The dynamic nature of the distribution network challenges the stability and control effectiveness of the microgrids in both grid-connected and autonomous modes. In this paper, linear and nonlinear models of microgrids operating in different modes are presented. Optimal design of LC filter, controller parameters, and damping resistance is carried out in case of grid-connected mode. On the other hand, controller parameters and power sharing coefficients are optimized in case of autonomous mode. The control problem has been formulated as an optimization problem where particle swarm optimization is employed to search for optimal settings of the optimized parameters in each mode. In addition, nonlinear time-domain-based as well as eigenvalue-based objective functions are proposed to minimize the error in the measured power and to enhance the damping characteristics, respectively. Finally, the nonlinear time-domain simulation has been carried out to assess the effectiveness of the proposed controllers under different disturbances and loading conditions. The results show satisfactory performance with efficient damping characteristics of the microgrid considered in this study. Additionally, the effectiveness of the proposed approach for optimizing different parameters and its robustness have been confirmed through the eigenvalue analysis and nonlinear time-domain simulations.

The aim of this paper is to design a speed controller of a DC motor by selection of a PID parameters using genetic algorithm (GA) and Adaptive Neuro-Fuzzy Inference System (ANFIS). DC motor could be represented by a nonlinear model when nonlinearities such as magnetic saturation are considered. To provide effective control, nonlinearities and uncertainties in the model must be taken into account in the control design. The model of a DC motor is considered as a third order system. And this paper compares three kinds of tuning methods of parameter for PID controller. One is the controller design by the Ziegler and Nichols, second is the controller design by the Genetic Algorithm method and third is the controller design by Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed methods could be applied to the higher order systems.

This paper proposes a two-stage stochastic energy management (EM) in an isolated microgrid (MG) to decide for the day-ahead optimal dispatch. The dispatch aims to effectively manage the MG power sources, including intermittent renewable energy sources (RESs), battery energy storage systems (BESSs), and diesel generators such that the expected operation costs, reactive power costs, spinning reserve, and load shedding are minimized. The Generative Adversarial Networks (GANs) is utilized in this paper as a data driven scenario generation method to model the uncertainties in the output power of the RESs to be used in the stochastic programming formulation. Then, the fast forward scenario reduction algorithm is used to reduce the number of scenarios with the help of SCENRED/GAMS software. Usually, fuel consumption costs of diesel generators are considered to be dependent on active power generation only. However, neglecting the related reactive power costs might result in increased operation costs and deviations in the dispatches from the optimal solutions. Hence, this paper co-optimizes the costs related to both active and reactive powers of diesel generators. In addition, this study considers the reactive power capability of inverter-interfaced distributed energy resources (DERs). Moreover, the detailed models for the different resources are presented, especially for diesel generators where the actual capability curves are used instead of the widely used box constraints. The problem is formulated as a nonlinear programming problem in the General Algebraic Modeling System (GAMS) software and is solved by the CONOPT solver.

This article presents a new approach for high-impedance fault analysis (detection, classification, and location) in distribution networks using the adaptive neuro fuzzy inference system. The proposed scheme was trained by data from simulation of a distribution system under various faults conditions and tested for different system conditions. Details of the design process and the results of performance using the proposed method are discussed in this article. The results show that the proposed technique has very good performance in detecting, classifying, and locating high-impedance faults. The third harmonics, magnitude, and angle for the three-phase currents give superior results for fault detection as well as for fault location in high-impedance faults. The fundamental components magnitude and angle for the three-phase currents give superior results for the classification phase of high-impedance faults over other types of data inputs.

A heuristic particle swarm optimization combined with Back Propagation Neural Network (BPNN-PSO) technique is proposed in this paper to improve the convergence and the accuracy of prediction for fault diagnosis of Photovoltaic (PV) array system. This technique works by applying the ability of deep learning for classification and prediction combined with the particle swarm optimization ability to find the best solution in the search space. Some parameters are extracted from the output of the PV array to be used for identification purpose for the fault diagnosis of the system. The results using the back propagation neural network method only and the method of the back propagation heuristic combination technique are compared. The back propagation algorithm converges after 350 steps while the proposed BP-PSO algorithm converges only after 250 steps in the training phase. The accuracy of prediction using the BP algorithms is about 87.8% while the proposed BP-PSO algorithm achieved 95% of right predictions. It was clearly shown that the results of the back propagation heuristic combination technique had better results in the convergence of the simulation as well as in the accuracy of the prediction of the fault diagnosis in the PV system.