9 results about "Fractional-order control" patented technology

The application discloses a parallel circulating current suppression method and device of a fractional order inverter, electronic equipment and a storage medium, and comprises the following steps: establishing a mathematical model of a fractional order inverter comprising a fractional order inductor and a fractional order capacitor, constructing a voltage and current double control loop based on a fractional order controller based on the mathematical model, and tracking and controlling the output voltage and current of the parallel fractional order inverters, and suppressing the circulating current by power sharing of the parallel fractional order inverters through droop control and fractional order virtual impedance. On the one hand, the voltage and current double control loop based on the fractional order controller can track the output voltage of the inverter, improve the tracking effect of the output voltage, reduce the voltage difference between the inverters, and suppress the parallel circulating current. On the other hand, the droop control combined with the fractional order virtual impedance can improve the power distribution response speed of the fractional order inverter, improve the power distribution accuracy between the inverters, and improve the suppression effect of the parallel circulating current.

This invention discloses a grid-connected frequency control method for a doubly-fed induction generator (DFIG) based on a fractional-order adaptive virtual synchronous generator (VSSG), belonging to the field of wind power grid-connected control technology. The method first establishes a mathematical model for DFIG grid connection and monitors grid frequency and active power in real time. Second, it constructs a grid-side VSG control model, analyzes the influence of rotational inertia J and damping coefficient D on the system's dynamic response, and designs adaptive VSG control. Based on this, fractional-order control theory is introduced, and a fractional-order VSG control module is designed. The system's frequency regulation performance is optimized using a non-integer-order differential operator. Finally, the controller parameters are optimized to achieve rapid dynamic response and frequency stability. By combining fractional-order control and adaptive VSG strategies, this invention effectively suppresses power oscillations and frequency overshoot during grid frequency fluctuations, improving the frequency support capability and transient stability of the DFIG grid-connected system. It is suitable for frequency control applications in power systems with a high proportion of renewable energy integration.

The application provides a micro-grid hierarchical fractional order robust control method based on neural network order estimation, and belongs to the technical field of micro-grid control. The application establishes the mapping relationship between the micro-grid operation state parameters and the fractional order PID controller order parameters by constructing a neural network order estimator, adopts the fractional order PID controller which has better dynamic performance than the integer order PID, increases the adjustable integral order and differential order, and combines the self-optimization ability of the LSTM neural network, so that the fractional order PID controller can obtain more suitable parameter configuration under different working conditions. Meanwhile, the application establishes a small signal model of the micro-grid closed-loop system, constructs the stability criterion and the stability domain boundary based on the frequency domain analysis method, and checks and corrects the order parameters. The application significantly improves the adaptability of the system to complex working condition changes, improves the transient control performance of the micro-grid, reduces the overshoot and shortens the regulation time.

The application discloses a semi-active vehicle ISD suspension system and a fractional order control method, and comprises the following working steps: step one, constructing a vehicle ISD suspension structure model based on fractional order semi-active control; step two, fractional order semi-active control method analytical expression; step three, solving variable values and objective functions by using an optimization algorithm; and step four, dynamic performance simulation analysis.The application has the beneficial effects that the dynamic characteristics of a complex system can be more accurately described, and the vehicle ride comfort is improved.

The application is a fractional order controller parameter setting method based on a rotating Hankel matrix and a multi-objective genetic algorithm NSGA3. The application relates to the technical field of target constraint controller optimization design, and utilizes the multi-objective genetic algorithm NSGA3 to optimize fractional order controller parameters, so as to complete the design of a fractional order controller meeting the required indexes of a system; the setting of a reference point, the adaptive standardization of a population, the correlation operation and the individual reservation operation. The fractional order controller design method provided by the application has a significant improvement in overshoot and regulation time compared with existing methods. Under the condition of meeting the same required indexes of a system, the slope of a phase characteristic curve obtained by the fractional order controller designed by the method of the application is closer to 0, and the robustness of the system is improved.

The application discloses an anti-interference learning control method based on a dynamic combination model, which is applied to a control system without model or weak model dependence. The method is characterized in that a weight system is added to the output of a controller, different order process local models of a controlled system are used as sub-models, a weight evaluation and updating mechanism is set, the connection weights of the sub-models are dynamically determined, the sub-models are combined into a dynamic combination model of the controlled system with variable order according to the connection weights, the comprehensive model parameters of the controlled system are calculated according to the dynamic combination model, and the anti-interference learning control of the controlled system is realized. The model structure of the dynamic combination model, the system order updating mode and the weight adjustment method are given. The dynamic combination model overcomes the problem of dynamic change of the system model in operation, realizes the modeling of the fractional order controlled system, and expands the application range of the dynamic combination model.

The application provides a micro-grid hierarchical fractional order robust control method based on neural network order estimation, and belongs to the technical field of micro-grid control. The application establishes the mapping relationship between the micro-grid operation state parameters and the fractional order PID controller order parameters by constructing a neural network order estimator, adopts the fractional order PID controller which has better dynamic performance than the integer order PID, increases the adjustable integral order and differential order, and combines the self-optimization ability of the LSTM neural network, so that the fractional order PID controller can obtain more suitable parameter configuration under different working conditions. Meanwhile, the application establishes a small signal model of the micro-grid closed-loop system, constructs the stability criterion and the stability domain boundary based on the frequency domain analysis method, and checks and corrects the order parameters. The application significantly improves the adaptability of the system to complex working condition changes, improves the transient control performance of the micro-grid, reduces the overshoot and shortens the regulation time.

A fractional-order optimization control method for the load frequency of an integrated energy system incorporating green hydrogen includes the following steps: Step 1: Establishing a mathematical model for load frequency control involving an electro-hydrogen-electric conversion link including an alkaline water electrolyzer, a hydrogen storage tank, and a hydrogen fuel cell; Step 2: Establishing a load frequency control system model for an integrated energy microgrid incorporating green hydrogen; Step 3: Designing a fractional-order PI... λ D μ Controller, Step 4: Establish the objective function for frequency control optimization of fractional-order loads; Step 5: Propose a controller parameter optimization strategy based on an improved sparrow search algorithm. The method of this invention can help improve the photovoltaic absorption capacity of integrated energy systems, thereby improving the frequency stability of the system.