17 results about "Fuzzy model" patented technology

The application provides a control method and device for a power converter of an energy storage system, a coupling relationship between active power and reactive power is analyzed through a power outer loop control model based on a virtual synchronous machine and a power coupling coefficient matrix, PI parameters of a reactive voltage compensation channel are adjusted based on the coupling relationship and a fundamental component extracted, a voltage reference value output by a PI controller after correction is obtained, a current value in a prediction time domain is predicted through a model-free predictive control method based on a bias format dynamic linearization in a current inner loop, and the voltage reference value and the current value are optimized in combination with an intuitionistic fuzzy model and a rough decision model, so that a target voltage reference value and a target current value generated after optimization are used for regulation and control. Therefore, through fusion of power decoupling, model-free predictive control, fundamental component extraction, and the intuitionistic fuzzy model and the rough decision model, adaptability, robustness and power quality of the power converter in a complex power grid environment are improved.

This invention proposes a dynamic scene deblurring method and system based on 4D Gaussian representation and pseudo-ground value supervision. The method includes: obtaining a sharp image and a rendered depth map from a 4D Gaussian representation; predicting the motion velocity of dynamic pixels using a velocity MLP network based on the rendered depth map; constructing a fuzzy weight network and predicting the contribution weight of each sampling point on the sampling trajectory; fusing the pseudo-sharp image with the original blurred input using a dynamic region mask to obtain a hybrid pseudo-ground value image; synthesizing a physically blurred image through weighted integration; and constructing a dual-domain reconstruction loss and geometry-motion regularization constraints by combining hybrid pseudo-ground value supervision and gradient decoupling strategies to jointly optimize the scene representation, motion parameters, and fuzzy model, thereby obtaining a sharp dynamic scene representation. This invention achieves explicit modeling of the physical blurring process through 4D Gaussian representation and a fuzzy weight network, improving the realism and view consistency of motion blur synthesis.

This invention discloses an energy management method for hybrid energy storage systems based on fuzzy model predictive control. It constructs a linear variable parameter model predictive control architecture incorporating fuzzy decision-making for closed-loop optimization control. First, an LPV prediction model for the system is established, and the load power sequence is introduced as a measurable feedforward disturbance into the state equation. During the rolling optimization preparation stage, a fuzzy inference mechanism is used to analyze multi-dimensional operating conditions such as system power fluctuations, SOC, and SOV in real time, dynamically adjusting the weighting factors of energy tracking, battery loss, and current smoothing in the cost function to achieve an adaptive trade-off in the control strategy. Simultaneously, a hierarchical setpoint tracking strategy is adopted, intelligently switching the voltage state reference target based on the real-time energy level of the supercapacitor. Finally, based on the time-varying model, dynamic weights, and the reference target, a quadratic programming problem is solved, outputting the optimal control quantity applied to the power converter, thereby significantly improving the system's adaptive coordination capability and overall operating efficiency under complex and variable operating conditions.

This invention discloses a large-signal modeling method for a photovoltaic-storage-charging energy routing system based on T-S fuzzy logic. The method includes: considering the coupling relationships between various units in the photovoltaic-storage-charging energy routing system, establishing a large-signal nonlinear state-space model of the system; identifying nonlinear terms in the large-signal nonlinear state-space model; performing fuzzy linearization on the nonlinear terms to obtain the linearized result; and integrating the linearized result to construct a T-S fuzzy model of the photovoltaic-storage-charging energy routing system. This invention can establish an accurate dynamic model covering the entire operating range of the system, providing an effective model foundation for large-signal stability analysis, robust controller design, and operational optimization of the system.

This invention discloses a dynamic risk assessment method and system for storage tanks, comprising the following steps: S1, identifying potential risk sources of the storage tank, taking tank leakage as the assessment object; S2, inputting the potential risk sources and their possible consequences into a fuzzy Bow-Tie model to form a preliminary qualitative analysis of consequences and measures; S3, transforming the Bow-Tie model into a Bayesian network based on mapping relationships; S4, having experts score the corresponding risk sources and converting the scores into prior probabilities of the root nodes of the Bayesian network, determining the weights of each expert using the analytic hierarchy process (AHP), and converting the expert evaluation results into fuzzy probabilities to obtain the prior probabilities of the root nodes and determine the conditional probability tables of other nodes; S5, calculating the posterior probabilities and analyzing and determining the priority relationships of failure modes; S6, providing corresponding risk control measures based on the priority judgment results. This method realizes management from risk source identification to control measure output, overcoming the problem that traditional methods cannot cope with complex dynamic environments.

The application belongs to the technical field of fault detection and analysis in control system, and discloses a kind of hot reactor fault detection method based on polytope. Through the discretization analysis of the nonlinear model of hot reactor, the system dynamics under the condition of hot reactor failure is described by T-S fuzzy 2-D model. To solve the problem of unknown antecedent variables, a T-S fuzzy state observer is designed to calculate the estimated value of the antecedent variables using state estimation. On this basis, a T-S fuzzy fault detector is designed to generate a residual signal. Using the estimated-based method, a fault detection mechanism based on polytope is established to determine whether a fault has occurred according to the residual signal. Finally, the feasibility and effectiveness of the application are verified through simulation. The application first designs a T-S fuzzy 2-D model fault detector for hot reactor, and conducts fault detection based on polytope under the condition of unknown antecedent variables.

This disclosure presents a disturbance rejection control method and device, electronic equipment, and storage medium for a gas storage chamber. Through this application, a global fuzzy model is constructed based on historical input-output data of the gas storage chamber pressure control system and transformed into a non-minimum state-space model. State variables are directly constructed using the input-output data without reconstructing unmeasurable state variables. Simultaneously, feedback control components and disturbance compensation components are designed to synthesize target control commands. Therefore, this addresses the system stability problem caused by the dynamic coupling of state estimation errors and feedback control in traditional state-space models, which rely on unmeasurable state variables and require reconstruction through observers. This, in turn, restricts the safe and efficient operation of compressed air energy storage power stations. The goal is to improve the operational stability of the gas storage chamber pressure control system, reduce control errors, and thus ensure the safe and efficient operation of compressed air energy storage power stations, better adapting to grid peak shaving and renewable energy consumption needs.

This invention relates to an online 3D fuzzy modeling method and application for distributed parameter systems based on reinforcement learning. The method includes the following steps: Step S1, constructing a dataset based on sensor data collected from the distributed parameter system and building a Markov decision process model; Step S2, establishing an online 3D fuzzy model of the distributed parameter system based on the Actor-Critic reinforcement learning model framework; Step S3, optimizing the online 3D fuzzy model of the distributed parameter system. Compared with existing technologies, this invention has advantages such as high modeling accuracy, dynamic adaptation to new data, wide application, and improved energy efficiency.

The application discloses a micro-grid voltage control method based on a T-S fuzzy adaptive sliding mode observer, and comprises the following steps: an islanded AC / DC hybrid micro-grid operation model is established through a differential equation; an islanded AC / DC hybrid micro-grid state space form is constructed through a state space method; fuzzy rules and membership functions are designed, and an islanded AC / DC hybrid micro-grid T-S fuzzy model is established; a state observer is established to estimate complete state information in real time and offset external disturbances; two sliding mode functions are constructed through estimation errors and tracking errors, so that system output states track reference model output states; according to the islanded AC / DC hybrid micro-grid T-S fuzzy model, a T-S fuzzy adaptive sliding mode controller is established based on the state observer and the two sliding mode functions, and voltage tracking control is realized by output feedback. Through the observer and the sliding mode control, the application realizes voltage control of the islanded AC / DC hybrid micro-grid T-S fuzzy model, and guarantees the stability of a closed-loop system.

The application discloses a resource attraction domain characterization method, system, equipment and medium based on Lyapunov theory, and belongs to the technical field of distribution network operation stability analysis, and comprises the following steps: a heterogeneous source-side resource mathematical model is established according to the composition and control mode of different types of source-side resources in a distribution network; the mathematical model is limited according to preset requirements, and a high-order nonlinear model is obtained; the high-order nonlinear model is subjected to fuzzy processing, and a fuzzy model is obtained; a Lyapunov energy function is constructed according to the fuzzy model; and the Lyapunov energy function is analyzed under the constraint of stability conditions to construct the attraction domain of source-side resources in the distribution network. The application solves the problems that the prior art is difficult to adapt to the nonlinear characteristics of source-side resources, is inefficient and cannot form a unified expression.

This invention discloses a photovoltaic maximum power point tracking (MPPT) control method based on a T-S fuzzy boost circuit. The method models the DC-DC boost converter circuit in the photovoltaic MPT system using a T-S fuzzy model. It determines the inductor current and output capacitor voltage at the system's maximum power output state through a maximum power point scanning method, constructing a photovoltaic MPT reference model. A controller computational model based on an observer observes the T-S fuzzy boost circuit system model and generates state estimates. These state estimates are compared with the state values ​​output by the photovoltaic MPT reference model to generate a PWM control voltage. The system output error is calculated based on the output voltages of the photovoltaic MPT reference model and the T-S fuzzy boost circuit system model, yielding the constraints. This control algorithm enables the photovoltaic MPT system to track efficiently and accurately.

The present application relates to the technical field of building load prediction, in particular to a cross-scale modular integrated building load interval prediction method and system, the method comprising: constructing a modular integrated fuzzy model, constructing a single-input single-output fuzzy prediction module corresponding to each input variable, training the fuzzy model using a building load multi-dimensional dataset to obtain an initial modular integrated fuzzy model; dividing the input space based on the initial modular integrated fuzzy model, constructing a local modular integrated fuzzy model for a multi-dimensional subspace with poor prediction effect, and integrating the initial global model and the local model to construct a cross-scale fuzzy model; performing quantile regression modeling based on the output results of the cross-scale fuzzy model under different quantile points to determine the lower quantile boundary and the upper quantile boundary of the building load prediction result respectively, and constructing a building load interval prediction model. The present application can improve the adaptability and accuracy of building load interval prediction.

The application provides a nonlinear truck trailer system fuzzy control method in a networked environment, comprising: modeling a nonlinear truck trailer system as a piecewise T-S fuzzy model, and designing a piecewise fuzzy state feedback controller with state adaptive saturation, and giving a controller design condition; designing a new piecewise event triggering mechanism, wherein an event triggering rule follows different space region information of state variables of the system model, and researching an event triggering fuzzy control scheme; the application gives a new event triggering control scheme, which can guarantee system stability in the case that the system is affected by outliers, and overcomes the problem that the event triggering mechanism and the controller need to be designed coordinately in the prior application; finally, the effectiveness of the technical method of the application is verified through an actual truck trailer system.

The application provides a self-adaptive locking structure control method and device applied to a box-type substation, and belongs to the technical field of box-type substation protection. The self-adaptive locking structure in the method comprises a paper-folding structure. Two sides of the paper-folding structure used for elastic expansion are respectively provided with magnetic components. The elastic expansion of the paper-folding structure is realized by inputting currents with different directions to the two sides of the magnetic components. The method comprises the following steps: acquiring state monitoring data of the two sides of the magnetic components; obtaining a control quantity through a preset fuzzy PID model based on the state monitoring data; in the case that the control quantity is less than a first threshold value, inputting a current with a first direction to the two sides of the magnetic components to make the paper-folding structure in a rigid state; and in the case that the control quantity is greater than or equal to the first threshold value, inputting a current with a second direction to the two sides of the magnetic components to make the paper-folding structure in a flexible state. The application can reduce the damage of equipment in the transportation and operation process of the box-type substation.

This invention discloses a method, device, and medium for improving the transient stability of a grid-connected converter during low-voltage ride-through (LVRT) based on a TS fuzzy model and attraction domain estimation. It belongs to the fields of power electronics technology and fault ride-through technology for new energy power generation systems. The method includes: using a logarithmic-exponential composite function to handle the non-differentiable characteristics of LVRT limiting control and establishing a full-order nonlinear differential-algebraic model; estimating the attraction domain of the grid-connected converter system based on the TS fuzzy model and linear matrix inequality method; and analyzing the influence of control parameters on transient stability. The beneficial effects of this invention are: constructing a Lyapunov function based on the TS fuzzy model and LMI method; quantitatively estimating the attraction domain of the grid-connected converter system; and analyzing the influence of parameters on transient stability. Simulations and experiments show that the grid-connected converter maintains transient stability when the initial LVRT switching point is within the attraction domain; and optimizing control parameters can improve the transient stability margin during the switching process.