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The application of arresters is critical for the safe operation of electric grids against lightning. Arresters limit the consequences of lightning-induced over-voltages. However, surge arrester protection in electric grids is challenging due to the intrinsic complexities of distribution grids, including overhead lines and power components such as transformers. In this paper, an optimal arrester placement technique is developed by proposing a multi-objective function that includes technical, safety and risk, and economic indices. However, an effective placement model demands a comprehensive and accurate modeling of an electric grid's components. In this light, appropriate models of a grid's components including an arrester, the earth, an oil-immersed transformer, overhead lines, and lightning-induced voltage are developed. To achieve accurate models, high-frequency transient mathematical models are developed for the grid's components. Notably, to have an accurate model of the arrester, which critically impacts the performance of the arrester placement technique, a new arrester model is developed and evaluated based on real technical data from manufacturers such as Pars, Tridelta, and Siemens. Then, the proposed model is compared with the IEEE, Fernandez, and Pinceti models. The arrester model is incorporated in an optimization problem considering the performance of the over-voltage protection and the risk, technical, and economic indices, and it is solved using the particle swarm optimization (PSO) and Monte Carlo (MC) techniques. To validate the proposed arrester model and the placement technique, real data from the Chopoghloo feeder in Bahar, Hamedan, Iran, are simulated. The feeder is expanded over three different geographical areas, including rural, agricultural plain, and mountainous areas. [ABSTRACT FROM AUTHOR]

Wind energy systems are pollution free and clean form of the renewable energy production. The dynamic model of a wind turbine system based on a doubly fed induction generator (DFIG) is exposed to external disturbances, uncertainties, and nonlinear dynamics. In this paper to ensure the system robustness against external disturbance and uncertainty in system parameters, a novel optimized fractional order robust adaptive sliding mode controller is proposed by utilizing a disturbance observer. The controller's main goal is to track the maximum power point of the wind turbine. In order to show the superiority of the proposed method, the results under normal conditions and in the presence of disturbance and uncertainty have been compared with the classical sliding mode control (SMC) and adaptive sliding mode control (ASMC). The parameters of all three controllers have been optimized by ant colony optimization (ACO) algorithm. The proposed method does not need the knowledge of the upper bounds of model uncertainty and disturbance. Also by using the fractional order operators in the control signal of the proposed method, its robustness against model uncertainty and disturbance is increased and it can extract the maximum power than the other compared methods. [ABSTRACT FROM AUTHOR]

These days, one of the biggest challenges in the world is dealing with the outbreak of the Co vid 19 virus. Recently, new variants of this virus have been identified that have a mucli liigher rate of transmission. To effectively control and manage the spread of the disease, a clear understanding of its transmission dynamics and effective control techniques to reduce or inllibit the spread of the virus is necessary. Although vaccine production and distribution are currently underway. Non-Pharmacological Interventions (NPI) continue to be an important and fundamental strategy for controlling the spread of the virus in various countries around the world. In this paper. Covid 19 dynamics is modeled using four well-known categories (SEIR): Susceptible-Exposed-Infected-Recovered. Since the parameters of the model have uncertainty, a robust control method should be designed. In this paper, using fractional calculus and fuzzy logic, a robust fuzzy fractional-order sliding mode controller (FOFSMC) for Covid 19 dynamics is proposed, which aims to control the prevalence of the disease using NPI. The proposed method is implemented both on the integer and fractional order model. Finally, the performance of the proposed controller on the new variant of the Covid 19 virus with a faster disease transmission rate will be evaluated. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID‐19) pandemic has impacted many nations around the world. Recently, new variant of this virus has been identified that have a much higher rate of transmission. Although vaccine production and distribution are currently underway, non‐pharmacological interventions are still being implemented as an important and fundamental strategy to control the spread of the virus in countries around the world. To realize and forecast the transmission dynamics of this disease, mathematical models can be very effective. Various mathematical modeling methods have been proposed to investigate the transmission patterns of this new infection. In this paper, we utilized the fractional‐order dynamics of COVID‐19. The goal is to control the prevalence of the disease using non‐pharmacological interventions. In this paper, a novel fractional‐order backstepping sliding mode control (FOBSMC) is proposed for non‐pharmacological decisions. Recently, new variant of this virus have been identified that have a much higher rate of transmission, so finally the effectiveness of the proposed controller in the presence of new variant of COVID‐19 is investigated. [ABSTRACT FROM AUTHOR]