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Provisional microgrids create by the connection of two beside microgrids to achieve economic benefits. So far, there have been many reviews on AC microgrids. However, due to the many advantages of DC microgrids, they cannot be ignored. To achieve the advantages of both AC and DC microgrids, hybrid structures were considered. The subject of controlling hybrid microgrids is relatively more complex. Due to the variable nature of the distributed generation resources, this complexity in islanding conditions is even greater than normal conditions. This paper proposes a robust frequency control for islanding provisional microgrid including hybrid AC/DC part as a master microgrid and AC conventional part as a slave microgrid. As authors are aware, there are no scientific reports for frequency control of the proposed microgrid. One of the issues affecting the performance of this controller is the choice of weighting functions. To achieve optimal controller, an algorithm is used to determine the best weighting functions according to the way the controller works, and by examining the sensitivity functions. Finally, simulations were performed to investigate the performance of the controller and the results of using this controller for this microgrid were examined. K E Y W O R D Shybrid microgrid, islanding mode, robust frequency control, uncertainty parameters, weighting functions | INTRODUCTIONA microgrid is one of the categories in which the generation process, consumption, and energy storage are practicable and useful simultaneously. In this context, generation and storage operations are mixed. 1 Electric loads, generators, and storages can be AC or DC type. Over the years, power grids have evolved to use AC structure. However, with the

Abstract. In the present paper, by Haagerup theorem, we show that if A ∈ M n is a non scalar strictly positive matrix and 0 < ν < 1 be a real number such that ν = 1 2 , then there exists X ∈ M n such that

Summary One of the energy management system (EMS) goals in the smart home (SH) is to achieve cost reduction besides consumer risk minimization. For these purposes, SH will be equipped with combined heat and power (CHP) generation, wind turbine (WT), and photovoltaic (PV) resources. EMS in SH faces uncertainty due to variable generation of these resources and in‐operation of the switch connected to the network. In this article, proposed comprehensive algorithm for EMS of SH including WT, PV, battery energy storage system, CHP considering probability of mal‐operation of tie‐switch between SH and grid. In this regard suggested algorithm provides consumer risk reduction in SH EMS problem regarding to uncertainty of market price. It is so crucial that all of triple uncertainties of PV and WT resources and tie switch mal‐operation are considered as residential consumer risk constraints to achieve accurate results. Genetic algorithm (GA) is used as optimization method for solving of risk‐based SH EMS problem. Proposed EMS algorithm is implemented for test SH via simulation studies using MATLAB software. Results indicate presented risk‐based GA increases the thermal and energy storage by 20.25% and 14.28% and reduces the consumer risk when a blackout occurs by increasing the spinning reserve.

In this paper a method for estimating the dimension of rectangular cracks is proposed. The use of Eddy current (EC) nondestructive testing (NDT) based on probe impedance changes on the crack regions is considered. The artificial neural network estimates the dimension of new cracks using impedance changes of the eddy current probe. The experimental results and finite element method (FEM) results are used for training the artificial neural network. By increasing the number of experiments, the results of the finite element method are not necessary. The simulation results are very promising.