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Nowadays, wind turbine energy has an increased importance in electrical power applications since when it is considered as an essential inexhaustible and broadly available energy resource. An aerogenerator is a device that transforms a part of the kinetic energy of the wind into available mechanical energy on a transmission shaft, and then into electrical energy through a generator, which is in our case a dual power asynchronous machine. An important characteristic of a wind turbine is that the avail, able maximum power is provided only in a single given operating point, called Maximum Power Point. Many classical methods and controllers have been widely developed and implemented to track the maximum power point. Among drawbacks of a classical PI controller is that its parameters are not constant, these conventional control laws may be are insufficient because they are not robust, especially when the accuracy requirements and other dynamic characteristics of the system are strict. The new idea in this paper is to introduce the Genetic Algorithms theory into the controlstrategy that used inthe conversion chain of the wind turbine, in order to improve stability. Simulation results approve that the application of Genetic Algorithms to the PI regulator, minimize or eliminate the drawbacks of the classical PI regulator, and greatly promote the efficiency and stability of the wind turbine systems.

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This paper presents the implementation of selective harmonic elimination (SHE) in a five-level inverter structure using artificial neural networks (ANNs). SHE is an effective low-frequency modulation technique to eliminate selected harmonics and control multilevel converters. The use of ANN-SHE requires the calculation of the optimum values of switching angles via the solving system of nonlinear equations for the total harmonic distortion (THD) reduction, where the nonlinear equations are founded by the complex Fourier series analysis of the inverter output voltage. The procured switching angle values are directly implemented by a multilayer perceptron (MLP) algorithm without a lookup table. The ANN model is obtained by training the neural network (NN), taking the modulation index (M) as an input and approximating switching angles as an output. A thorough analysis was carried out to show the programming steps of the proposed ANN-based SHE using Matlab/ Simulink environment. A realized inverter prototype steered by the proposed ANN-based SHE was tested with various modulation indexes on a real-time mode using a digital signal processor (DSP) C2000 Delfino-TMS320F28379Dembedded board. A comparison between the simulation results and the experimental data is presented. The obtained results illustrate that the experimental results match the simulation closely, and the ANN model provides a fast and precise estimate of the switching angles for each value of the modulation index.

This article presents the application of the harmony search (HS) optimization algorithm for selective harmonic elimination PWM (SHEPWM) in a new topology of multilevel inverters with reduced number of electronic switching elements. The main objective of the harmonic elimination strategy is eliminating undesired low-rank harmonics in order to improve the quality of the output waveform. The harmonic elimination strategy is achieved by solving a system of nonlinear equations. In this paper harmony search optimization is applied using artificial neural networks (ANNs) on a new 21-level inverter topology. The algorithm is based on a music improvisation process. MATLAB programming software is used to develop a harmony search optimization program for harmonic elimination. A small-scale laboratory of the proposed 21-level inverter is built to validate the simulation results and to prove the efficiency of the proposed control scheme.

This paper addresses to integrate an optimal Proportional-Integrator-Derivative controller for frequency regulation in an isolated microgrid power system based renewable generation. This autonomous microgrid system is composed of Distributed Energy Sources like wind, solar, Diesel Engine Generator, Fuel Cells system, and two different storage devices such as Battery Energy Storage System and Flywheel Energy Storage System. Optimal tuning of the investigated controller is considered as the main problem to be resolved using the Krill Herd algorithm through an objective function. The obtained results are also accomplished with and without the battery energy storage system. The comparison of system performance shows that the proposed control scheme based Krill Herd Algorithm is better than the Genetic Algorithm in the improvement of system performance.

This paper presents the description, analysis and control of an LLC resonant inverter suitable for induction heating applications. The output power of the proposed inverter has to be controlled by adjusting the duty cycle of the switches using a power loop circuit based on fractional order PI λ controller. A phased locked loop (PLL) is used as frequency tracking control circuit. The complete closed loop control model is obtained using small signal analysis. The validity of the proposed control is verified by simulation results. Results of this simulation are compared to those obtained by using a PI controller. They show that the improved PI λ controller exhibits a much better behaviour.