245 results about "Nonlinear model" patented technology

The invention relates to the technical field of industrial mechanism models, in particular to a shock absorber performance optimization control method based on model fusion, which comprises the following steps: extracting a low-frequency disturbance state variable and inputting the variable-topology industrial mechanism model to generate a nominal reference state trajectory; utilizing a depth state observation network fused with energy passivity constraint to calculate a non-linear model mismatch compensation amount and an adaptive weighting parameter; performing dynamic fusion on the nominal reference state trajectory and the compensation amount based on the adaptive weighting parameter to generate a generalized state estimation value; and executing dynamic multi-objective optimization based on the generalized state estimation value, and generating mixed mode control input acting on an execution end. According to the invention, through adaptive fusion of a mechanism model and a data driving method, physical consistency, calculation real-time performance and robustness of a control process are considered.

Disclosed in the present invention are a data-driven linear MPC-based assessment method and apparatus for a frequency regulation capability of a wind farm, and a control method and apparatus. The assessment method comprises the steps of: offline acquiring historical operation data sets of wind turbines, and training a linear predictive control model, wherein the linear predictive control model is obtained by applying a dimensionality-increasing transformation process to a wind farm frequency modulation dynamic nonlinear model on the basis of a Koopman operator theory; acquiring real-time operation data of the wind turbines, and using the acquired real-time operation data to solve for a linear MPC optimization model, to obtain droop coefficients of the wind farm, wherein the linear MPC optimization model is constructed on the basis of the trained linear predictive control model and by taking the maximum droop coefficient of the wind farm as an objective and setting a safety rotational speed constraint condition; and assessing a frequency regulation capability of the wind farm on the basis of the obtained droop coefficients of all the wind turbines in the wind farm. The present invention has the advantages of ease of implementation, high assessment efficiency and accuracy, and strong scalability, and the like.

The invention discloses a method and a system for evaluating the nonlinear influence of a greenbelt landscape pattern on a rainfall flood regulation service. The method comprises the following steps: step 1, constructing a basic database; 2, processing data variables; 3, constructing a nonlinear model: taking a green land landscape pattern factor as a core explanation variable, taking a rainfall flood regulation service index as a response variable, introducing a natural environment factor and a social economic factor as control variables, training the random forest model, and constructing a rainfall flood regulation service prediction model; 4, key factors are identified, wherein the top-ranked factors are regarded as key factors playing a role in rainfall flood adjustment service; step 5, analyzing a key threshold: performing visual analysis on a nonlinear relationship between the key factor and the rainfall flood adjustment service by adopting a partial dependency graph algorithm; and by observing positive and negative influence trends and change rates of the key factors on the rainfall flood regulation service indexes in different value intervals, identifying potential key thresholds.

The invention relates to the technical field of meteorology and hydrology, in particular to a rainfall space reconstruction method, system and device based on terrain and meteorological multi-factor fusion driving and a medium, and the method comprises the steps: obtaining space terrain factors and meteorological observation data of a target area, and employing a BP neural network to progressively interpolate missing measurement values of meteorological driving variables in a layered manner; setting a missing detection elimination rule based on a wet season and a dry season to process precipitation data; constructing a 14-dimensional high-dimensional input feature system fusing a space terrain factor, a time sequence factor and a meteorological driving factor; nonlinear models such as an XGBoost model, a BP neural network model or an LSTM model are used for training, and finally the monthly scale precipitation space reconstruction of the grid is achieved. According to the method, the problems that a traditional interpolation method is poor in adaptability in a complex terrain area and insufficient in multi-factor driving relation description are effectively solved, and the precision and the physical consistency of precipitation space distribution are remarkably improved.

The invention provides a multi-dimensional data preferential analysis method and device based on a gradient boosting decision tree model, and relates to the technical field of data mining, in the method, a target gradient boosting decision tree model is adopted, the nonlinear modeling problem of strategy optimization in multi-dimensional data is solved, and the multi-dimensional data is optimized according to the target weight determined in the training process. According to the method, key features can be accurately identified, the processing efficiency and precision are high, compared with a traditional linear regression or single decision tree method, the accuracy and real-time performance of strategy optimization can be improved, high interpretability and robustness are achieved, an optimization strategy can be adjusted in a self-adaptive mode in a changeable environment, and the method is suitable for popularization and application. The method is widely applied to multiple fields of industrial optimization, intelligent decision making and the like.

The invention provides a green electricity hydrogen production system optimization method and system considering multi-tank service life balance, and belongs to the technical field of new energy and electrochemistry, the method comprises the steps of obtaining market data, power grid data and downstream hydrogen load demand data in a preset scheduling period, and obtaining initial state data of a plurality of electrolytic tanks deployed in a hydrogen production station; constructing a mixed integer quadratic programming optimization model with the goal of maximizing the comprehensive benefit of the system; the model is solved, and the optimal scheduling strategy of the start-stop state and the operation power of each electrolytic cell in each time period in the scheduling period is obtained; and converting the optimal scheduling strategy into a control instruction, and issuing the control instruction to a controller of a hydrogen production station to complete collaborative optimization of the green electricity hydrogen production system. The method aims at solving the problems that in an existing green electricity hydrogen production system scheduling method, the calculation cost of a nonlinear model is high, the nonlinear model is difficult to apply to an actual large-scale system, and short-term market income and long-term equipment life cannot be effectively coordinated and balanced.

The invention relates to the technical field of wireless positioning, in particular to a dynamic-dynamic TDOA (time difference of arrival) cooperative positioning method based on an unmanned aerial vehicle-mounted mobile base station. The method comprises the following steps: constructing a cooperative sensing network comprising at least four unmanned aerial vehicles carrying unmanned aerial vehicle-mounted base stations, and realizing microsecond-level high-precision time synchronization of the whole network through a bidirectional distance measurement technology in combination with an introduced clock error model. All the base stations receive signals transmitted by the moving target to be measured under the unified time base, the arrival time is recorded, and a TDOA measurement value is calculated with the main node base station as the reference. And based on the group of TDOA measurement values, tracking and positioning the to-be-measured moving target by adopting an extended Kalman filtering algorithm, and estimating and outputting the three-dimensional position and speed of the to-be-measured moving target in real time by linearizing the nonlinear model. The method effectively overcomes the technical problems of difficult time synchronization and serious error coupling in a dynamic-dynamic scene, and has the advantages of being independent of ground infrastructure, high in positioning precision, strong in robustness and remarkable in cooperative gain.

The invention relates to the technical field of data analysis, in particular to a newborn health data analysis system which comprises a physical sign data construction module, a trend recognition module, a weight distribution module, a nonlinear modeling module and a prediction curve output module. According to the method, the growth cycle of the newborn is subdivided into continuous stages with different physiological meanings, the change rates of the sign data between the adjacent stages are deeply calculated and compared, the dynamic trends such as acceleration, stability or slowing down of body weight and height changes can be accurately recognized, and then quantitative weights are given to the different growth trends; according to the method, the positive growth situation is more evaluated in subsequent analysis, the growth and slowdown situation is correspondingly adjusted, a nonlinear prediction model is constructed on the basis, the non-uniform-speed natural law of growth and development of the newborn can be fit, the staged trend can be dynamically fused into long-term change track prediction, and the prediction accuracy of the growth and development of the newborn is improved. And the health conditions of the newborn in different developmental window periods are disclosed.

The invention belongs to the field of geological disaster prediction, and particularly provides a reservoir landslide displacement prediction method based on dynamic lag recognition and fuzzy entropy optimization, which comprises the following steps: acquiring and preprocessing landslide time sequence monitoring data; combining the distributed lag nonlinear model with the maximum information coefficient, dynamically analyzing the lag relationship between the displacement and the rainfall and reservoir water level through a sliding window, outputting a self-adaptive lag stage and constructing a lag feature set; adaptively decomposing the displacement sequence by using variation mode decomposition of fuzzy entropy optimization, determining the optimal mode number according to the minimum fuzzy entropy, and reconstructing the intrinsic mode function into trend, period and random items; the method comprises the following steps: extracting local features of a multi-lag feature space through CNN, inputting reconstructed displacement components into GRU to capture time dependence, introducing an attention mechanism to weight a key time step, and outputting a predicted value and a confidence interval through quantile regression; according to the method, dynamic lag capture, adaptive decomposition and CNN-GRU-Attention are fused, and high-precision and high-robustness prediction is realized.

The invention relates to the technical field of electromagnetic measurement and state monitoring, and discloses a global state sensing system and method for an electromagnetic voltage transformer. The technical problems that in the prior art, data robustness is insufficient and actual working condition changes of a power grid are difficult to adapt due to the fact that accurate resonance circuit parameters are depended and a single calculation path is adopted are solved. According to the method, ferromagnetic resonance phenomena under various faults are simulated based on electromagnetic transient simulation, phenomenon data are obtained, and influence factors are analyzed to obtain influence rule data. And taking the rule data as input, and adopting a comprehensive solving method combining programming calculation, system structure chart calculation and electromagnetic transient software simulation to obtain a high-reliability state quantity time sequence. Based on the time sequence, a nonlinear model which takes loss into account and does not depend on precise resonant circuit parameters is derived, and explicit mathematical functions of primary current and transient overvoltage of the voltage transformer are obtained.

The invention relates to an aiming point dynamic stabilization and automatic calibration method fusing inertial measurement unit data, and belongs to the technical field of aiming equipment dynamic control. The method comprises the following steps: acquiring attitude data of an inertial measurement unit, and constructing a multi-source data matrix in combination with real-time position data of an aiming point and environmental interference data; after time sequence synchronization and drift suppression are carried out on multi-source data, associated feature vectors are extracted through multi-modal feature fusion; inputting a self-adaptive extended state observer to estimate total disturbance and generate an anti-interference compensation amount, constructing a kinetic model in combination with a nonlinear model prediction controller, solving a multi-objective optimization problem, and generating an attitude compensation control sequence to realize dynamic stability of an aiming point; and finally, aiming deviation is calculated in real time to trigger double-closed-loop calibration, an inner ring corrects the drift error of the inertial measurement unit, and an outer ring optimizes parameters of the observer and the controller. According to the invention, environmental interference and inertial drift are effectively suppressed, the dynamic aiming stability is improved, and the long-term aiming reliability is guaranteed through closed-loop calibration.

The invention provides a dynamic risk perception prediction stability control system and method for a cold-chain logistics transport vehicle, and belongs to the technical field of vehicle control. Comprising three core modules: a physical-data mixed residual state predictor, a quantitative stability evaluation module based on a maximum Lyapunov index, and a self-adaptive nonlinear model prediction controller fusing predetermined performance control. And finally, the control instruction is distributed to the four in-wheel motors through a multi-target torque distributor. According to the method, the prediction precision is remarkably improved, continuous risk quantification is realized, and meanwhile, the control performance and the system robustness are synchronously improved.

The invention discloses a train rail coupling vibration control method, device and equipment and a readable storage medium, and is applied to the technical field of intelligent control, and the method comprises the steps: obtaining a nonlinear model considering a vibration mode of a rail based on a high-speed maglev train structure of a lap joint structure, a rail vibration equation and an electromagnet kinetic equation; determining motion characteristics of the nonlinear model to obtain a motion state space equation; determining a reference model corresponding to the nonlinear model, and determining a control law when the difference between the motion state space equation and the reference model is minimum; and determining target vibration control parameters by using a control law by taking the nonlinear model as a control object reference model as an ideal target. According to the method, the nonlinear model considering the vibration mode of the track is obtained through the high-speed maglev train structure based on the lap joint structure, the corresponding reference model and control law are designed, and due to the fact that the vibration mode of the lap joint structure is considered in the nonlinear model, the suppression effect on coupling vibration of the high-speed maglev train track is better.

This invention provides a low-computational-complexity, pre-set performance-tolerant control method for steer-by-wire systems. The control method includes: determining the control objective based on a pre-set performance control method; subsequently designing a new pre-set performance function and error transformation function; designing virtual control laws and control laws in the controller using a robust control method; establishing a dynamic model of the steer-by-wire system based on system dynamics; establishing a dead-zone model and a fault model based on the impact of dead zones and faults in the vehicle actuators on the control performance of the steer-by-wire system; simultaneously obtaining the input nonlinear model of the steer-by-wire system; subsequently establishing a state-space model of the steer-by-wire system; and finally, verifying the controller's control effect and optimizing its internal parameters using the state-space model. This invention considers fault and dead-zone issues, improving the robustness of the controller; and considers the limited computational resources of the vehicle, with a simple controller structure that improves the overall computational efficiency of the vehicle.

The invention discloses a digital self-interference cancellation method based on transceiver nonlinear behavior modeling, and belongs to the technical field of electronic reconnaissance and interference. The digital self-interference cancellation method comprises the following steps of performing linear combination on memory components of a broadband signal, and generating cross terms with different phase structures by using a nonlinear effect so as to construct a Wiener model; establishing a multipath self-interference channel model; nonlinear modeling is carried out on linear components of interference signals received by a radio frequency front end at a receiver, and nonlinear component modeling is a linear model; for a nonlinear model constructed by a linear component in an interference signal received by a radio frequency front end, adopting dynamic reduction measurement to reduce the complexity of the model; parameter preliminary estimation is carried out by using the characteristics commonly represented by the intermediate variables, and parameter estimation is optimized by combining a two-step iterative algorithm; and reconstructing the transmitting signal in the digital domain according to a parameter estimation result to generate a reference signal, and realizing interference cancellation in the digital domain of the receiving end.

The application discloses a kind of charging equipment health state evaluation method and its device, electronic equipment, it is related to new energy technology field, wherein, the evaluation method includes: the actual parameter value of the component parameter of target charging equipment is obtained, based on actual parameter value, using preset nonlinear model determines the benchmark value and increment value of different component parameters, for different components under each circuit, based on benchmark value and increment value, determine the health value of each different component, based on the health value of each different component, determine the health state of target charging equipment.The application solves the technical problem that the accuracy of related art for evaluating the health state of charging equipment is low.

The application discloses a kind of unmanned plane path planning and control integration optimization method, this method is directly optimized control input, simultaneously solve path planning problem and optimal control problem, secondly, unlike the secondary planning method of the past, the particle swarm algorithm is introduced in this paper to solve complex nonlinear model prediction problem, finally, in view of the deficiency of artificial potential field method, the subsequent navigation point is predicted using model predictive control, and the control input is optimized using particle swarm optimization algorithm, which solves the influence of traditional artificial potential field method on path planning and control, and realizes the optimization of unmanned plane path and control input. This method optimizes the path and control input simultaneously, can realize the optimal path planning while the control input required for driving is also the optimal solution, realizes the integration optimization of unmanned plane path planning and control.

The invention provides an active-disturbance-rejection fault-tolerant control method and system for aerial refueling of an unmanned aerial vehicle, and belongs to the technical field of aerial refueling anti-interference control, and the method comprises the steps: introducing a virtual control variable, building a six-degree-of-freedom motion equation of the unmanned aerial vehicle, converting the six-degree-of-freedom motion equation into an affine nonlinear model, and dividing the state variable of the unmanned aerial vehicle into five control loops; acquiring real-time position information of the refueling taper sleeve, generating a smooth docking reference trajectory by using a three-section method, and converting the expected position instruction into an expected position instruction of the mass center of the unmanned aerial vehicle; based on an ADRC algorithm, an LESO and a controller are designed for each control loop; generating a control signal of each control loop according to the virtual control variable; and in each stage of air refueling operation, adopting a mode fading algorithm to realize smooth switching between a guidance strategy and a controller in each stage, outputting a comprehensive control signal, outputting the comprehensive control signal to an unmanned aerial vehicle execution mechanism, and driving the unmanned aerial vehicle to track an expected position instruction of the mass center of the unmanned aerial vehicle. The safety of aerial refueling of the unmanned aerial vehicle is guaranteed.

The application discloses a multi-power unit high-speed train control method, and relates to the technical field of train control.The method comprises the following steps: performing stress analysis on a multi-power unit high-speed train to determine a high-speed train dynamics model; determining a discrete-time nonlinear model based on the high-speed train dynamics model; constructing control force and control force change rate constraints; converting the discrete-time nonlinear model to obtain a dynamic linearization data model; designing a parameter estimation algorithm and an adaptive extended state observer; designing a discrete-time terminal sliding mode function and a hyperbolic curve type reaching law; determining a data-driven terminal sliding mode control scheme based on the dynamic linearization data model, the parameter estimation algorithm, the adaptive extended state observer, the discrete-time terminal sliding mode function and the hyperbolic curve type reaching law; and performing tracking control operation on the multi-power unit high-speed train by using the data-driven terminal sliding mode control scheme.The application improves the control precision of high-speed train operation while reducing the sliding mode chattering.

The invention discloses an online parameter identification and hysteresis compensation method for a piezoelectric positioning platform. The method comprises the following steps: S1, signal acquisition: acquiring an input signal u (t) and an output signal y (t); s2, filtering processing: constructing a filtering variable and a regression vector phi (t) by using a filtering technology; s3, auxiliary variable calculation: solving an auxiliary regression matrix M (t), a vector N (t) and a vector R (t); s4, estimation error information extraction: constructing a parameter approximate estimation error vector E (t); s5, parameter estimation updating: updating an estimation vector according to a parameter estimation adaptive law based on a preset time-space obstacle function; and S6, hysteresis compensation: realizing hysteresis compensation based on a feedback linearization strategy. According to the method, the asymmetric dynamic hysteresis nonlinear model for accurately describing the piezoelectric positioning platform can be established, online parameter identification and real-time hysteresis compensation are realized for parameter perturbation possibly generated by a system under an uncertain working condition, and the positioning precision of the piezoelectric positioning platform in the ultra-precision positioning field is effectively improved.

The invention particularly relates to a direct force carrier landing control method based on predefined time active disturbance rejection control, and belongs to the technical field of aviation. Aiming at the problems of high landing accident rate and large wake flow disturbance influence of a shipboard aircraft, uncertain convergence of a traditional control method and the like, the method comprises the following steps: firstly, establishing a six-degree-of-freedom nonlinear model, an aerodynamic force and an aerodynamic moment model of the shipboard aircraft, and fusing the wake flow disturbance influence; constructing a torque control channel and a direct force control channel, and converting the torque control channel and the direct force control channel into a control-oriented model; and then designing PTADRC-based attitude angle, angular rate and track angle control laws, and realizing total disturbance estimation and compensation through PTESO. Simulation verification shows that the method has no overshoot and no steady-state error tracking instruction in the predefined time, is high in anti-interference capability, can guarantee the stability and accurate control of the landing attitude of the shipboard aircraft, and remarkably reduces the landing risk.

The invention discloses a dynamic updating method and device for biological characteristics, equipment and a storage medium. Comprising the steps of collecting real-time authentication features of a user, and extracting historical fusion features of the user; calculating a feature quality factor, a similarity factor, a time interval weight and a historical cumulative weight based on the real-time authentication feature and the historical fusion feature; training a fusion weight prediction model based on the multi-layer perceptron neural network model; inputting the feature quality factor, the similarity factor, the time interval weight and the historical cumulative weight into a fusion weight prediction model, and outputting a dynamic fusion weight; and based on the dynamic fusion weight, fusing the real-time authentication feature and the historical fusion feature to obtain an updated fusion feature. According to the method, the newly collected features and the historical fusion features are combined to update the feature library, the feature fusion weight is dynamically adjusted through the multi-dimensional feature factors and the nonlinear model, and the complex relationship among the factors is captured through the model, so that the weight adjustment is more flexible and accurate.

The invention relates to a POD secondary manifold lower chamber flow field nonlinear model order reduction method based on anchor point optimization, and the method comprises the steps: obtaining a two-dimensional lower chamber computational fluid mechanics model as a full-order model, and building a snapshot matrix according to the full-order model; carrying out POD order reduction on the snapshot matrix, and calculating a POD reconstruction error matrix and a secondary manifold coefficient matrix of the snapshot matrix; determining an anchor point, and calculating a quadratic manifold modal matrix at the anchor point according to the POD reconstruction error matrix and the quadratic manifold coefficient matrix; according to the secondary manifold modal matrix at each anchor point, obtaining a secondary manifold modal matrix at the test point; and according to the quadratic manifold modal matrix at the test point, calculating an approximate snapshot of the POD quadratic manifold nonlinear reduced-order model at the test point. According to the method, the representative anchor points are introduced into the parameter sampling space, so that self-adaptive calculation of the secondary manifold modal matrix is realized, and the approximation precision of the reduced-order model is improved.

The application discloses a stable domain constraint trajectory planning and control method for a modular distributed electric drive heavy load vehicle, comprising the following steps: constructing a nonlinear dynamics model of the modular distributed electric drive heavy load vehicle, fitting a tire lateral force model through a rational polynomial, and establishing a vehicle system state space expression based on the rational polynomial form; adopting a sum of squares planning method to estimate the stable domain of the vehicle under different vehicle speeds, road adhesion coefficients and front, middle and rear wheel turning angles, and analyzing the influence of each state quantity on the shape and boundary of the stable domain; embedding the stable domain constraint into a trajectory planning layer based on a quintic polynomial to generate an obstacle avoidance trajectory meeting the dynamics stability; designing a multi-axle proportional steering strategy based on a steady state assumption, and combining a nonlinear model predictive controller to realize trajectory tracking control, so that the stability and control feasibility of the vehicle in a high-speed dynamic obstacle avoidance process are ensured. Through construction of a high-speed dynamic obstacle avoidance simulation scene, the effectiveness, stability and control feasibility of the stable domain constraint trajectory planning and control framework are verified.

The invention provides a modeling method of a GaN HEMT nonlinear model, and relates to the technical field of semiconductors. Comprising the following steps: carrying out PIV test on a GaNHEMT in a temperature range of 25-175 DEG C to obtain PIV test data; improving the initial model equation of the drain-source current of the GaNHEMT into a target model equation based on the environment temperature; embedding the target model equation into ADS software by using a symbol definition device; and fitting the PIV test data with the output characteristic curve of the target model equation in the ADS software, and determining heat effect related parameters. According to the method, an initial model equation of source-drain current is improved into a target model equation in consideration of different working environment temperatures of the GaNHEMT, the improved model can accurately predict output characteristic curves at different temperatures, an accurate drain-source current model at high and low temperatures is established, and it is ensured that a nonlinear model of the GaNHEMT is more accurate during microwave simulation.

The application discloses a kind of based on multi-dimensional structure coupling and phase change determination digital content asset evaluation method and system.The method comprises: obtaining multidimensional state data;System state characteristics are constructed;Phase change determination is carried out based on state variable derivative change;When detecting phase change, value transition analysis is carried out;Output value evaluation result.Wherein, the system state characteristics are mapped by multivariate function containing structure coupling degree function, the phase change determination is realized by comparing first derivative or second derivative of state variable with preset threshold, and the value transition analysis is realized by nonlinear model containing piecewise function or exponential function.The application identifies the transition process of digital content from exchange value to structural value, solves the technical problems that the value evaluation dimension is single and the dynamic adaptability is poor in the prior art, and can be widely applied in AI content evaluation, digital asset pricing, public opinion analysis and other fields.

The invention discloses a ground subsidence springback nonlinear simulation method coupling a physical mechanism and machine learning, and belongs to the technical field of ground subsidence-springback simulation, and the method comprises the steps: fusing the long-time-sequence trend features of a recurrent neural network (RNN) with the local detail features of a convolutional neural network (CNN), employing a mode of fusing the mechanism into learning, and carrying out the recognition of the long-time-sequence trend features of the RNN and the local detail features of the CNN; embedding a physical mechanism into a recurrent neural network (RNN) model unit structure after local detail feature fusion to obtain a coupling model based on physical mechanism constraint, introducing a nonlinear factor n into the coupling model to reflect a nonlinear process of soil compression-springback, and modifying the recurrent neural network unit structure to obtain a soil compression-springback model. Based on physical parameters and construction of a nonlinear coupling model, the nonlinear coupling model is used for outputting a predicted value of ground subsidence-springback, a nonlinear model coupling a physical mechanism and machine learning can be constructed, and accurate description of a subsidence-springback evolution process is realized.

The invention discloses a permanent magnet synchronous motor sliding mode integrated control method based on a disturbance observer, and the method comprises the steps: constructing a hyperlocal mathematical model of a speed ring of a permanent magnet synchronous motor, combining a state equation with a first-order nonlinear model, and reducing the dependence on a precise system model; designing a model-free adaptive fast integration terminal sliding mode controller; a current loop backstepping controller is designed, electromagnetic parameter disturbance is considered, d-axis and q-axis current control laws are recursively constructed, and the anti-interference capability of a current loop is enhanced; and designing an extended super-spiral disturbance observer, estimating total unknown disturbance of the system and performing feed-forward compensation, thereby improving the control precision. The speed tracking precision of the system is improved, and the steady-state error is reduced.

The invention discloses an AC / DC hybrid micro-grid large signal stability improving method and device based on a super capacitor integral droop control strategy and a medium, and belongs to the technical field of power electronic technology and AC / DC hybrid micro-grid system.The AC / DC hybrid micro-grid large signal stability improving method comprises the steps that a super capacitor is connected to a DC bus in parallel, and the super capacitor and a VSC work cooperatively; the supercapacitor employs integral droop control to manage the discharge rate, and the VSC employs voltage-current droop control. And establishing a large-signal nonlinear model of the system and performing stability analysis by using a mixed potential function theory. According to the invention, a voltage source converter adopting traditional U-I droop control and a super capacitor adopting integral droop control work cooperatively: when large disturbances such as constant power load sudden change occur, SC controls a high-frequency component required by instantaneous compensation power through ID by virtue of a quick response characteristic of the SC; and the VSC responds to smooth change of load power and provides low-frequency power support through U-I droop control.

This application discloses an echo cancellation method, electronic device, storage medium, and program product. The echo cancellation method includes: determining an echo simulation signal based on a first input signal using a nonlinear model; the nonlinear model describing the nonlinear relationship between the echo simulation signal and the first input signal; and performing echo cancellation processing on a second input signal based on the echo simulation signal. This application introduces a nonlinear model to determine the echo simulation signal. The nonlinear model can accurately describe the complex nonlinear relationship between the echo simulation signal and the first input signal, thereby more accurately simulating the actual echo generation process, making the echo simulation signal closer to the real echo signal. Based on this, using the echo simulation signal to perform echo cancellation processing on the second input signal picked up by the microphone can more accurately eliminate echo components, improve communication quality, and provide users with a complete and clear voice communication experience.