This paper presents a new decentralized robust power/current/voltage/frequency control strategy for islanded and grid-connected modes to enhance power-sharing and small- and large-signal stability of a wind/photovoltaic/fuel cell microgrid and improve its performance for nonlinear and unbalanced loads. First, the distributed energy resources are modeled for unbalanced voltage condition. Then, in order to improve power sharing, regulate voltage and active/reactive powers injected by these resources, and moreover, harmonic and negative-sequence current control in the presence of nonlinear and unbalanced loads, three separate controllers for positive-sequence voltage and power control and negative sequence current control are designed based on the sliding mode control, Lyapunov function theory, and fractional-order sliding mode control, respectively. The theoretical concept of the proposed control strategy, including the mathematical modeling of microgrid components, basic theorems, controller design procedure, and robustness/closed-loop stability analysis, is outlined. Also, this direct power/current/voltage/frequency control scheme is governed by a hierarchical control scheme that exploits a voltage compensation scheme and harmonic virtual impedance loop. To show the effectiveness of the proposed control scheme, offline time-domain simulation studies are performed on a wind/photovoltaic/fuel cell microgrid with nonlinear and unbalanced loads in MATLAB/Simulink environment and the results are verified by OPAL-RT real-time digital simulator.

This study presents a novel approach for robust, balanced and unbalanced power‐flow analysis of microgrids including wind/solar, droop‐controlled and electronically‐coupled distributed energy resources. This method is based on using radial basis function neural networks that can be applied to a wide range of non‐linear equation sets. Unlike conventional Newton–Raphson, the presented method does not need to calculate partial derivatives and inverse Jacobian matrix and so, has less computation time, can solve all the equation sets for the power grid and distributed energy resources exactly and simultaneously, and has enough robustness with respect to the R / X ratio and load changes. Also, because the power electronic interface provides some degrees of freedom in the steady‐state and dynamic models, a new approach is required to solve the non‐linear set of the power grid and distributed energy resource equations even with unequal number of equations and variables. The proposed method is a general method applicable to all types of power networks, including radial, meshed, and open‐loop, and includes all types of buses, i.e. PQ, photovoltaic and slack buses. This method is tested on different microgrid test systems, and the comparative results validate its efficiency and accuracy.

Permanent magnets (PMs) have been widely used in different types of electrical machines to improve their performance. This paper introduces a novel multi-tooth hybrid-excited switched reluctance motor (MT-HESRM) with PMs placed between the end teeth of the adjacent modules. Thanks to the innovative method of embedding PMs, a unique design is developed. First, the operating principle of the motor is explained and the magnetic circuit model (MCM) of the motor is analyzed. The magnetic characteristics in terms of flux density, flux linkage, inductance, and torque are obtained and compared with those of its PMless counterpart. The mathematical model of the reluctance and PM torques is presented. The reluctance and PM torques are decoupled using the finite element analysis and the torque contribution of the PMs is discussed. The steady-state operations of both motors with both current chopping and single-pulse controls are analyzed and compared under different speeds. Finally, both motors are manufactured, the laboratory tests are done, and the experimental results are extracted. Both the simulation and test results elucidate that the MT-HESRM, which has only three small PMs as auxiliary flux sources, has unique features in terms of high output power and torque with a negligible cogging torque.

In this paper, a multi-objective algorithm is presented for optimal power management and design of a hybrid Wind/Photovoltaic/ generation system with hydrogen energy storage system including electrolyzer, fuel cell and hydrogen tank to supply power demand in a microgrid system. The generation units are intrinsically non-dispatchable and moreover, the major components of the system i.e. wind turbine generators, photovoltaic arrays and DC/AC converter may be subjected to failure. Also, solar radiation, wind speed and load data are assumed to be entirely deterministic. The goal of this design is to use a novel multi-objective optimization algorithm to minimize the objective functions i.e. annualized cost of the system, loss of load expected and loss of energy expected and provide optimal energy management in the microgrid. The system costs involve investment, replacement and operation and maintenance costs. Prices are all empirical and components are commercially available. The simulation results for different cases reveal the impact of components outage on the reliability and cost of the system. So, they are directly depends on component’s reliabilities, i.e. outages lead to need for a larger and more expensive generation system to supply the load with the acceptable level of reliability. In addition, an approximate method for reliability evaluation of hybrid system is presented which lead to reduce computation time. Simulation results show the effectiveness of proposed multi-objective algorithm to solve optimal sizing problem in contrast with traditional single objective methods.

The minimisation of discrimination time between main and backup overcurrent (OC) relays is one of the most important issues in power system relays coordination. On account of the massive computation burden and complexities for determining power system breakpoint, the implementation of the previously reported OC protection methods in large interconnected networks is almost impossible or at least cumbersome. In this regard, a variety of optimisation algorithms have been presented for coordination between relays and finding optimal operation time (OT) of the protection system. The previously reported single‐objective optimisation algorithms have some limitations/drawbacks regarding OT of relays and their coordination. In this study, a new multi‐objective optimisation algorithm is presented for coordination of OC relays in interconnected networks, based on multi‐objective particle swarm optimisation (MOPSO) and fuzzy decision‐making tool (FDMT). Then, using some useful assumption and recommendations of IEC‐6090 and fault calculations for the microgrids including distributed energy resources, the proposed method is generalised for OC relay coordination in microgrids. Finally, the proposed method has been successfully implemented on different test systems and the obtained results have been compared with other reported methods to prove accuracy, authenticity, and efficiency of the MOPSO/FDMT‐based protection and relay coordination algorithm.

The probabilistic power flow (PPF) of active distribution networks and microgrids based on the conventional power flow algorithms is almost impossible or at least cumbersome. Always, Mont Carlo simulation is a reliable solution. However, its computation time is relatively high that makes it unattractive to be a reliable solution for large interconnected power systems. This study presents a new method based on fuzzy unscented transform and radial basis function neural networks (RBFNN) for possibilistic‐PPF in the microgrids including uncertain loads, correlated wind and solar distributed energy resources and plug‐in hybrid electric vehicles. When sufficient historical data of the system variables is not available, a probability density function might not be defined, while they must be represented in another way namely possibilistically. When some of system uncertain variables are probabilistic and some are possibilistic, neither the conventional pure probabilistic nor pure possibilistic methods can be implemented. Hence, a combined solution methodology is needed. The proposed method exploits the ability of RBFNN and unscented transform in non‐linear mapping with an acceptable level of accuracy, robustness and reliability. Simulation results for the proposed PPF algorithm and its comparison with the reported methods for different test power systems reveals its efficiency, accuracy, robustness and authenticity.