Abstract-The objective of this paper is to develop an

In this paper, a sensorless speed and armature resistance and temperature estimator for Brushed (B) DC machines is proposed, based on a Cascade-Forward Neural Network (CFNN) andQuasi-Newton BFGS backpropagation (BP). Since we wish to avoid the use of a thermal sensor, a thermal model is needed to estimate the temperature of the BDC machine. Previous studies propose either non-intelligent estimators which depend on the model, such as the Extended Kalman Filter (EKF) and Luenberger's observer, or estimators which do not estimate the speed, temperature and resistance simultaneously. The proposed method has been verified both by simulation and by comparison with the measurements and simulation results available in the literature.

This article <span lang="EN-US">presents an improved vector control scheme based on super twisting continuous sliding mode for a permanent magnet synchronous generator integrated in a dual roror wind turbine system. To augment the energy effectiveness of wind systems, several research has recently been realized by different researchers and in various technologies fields. The field of machine control occupied a large part of this research as the objective was always to find the most optimal control solution. Two main objectives are targeted in this work. The first goal is to develop the vector control performance of the permanent magnet synchronous generator by using second order continuous sliding mode controller, which is known for their robustness and ability to reduce chattering phenomenon. The second objective of this work is to use a dual rotor wind turbine in order to increase the energy efficiency of the wind power system used. The obtained simulation results showed the efficacy of the techniques used.</span>

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The main of this paper is to provide optimal control of a state microgrid system. The proposed configuration composes of renewable generation systems such as solar photovoltaic systems and wind turbine generator with a Diesel Engine Generator and Fuel-Cell. An Aqua electrolyzer and other energy storage systems such as battery and flywheel are also used in the proposed microgrid. A standard PID (Proportional Integral Derivative) controller scheme is introduced whose its parameters are determined using different optimizations algorithm such as Algorithm Genetic, Particle Swarm Optimization, and Krill Herd algorithm for minimizing frequency and power deviations, in order to enhance the operation of this system. The PID controller gains are optimized by resolving an objective function. The simulation results are shown, and given that the Krill Herd algorithm improves the performance of the system in comparison with GA and PSO based on PID. The efficiency of the system is improved.