spraying control precision and the denitration efficiency are improved, and intelligent prediction of ammonia escape and self-adaptive control of NOx are achieved.

The invention discloses a multi-vehicle cooperative control method and system considering time-varying directed communication topology, and belongs to the technical field of vehicle control. The method comprises the following steps: firstly, modeling an unreliable communication topological structure, and representing the unreliable communication topological structure by adopting a continuous time Markov chain in order to describe the randomness of the communication topology; secondly, respectively establishing longitudinal dynamic models for the following vehicle and the pilot vehicle; thirdly, designing a fully-distributed adaptive observer to obtain state estimation information of the pilot vehicle; and finally, constructing an optimization control problem by using the pilot vehicle state and the front vehicle state information observed by the self-vehicle observer, introducing constraint conditions including chord stability constraints, and solving the optimization problem by adopting a DMPC framework to obtain an optimal control quantity so as to realize tracking control of the pilot vehicle state.

The invention discloses a power system scheduling method, device, equipment and medium based on adaptive step length and small-interference stability constraint, and relates to the field of power systems and automation thereof, and the method comprises the steps: judging whether an initial power scheduling result outputted by an original optimal power flow model of a power system meets the small-interference stability constraint or not; if the obtained first judgment result is negative, performing iterative solution in a neighborhood of the initial power dispatching result based on a damping ratio threshold, a fluctuation interval of renewable energy output and a preset adaptive step iterative solution strategy to obtain a target solution result; carrying out robust small interference stability constrained boundary key local positioning based on a target solving result, and collecting sample data according to a positioning result; and performing explicit equivalent modeling based on the sample data, the linear kernel function, the penalty factor and the positioning result, and determining a target power dispatching result by using a modeling result and an original optimal power flow model. And the robust small-interference stability of the power system under renewable energy fluctuation can be ensured.

The invention relates to the technical field of direct-current micro-grids, in particular to a direct-current micro-grid large-signal stability judgment method and system, and the method comprises the steps: firstly carrying out the simplification and equivalence of a direct-current micro-grid, and obtaining a simplified equivalent diagram of the direct-current micro-grid, and a correlation expression of the simplified equivalent diagram; secondly, constructing an equivalent mathematical model of the direct-current micro-grid system according to a simplified equivalent diagram correlation expression; then, according to the equivalent mathematical model of the DC micro-grid system, constructing a state equation of the DC micro-grid system; then a Lyapunov direct method is utilized to limit a state equation, and a stability constraint condition for judging the stability of the large signal of the direct-current micro-grid is deduced; and finally, according to a stability constraint condition, constant power load limit values are set in a classified manner, a judgment process is designed, and the large signal stability of the direct-current micro-grid is judged according to the constant power load limit values. The problem that the large-signal stability of the single direct-current micro-grid with the comprehensive load cannot be simply and quickly judged is solved.

The invention discloses a transient voltage stability constraint iterative solution method based on mechanism explanation, which comprises the following steps of: establishing a transient voltage stability mechanism analysis framework combining qualitative analysis and quantitative analysis, identifying key factors influencing transient voltage stability through qualitative analysis, analyzing a response trend under a set condition, and calculating a transient voltage stability constraint iterative solution. Obtaining the range of the key characteristic value; local interpretation of the LIME is expanded to global interpretation, so that an interpretable analysis result of the LIME can be applied to an optimization problem; building an under-voltage load reduction model, and adding transient voltage stability constraint on the basis of the traditional load reduction model; building a constraint iteration solving algorithm based on sequential LIME to obtain an optimal load reduction scheme for guaranteeing the transient voltage stability of the system; and performing fault simulation on the traditional solution and the optimal solution considering the stability constraint, simulating the condition that the system operation is disturbed, and observing whether the transient voltage can be recovered to the standard level or not.

The invention discloses a static voltage-power angle dual stability constraint-based power system optimization scheduling method, device and equipment and a medium, and relates to the technical field of power systems and automation thereof, and the method comprises the steps: carrying out the pre-solving of a preset power system optimal power flow model under a preset constraint condition; searching a target feasible solution of the optimal power flow model of the power system from a first neighborhood of the pre-solving result; generating a sample set based on a second neighborhood of the target feasible solution, classifying samples, and determining a stability constraint type according to a classification result; using a preset support vector machine technology to segment the sample according to the classification result so as to generate an optimized stability constraint condition; and optimizing the optimal power flow model of the power system by using the optimized stability constraint condition, and performing optimal scheduling of the power system based on the optimized optimal power flow model of the power system. Therefore, various stability constraints can be efficiently embedded into the power system optimization model to provide a more reliable optimization scheduling scheme for the operation of the power system.

The invention relates to the technical field of space optimization, and discloses a container loading space optimization method based on robot collaboration, and the method comprises the steps: building a multi-dimensional physical model of a package through obtaining the point cloud data and weight distribution of the package; generating a loading scheme containing a target pose, a pressure tolerance threshold and a loading sequence based on a reinforcement learning algorithm; the mechanical arm generates a motor current-position composite control instruction according to a pre-trained grabbing dynamical model, and accurate grabbing is achieved; in the loading process, the pose of the parcel is monitored in real time through a Kalman filtering algorithm, when deviation exceeds a threshold value, a local space KD tree is constructed, a collision probability gradient field is calculated, and a compensation scheme meeting stability constraints is generated; and finally, updating the quad-tree index and feeding back to the reinforcement learning algorithm for online optimization. The problem that in the robot loading process, the grabbing force control is not accurate, and the efficiency is reduced due to execution deviation accumulation is effectively solved, and efficient and accurate automatic loading is achieved.

The invention provides a wind power plant fatigue suppression active control method based on data mechanism hybrid modeling, and belongs to the technical field of wind power plants, and the method comprises the steps: building a continuous state space model, carrying out the discretization of the continuous state space model through a sampling period, and obtaining a discrete state space model, calculating a threshold value of a switching control strategy of a wind speed region based on the discrete state space model, and further obtaining a prediction model of main shaft torque and a prediction model of tower thrust, and recording the prediction models as wind turbine generator linear prediction models; an equivalent fatigue load linearization model is obtained through main shaft torque time sequence data, and after the equivalent fatigue load linearization model and a wind turbine generator linear prediction model are combined, a data mechanism dual-drive fatigue load model is obtained; and performing active optimization control solution on the data mechanism dual-drive fatigue load model to obtain an active power distribution optimal solution meeting the safety and stability constraints of the wind power plant.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a cross-platform data interaction sharing method and system based on artificial intelligence. Comprising the following steps: constructing an edge cloud collaborative architecture for health degree perception, and establishing a dynamic health degree baseline for nodes; constructing a correction reward function taking the node health degree as a stability constraint, and training by adopting a multi-agent reinforcement learning model to obtain a Nash equilibrium optimal strategy; decoding the optimal strategy into an executable cross-platform data interaction planning graph through a strategy interpreter network, and coordinating resource conflicts by utilizing a game theory; on the basis of the planning graph, proxy re-encryption and hierarchical homomorphic encryption of context awareness are executed, and an authority token chain is established to realize fine-grained authority tracking; closed-loop optimization of strategies and system self-evolution are carried out through multi-dimensional quality scoring, multi-layer traceability analysis and an elastic weight solidification technology; cooperative gains of cross-platform data interaction in three dimensions of efficiency, security and long-term stability are realized.

The invention discloses a stability constraint LQR single-rotor unmanned aerial vehicle trajectory tracking control method based on GWO-PSO optimization. The method comprises the following steps: constructing a nonlinear dynamic model of a single-rotor unmanned aerial vehicle; defining a hovering point as a unique balance point for stable work of the single-rotor unmanned aerial vehicle, and obtaining a linearization model surrounding the balance point according to the nonlinear dynamic model based on the balance point; establishing an optimization model considering model uncertainty and external interference according to the linearization model so as to design an initial feedforward feedback LQR control law based on stability constraint; taking a weight matrix Q and a weight matrix R in the initial feedforward feedback LQR control law as a group of parametric variables, and optimizing the parametric variables based on the constructed GWO-PSO hybrid optimization algorithm to obtain an optimal variable; and obtaining an optimal feedforward and feedback LQR control law according to the optimal variable, thereby realizing trajectory tracking control of the single-rotor unmanned aerial vehicle. According to the invention, the problem that the existing method is insufficient in trajectory tracking precision, anti-interference capability and robustness is solved.

This invention discloses a method for joint evaluation and optimization of buckling and bending of arbitrary quadrilateral laminates based on physical information neural networks, relating to the field of composite material structure design technology. The method, for arbitrary quadrilateral laminates, first constructs a method based on classical laminate theory... USA The stiffness matrix explicitly reflects the influence of ply parameters, and a physical domain is transformed to a reference domain through a bitriangular affine mapping to establish a physical information neural network mechanical model. The model is trained with the goal of minimizing the total potential energy or Rayleigh quotient, solving for the buckling factor and layer-by-layer strain limits, and adapting to four typical boundary conditions through a boundary embedding function. Subsequently, using ply angle and thickness as variables, optimization is performed using a genetic algorithm, with the goal of minimizing mass, and the buckling factor and strain limits as constraints. The final output is the optimal ply scheme that satisfies specific boundary conditions. This invention can optimize ply angle and thickness while satisfying buckling stability and strain strength constraints.

The invention discloses a large model reasoning stability enhancement method and system in a text scene, and relates to the technical field of knowledge enhancement deep learning, and the method comprises the steps: building a structured text knowledge graph based on a text knowledge base, and generating a dynamic text knowledge embedding matrix through a graph attention network; inserting a text knowledge gating cross attention module into a decoder selection layer of the pre-trained large model, taking the text knowledge gating cross attention module as an external knowledge source, obtaining a knowledge enhanced hidden state after gating fusion, and constructing a transformation model; semantic equivalent perturbation is carried out on an input text to obtain a perturbation sample, the perturbation sample is input into the transformation model in parallel to obtain extraction probability distribution, and divergence and gradient direction consistency loss between two distributions are calculated; and combining cross entropy loss and gradient direction consistency loss to train and transform the model, and updating parameters to convergence to obtain a final large model. According to the method, the fact consistency of output can be improved, common optimization of knowledge guidance and stability constraint is realized, and the result is accurate and reliable.

The invention belongs to the technical field of power system dynamic safety analysis and renewable energy source grid connection, and provides a method for determining the permeation limit of high renewable energy sources under multi-dimensional stability constraint. The method takes physical mechanism depth modeling-multi-dimensional stability coupling theory-intelligent data driven screening as a core, constructs a quantitative correlation model of frequency stability, transient stability and small disturbance stability through rigorous theoretical derivation, proposes a global-region double-layer dynamic inertial constraint mechanism, and combines an AI-driven critical scene screening and clustering method to obtain a critical scene clustering algorithm. And finally determining the safe RE penetration limit. According to the method, the limitation of traditional single-dimension evaluation and static constraint is broken through, all theoretical derivation is based on the dynamic characteristics and the statistical learning principle of the power system, moderate data verification is assisted, and a complete theoretical system and an engineering tool are provided for RE safety integration of the low-inertia power grid.

The invention relates to the technical field of power grids, in particular to a distributed new energy acceptance capacity dynamic calculation method and system, and the method comprises the steps: constructing a combined small-disturbance dynamic model for distributed new energy access, and obtaining a stable constraint boundary; establishing an optimization model by taking distributed new energy acceptance capacity maximization as a target function; and solving the optimization model by adopting a self-adaptive particle swarm algorithm to obtain the maximum acceptance capacity of the distributed new energy in the current operation state of the power distribution network. According to the invention, based on the equivalent dynamic model of the network-forming inverter and the state-space equation of the power distribution network, the combined small-disturbance dynamic model is constructed and the stability analysis is carried out, the stability constraint is directly introduced into the acceptance capacity optimization, and the adaptive particle swarm algorithm is adopted for solving, so that the maximum access capacity of the distributed new energy can be accurately evaluated. Meanwhile, small-interference stability of the system and operation constraints of the power distribution network are considered, and calculation precision, efficiency and engineering application value are improved.

The application discloses a power transformer temperature monitoring method, device and equipment, and relates to the technical field of power equipment state monitoring and intelligent modeling. First, according to the load factor, the average winding temperature and the position of the tap switch of the power transformer at the current moment, the physical model of the power transformer is used to calculate the heat generation power at the current moment; the heat generation power at the current moment is brought into a heat balance equation for solving to obtain the top-layer oil temperature simulation value at the current moment; then, the physical characteristics at the current moment and a preset number of moments before the current moment are combined to form an enhanced feature sequence; the enhanced feature sequence is input into a deviation prediction model to obtain the dynamic deviation of the top-layer oil temperature at the current moment, the top-layer oil temperature simulation value at the current moment is calibrated, and the top-layer oil temperature monitoring value is obtained. The application provides prior knowledge through the physical model, combines data-driven learning of the deviation dynamic characteristics, and applies stability constraints, so that high-precision and stable oil temperature prediction is realized.

The invention provides a new energy power system reliability evaluation method based on environment variables, and relates to the technical field of power systems. According to the method, a non-parametric self-adaptive quantile regression forest model is adopted to perform modeling on wind speed and sunlight intensity, and online incremental updating is triggered based on Kolmogorov-Smirnov statistics; setting a cross-scale state coupling interface layer in the double-layer nested reliability evaluation architecture, defining a shared state vector including an energy storage charge state, an equipment aging index and an available reserve capacity, and applying Lyapunov stability constraint; a period economic value dynamic pricing module based on Q-learning is integrated in a short-term evaluation layer, and a real-time electricity price, a user interruption contract and a load elastic coefficient are used as state spaces to correct a load shedding strategy. According to the scheme, through adaptive modeling of environment variable distribution and cross-scale state stable transmission, the problem of evaluation result lag is effectively solved, and the real-time performance and economical efficiency of reliability evaluation are improved.

The invention discloses a VSG-controlled inverter inertia margin calculation method based on a state-space equation, and the method comprises the steps: 1, building an inverter state-space equation under a VSG control strategy, and obtaining a constraint condition of VSG control parameter design under a stability requirement through feature value analysis; 2, based on a VSG equivalent inertia constant expression, determining a quantitative relation between a VSG control parameter and an inverter inertia under VSG control; and 3, calculating an inverter inertia margin under VSG control by combining a VSG control parameter constraint condition and a VSG equivalent inertia constant expression. According to the method, the adjustable range of the VSG control parameter under the stability constraint is determined based on the state-space equation, and the influence of the VSG control parameter on the inertia is quantitatively described according to the equivalent inertia constant, so that the calculation of the inverter inertia margin under the VSG control is accurately and effectively realized.

The application discloses a kind of low-loss proportional amplification fluxgate current sensor measurement methods, it is related to current measurement technical field, including, obtaining the observation set of synchronous demodulation by phase-locked;Based on the scheduling optimization function of prediction set construction, apply soft opening constraint, amplitude stability constraint, phase consistency constraint and saturation depth constraint, output corresponding control variable;According to control variable setting excitation signal parameter and executing excitation drive and compensation injection, extract the harmonic amplitude-phase response of current cycle in demodulation window position;Harmonic amplitude-phase response is converted into linear measurement output of current cycle, while updating observation set.The application implements proportional closed-loop conversion in I / Q rectangular coordinates, combines amplitude error and phase error into compensation current update amount, and combines calibration proportionality coefficient and output amplitude limiting, so that closed-loop converges amplitude-phase deviation in linear interval quickly, improves compensation current stability and response speed and reduces energy consumption.