Research on Voltage Stabilizing Control Strategy of Critical Load in Unplanned Island Based on Electric Spring

Aiming to solve the problem of voltage fluctuation of critical load caused by lack of control when an unplanned island occurs in a microgrid, a voltage stabilizing control strategy of critical load based on electric spring is proposed in this paper. When unplanned islanding occurs in a microgrid system, the system bus voltage fluctuates dramatically due to instantaneous power imbalance, compromising the power supply safety of important loads on the bus. In this paper, the electric spring control mode is integrated into the voltage stabilizing control strategy of critical loads in an unplanned island for the first time to realize the protection of critical loads. First of all, a model of an optical storage AC/DC hybrid microgrid is built, the overall system architecture is determined, and the microgrid is divided into four working states. Second, the working principle of electric spring is introduced, and a decoupling control strategy based on double closed loop is proposed. Finally, the experimental simulation of the proposed control strategy is experimentally simulated in Matlab/Simulink environment. The simulation findings show that when the bus voltage and current of microgrid change due to an unplanned island, the proposed control strategy based on electric spring may achieve the stability of voltage and current on critical loads.

Application of regression in algorithm of nonlinear stochastic adaptation of unstable multidimensional objects

The results of a study of applicability of kernel estimation in the synergetic control systems for the objects unstable in an open-loop state (without a stabilizing control) have been presented. The effectiveness of kernel estimates has been shown for four nonlinear objects with unstable limiting states. The estimate the effectiveness of embedding the kernel predictive estimate of the state variables of a nonlinear object, subjected to disturbances of an unknown nature, into the system of synergetic control is demonstrated.

On asymptotically stabilizing control of nonlinear fractional control systems using an optimization scheme

In this paper, stabilization of the nonlinear fractional order systems with unknown control coefficients is considered where the dynamic control system depends on the Caputo fractional derivative. Related to the nonlinear fractional control (NFC) system, an infinite-horizon optimal control (OC) problem is first proposed. It is shown that the obtained OC problem can be an asymptotically stabilizing control for the NFC system. Using the help of an approximation, the Caputo derivative is replaced with the integer order derivative. The achieved infinite-horizon OC problem is then converted into an equivalent finite-horizon one. According to the Pontryagin minimum principle for OC problems and by constructing an error function, an unconstrained minimization problem is defined. In the optimization problem, trial solutions are used for state, costate and control functions where these trial solutions are constructed by using a two-layered perceptron neural network. A learning algorithm with convergence properties is also provided. Two numerical results are introduced to explain the main results.