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The optimal P-Q control issue of the active and reactive power for a microgrid in the grid-connected mode has attracted increasing interests recently. In this paper, an optimal active and reactive power control is developed for a three-phase grid-connected inverter in a microgrid by using an adaptive population-based extremal optimization algorithm (APEO). Firstly, the optimal P-Q control issue of grid-connected inverters in a microgrid is formulated as a constrained optimization problem, where six parameters of three decoupled PI controllers are real-coded as the decision variables, and the integral time absolute error (ITAE) between the output and referenced active power and the ITAE between the output and referenced reactive power are weighted as the objective function. Then, an effective and efficient APEO algorithm with an adaptive mutation operation is proposed for solving this constrained optimization problem. The simulation and experiments for a 3kW three-phase grid-connected inverter under both nominal and variable reference active power values have shown that the proposed APEO-based P-Q control method outperforms the traditional Z-N empirical method, the adaptive genetic algorithm-based, and particle swarm optimization-based P-Q control methods.

Fractional order proportional-integral-derivative(FOPID) controllers have attracted increasing attentions recently due to their better control performance than the traditional integer-order proportional-integral-derivative (PID) controllers. However, there are only few studies concerning the fractional order control of microgrids based on evolutionary algorithms. From the perspective of multi-objective optimization, this paper presents an effective FOPID based frequency controller design method called MOEO-FOPID for an islanded microgrid by using a Multi-objective extremal optimization (MOEO) algorithm to minimize frequency deviation and controller output signal simultaneously in order to improve finally the efficient operation of distributed generations and energy storage devices. Its superiority to nondominated sorting genetic algorithm-II (NSGA-II) based FOPID/PID controllers and other recently reported single-objective evolutionary algorithms such as Kriging-based surrogate modeling and real-coded population extremal optimization-based FOPID controllers is demonstrated by the simulation studies on a typical islanded microgrid in terms of the control performance including frequency deviation, deficit grid power, controller output signal and robustness.

Optimal frequency control of an islanded microgrid has been a challenging issue in the research field of microgrids. Recently, fractional-order calculus theory and some related control methods have attempted to handle this issue. In this paper, a novel fractional-order model predictive control (FOMPC) method is proposed to achieve the optimal frequency control of an islanded microgrid by introducing a fractional-order integral cost function into model predictive control (MPC) algorithm. Firstly, a discrete state-space model is derived for the optimal frequency control problem of an islanded microgrid. Afterward, a fractional-order integral cost function is designed to guide the FOMPC algorithm to obtain optimal control law by borrowing the Grünwald-Letnikov (GL) definition of fractional order calculus. Six simulation studies have been carried out to illustrate the superiority of FOMPC to conventional MPC under dynamical load disturbances, perturbed system parameters and random dynamical power fluctuation of wind turbines.

This paper puts forward the ML-FEM coupled model for analyzing the transient behavior of stator ground fault in generators, which possesses the distinguished capability to accurately take account of not only the distributed capacitances of the stator windings to the ground, but a variety of generator nonlinearities. The numerical simulation of stator ground fault in generators, along with the related experiments, has been completed. The simulation and experiment results explicitly demonstrate that the ML-FEM coupled model is superior to the ML model presented previously, and more preferable for transient analysis of stator ground fault in generators. As paves the way to develop and improve the relay protection scheme against this type of fault.

Based on the multi-loop circuit method of calculating the inductance parameters of AC machine windings, a comprehensive and transient simulation model is established for various faults in the stator windings of a salient-pole synchronous generator. In term of different fault styles, the fault-associated matrix in the state equations is modified, and all of faults in stator windings including internal faults and ground faults can be simulated in this unified model. Simulation results are compared with those of experiments to verify the proposed method. Using the model to study the variation of current and voltage is conductive to analyze the features of faults, and design the relevant protection scheme.

This paper puts forward a developed scheme against the deficiencies of the out-of-step protection based on the three-impedance element principle, which is widely applied at present. Compared with the original scheme, the developed scheme can predict the out-of-step caused by steady stability disruption or transient instability by using the least square method in advance and monitor the limit of steady stability. So the performance of the out of step protection is improved. The paper gives the whole developed protection scheme. The results of dynamic simulation experiments verify the scheme.