In this paper, a novel improved Stochastic Fractal Search optimization algorithm (ISFSOA) is proposed for finding effective solutions of a complex optimal reactive power dispatch (ORPD) problem with consideration of all constraints in transmission power network. Three different objectives consisting of total power loss (TPL), total voltage deviation (TVD) and voltage stabilization enhancement index are independently optimized by running the proposed ISFSOA and standard Stochastic Fractal Search optimization algorithm (SFSOA). The potential search of the proposed ISFSOA can be highly improved since diffusion process of SFSOA is modified. Compared to SFSOA, the proposed method can explore large search zones and exploit local search zones effectively based on the comparison of solution quality. One standard IEEE 30-bus system with three study cases is employed for testing the proposed method and compared to other so far applied methods. For each study case, the proposed method together with SFSOA are run fifty run and three main results consisting of the best, mean and standard deviation fitness function are compared. The indication is that the proposed method can find more promising solutions for the three cases and its search ability is always more stable than those of SFSOA. The comparison with other methods also give the same evaluation that the proposed method can be superior to almost all compared methods. As a result, it can conclude that the proposed modification is really appropriate for SFSOA in dealing with ORPD problem and the method can be used for other engineering optimization problems.

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Due to lack of situational awareness, automated analysis, poor visibility, and mechanical switches, today's electric power grid has been aging and ill-suited to the demand for electricity, which has gradually increased, in the twenty-first century. "esides, the global climate change and the greenhouse gas emissions on the Earth caused by the electricity industries, the growing population, one-way communication, equipment failures, energy storage problems, the capacity limitations of electricity generation, decrease in fossil fuels, and resilience problems put more stress on the existing power grid. Consequently, the smart grid SG has emerged to address these challenges. To realize the SG, an advanced metering infrastructure "MI based on smart meters is the most important key.

Traditional Friedel‐Crafts acylation with acid halides suffers from waste byproducts such as SO2, HCl, and the hydrolysis of Lewis acid catalyst. Thus, the environmentally benign intermolecular and intramolecular Friedel‐Crafts acylation of arenes with carboxylic acids as acylating agents has been investigated using a mixture of deep eutectic solvent and an ionic liquid as reaction media under microwave irradiation. The Friedel‐Crafts acylations with carboxylic acids as acylating agents are challenging due to the low reactivity of carboxylic acids. In this paper, the desired products were obtained at moderate to good yields under microwave irradiation for a short reaction time. The Friedel‐Crafts cyclization of 4‐phenylbutyric acid is more effective than 3‐phenylpropionoic acid under the present method. The reaction mechanism is proposed by combining experiments with density functional theory calculations, highlighting the critical role of ZnCl2, triflate anion (CF3COO−) in binary ionic liquids, and acidic conditions in accomodating a kinetic accessible pathway making the C−C bond.

This paper proposes a new load shedding method based on the application of a Dual Neural Network (NN). The combination of a Back-Propagation Neural Network (BPNN) and of Particle Swarm Optimization (PSO) aims to quickly predict and propose a load shedding strategy when a fault occurs in the microgrid (MG) system. The PSO algorithm has the ability to search and compare multiple points, so the proposed NN training method helps determine the link weights faster and stronger. As a result, the proposed method saves training time and achieves higher accuracy. The Analytic Hierarchy Process (AHP) algorithm is applied to rank the loads based on their importance factor. The results of the ratings of the loads serve as a basis for constructing the load shedding strategies of a NN combined with the PSO algorithm (ANN-PSO). The proposed load shedding method is tested on an IEEE 25-bus 8-generator MG power system. The simulation results show that the frequency recovery of the power system is positive. The proposed neural network adapts well to the simulated data of the system and achieves high performance in fault prediction.

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